Innovating in the Open Lab: The new potential for interactive value creation across organizational boundaries 9783110633665, 9783110628210

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Innovating in the Open Lab

De Gruyter Studies in Innovation and Entrepreneurship

Series Editor John Bessant

Volume 1

Innovating in the Open Lab The new potential for interactive value creation across organizational boundaries Edited by Albrecht Fritzsche, Julia M. Jonas, Angela Roth, Kathrin M. Möslein

ISBN 978-3-11-062821-0 e-ISBN (PDF) 978-3-11-063366-5 e-ISBN (EPUB) 978-3-11-062997-2 ISSN 2570-169X Library of Congress Control Number: 2020930322 Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at http://dnb.dnb.de. © 2020 Walter de Gruyter GmbH, Berlin/Boston Typesetting: Integra Software Services Pvt. Ltd. Printing and Binding: CPI books GmbH, Leck www.degruyter.com

In memory of Prof. Mitchell Tseng, a colleague, friend and inspiration to us all

Preface Laboratories enjoy extraordinary popularity in business and society these days. There is hardly any institution left that does not advertise something as a laboratory. The word carries an aura of wonder and mystique. It evokes images of discovery and progress that seem to be compatible with any kind of human activity. Walking through the streets of our cities, one can encounter tango labs, espresso labs, insurance labs, and ethics labs.1 All of them provide spaces for experimentation and the quest for novelty, which, of course, attracts the attention of innovation management. In search for a systematic of different types of new laboratories, variations can be observed in several dimensions. Laboratories can be established on the ground of a permanent infrastructure or consist of pop-up solutions for specific events. They can be operated by a focal institution that collects input from other parties or by an intermediary that facilitates the interaction from a neutral point of view. Laboratories can be dedicated to the solution of very specific problems or allow for more diversity; they can enforce structured procedures or give everyone the freedom to decide what they want to do. Last but not least, they can be driven by very clear commercial interests or involve a broader range of social and political agendas. A common element of all the new laboratories that are currently established, however, seems to be the attempt to overcome borders: people in the laboratory are invited to share a wide range of thoughts and suggestions with one another, experts are given the opportunity to exchange across the boundaries of their domains, different social interest groups are given a place where they can work together to solve problems. In contrast to the laboratories that have long been used in academia and industry, the new laboratories are not created as contained spaces for science and engineering, but the exact opposite: open spaces for participation and collaboration. In view of this development, one can ask oneself whether the use of the word “laboratory” is appropriate at all. With increasing openness, many typical attributes of laboratory work disappear. Work does not take place in secret anymore; it is not focused on experiments that reproduce specific effects, but expands to a variety of different activities that go far beyond the classical tasks of researchers and engineers. To speak of a laboratory in this context seems at best to be a distant reference to scientific and technical excellence that should serve as inspiration for the contributors. However, there is one element of laboratory work that remains intact in open laboratories and radically distinguishes them from most other work environments. On the assembly line, in the office, in the classroom or operating theatre, the focus is set on repetitive routine work. The activities are oriented towards a

1 see www.tangolab.ch; www.espressolab.com; www.insurelabs.de; ethicslab.georgetown.edu; https://doi.org/10.1515/9783110633665-202

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clearly defined outcome. Laboratory work is different. It is essentially linked to the search for something new. Linguistically, the word laboratory belongs to the same family as labour and laborious, but also the word lapse. They all have their origin in the Latin verb labare, which could be translated as “staggering under a load”. This expresses a different idea of work than a regular nine-to-five job. There is something radical, existential going on, something that brings someone to the edge. Laboratory work, one might say, requires true commitment. It does not leave you uninvolved. When you enter a laboratory, you must be prepared to leave it as another person. In the lab, you expose yourself to the possibility of finding something that forces you to revise your view of the world. For researchers, this normally implies a scientific insight, for engineers, an invention. For other people, laboratory work may involve a very different kind of novelty: a solution to a daily problem, an experience of a new part of reality, a new understanding of oneself, or new feelings towards something or someone. All this seems possible in an open laboratory. Understanding an open laboratory as a space for novelty puts it on the agenda of innovation management. It raises the question of how to organize such a place to inspire creativity, enable exploration and encourage exchange. In addition, it must also be asked how novelty that results from work in the laboratory can unfold real value. This question is particularly important for decisions about investing in open laboratories. Over the last decade, considerable amounts of money and effort have been spent on the establishment of open laboratories by industrial as well as public institutions. Numerous case studies give insight into these activities and their outcomes. Surprisingly little, however, has been said so far about the managerial tasks that need to be performed in order to make the lab successful. Much too often, the open laboratory is approached like the famous field of dreams in the Kevin Costner movie by the same name: you just build it, and then something magical will happen. Neither a researcher nor a practitioner can be satisfied by this. The following chapters therefore look in more detail into the added value of open laboratories for innovation activities and the interventions that can help to maximise it. This book is aimed at a broad audience in academia, industry and public administration. It does not only want to provide theoretical knowledge, but also actionable guidelines for management. For this reason, the book starts with an introduction to the look and feel of an open laboratory on the specific example of JOSEPHS® in Nuremberg, Germany, an open laboratory established by the Fraunhofer Institute for Integrated Circuits in collaboration with Friedrich-Alexander-University ErlangenNuremberg. The first part of the book gives insight into the strategic considerations behind JOSEPHS®, its role in the local innovation ecosystem and the overall open labs movement. The second part of the book presents findings from five years of work in the open laboratory, reported from practitioners on site as well as leading scientific researchers.

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Part three of the book turns the attention from the specific case of JOSEPHS® towards more general questions of customer engagement and value co-creation in open laboratories. Part four puts the phenomenon of open laboratories in the larger context of innovation management from different theoretical perspectives. Part five concludes the book with an exploration of further possibilities to use open laboratories for innovation in the digital age. We would like to thank all the authors for their excellent contributions, which together create a colourful picture of the various facets of innovation management in open laboratories. We would also like to thank all the members of the team at the Fraunhofer Institute for Integrated Circuits and the Friedrich-Alexander University Erlangen-Nuremberg who have worked on JOSEPHS® in recent years, as well as the Bavarian Ministry of Economic Affairs, Regional Development and Energy and the City of Nuremberg who got the ball rolling with their support. Furthermore, we are very grateful for the good cooperation with Professor John Bessant, the editor of this book series at De Gruyter, and all the publishing staff who supported us. Finally, Agnieszka Lubkiewicz at FAU deserves special thanks for her help in preparing the manuscript. We hope that our book will prove to be valuable for researchers and practitioners in the field of innovation management, as well as all other persons interested in learning more about open labs and their potential for innovation management. Nuremberg, October 2019 Albrecht Fritzsche Julia M. Jonas Angela Roth Kathrin M. Möslein

Contents Preface

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Part I: Open Labs and Innovation Strategies Angela Roth 1 Piloting in Open Innovation Labs – A Challenge for Local Ecosystems

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Heike Karg 2 The First 100 Days of JOSEPHS® – The Open Innovation Lab in Nuremberg 11 Michael Fraas 3 Where City Innovation Comes Alive – The JOSEPHS® Innovation Lab in Nuremberg 21 Alexander Pflaum and Albert Heuberger 4 JOSEPHS® as an Anchor Point for the Development of Smart Products and Services in an Increasingly Digitized World 35 Frank Danzinger, Rebekka Schmidt, Fabian Memmert, and Michaela Pichlbauer 5 Open Lab Functionalities in Offline-Retail – A Step Towards Future Retail? 49

Part II: Managing Innovation in Open Labs Albrecht Fritzsche 6 The Many Facets of Open Laboratories and Their Implications for Innovation Management 73 Katharina Greve, Julia M. Jonas, Andy Neely, and Kathrin M. Möslein 7 Unlocking Unique Value Through Co-Creation in Open Laboratories Ingeborg Steinmetz and Lisa Hübner 8 Working in the Open Lab – Mediation, Trading and Translation

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Stefan Wolpert and Maximilian Perez Mengual 9 Professional Leadership as Key to Innovation Projects in Open Laboratories 103 Sebastian Engel 10 Driven by the Same Spirit – Entrepreneurship, Incubation and Open Labs in the Business Ecosystem of Central Franconia 113

Part III: Co-creating Value with Open Labs Julia M. Jonas 11 Co-creating Value with Open Labs

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Julia A. Fehrer, Roderick J. Brodie, Valtteri Kaartemo, and Maximilian Reiter 12 The Role of Engagement Platforms in Innovation Ecosystems

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Anna-Greta Nyström, Wilhelm Barner-Rasmussen, and Valtteri Kaartemo 13 B2B Vertical Collaboration and Open Innovation – The Case of 5G in Finland 141 Kyrill Meyer, Jörg Härtwig, and Jürgen Anke 14 An Innovation Network for Collaborative Engineering of Smart Service Systems – The LESSIE Approach 153 Christofer F. Daiberl and Angela Roth 15 Driving Service Productivity of Open Innovation Labs

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Part IV: Open Labs as Innovation Spaces Kathrin M. Möslein 16 Understanding Open Labs – The Challenge of Place and Space John Bessant 17 Creating the Creative Open Lab

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Susanne Ollila and Anna Yström 18 Open Laboratories as “In-between Spaces”

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Jan Mehlich and Mitchell M. Tseng 19 Navigating in the Vastness – Making Sense of the Dynamics of Consumer Choices 213 Pramoth Kumar Joseph, Srinivasan R, and Sandeep Lakshmipathy 20 Innovating in the Open lab – Archetypes of OI Strategies and Capabilities 227

Part V: New Frontiers for Open Labs Albrecht Fritzsche 21 Open Labs as Islands of Reason in the Digital Age

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Steve Rader and Amy P. Kaminski 22 A Virtual Laboratory for Open Innovation in Space Exploration: The NASA Tournament Lab 253 David Sarpong and Amit Rawal 23 From Open Labs to DiY Labs – Harnessing ‘the wisdom of crowds’ for Innovation 263 Anne Krefting and Hanan Prince 24 Tapping into Cultural Richness – Open Labs in Nubia

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Max Jalowski 25 Facilitating Participatory Design in the Cyber-Physical Lab List of Figures

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List of Tables

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Contributors Index

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Part I: Open Labs and Innovation Strategies

Angela Roth

1 Piloting in Open Innovation Labs – A Challenge for Local Ecosystems 1 Piloting in Open Innovation Labs The idea of piloting is not new. It is a standard element in development processes, especially in product oriented, high tech industries. However, in recent years, there have been some exciting developments in piloting. Though living labs and innovation labs are not new, they have become more popular, and many companies have started to establish living labs from their unique perspectives (Leminen, Westerlund, 2019; Westerlund, Leminen, 2011; Hossain et al., 2019). Living labs are designed to strengthen the relationship between a company and its customers or potential customers during or before the development of products and services. By interacting and co-creating constantly with them, companies may develop fitting products and services to make their business successful (Dell’Era et al., 2019; Westerlund, Leminen, 2011). This idea of co-creating is also used in research into open innovation and is widely implemented in online solutions, like online open innovation platforms, open innovation communities, open innovation contests or market places (Huff et al., 2013; Adamczyk et al., 2012; Hallerstede, 2013). Open innovation (OI) has evolved since Chesbrough (2003) first elaborated the term. It has moved from innovation in the context of ‘inside’ or ‘outside’ and ‘external ideas and paths’ (Chesbrough, 2003, p. 43) to ‘purpose inflows and outflows to accelerate internal innovation’ with a focus on the role of ‘external innovation’ (Chesbrough, 2006, p. 1). Open innovation platforms and communities are widely used to involve people in piloting processes. There are also professional intermediaries, who offer platforms or build communities focusing on certain topics (for example, innosabi). Research has been conducted on what platforms should look like and success factors (Nambisan et al., 2018; Daiberl et al., 2019; Roth et al., 2017; Bogers et al., 2018; Enkel et al., 2009). However, open innovation via online tools is not always appropriate. When a product or service must transmit haptic elements or feelings and impressions, online tools can barely enable people to give feedback and co-create products and services. This is true for services, business models or store concepts and for products that include service processes or have their main effect via haptic features. In these cases, offline open innovation is an option, and this is where living labs and real open labs come into play. There are different types of these real spaces (Roth et al., 2014). For example, labs may be permanently established or exist only for a

Angela Roth, FAU Erlangen-Nuremberg, Chair of Information Systems - Innovation and Value Creation, Nuremberg, Germany https://doi.org/10.1515/9783110633665-001

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certain amount of time. In addition, labs may be run by firms for their own purposes or by intermediaries, who offer the space to third parties. Finally, there are multiple possibilities that follow the concept of open innovation in terms of how they interact with customers online or offline. However, who should be involved in open innovation processes and how the results can be implemented remain open questions for firms. One way to answer these questions is to focus on offline open innovation in real open innovation labs, and to follow the need for firms to engage in networks or ecosystems to be successful. Thus, it is of interest to determine how open innovation labs can be used to engage in local ecosystems.

2 The Need to Engage in Ecosystems In traditional business literature, basic elements that constitute a firm have been considered the most important. These include factors of production, such as land, labour and capital (Smith, 1776; Gutenberg, 1983). These factors have been developed, and variants appeared, including knowledge, machines, management, technology, entrepreneurship and materials and labour. Additionally, the idea that data is the new oil has circulated, which implies that data could be listed as a production factor. The aim of this article is not to discuss these factors and determine their worth. Rather, the aim is to shed light on the fact that nowadays, challenges and tasks are so complex that only complex solutions can be used to complete them. In other words, if tasks and challenges for customers and companies arise within complex networks or ecosystems comprising firms and stakeholders, then solutions can only be achieved through the use of complex networks. This is also known as “Ashby’s law” (“Law of requisite variety”, Ashby, 1956). Consequently, networks must become part of the list of production factors and be considered in terms of the core competencies of a firm. In terms of services, a study showed that managing network and community competencies was part of a framework for service innovation competencies (Roth, 2015). In the world of products, networks and ecosystems have been also recognised as important basic elements. Open innovation, especially that conducted in open innovation labs, takes place in a network of multiple stakeholders, which tends to be part of a local ecosystem. Therefore, it is worthwhile analysing whether conducting an open innovation project at an open innovation lab improves the “network production factor” by engaging in or building local ecosystems. Besides being considered as places where open innovation takes place, real open innovation labs can also be described as multi-sided platforms. A multitude of stakeholders is involved in providing and orchestrating this platform, and many stakeholders use and interact on the platform in many different ways and directions, fostering network effects. In a similar vein, ecosystems have an interest in establishing such places for piloting and using open

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innovation labs as a means for engaging stakeholders to build relationships and a diverse, widespread, interdisciplinary ecosystem. Consequently, integration into and of local ecosystems should play an important role in the development of real open innovation labs. Therefore, piloting in such labs means to co-create with people to find innovative solutions and to engage in local ecosystems to support the implementation of the solutions developed within the lab. Consequently, how a firm can establish or join an open innovation lab or local ecosystem is an important topic. Such endeavours may involve establishing an appropriate environment for piloting in such ecosystems, which is an important topic in itself. This is the focus of the first chapter.

3 Perspectives on Piloting in Open Innovation Labs as Challenge for Local Ecosystems The following articles have shed light on how open innovation labs can join ecosystems, how open innovation labs can be established and how the right environment for piloting in such ecosystems can be established. One example of an open innovation lab embedded in a local ecosystem is JOSEPHS®. JOSEPHS® is situated in Nuremberg and was established in 2014 as a joint lab between the Fraunhofer Institute for integrated circuits and the Friedrich-Alexander University ErlangenNuremberg. Over 80 companies have co-created with customers at JOSEPHS® through research projects and presentations of products, services and business models so far. Such projects and presentations typically run over a period of three months and recruit participants who attend the company at JOSEPHS® during that time. Much experience is gathered from stakeholders through these processes. JOSEPHS® started as a funded research project but has matured into its own organisation in the meantime. The need for an open innovation lab like JOSEPHS® has been shown by the fact that it is an own organisation now. JOSEPHS® can be seen as a multi-sided real open innovation lab involving many different stakeholders. The following first three articles focused on JOSEPHS® from different perspectives. The first took the perspective of an operative project manager and looked at the first 100 days since the opening of JOSEPHS®. The second was written by a representative of the city of Nuremberg, while the third analysed the potential of JOSEPHS® to create digitalised smart products and services from the perspective of researchers. The articles have shown the different ways in which JOSEPHS® operates as an open innovation lab and as part of an ecosystem. Finally, a fourth article looked beyond this scope to the retail sector and analysed a different lab, which was initiated by Swisscom. Parallels to JOSEPHS® have been identified, and the final conclusions showed the important role open innovation labs can play in local ecosystems. For those interested in the basic elements

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and development of JOSEPHS®, further reading can be found in articles and conference papers published via various outlets. Finally, the company’s website is available at https://www.josephs-innovation.de or JOSEPHS® can just be visited on site: Journal articles, book chapters, etc.: – Roth, A., Fritzsche, A., Jonas, J. M., Danzinger, F. & Möslein, K. M. (2014). Interaktive Kunden als Herausforderung: Die Fallstudie “JOSEPHS® – Die Service-Manufaktur”. HMD Praxis der Wirtschaftsinformatik, 51, 883–895. – Roth, A., & Möslein K. M. (2014). Produzenten als Dienstleister: Auf dem Weg zu interaktiven hybriden Wertschöfpungssystemen. (Schuh, G., & Stick V., Ed.). Enterprise-Integration – Auf dem Weg zum kollaborativen Unternehmen. 139–151. – Srinivasan R. (2016). Josephs® – The Service Manufactory. Harvard Business Review Case Study – Möslein, K. M. & Fritzsche, A. (2017). The evolution of strategic options, actors, tools and tensions in open innovation. In Pfeffermann, N. & Gould, J., Strategy and Communication for Innovation (61–76). Cham: Springer. – Fritzsche, A. (2018). Corporate foresight in open laboratories: a translational approach. Technology Analysis & Strategic Management, 30, 646–657. – Roth, A., & Jonas J. M. (2018). Dienstleistungsentwicklung im offenen Innovationslabor – Ein Blick durch die Unternehmensbrille. (Bruhn, M., & Hadwich K., Ed.). Service Business Development. Band 2, 65–82. – Roth, A., Möslein K. M., & Reichwald R. (2018). Der Kunde als Mitentwickler – Herausforderungen für die marktorientierte Führung. (Bruhn, M., & Kirchgeorg M., Ed.). Marketing Weiterdenken. Zukunftspfade für eine marktorientierte Unternehmensführung. 143–156. Conference papers: – Fritzsche, A., Jonas, J. M., Roth, A. & Möslein, K. M. (2014). Systematic service development: Exploring the role of the setting. R&D Management Conference, Stuttgart. – Jonas, J. M., Roth A., & Möslein K. M. (2014). Open Service Design? Exploring Customer Co-Creation in a Service Manufactory. ServDes Lancaster. – Roth, A., Möslein K. M., & Jonas J. M. (2014). Bringing interactive hybrid value creation to downtown retailers – towards a service-manufactury. Cambridge Service Week, Academic Conference. – Fritzsche, A., Möslein, K. M. (2015). Accelerating Scientific Research with Open Laboratories. British Academy of Management Conference BAM, Portsmouth. – Fritzsche, A. (2015). Communication patterns in open innovation laboratories – a conversation analysis. Communication Readings: From Theory to Practice. Moscow. – Fritzsche, A. (2015). Can customers innovate business models? International Society for Professional Innovation Management Conference, Budapest.

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– Fritzsche, A., Roth, A. & Möslein, K. M. (2015). Open innovation for innovation tools: The case of co-design platforms. International Symposium on Open Collaboration, San Francisco. – Roth, A., Jonas, J. M., Fritzsche, A., Danzinger, F. & Möslein, K. M. (2015). Spaces for value co-creation: The case of “JOSEPHS® – The Service Manufactory”. European Academy of Management, Warsaw. – Daiberl, C., Höckmayr B., Roth A., & Möslein K. M. (2016). Developing new services for e-mobility: An integrated online-offline co-creation approach.. innteract 2016. – Daiberl, C., Roth A. & Möslein K. M. (2016). Conceptualizing productivity within a service network: The case of JOSEPHS®. EurOMA. – Daiberl, C., Naik H S., & Roth A. (2018). Proposing the NSPIRE Technique: Improving Productivity of Networked Service Delivery. R&D Management Conference (RADMA). Detailed summaries of the articles in this chapter are as follows: Heike Karg wrote the field report, The First 100 Days of JOSEPHS® – The Open Innovation Lab in Nuremberg, which offered insights from a practitioner’s perspective. Having been the on-site operative project manager of JOSEPHS®, she drew an interesting picture of its first 100 days. She began with a description of the idea, concept and main elements of JOSEPHS® and its stakeholders. During the first 100 days, specific challenges arose that might be typical of an open innovation lab. Karg shed light on some of these challenges and showed how the JOSEPHS® Team coped with them. Currently, JOSEPHS® is looking back on more than five years of experience in establishing and running an open innovation laboratory and integrating it into existing local ecosystems. Therefore, the way challenges were met can be judged in retrospect. Overall, a snapshot of JOSEPHS® as an open innovation laboratory has been offered, which was based on practitioner insights and experiences. Michael Fraas was involved in the development and establishment of JOSEPHS® as a representative of the city of Nuremberg. The city of Nuremberg was one of the first cities in which this kind of open innovation lab was opened. Representatives of the city of Nuremberg were not only participating stakeholders but also played a formal role in granting permissions and concessions. Additionally, the city of Nuremberg played an important role in positioning JOSEPHS® within the local ecosystem and establishing it as a driving force for cooperation, innovation and the urban development of Nuremberg. In his article Where City Innovation Comes Alive – The JOSEPHS® Innovation Lab in Nuremberg, Fraas offered practitioner insights and described a cocreation journey through Nuremberg. After presenting articles about JOSEPHS® from a practitioners’ perspective, an academic article by Alexander Pflaum and Albert Heuberger, JOSEPHS® as an Anchor Point for the Development of Smart Products and Services in an Increasingly Digitised

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World has been presented. In this article, the authors analysed how JOSEPHS®, as an open innovation laboratory, can meet the challenges of the current digitalisation megatrend with respect to the development of smart products and services within a complex innovation ecosystem. Pflaum and Heuberger described key challenges in the development of smart services and what a reference process for digital transformation in this context could look like. They elaborated on business model thinking, customer integration, dynamic and agile processes, innovation in ecosystems and the development of transformational capabilities inside a company. The article showed that innovation laboratories make an essential contribution to the successful transformation of companies and represent an anchor point for cooperation between the individual participants of an innovation ecosystem. Frank Danzinger and his co-authors, Fabian Memmert, Rebekka Schmidt and Michaela Pichlbauer, transferred the idea of open labs into the retail sector in their article, Open Lab Functionalities in Offline-Retail – A Step Towards Future Retail? This sector is under pressure, as it is dependent on new concepts and business models to cope with rising competition from online retail. Danzinger and his colleagues asked whether open lab functionalities could be embedded into offline retail. To answer this question, they presented two studies. First, the needs of offline retail environments were analysed and connected to open lab functionalities. Second, a comparative case study was conducted, which integrated open lab functionalities into an offline retail setting at Swisscom. Findings showed that customers were positively affected by the lab and new value was created. Additionally, the retail organisation was fundamentally altered in terms of organizational issues.

4 Conclusion Various insights can be drawn from the articles. First, real open innovation labs are a means to pilot in local ecosystems but must be orchestrated as such. Second, they are a means to establish or structure local ecosystems and support the need for network building as a basic element of firms. Third, strong cooperation with and within local ecosystems is important to foster network effects and to support the implementation of co-created solutions. Fourth, open innovation labs and their ability to engage in local ecosystems are not limited to special branches; their effects may be true in several different fields and branches. Fifth, the idea and concept of open innovation labs can be transferred to industries like retail and may help companies cope with trends and competition. Finally, JOSEPHS® demonstrated the different effects open innovation labs can have in local ecosystems and how challenges and solutions can look. The development of offline open innovation labs is still in its infancy and promises many interesting ideas, concepts and network effects in the coming years.

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Literature Adamczyk, S., Bullinger, A. C. & Möslein, K. M. (2012) Innovation Contests: A Review, Classification and Outlook. Creativity and Innovation Management, 21(4), pp. 335–360. Ashby, W.R. (1956). An introduction to Cybernetics. New York: Wiley. Bogers, M., Chesbrough, H. & Moedas, C. (2018). Open innovation: research, practices, and policies. California Management Review, 60(2), 5–16. Chesbrough, H. (2003). Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business School Press, Boston, MA. Chesbrough, H. (2006). Open Business Models: How to Thrive in the New Innovation Landscape. Boston, MA: Harvard Business School Press. Daiberl, C., Oks S. J., Roth A., Möslein K. M., & Alter S. (2019). Design principles for establishing a multi-sided open innovation platform: lessons learned from an action research study in the medical technology industry. Electronic Markets, 1–18. Dell’Era, C., Landoni P. & Gonzalez, S.J. (2019). Investigating the innovation impacts of usercentred and participatory strategies adopted by European living labs. International Journal of Innovation Management, 23(5), 1950048. Enkel, E., Gassmann, O. & Chesbrough, H. (2009). Open R&D and open innovation: exploring the phenomenon .R&D Management, 39(4),311–316. Gutenberg, E. (1983). Grundlagen der Betriebswirtschaftslehre – Band 1: Die Produktion; Berlin: Springer-Verlag. Hallerstede, S. H. (2013). Managing the lifecycle of open innovation platforms. Wiesbaden: Springer. Hossain, M., Leminen, S. & Westerlund, M., (2019). A systematic review of living lab literature. Journal of Cleaner Production, 213, 976–988. Huff, A., Moeslein, K.M. & Reichwald, R. (2013). Leading Open Innovation, MIT Press. Leminen, S. & Westerlund, M. (2019). Living labs: From scattered initiatives to a global movement, Creativity and Innovation Management 28 (2), pp. 250–264. Nambisan, S., Siegel, D. & Kenney, M. (2018). On open innovation, platforms and entrepreneurship, Strategic Entrepreneurship Journal, 12(3), pp. 354–368. Roth, A. (2015). On the Way to a Systematic Service Innovation Competence Framework. (Agarwal, R., Selen W., Roos G., & Green R., Ed.). The Handbook of Service Innovation. Roth, A., Dumbach M., Schliffka B. & Möslein K. M. (2017). Successful management of diverse corporate innovation communities. Journal of Strategy and Management. 10(1),2–18. Roth, A., Fritzsche, A., Jonas, J. M., Danzinger, F. & Möslein, K. M. (2014). Interaktive Kunden als Herausforderung: Die Fallstudie “JOSEPHS® – Die Service-Manufaktur”. HMD Praxis der Wirtschaftsinformatik, 51, 883–895. Smith, A. (1776). The Wealth of Nations, B.I, Ch. 6, Of the Component Parts of the Price of Commodities in paragraph I.6.9. Westerlund, M. & Leminen, S. (2011). Managing the challenges of becoming an open innovation company: experiences from Living Labs. Technology Innovation Management Review, 1(1).

Heike Karg

2 The First 100 Days of JOSEPHS® – The Open Innovation Lab in Nuremberg Insights from an Operative Management Perspective

1 Introduction On 19 May 2014, JOSEPHS® opened its doors in the city centre of Nuremberg as a unique open innovation laboratory. As science project of the Fraunhofer Center for Applied Research on Supply Chain Services SCS, the laboratory was initiated in cooperation with the Chair of Information Systems, Innovation and Value Creation at Friedrich-Alexander-University Erlangen-Nuremberg. JOSEPHS® was funded by the Bavarian Ministry for Economic Affairs and Media, Energy and Technology from 2013 to 2019, after which it operated as a spin-off.

2 The Concept 2.1 The Idea Established as a meeting point for curious and engaged co-developers and creative minds, JOSEPHS® encourages every individual to experience new products and services and to share their ideas and experiences during the development of new products and services. These shared experiences ensure that future offerings will perfectly fit specific needs. JOSEPHS® invites individuals to experience ongoing innovation journeys of established brands and new start-ups. In an open setting consisting of a 400-square metre shop floor, visitors can actively participate in the development, implementation and marketing of the innovations of respective companies and thus become “co-creators” (Roth, Fritzsche, Jonas, Danzinger & Möslein, 2014). Every three months, JOSEPHS® opens a new “theme world” consisting of as many as five research projects, which are designed as so-called “research islands” in cooperation with companies and institutions. All islands run under the umbrella of a common theme world, e.g. “Smart Future” or “Intelligent and Digital”.

Heike Karg, Fraunhofer IIS – Center for Applied Research on Supply Chain Services SCS, Nuremberg, Germany https://doi.org/10.1515/9783110633665-002

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For the companies engaging with the open innovation lab, projects start with a briefing and research design. That means, every company is required to come prepared with a research question, e.g. “Which fields of application do people think of when interacting with our technology?” or “Which store concept is more appealing to people?”. The JOSEPHS® team and the company work together on a concept for the research island within a particular theme world. During the three-month test phase, visitors can test the products and services and provide feedback, and the JOSEPHS® team collects the data. After the test phase, the outcome is presented to the company, including any recommended actions.

2.2 The Name The name “JOSEPHS®” was chosen in memory of Joseph von Fraunhofer, after whom the Fraunhofer organization was named. The optician and physicist, born in the Bavarian town of Straubing in 1787, uniquely embodies the combination of scientific research and its application together with an unresting entrepreneurial spirit (Fraunhofer-Gesellschaft, 2009; Trischler and vom Bruch, 1999). There was also a practical reason for choosing this name: the team feared that a name like “Fraunhofer (Science) Laboratory” could pose a barrier that would discourage visitors to enter: such a name might give the impression that the open laboratory’s use would be intended only for specially, or academically, trained individuals. The aim in choosing the name JOSEPHS® was to draw a resemblance to a socializing space, such as a café, and thus reduce any hesitations or doubts potential visitors may have. JOSEPHS® was originally called “JOSEPHS® – The service manufactory”. This name, however, confused some people: they did not understand the link between what they experienced at JOSEPHS® and the subtitle, especially in light of the fact that the coffee shop in JOSEPHS® was self-service. Knowing that and experiencing the development at JOSEPHS®, the team decided after a little more than two years to replace the words, “The service manufactory” with “The open innovation lab,” which hits the nail on the head thereafter, the subtitle has not been a point of discussion. Even in terms of marketing, the change was very helpful.

2.3 The Location JOSEPHS® is situated on the ground floor of a historic building in the middle of the old town of Nuremberg and is surrounded by shops, restaurants and offices. This location was chosen very carefully. It is not a prime location in the pedestrian precinct but a very good location next to it. The intent was that the laboratory would not only be a planned destination for visitors but would also attract passers-by. In addition, there should not be hundreds of people inside at the same time, as this

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does not contribute to JOSEPHS® main purpose: to gather feedback and high-quality ideas regarding innovative development. Other reasons for choosing a central location is good reachability, access to public transport and visibility. As JOSEPHS® also claims to be a good place for testing store concepts, its proximity to retail outlets is also beneficial.

2.4 The Design JOSEPHS® was designed by an agency specializing in store design (cf. Figure 2.1) and based on several rounds of brainstorming and prototyping with the JOSEPHS® project team and key stakeholders. Following the ideas of the design experts, JOSEPHS® consists of different areas that are related to each other and create a great experience for all people who come to the laboratory, whatever the purpose of their visit was.

Figure 2.1: The construction and design of JOSEPHS® from an interior perspective (Quelle: Fraunhofer SCS/IIS).

2.5 The Different Areas in JOSEPHS® The WERKSTATT (translation: service workshop or factory) serves as the centrepiece of JOSEPHS®. Here, companies, science projects, start-ups etc. have the chance to get in contact with their future users, customers and various other interested individuals.

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As JOSEPHS® is an open lab, the range of people coming to visit, test and give feedback is very wide. The DENKFABRIK (think tank) is a well-equipped, 90-square-metre workspace that can be used for a variety of purposes (e.g. meetings, workshops and lectures). In addition to hosting events initiated by the JOSEPHS® team, it is made available to companies and other organizations who wish to rent it for their own events. The GENUSSWELT (coffee shop) is designed as a space in which to drink coffee, enjoy snacks, relax, meet people, work on laptops and so on. This area is not run by the JOSEPHS® team itself but by alternating professional gastronomic partners (a franchised coffee shop and an Italian family business). At the GADGET-SHOP, visitors can purchase books, games and gadgets related to innovation, co-creation, user integration and design thinking. The offerings are also related to the topics of the current theme worlds. These four areas have been designed to provide visitors with an overall experience at JOSEPHS®, to have a good time and to feel comfortable (Roth et al., 2014).

2.6 The People at JOSEPHS® JOSEPHS® is a place where several different types of people come together for many different reasons and purposes. One such group is the JOSEPHS® team, consisting of full-time employees, student assistants, JOSEPHS® interns as well as those from nearby scientific institutes, such as the Fraunhofer Institute for Integrated Circuits IIS, and the Chair of Information Systems, Innovation and Value Creation at FriedrichAlexander University Erlangen Nuremberg. Another group comprises employees of industry partners that are running research projects in cooperation with JOSEPHS®. The visitors can be divided into three types: co-creators, event participants and visitors. One person can represent all three types or only one or two types. Most important to JOSEPHS® are the co-creators, as they are the ones who are really interested in getting to know and test new products and services and to help make them better/more useful by providing feedback and sharing and drafting their ideas. Participants, either for job-related reasons or for reasons of personal interest, join open or closed events in the DENKFABRIK. Visitors to JOSEPHS® may be people who wish to observe research projects but do not co-create; some may want to simply have a coffee or a snack, buy something at the Gadget-Shop or just work on their laptops using the free Wi-Fi. The external users of the DENKFABRIK can also be co-creators or visitors, depending on their engagement in terms of the science islands during their stay. Yet, the goal for the JOSEPHS® team is that everyone who enters JOSEPHS® becomes an active co-creator.

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3 Challenges of the First 100 Days 3.1 Selection, Induction and Education of the Team One of the biggest challenges at JOSEPHS® was to create the perfect team for something that no one had done before and something whose evolution, as well as the challenges posed to all stakeholders involved, would be unknown. When our team started to look for personnel, we thought that the most important assets for a candidate were service orientation and experience, along with openness and the ability to familiarize oneself quickly with new topics, coupled with mental flexibility. As we now know, we were right about these points. However, we were wrong about some others: at the beginning of our recruiting process, we thought that the perfect match would be a salesperson with a number of years of experience in retail who is a technophile and interested in hot topics, such as digitalization. During the interviews, however, we noticed that most applicants had difficulty understanding the scientific features of the JOSEPHS® concept. Ultimately, not only service and sales skills were relevant but especially the ability to build bridges between the feedback of visitors and co-creators and the research questions asked by the participating companies and institutions on the research islands. Additionally, the applicants could only barely imagine what JOSEPHS® could be and look like some years in the future. In the end, it was not surprising that the three successful candidates all had an academic background combined with an open mind and entrepreneurial thinking. JOSEPHS® did not provide any training, in the conventional sense, to its employees, because at that time nobody knew what to expect. We could only make assumptions as a team and predefine processes and procedures on that basis. Otherwise, we had to wait and see how everything developed and react to it accordingly, day by day. Little has changed since the beginning, because JOSEPHS® is still a prototype that adapts to constantly changing conditions and requirements in many areas. The team grew – and is growing constantly – with these challenges and has shaped JOSEPHS® with innovative solutions to give the lab its very own character. It was therefore important to hire employees having a variety of abilities and strengths who, in addition to their individual skills and special knowledge, have one thing in common: a hands-on mentality and a start-up mindset.

3.2 Organizing and Coordinating a Daily Life Process As is typical in a start-up setting, it was difficult to create a clear structure with precisely defined processes right from the start at JOSEPHS®. Of course, there were points that could be specified, which thus created a certain framework. These were mainly organizational details without content or interpersonal components. From

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the beginning, we had a duty roster for covering the opening hours as well as checklists delineating what to do at opening and closing. We continuously adjusted the duty roster as soon as we had collected the first experience indicating the time distribution of visitors and found areas of improvement. The checklists were also subject to regular checks and adjustments; particularly at the beginning and end of each theme world, they were given a general overhaul. The rest was learning by doing and learning by trying. Experiments are still part of the daily business today. The range varies from experiments on how best to address visitors while considering time, place and the wording of questions to possibilities of how to generate feedback for JOSEPHS® itself, be it with manually completed questionnaires or surveys on mobile devices at a precisely defined feedback point. All of these experiments have helped to define processes, create structures and rethink and redesign the tried and tested. They play a major role in today’s daily business being organized and structured in a way that still allows the freedom of constant change; at the same time, business functions smoothly and can be transferred to other similar test laboratories and facilities (Roth et al., 2014).

3.3 Bringing in the First Co-creators It is important to understand that JOSEPHS® does not sell a product or provide a service that has already been on the market. On the contrary, the team had to master the challenge of engaging the population’s curiosity about JOSEPHS® and finding the trigger that would make them become active participants. Traditional advertising was not an option, because the operator of JOSEPHS®, the Fraunhofer Gesellschaft, as a basic-financed and non-profit research institution, is not allowed to use it. The first co-creators came to JOSEPHS® mainly due to the strong press response. Both local and national press published many contributions in the first weeks. The focus for the team was therefore on creative forms of attention-raising, such as information stands in the city centre, trade fair participation and voucher campaigns, together with the coffee shop and dissemination by word of mouth. The latter worked very well after the first co-creators left JOSEPHS® enthusiastically and shared this enthusiasm with their friends and acquaintances. Another strategy we used to make JOSEPHS® known and to attract co-creators was to offer events that would appeal to many individuals, which will be discussed in more detail in section 3.5. In addition to these measures, we used all available channels while working together with the different stakeholders of the lab to reach diverse groups: our own colleagues, university students, guests of the coffee shop etc.

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3.4 Getting Started with Research Partners The biggest challenge in terms of acquiring research partners was to find the first ones and to design the first theme world together with them. The acquisition was particularly difficult, as we could talk only about plans and expectations but had nothing concrete to show. Fortunately, there were a few companies with a pioneering spirit and a desire to try something new and thus take a completely new approach to customer involvement in the production process. We tried to think through and anticipate as many scenarios as possible in the run-up to the opening and considered the previous design and planning of the construction of the individual research islands. We dealt with topics such as our visitors’ movement patterns within JOSEPHS®, possible interaction on the islands, feedback recording and customer journeys. During this preparatory process, and especially during the workshops for the development of research design, the research partners as well as the team members profited greatly from each other’s respective experiences. A decisive moment came with the physical construction of the research islands. On the one hand, some of the conversion work had not yet been completed, so that team, research partners and craftsmen had to work in parallel a few days before the opening, under pressure with the knowledge that everything had to be completed on time for the opening, to which many important guests were invited. On the other hand, some planned superstructures or arrangements proved not to be feasible, for example because they were too large, or too heavy or prevented the co-creator from continuing smoothly. It was now a matter of spontaneously finding creative solutions. For example, talented colleagues made special suspensions for televisions, or furniture was moved back and forth many times until the supposedly optimal setup was achieved. In summary, we have gone many ways together with our research partners during the preparation and the structure of the first theme world; we have tried a lot of things and also quickly rejected them again. During this time, we have lived a distinct trialand-error philosophy, which has brought us closer to our goal with every failure.

3.5 Establishing an Event Culture Events on current topics at the DENKFABRIK were a part of the JOSEPHS® concept from the very beginning. The principle was as follows: JOSEPHS® gives speakers a stage for presenting their topics free of charge and takes care of the organization and promotion of the event. The speaker creates a 90-minute interactive event on an interesting topic, according to the JOSEPHS® “try and participate” principle. Ideally, this topic correlates with the current theme world or is even designed directly by one of the research partners who currently has an island in the WERKSTATT. This principle should lead to all sides profiting from the events: the participants, because they can actively experience exciting topics; the speakers, because

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they have an interested audience for their topic and can use it for such things as networking; and JOSEPHS®, which hopes to make itself known and to win further co-creators. The first events went rather badly: only a handful of visitors attended many of them, and we often could not understand why. There were a few exceptions: for example, an event at which visitors could try out the relatively new Google glasses was very well attended. Of course, the speakers were not enthusiastic about the low number of visitors, and some time passed before they were motivated to try again. We remained persistent and tried many different types of events and topics. The team put a lot of energy into the search and acquisition of speakers, often infecting them with their own enthusiasm for JOSEPHS® on first contact. We often heard the remark, “Well, if you are so excited about this JOSEPHS®, I have to take a look at it myself”. Most of them were infected by the enthusiasm after visiting us and becoming familiar with the JOSEPHS® idea. As they really liked what they saw and heard, many started an event experiment together with JOSEPHS®. This exceptional commitment of the team paid off. Soon, word about the high quality of our events spread, and our DENKFABRIK filled up more and more with each event. It did not take long before there were events whose attendance exceeded the capacity of the DENKFABRIK; we therefore started to limit the number of participants and to require pre-registration. Today, the events are booming, and the speakers are queuing up to get slots for themselves. JOSEPHS® is now known and appreciated far beyond the borders of Nuremberg for its attractive events and well-known speakers. People continue to return again and again to JOSEPHS®. However, turning these visitors from event participants into co-creators remains a challenge even after almost five years.

3.6 Interacting with Users Already on the first opening day of JOSEPHS®, the team learned that our idea of how visitors would get involved in the laboratory was completely wrong. The original plan was that interested people would look around the theme world and the research islands independently or guided by a mobile device and submit their written feedback at each area. In this respect, we had underestimated the German mentality, which is to “Have a look at it, but don’t touch it”. This mindset was observed in our WERKSTATT. Many interested people came, stopped at the science islands, observed them from a safe distance and shortly moved to the next island without providing any feedback. Of course, the team responded promptly by adapting our visitor approach to purposefully accompany and encourage feedback from them. In addition, in this case we had to try out and iteratively test what kind of approach worked for whom. We also regularly changed the way we recorded feedback (e.g. writing on post-its,

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talking to employees directly or to a dictation machine, filling in a questionnaire on a mobile device) and phrased the questions we asked in order to achieve the optimal approach. It was necessary to adjust to the visitors’ behaviour and expectations. Finally, it turned out that visitors preferred to interact with people rather than with technology. The main goal of JOSEPHS® is to ensure that all visitors want to come to the lab and go home with a good feeling. They should do everything willingly and, ideally, come back for additional visits again.

3.7 Integrating the Coffee Shop There were several reasons why integrating the coffee shop into the overall concept of JOSEPHS® was a challenge; however, the overall open design of the laboratory made this possible. There were no visible boundaries. Nevertheless, such boundaries quickly manifested themselves in the minds of some visitors. The original idea was that JOSEPHS® visitors would have a great experience and enjoy themselves. This would individually encompass all the following: exploring the theme world and giving feedback, a comfortable break while enjoying a delicious coffee and a snack, experiencing an interesting event, purchasing a great gadget or simply exchanging ideas with interesting people. The coffee shop should play a central and connecting role in this combination of experiences. We hoped that many guests who would primarily come for coffee would also develop an interest in the other areas of JOSEPHS®. Therefore, it was necessary for coffee shop employees to know about the lab and to make an effort to further acquaint the guests with its offerings. Unfortunately, this worked only to a limited extent, as some of the employees were too busy, and others could not really bridge between the coffee shop and the innovation lab. We started various attempts to arouse the enthusiasm of the coffee shop employees and to involve them in the overall concept; although this worked for some, it did not lead to the desired effect overall. Joint incentive campaigns also failed. In summary, a coffee shop is a good opportunity for visitors to combine a JOSEPHS® visit with relaxation. Conversely, only a few guests of the coffee shop spontaneously became co-creators. However, the presence of a caterer on site represents a competitive advantage when it comes to third-party use of the DENKFABRIK for company events.

4 Conclusion Even after almost five years, JOSEPHS® remains a prototype that reinvents itself every day. Even though the first 100 days were a special challenge, many of the

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principles implemented at that time, for example trial and error, can still be found in the daily life of the lab despite standardized processes and established routines. The principle of early failure and iterative testing is still one of the success factors of JOSEPHS®. Its application enables the lab to keep pace with the pulse of time. Thus, it offers a perfect platform for all those concerned with innovation and current topics in today’s world and want to engage intensively in idea exchange.

References Roth, A., Fritzsche, A., Jonas, J., Danzinger, F., & Möslein, K. M. (2014). Interaktive Kunden als Herausforderung: Die Fallstudie „JOSEPHS®–Die Service-Manufaktur“. HMD Praxis der Wirtschaftsinformatik, 51(6), 883–895. Trischler, H., Vom Bruch, R. (1999). Forschung für den Markt: Geschichte der FraunhoferGesellschaft. CH Beck. Fraunhofer-Gesellschaft (2009). Joseph von Fraunhofer. Forscher und Unternehmer. Available at: https://www.fraunhofer.de/content/dam/zv/de/documents/Joseph_von_Fraunhofer_tcm7782.pdf (Last access August 1, 2019)

Michael Fraas

3 Where City Innovation Comes Alive – The JOSEPHS® Innovation Lab in Nuremberg 1 This is a Test Dear Readers Once you are skimming this text, you will probably already have read many articles describing the concept of the JOSEPHS® open innovation lab and reflecting on its creation. And naturally we hope that all of these texts have been as informative as they have been entertaining. But how can we be sure? In keeping with the JOSEPHS® mentality, we would like to focus now on you, the reader. What are your expectations for this text? Is it easy to read? Is it better like this? Or what about this? Run the paper through your fingers. What does it feel like? Does the paper have a pleasant feel? Is it too thin? Or too thick? Grab a pen and jot down what you think. What do you expect from this text? Write down what you would like this text to do in the space below, which we have reserved exclusively for you. I expect the following from the text “Where city innovation comes alive – The JOSEPHS® innovation lab in Nuremberg” 1) _______________________________________________________________________ 2) _______________________________________________________________________ 3) _______________________________________________________________________ 4) _______________________________________________________________________ 5) _______________________________________________________________________ So have you written down all your ideas? Great. We’ll come back to them later, we promise! We hope you took our little exercise seriously. We do. Because we take JOSEPHS® very seriously. The basic idea behind it is both simple and fascinating. Would it not be possible to design products and services together with all of the people who will ultimately be using them? And in a wider sense, can we not also use JOSEPHS® as a hands-on laboratory to design a city with all of the people that will be using that city together, in other words the citizens? This is a question that we are going to tackle below using the example of Nuremberg. But first of all we need a little bit of context. We will start with an explanation of why innovation is important to a city like Nuremberg and what the

Michael Fraas, Deputy Mayor for Economic Affairs, City of Nuremberg, Germany https://doi.org/10.1515/9783110633665-003

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City of Nuremberg has expected from JOSEPHS®. We will then share with you our experiences and assessments, and attempt to consider the future of this innovation lab for all from a city perspective.

2 Harnessing Innovation for Structural Change As a city with a strong industrial tradition, Nuremberg had begun embracing economic innovation and transformation, as a means of promoting a sustainable economic structure, by the 1990s at the latest. The promotion of innovation became one of the main focuses of the city’s economic policy. Nuremberg’s economic policy currently does not only support innovation in enterprises, but also technology-oriented sector clusters, new start-ups, research and development as well as digitalisation in local companies (see Table 3.1). JOSEPHS® is a key tool in this approach. A combination of engaged companies and a sound innovation policy has made Nuremberg a high-tech location with future-oriented industries and a modern services sector. This trend is also reflected in the city’s economic figures. During the ten years from 2008 to 2017, the number of people in employment in Nuremberg has grown by around 36,000 (from 269,000 in 2008 to 305,000 by 2017) (Wirtschaftsstandort Nürnberg Positionsbestimmung 2018, 2019). Over the same period, the unemployment figures have fallen by around 28 percent (Wirtschaftsstandort Nürnberg Positionsbestimmung 2018, 2019). Compared with Germany’s other major cities1 Nuremberg ranks in the top 5 for unemployment with a jobless rate of 5.1 percent (in December 2018) (Wirtschaftsstandort Nürnberg Positionsbestimmung 2018, 2019). Nuremberg is recording particularly strong growth in the field of knowledgeintensive industries (see Figure 3.1). These sectors typically employ a disproportionately high number of graduates, scientists and engineers. Over the period from 2010 to 2017, the number of people in employment and contributing to social security who were working in knowledge-intensive services in Nuremberg rose by 22 percentage points (from 67,500 to 82,500 in absolute terms) (Federal Employment Agency, 2018 and the Commission of Experts for Research and Innovation (EFI), 2010). During the same period, the number of people in employment and contributing to social security working in knowledge-intensive industries grew by 18 percentage points (from 28,000 to 33,000 in absolute terms) (Federal Employment Agency, 2018; the Commission of Experts for Research and Innovation (EFI), 2010). Nuremberg has retained a strong industrial core. Some 15.6% of the city’s workforce are employed in manufacturing companies (in 2017). In the ranking of Germany’s 20 largest cities, Nuremberg places among the top third for locations in

1 Cities with a population of least 350,000

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Table 3.1: Tools used to promote innovation as part of Nuremberg’s economic policy. Tools

Examples

Promotion of innovation in enterprises

Free one-to-one innovation consultations (Nuremberg Innovation Consultation Days) Free outreach consultations on resource and energy efficiency (Consultation Days on Resource and Energy Efficiency) Free profitability studies into self-consumption of photovoltaicgenerated electricity in the business sector (Solar checks for Nuremberg companies)

Promotion of clusters

Sector cluster for information and communication (Nürnberger Initiative für die Kommunikationswirtschaft e.V.) Sector cluster for transport and logistics (Cluster for Transportation and Logistics – neuer Adler e.V.) Sector cluster for energy technology (ENERGIEregion Nürnberg e.V.) Sector cluster for power electronics (European Center für Power Electronics e.V.)

Expansion of research and development

Energy Campus Nuremberg (energy research) Nuremberg Campus of Technology (smart city research) Embedded Systems Institute (Application Centre for Embedded Systems) ADA Lovelace Center (AI research from  onwards) New Technical University in Nuremberg (TUN) by 

Entrepreneurship

ZOLLHOF Tech Incubator (digital start-ups) Startup.Digital.Nürnberg (digital start-ups) Energy Technology Centre (energy start-ups) ESA BIC (aerospace start-ups) Startup Demo Night (start-up pitches)

Digitalisation

Digital Nuremberg (overarching digitalisation strategy for the City of Nuremberg) Digitalisation Agenda for Nuremberg (digitalisation strategy for the Nuremberg economy) IoT innovation lab for Industry . applications Energie.Digital (specialist events on digitalisation in the energy sector)

which the industrial sectors still play a key role (Wirtschaftsstandort Nürnberg Positionsbestimmung 2018, 2019). Nuremberg’s industries have gone high tech, however. Just under 70% of people working in industry are employed in knowledgeintensive sectors. This is well above the German average (45%), not to mention the average for Bavaria (51%) or for Germany’s twenty largest cities (59%) (Federal Employment Agency, 2018; the Commission of Experts for Research and Innovation (EFI), 2010).

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1,00,000 Employees contributing to social security

Employees contributing to social security

34,000

32,000

30,000

28,000

26,000

24,000

2010

2017

Knowledge-intensive services

80,000

60,000

40,000

20,000

0

2017 2010 Knowledge-intensive industries

Figure 3.1: Employment in the knowledge-intensive sectors in the City of Nuremberg in the years 2010 and 2017 (based on statistics from: Federal Employment Agency, 2018; Comission of Experts for Research and Innovation (EFI), 2010).

There is another indicator that demonstrates how much the Nuremberg economy is geared towards the future (cf. Figure 3.2). More than 10% of those in formal employment work in information and communication technologies. In terms of all things digital, Nuremberg comes second only to Munich among Germany’s major cities. This excellent second place provides the ideal platform for structural change as the economy goes digital (Wirtschaftsstandort Nürnberg Positionsbestimmung 2018, 2019). This process of structural change is already well advanced. And it is now Nuremberg’s turn to get in on the act, using digital transformation to benefit the economy. We can already see that innovations are growing increasingly fast-paced, more interlinked and complex. Innovation cycles are shortening, with business models being replaced by new, digital solutions from one day to the next. Companies are being forced to reinvent themselves using innovation and creativity. All of this can be observed in Nuremberg today. Many companies are setting up their own innovation spaces and labs, and opening themselves up to innovative impetus from the outside. In this way a further trend has taken hold. In keeping with the theme of “open innovation”, companies are encouraging third parties to get involved in the development of their products, services and processes. The concept of “co-creation” is a very specific form of open innovation and refers to companies involving their customers in their innovation process. (https://de.wikipedia.org/wiki/Co-Creation) After all, despite all the available methods of market research, between 50 and 80 percent of all new products fail. Generally, this is not down to poor product quality but simply because the new products do not provide sufficient benefit to customers. They are out of touch with the market. In contrast, an interactive product development process

2.4%

5.5%

4.9%

4.7% 4.5% 4.0% 3.6%

6.2% 6.4%

9.4%

Proportion of employees in the information and communication technology sector as a percentage of total employees, June 2017

8.7% 8.7% 8.2% 7.7% 7.6% 7.6% 7.0% 7.0% 6.9% 6.4% 6.4%

11.1% 10.4% 9.9%

Figure 3.2: No. of employees working in information and communication technologies, a comparison by city Wirtschaftsstandort Nürnberg Positionsbestimmung 2018, 2019.

Central Franconia Metropolitan region of Nuremberg Bavaria Germany

Munich Nuremberg Bonn Dresden Colone Stuttgart Frankfurt a M Hamburg Berlin Leipzig Münster Düsseldorf Dortmund Hannover Essen Bremen Bielefeld Bochum Wuppertal Duisburg

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that involves customers can help to tackle this problem. Customers can (help to) develop the products that they want to use. The resulting products are therefore closer to customers’ actual needs. There are now well documented examples of this form of cooperative product development being a success. The Danish toy company Lego has a “Lego Ideas” platform, by means of which the lego community can contribute its own ideas for new lego sets. If a proposed design gets 10,000 votes on the website, the company reviews whether to include the idea in its range (Hensberger, 2018). The T-shirt company Threadless actively involves its customers in selecting the designs for its printed Tshirts and in its marketing activities. Its community can submit designs for T-shirts, which are then scored or improved by other members. Customers can also model the products and create photos for the company’s catalogue. Threadless itself sets the rules, pays the customer-designers and manages the production process (manufacture and distribution) (Apel, 2011). The furniture chain IKEA works with its customers on its “Kindern eine Zukunft schenken” (Giving children a future) campaign. Children from all over the world are invited to take part in a competition by sending in their pictures of animals. The winning pictures are then turned into cuddly toys which are sold in IKEA stores. The revenue from the sale of these toys is donated to good causes (IKEA, n.d.). The logistics company DHL worked with its customers in Germany and Singapore to improve the quality of its services. A customer came up with the idea of using a drone to carry parcels, and this has been developed and subsequently realised. The “DHL Parcelcopter” is intended primarily for situations that mesh poorly with established infrastructures or where standard delivery methods would take considerably longer, for example in relation to global development cooperation projects. This drone was developed between 2013 and 2018 in four iterations. Meanwhile, the Canadian mining company GoldCorp is using its customers’ knowledge in a different way. Back in 2000 the company was on the brink of insolvency having failed to find any new gold deposits. It decided to make all of its geological data publicly available and offered a financial reward for help. More than 1,000 geologists and amateur researchers worked on the data and came up with suggested locations of deposits. The campaign was very successful, not just ensuring GoldCorp’s survival but also generating significant growth (Apel, 2011). The JOSEPHS® concept is building on these and other experiences, taking them a step further. In this way, the idea of a city-centre innovation space for co-creation was born. This is a space where companies can engage with customers and potential customers, exchanging ideas about new products and services, and receiving direct feedback. This approach fits perfectly into Nuremberg’s economic policy with its focus on innovation. Consequently, the Department for Economic Development of the City of Nuremberg supports the JOSEPHS® project, helping for example in the search for suitable premises and with the requisite building permits.

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3 Down the Rabbit-Hole – JOSEPHS® takes Shape as It Shapes the Future Alice was beginning to get very tired of sitting by her sister on the bank, and of having nothing to do: once or twice she had peeped into the book her sister was reading, but it had no pictures or conversations in it, `and what is the use of a book,' thought Alice `without pictures or conversation? ’So she was considering in her own mind (as well as she could, for the hot day made her feel very sleepy and stupid), whether the pleasure of making a daisy-chain would be worth the trouble of getting up and picking the daisies, when suddenly a White Rabbit with pink eyes ran close by her. There was nothing so very remarkable in that; nor did Alice think it so very much out of the way to hear the Rabbit say to itself, `Oh dear! Oh dear! I shall be late!' (when she thought it over afterwards, it occurred to her that she ought to have wondered at this, but at the time it all seemed quite natural); but when the Rabbit actually took a watch out of its waistcoat-pocket, and looked at it, and then hurried on, Alice started to her feet, for it flashed across her mind that she had never before seen a rabbit with either a waistcoat-pocket, or a watch to take out of it, and burning with curiosity, she ran across the field after it, and fortunately was just in time to see it pop down a large rabbit-hole under the hedge. In another moment down went Alice after it, never once considering how in the world she was to get out again. (Lewis Caroll, Alice’s Adventures in Wonderland)

Lewis Carroll’s story is a classic, and familiar to readers all over the world. Alice’s amazing journey begins when she disappears down the rabbit-hole. What happens next is a well-known tale. Small becomes big, big becomes small, the royal household is a pack of playing cards, with the merciless Queen of Hearts at its head, and the game of croquet features flamingos as mallets and hedgehogs as balls. The entrance to Nuremberg’s “rabbit-hole” is located in the corner between Hintere Lederergasse and Karl-Grillenberger-Straße.2 Everyone who steps inside JOSEPHS® must feel just a little bit like Alice. Here too, there is always something new or exciting waiting to be discovered. Some things are so fantastic that they would barely be conceivable outside of JOSEPHS®. There might not be any “Eat me!” or “Drink me!” signs, but everything at JOSEPHS® is designed to be touched and tried out, awakening visitors’ natural curiosity. JOSEPHS® is transformed every three months, presenting new ideas, concepts, services, products and theme worlds ranging from “Smart future” to “Senses in a Digital World”, “Join-in media” and “Creative – Hands and Feet”. Here are just some of the examples of recent exhibits at JOSEPHS®: – Smart sports clothing that can warn wearers when they are over-doing it. – Software that can answer the telephone and make appointments for callers. – Smart school bags that are comfortable to carry.

2 JOSEPHS´ address

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– – – – – – – –

Michael Fraas

A talking tent that can give pitching instructions. A digital shopping assistant to take round the supermarket. An external motor for in-line skates. A universal picture language for warehouse workers. An active bed that uses targeted movements to relax the sleeper’s back. A cube that can stop fresh vegetables going off. Software to help with learning a musical instrument. Technology: that can be used to feel music, that enables viewers and listeners to customise their television or radio experience or that uses the sound of a voice to form shapes.

JOSEPHS® is living proof of the ingenuity of the region, although it goes without saying that companies from elsewhere have always been just as welcome.

4 What does the City of Nuremberg Expect from JOSEPHS®? The key question is whether all of the good ideas also generate new developments for Nuremberg. The City of Nuremberg’s expectations were correspondingly high. Nuremberg set the following requirements: – JOSEPHS® will help to improve innovation in and the economic strength of companies in Nuremberg and the surrounding region. – JOSEPHS® will provide an experimental space for local retailers in Nuremberg. – JOSEPHS® will be a drop-in centre for innovation in the city administration and community. – JOSEPHS® will be perceived locally and beyond the region, improving Nuremberg’s visibility. We are therefore going to consider whether JOSEPHS® has lived up to these expectations.

4.1 Improving Innovation and Economic Strength Over a five-year period more than 100 partners have showcased their projects, products, services and ideas at the different “theme islands”,3 among them 55 companies

3 These “islands” are individual exhibition spaces in which the tenants can display their themes with the support of the Fraunhofer research team.

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and 47 institutions (mainly research establishments but also foundations and authorities, cf. Table 3.2). Table 3.2: Overview of participation in the 19 theme islands at JOSEPHS® (2014–2019) (based on JOSEPHS, 2019). Companies 

of which from the metropolitan region of Nuremberg

Institutions

of which from the metropolitan region of Nuremberg

 (%)



 (%)

The question of whether the JOSEPHS® offering is highly regarded by companies and institutions in the region is relevant to the assessment of how it contributes to innovation and economic strength in the European Metropolitan Region of Nuremberg.4 More than 60% have a positive view of JOSEPHS®. The figure is as high as 64% for companies (JOSEPHS, 2019). A third of the companies involved (18 in absolute terms) came directly from the City of Nuremberg. In other words, local companies have responded well to the offering from JOSEPHS®. These companies include online retailers, banks and insurance undertakings, providers of health and fitness equipment, toy companies, media houses and industrial enterprises (JOSEPHS, 2019). A broad mix of sectors is therefore represented at JOSEPHS®. The services available from JOSEPHS® are obviously relevant to a broad range of Nuremberg companies, as well as to companies from the metropolitan region of Nuremberg. JOSEPHS® gives companies the opportunity to try out their innovations and gain a better understanding of what customers need. The Nuremberg-based online jewellery company Amoonìc is the perfect illustration of this, having gained important impetus for the design of its online shop. Amoonìc customers can design their own jewellery. The company was awarded the Deutscher Gründerpreis, which is awarded to Germany’s top start-ups, in 2017 (JOSEPHS, 2019, Deutscher Gründerpreis, 2017). Overall, it is clear that the city’s expectations have been fulfilled in this regard.

4 The metropolitan region of Nuremberg includes 23 administrative districts and 11 urban municipalities in Central Franconia, Upper Franconia and parts of Lower Franconia, Upper Palatinate and Thuringia. Some 3.5 million live in the metropolitan region. https://www.metropolregionnuern berg.de/daten-fakten/daten-fakten.html

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4.2 Experimental Space for Local Retailers One of the initial ideas behind JOSEPHS® was to create an experimental space for local retailers to use. Retailers would be able to come to JOSEPHS® to try out their ideas. During the first five years, retailers were only represented in one of the theme islands, however (one sports retailer from the region). Retailers with a digital business model were much more heavily represented (10 companies, of which 2 from Nuremberg), as were companies offering dedicated services to the retail sector (5 companies, of which 1 from Nuremberg). There has also been a whole series of events dedicated to retail topics. Future topics relevant to retail have therefore been covered very well by JOSEPHS®, albeit with the focus on internet-based selling.

4.3 A Drop-in Centre for Innovation in the City Administration and Community JOSEPHS® has made a name for itself as an innovation space dedicated to exciting issues for the city administration and community. With its open approach it has grown into a biotope for the most diverse range of formats and questions, including sustainability, innovation, the future of retail, start-up culture, digitalisation, urban history as experience, the future of work, urban mobility, development cooperation and much, much more. In this way it contributes to creative restlessness in the city, triggering new approaches and solutions to the challenges facing Nuremberg. As an innovation space it is now very familiar to many people in the city and region. From August to October 2015, the City of Nuremberg made use of JOSEPHS® as part of the “Playful Development of Innovations” theme world. In cooperation with SAP AG it was able to learn what local people wanted to see included in the planned City App (see Figure 3.3). Using an interactive format, the visitors were able to answer questions on such aspects as the type and scope of the app by means of gesture control. Unfortunately, the City App was never realised, this being another outcome that can result from the use of JOSEPHS® in the very early stages of development. The list of JOSEPHS® users also includes Nuremberg’s housing company Städtische Wohnbaugesellschaft wbg and Nuremberg Airport (in which the City holds a 50% stake) There have also been various JOSEPHS® events based on cooperation arrangements. Overall, however, there would have been scope for even more cooperation between the city authorities and JOSEPHS®.

4.4 Visibility Beyond the Region JOSEPHS® has received many awards and featured in many (trade) publications. In 2018 the innovation lab was the recipient of the 2018 Science Prize awarded by the

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Figure 3.3: City of Nuremberg theme island in JOSEPHS® (https://www.josephs-service-manufaktur. de/besucher/vorherige-themenwelten/themenwelt-innovationen-spielend-entwickeln/).

EHI foundation and GS1 Germany. This Prize recognises scientific cooperation projects that are particularly relevant to and promote innovation in the retail sector. During the previous year JOSEPHS® was recognised as “A centre of excellence in the land of ideas” in a nationwide competition designed to showcase lighthouse projects for Germany as a base for business. A further indicator of the lab’s visibility beyond the region lies in the origins of the companies taking part in the theme islands. 36% of companies that have exhibited at JOSEPHS® over recent years are based outside of the European metropolitan region of Nuremberg. This means that the service on offer from JOSEPHS® is relevant beyond the region and growing in stature. With regard to institutions (mainly R&D), the proportion is actually slightly higher still, at 40%. Overall, JOSEPHS® has made a significant contribution to the visibility of the City of Nuremberg in terms of open innovation.

5 Everything is a Test Given the positive results for JOSEPHS® from a city perspective, it is appropriate to consider what the future might hold. JOSEPHS® would not be what it is if it would

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not look to build its own future. We want to take this forward. The following is an imaginary interview: Editor: JOSEPHS® is a huge success story. So much has been done, there is very little left to do. But there’s still some scope for getting the retail sector more involved. JOSEPHS®: That’s old news. We’ve been working on how to approach the retail sector more effectively. Our “Smart Future Retail” project is aimed at specifically this area. It’s a bit like open heart surgery for the retail industry, with a real retailer in a real shop implementing the latest retail technologies and ideas, and using them to make money. It’s exciting. We’re already looking forward to it. Editor: That sounds great. It’s a bit like the first JOSEPHS® baby. You are a huge success story. Aren’t there other people wanting to get involved? JOSEPHS®: Yes, obviously. That’s why I am now going it alone. I’m not a project any more, but a real company. And I am roving the country with my concept. But my heart will always be in Nuremberg. Editor: And how will you cooperate more effectively with the City of Nuremberg in future? In terms of innovation, JOSEPHS® and the city administration could still nudge closer together . . . JOSEPHS®: There are obviously some ideas. One idea is that the city could use me as an innovation space for administration issues. But that’s all still up in the air at the moment. Editor: Things are still looking exciting then.

In conclusion, we are coming back to you, dear readers, just as we promised at the start. This is your opportunity to give feedback on this article. We hope you can remember the wishes that you jotted down earlier? Can you? Great. In that case, it’s time to do the evaluation. Did the text meet your wishes? Our scale is from –2 (not fulfilled) to +2 (all fulfilled): –

–



+

+

Wish ) Wish ) Wish ) Wish ) Wish )

If your score is a negative number, what we’ll say is “This was a test”. You are also now officially allowed to rip out this article. But if your score was very positive, it would be nice if you could recommend it to other people and help spread the word about the first co-creative open innovation lab of its type in Nuremberg.

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References Apel, P. (2011). Customer Co-Creation: Wenn Kunden mit Unternehmen gemeinsam Produkte herstellen. Available at: https://www.peter-apel.de/blog/customer-co-creation-wenn-kundenmit-unternehmen-gemeinsam-produkte-herstellen (Last access January 10, 2019) Deutscher Gründerpreis. (2017). Girl’s best friends. Available at https://www.deutschergruender preis.de/preistraeger/2017/amoonic/ (Last Access January 07, 2019) Hensberger, A. (2018). Co-creation: Wie Sie Ihre Kunden in den Innovationsprozess integrieren. LEAD Innovation Management. Available at: https://www.lead-innovation.com/blog/kundenin-den-innovationsprozess-integrieren (Last access January 09, 2019) IKEA. (n.d.). IKEA Family Malwettbewerb. Available at: https://www.ikea.com/ms/de_DE/goodcause-campaign/soft-toys-for-education/kids-design-for-good-cause/ (Last access January 11, 2019) JOSEPHS. (2019). Josephs Service Manufaktur. Available at: https://www.josephs-servicemanufaktur.de/ (Last Access January 07, 2019). Wikipedia.de, Available at https://de.wikipedia.org/wiki/Co-Creation (Last Access July 23,2019) Wirtschaftsstandort Nürnberg Positionsbestimmung 2018, 2019 Statistical Data from Federal Employment Agency 2018 and the Commission of Experts for Research and Innovation (EFI) 2010

Alexander Pflaum and Albert Heuberger

4 JOSEPHS® as an Anchor Point for the Development of Smart Products and Services in an Increasingly Digitized World 1 Introduction Modern information and communication technologies such as the Internet of Things (IoT), Cloud and Mobile Computing, Blockchain or Artificial Intelligence (AI) are fundamentally changing the way we live. The increasing use of these technological innovations is accompanied by digitalization, which is now seen as a megatrend that has a significant impact on both society and the economy. High potential for change is attributed above all to smart products and the associated smart or data-driven services (Porter and Heppelmann 2014). In agriculture, for example, smart machinery helps to increase the yield per hectare and thus supply more people. Production systems become more efficient and flexible through the use of machine tools 4.0, autonomous transport systems and intelligent containers, and can react much more flexibly to changes in the corporate environment. Smart toothbrushes and garments help people live healthier lives and reduce healthcare costs. Intelligent footballs and ice hockey pucks lead to even more intense experiences in stadiums or in front of television sets. With the development, implementation and use of smart products and services, business processes, business models and industry structures are changing as well. “Digital transformation” is the magic word (Becker et al. 2019). However, for the individual company, the changes associated with digital transformation represent an immense challenge that cannot be mastered easily. It is the task of scientific institutions in general and the Fraunhofer Society in particular to support companies in coping with such central challenges. In the context of digital transformation, the Fraunhofer Institute for Integrated Circuits IIS concentrates on the development of sensors and microelectronic circuits for the realization of smart products, communication systems for the transfer of data between product and digital platform in the cloud, data analytics and artificial intelligence as a basis for the prognosis and derivation of recommendations for action as well as new services and business models for different fields of application. The institute thus masters the entire value chain from data generation with the help of smart products, its transmission, processing, analysis and monetization through

Alexander Pflaum, Fraunhofer Institute for Integrated Circuits IIS, Center for Supply Chain Services, Nuremberg, Germany Albert Heuberger, Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany https://doi.org/10.1515/9783110633665-004

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innovative and economically sustainable data-driven services. In addition to technological issues, the institute has also been dealing with questions of technology and innovation management for many years. Researchers with a background in business administration, mathematics and business informatics are working intensively on the development of models, methods and tools that support companies in the development, implementation and operation of data-driven services and the associated digital transformation. The innovation laboratory JOSEPHS® is ultimately one of these tools. The aim of this article is to explain how the open innovation laboratory contributes and what role it plays in the innovation ecosystem of Fraunhofer IIS today. The following chapter deals with the concept of digital transformation. Starting from a simple framework, the special characteristics of data-driven companies are presented. A reference process for the transformation of product-oriented companies into data-driven counterparts is explained. Based on this, challenges can be derived that companies have to deal with on their way to becoming a data-driven company. The third chapter describes how JOSEPHS® can support companies in meeting this challenge today. Finally, the article describes the innovation ecosystem JOSEPHS® is part of. The last, fourth chapter summarizes briefly and shows which further developments are conceivable for the future.

2 Digital Transformation 2.1 Framework for Digital Transformation There is still no scientifically elaborated and generally accepted definition of the concept of digital transformation. Figure 4.1 shows a framework for digital transformation, which was developed at the Fraunhofer Centre for Supply Chain Services SCS in Nuremberg in close cooperation with the Otto-Friedrich-University in Bamberg (Klötzer and Pflaum 2017). The starting point is a manufacturing company that pursues a traditional product-oriented business model. The endpoint is the digitized counterpart, the “data-driven company”, which in turn is based on a digital business model (Teece 2010, Zott and Amit 2017, Pflaum and Schulz 2018). The transformation from a product-oriented to a data-driven company can take place in different paths. On the one hand, it is possible to turn the traditional product into a smart one (Klötzer and Pflaum 2015) using embedded microelectronics. The result can then be integrated into a smart service (Arbeitskreis Smart Service Welt 2015, Beverungen et al. 2017) and embedded into a corresponding business model in order to generate new revenues (dimension “Realization of smart products/services” in Figure 4.1). Examples include smart agricultural machinery, the intelligent toothbrush or the smart ice hockey puck. On the other hand, smart products from other manufacturers can be used to make one’s own processes more efficient, more

4 JOSEPHS® as an Anchor Point for the Development of Smart Products and Services

Digitized internal processes

Fully digitized enterprise

No digitization visible

Digitized Services

Product orientation

Service orientation

Data-driven enterprise: Data enables new services, enhances efficiency, flexibility agility and leads to additional turnover and business models Application of smart products/services

Traditional Smart processes processes

Realization of smart products/services

37

Figure 4.1: Framework for digital transformation (Klötzer und Pflaum 2017).

effective, more flexible and, above all, more agile (dimension “Application of smart products/services” in the Figure 4.1). Examples would be the machine tool 4.0 or the smart container in production. Further examples can be found in the literature under the keyword “Industry 4.0” (Burmeister et al. 2016). In principle, both approaches can be pursued separately or combined with each other. The considerations above raise the question to which extend product-oriented and data-driven business models and companies differ from each other. In the next subchapter, this question will be answered in detail.

2.2 The Data-driven Enterprise Table 4.1 shows how product-oriented and data-driven companies differ from each other (Pflaum and Klötzer 2018). In contrast to the product-oriented company, the data-driven enterprise is not based on traditional, but on smart and networked products. With the help of embedded microelectronics, traditional physical products are transformed into their smart counterparts und brought to the market based on an “as-a-service” approach. In the data-driven enterprise the physical product itself loses importance. Data generated by the smart product in combination with other data available within and outside the company is the essential asset. The real challenge is no longer the efficient development, manufacturing and marketing of a physical product, but the automated generation, transmission, processing and exploitation of data. Data lakes and linked data technologies guarantee the availability of data needed for different

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Table 4.1: Characteristics of product-oriented and data-driven enterprises (according to Pflaum and Klötzer 2019). Dimensions

Product-oriented enterprise

Data-driven enterprise

Offering

Focus on products and accompanying services

Focus on data-driven and product related services

Value carrier

The product itself, accompanying services delivering additional value

Data available within the company and data from external sources

IT-Systems

Hardware & software for design, production, distribution, maintenance and repair of physical products and value-added services

Hardware and software for creation, procurement, storage, processing and exploitation of data coming from different sources

Process Linking design, development, organization transformation and transfer activities to create standard processes (SCM, SRM, CRM, PLM)

Fully digitized and automated information processes spanning the whole data life cycle from creation to analysis and exploitation

Structural Traditional structures with a focus on organization design, development, production and distribution of products/services

To the largest possible extent virtualized enterprise with permeable and simple organizational structures

Cooperation Integration of companies into traditional Understanding the enterprise as a part of pipelines or supply chains spanning from a business ecosystem dominated by a primary production to end-consumers digital service platform in the cloud Human resources

Engineers, computer scientists, business Service designers and engineers, economists, technicians computer and data scientists

Culture and mindset

Normally strong hierarchical structures, closed innovation processes, focus on intelectual property, product orientation

Permeable hierarchies, open innovation processes, flexible, agile, focus on customer and network value, service orientation

types of services. The pure amount and the variety of data, the speed at which data is created require digital platforms to support the data-driven value creation process. The use of such platforms fundamentally changes the business models of companies and, in this context, their roles and cooperation behavior within value creation systems. In principle, platform business models offer the possibility of transforming not only one’s own products but also those of competitors into their smart counterparts and thus increase the scalability of the business. Following this line of thinking digital transformation even eliminates the need to develop and manufacture products or to maintain production facilities. Smart products can be purchased or made available by third parties as “commodity goods”. The originally product-oriented and pipeline-like supply chains eventually become data driven

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and platform-based ecosystems. The data-driven enterprise is also associated with new demands on employee qualifications and corporate culture. Creating value from data requires people with creative skills, service designers, engineers and data scientists. Hierarchies become flatter, processes faster and more agile. Innovations are no longer predominantly generated inside the company, but are primarily obtained from the market using open innovation approaches.

2.3 Reference Process for Digital Transformation For the transformation of traditional product-oriented companies into their datadriven counterparts, a reference process has been developed at Fraunhofer IIS in close cooperation with the University of Bamberg. The process is shown in Figure 4.2 and briefly explained in the following section (Pflaum und Gölzer 2018).

BUSINESS STRATEGY: Picture of the future devolopment Identification of data-driven Services Evaluation based on portfolio analysis Roadmapping process SS NE Y EG I. B U STR SI AT

ION IS G N KI

E DG LE ON TI

DATA

II. K N CR OW EA

DATA SOURCES: – Internal and external data sources – Creation of a data catalogue – Application of linked data technology

IV. DECISION MAKING: – Quantification of implementation costs – Definition of business model – Creation of Decision template – Adaptation of vision and implementation roadmap

III. APP KNO LIC W A

IV. KNOWLEDGE CREATION: – Solution Conceptualization – Method selection – Data procurement – Data curation

IV. D MA EC

E DG LE N O TI

I. – – – –

III. KNOWLEDGE APPLICATION: – Solution implementation – Proof of Concept – Quantification of benefits

Figure 4.2: Reference process for digital transformation (according to Pflaum und Gölzer 2018).

The transformation process begins with the generation of a vision for the data-driven company of the future. The vision must then be translated into a set of data-driven applications or services as part of the development of the business strategy (step 1 in Figure 4.2). In order to avoid friction losses during the implementation, the right framework conditions have to be set in the background regarding the IT infrastructure, the partner network, the own organization, the required human resources and the corporate culture, based on an assessment of the digital maturity level of the

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company. The individual services are to be evaluated, prioritized and translated into an implementation roadmap with regard to their attractiveness for the company and their technical and organizational feasibility. Subsequently, the best rated service can be implemented. In the context of knowledge creation (step 2 in Figure 4.2), a data model is generated for this service with all required information and filled from different data sources inside and outside the company. In the next step, the knowledge collected in this way can be used to describe reality, forecast technical or business parameters, or automatically generate recommendations for action or control instructions. The methods and algorithms used here come from the fields of statistics, mathematics, machine learning and artificial intelligence. They must be integrated into a technical solution (step 3 in Figure 4.2) whose final implementation is decided in the last step of the procedure model (step 4 in Figure 4.2). After the decision and the implementation of the service the vision of the future and the corresponding roadmap have to be revised. On this basis, the implementation of further data-driven services can then be tackled. The result is an iterative process that brings the company closer to the constantly updated vision and the data-driven company, step by step.

2.4 Challenges in Developing Smart Services A number of challenges that have to be overcome in connection with the development and implementation of data-driven services can be derived from the considerations above as well as from previous transformation projects conducted at Fraunhofer SCS: Customer Integration Smart products have their own identity and are capable of collecting, storing, processing data, communicating with the environment and collaboratively solving specific tasks. The original function of the product is extended by additional, customizable data-driven services. In many cases, the development of such services requires the cooperation of the consumer. Here, customer co-creation is the keyword that has occupied both science and practice intensively for years (Turber 2014). Through the direct cooperation with the customer, the acceptance of smart services can be significantly increased. Agile Development Smart products and services are usually linked to digital platforms in the cloud (Papert and Pflaum 2017). Platforms in turn follow their own business models, which are based on network effects and have considerable disruption potential (Engels et al. 2017, Christensen 1997, 2006; Christensen et al. 2015, 2016). Companies are thus forced to innovate comparatively quickly and to significantly accelerate the corresponding

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processes. Agile development is the keyword here. Modern concepts and procedures such as MVP (Minimum Viable Product) and SCRUM or FaaS (Failure as a Service; Gunawi et al. 2011) can support this. Innovation in Ecosystems The complexity of smart products and services and the competence profiles required for their conception, realization and operation challenges individual companies. Ultimately, companies are forced to innovate together with partners within complex ecosystems (Turber 2014, Papert and Pflaum 2017). The prerequisite for success is comprehensive knowledge of the required roles and actors. Companies that want to implement and market smart products and services must either position themselves within existing ecosystems or, if such ecosystems are not available, build their own. Of particular interest in this context is the handling of digital platforms in the cloud. Transformational Capabilities In many cases, companies are still not prepared for digital transformation. Even if the necessary awareness exists at board level and the will for transformation is there, middle management and the operative base must be put in a position to successfully shape the digitization process. The models, routines, methods and tools required for this must be taught or trained within executive education programs. Application-oriented science is required to integrate and offer the fundamentals developed at universities in corresponding executive education modules. Ensuring Business Success A suitable value contribution is a necessary but not yet sufficient condition. The aim must be to develop a suitable business model around the value proposition that guarantees the business success of the new service offering as far as possible. On the customer side, the appropriate marketing channels must be identified and suitable customer relationship management processes set up. Above all, a suitable payment model has to be selected. On the resource side, key processes and resources must be defined and implementation partners selected. The identification of cost factors and cost drivers is also essential.

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3 JOSEPHS® as a Tool for Digital Transformation 3.1 Current Range of Services Since the foundation of JOSEPHS®, various service offerings have been created to support the conceptualization and implementation of innovative services based on smart products. In Table 4.2, the individual service components are listed, briefly described and assigned to the challenges identified in the last chapter. In total, the table shows that the range of services presented addresses all the challenges mentioned above. JOSEPHS® thus increases the probability of success in the development of smart services in a significant way and makes an important contribution to the digital transformation of companies. However, the successful conception, development and implementation of smart products and services requires intensive cooperation with partners within the framework of a more comprehensive innovation ecosystem. The following section deals with this topic in larger detail.

4 Position of JOSEPHS® within Fraunhofer IIS’s Innovation Ecosystem Fraunhofer IIS has been involved in R&D projects in the field of smart products and services for many years. Over time, an innovation ecosystem has been developed. The different roles and the connections between them are shown in Figure 4.3. The Fraunhofer Institute for Integrated Circuits IIS, the Fraunhofer Center for Supply Chain Services SCS and the innovation laboratory JOSEPHS® represent the core of the innovation ecosystem. JOSEPHS® is operated by Fraunhofer SCS with strong support from Friedrich-Alexander-University FAU in Erlangen and Nuremberg. Fraunhofer IIS cooperates with product manufacturers and suppliers of embedded hardware and software in the design and realization of smart products. Fraunhofer SCS works together with users, consumers, service providers and software application providers on data-driven, smart services. Together with Fraunhofer, platforms operators, telecommunications companies and standardization organizations ensure the necessary connectivity and create resources and infrastructures for the operation of services in the cloud. Cooperation partners such as FAU, the Otto-Friedrich-University in Bamberg and the Indian Institute for Management in Bangalore IIMB also work together with Fraunhofer IIS and SCS in the area of executive education. The individual roles of the partners are outlined at the next level of detail in Table 4.3. With the help of the innovation ecosystem described above, Fraunhofer IIS/SCS is in a position to drive the development of smart products and services from the ideation to implementation and monetization. Fraunhofer concentrates above all on application-oriented research and on the transfer of results from basic research

Challenge

X

X

X

Innovation isles

JOSEPHS® forum

X

X

X

X

X

Customer Agile Innovation Transformational integration development in capabilities ecosystems

Public talks

Component

Table 4.2: Service offerings at JOSEPHS® and their contribution.

X

X

Business success

The forum provides the actors of the innovation ecosystem the opportunity to network with each other and to exchange ideas on topics related to smart products, smart services and digital transformation. In addition, the forum can is used for business model workshops by Fraunhofer SCS and its customers. Executive education courses are also held in the forum. The participants benefit from the creative environment and learn about models, routines, methods and tools for the management of the transformation process.

Smart products and services can be presented over a period of three months in theme isles, and their value contribution can be discussed with potential customers. It is also possible to update products and services with a new version at fixed intervals. Thus, SCRUM processes running in the background can be easily supported.

The innovation laboratory offers public talks and discussion events on various topics related to smart products, smart services and digital transformation. New scientific findings coming from Fraunhofer and partner organizations are presented here. Practitioners report on successful development projects and digitization processes. These events bring together the most diverse actors and create synergies between the public, industry and science.

Description

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Service provider

Software provider Product manufacturer

User

JOSEPHS®

IIMB

Fraunhofer IIS/SCS

ES provider

FAU Telco provider

OFU

Digital platforms

Standardization

Figure 4.3: Smart service innovation ecosystem.

Table 4.3: Ecosystem partners and value contribution. Partner

Value Contribution

Fraunhofer IIS/SCS

– – – –

–

Development of cognitive sensors and smart products Closing gaps in the field of communication technologies Development of data analytics algorithms and toolbox Development of management tools and transformation toolbox Operation of the open innovation laboratory JOSEPHS® Executive education programs related to digital transformation (focus on technologies and transformation management processes etc.) Participation in standardization processes

JOSEPHS

– – – – –

Organization of public talks and discussions User integration based on innovation isles Evaluation processes during agile development projects Organization of workshops with focus on Digitalization Support of executive education programs

Product manufacturer

– –

Integration of embedded systems into products Realization of open software interfaces

Embedded System Provider

– –

Realization of embedded systems for smart products Establishing standard conformity

– –

®

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Table 4.3 (continued ) Partner Software Provider

Value Contribution – – –

Service Provider

– – – –

Realization of embedded software for smart products Realization of application software for mobile/stationary devices Integration of data analytics algorithms into software solutions Development of smart service concepts Integration of hardware and software modules into systems Development of business models for smart services Operation of smart services on digital platforms in the cloud

Platform Provider

– – – –

Operation of digital service platforms in the cloud Matching processes between service Providers and Users Attraction of complementary service offerings Development of context specific service systems

Customers and Consumers

– –

Articulation of concrete needs during talks and discussions Discussion of value contributions presented on innovation isles Co-Creation activities during workshops and research projects

– Friedrich-Alexander University Erlangen-Nuremberg

– – – –

Development of open innovation environments Support during operation of JOSEPHS® Organisation of innovation workshops Executive education programs related to digital transformation (focus on open innovation, customer cocreation, leadership etc.)

Otto-Friedrich University Bamberg

– – – –

Development of digital transformation reference process Development of models, routines, methods and tools Research on digital and digitalized business models Executive education programs related to digital transformation (focus on reference process, business models etc.)

Institute for Management Bangalore

–

Executive education programs related to digital transformation (focus on history of digital transformation, platform business models, leadership in the digital world, emerging markets etc.)

Telecommunication Provider

–

Ensure connectivity between smart products, digital platforms in the cloud and users or consumers

Standardization Organizations

–

Creation of standards in particular for secure and robust end-2-end data transmission

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to manufacturing companies and the service economy. With technologies, algorithms, models, methods and tools, Fraunhofer IIS and SCS provide the technological core and management know-how required for successful digital transformation based upon smart products and services. Within the ecosystem, the innovation laboratory JOSEPHS® provides the physical platform on which the individual players and stakeholders are brought together. From Fraunhofer’s point of view, the open innovation laboratory thus forms a central anchor point for the conception and realization of smart services based on smart products in particular as well as the digital transformation of companies and business models in general not only in the European metropolitan region of Nuremberg but far beyond. In recent years, a large number of innovation projects have been successfully carried out. The fact that JOSEPHS® was recognized by the federal government as an excellent location in the “Land of Ideas” in 2018 clearly underlines the significance of the open innovation laboratory for the digital economy.

5 Summary and Outlook In the introduction to this contribution, it became clear that smart products and services must be understood as vehicles of digital transformation and that their continuous development and implementation will fundamentally change business. Traditional manufacturing companies are transformed into their data-driven counterparts. The transformation concerns strategy, offering, information technology infrastructures, cooperation, structural and process organization, employee qualification as well as innovation and corporate culture. A reference process for digital transformation was presented that begins with the development of a corporate vision and iteratively goes through the steps “strategy development”, “knowledge generation”, “knowledge application” and “decision making”. The result of the first step is an implementation roadmap for data-driven services. The other three steps relate to design, prototypical implementation and subsequent implementation of individual data-driven services in the corporate context. Data-driven services can be used in the company’s own processes or offered at the interface to the customer. They can be closely linked to the company’s own physical product or based purely on data available to the company. It was pointed out that digital platforms in the cloud are particularly important for the operation of data-driven services. Based on these considerations, a number of challenges were derived which are connected with the digital transformation in general and with the conception and realization of data-driven services in particular. The following were mentioned explicitly: intensive involvement of the customer in the development process, agile development processes, innovation in business ecosystems, rapid development of transformational capabilities in the company as well as increasing the probability

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of success by using a business model approach. It was shown that the different service modules of JOSEPHS® address all these challenges and that the open innovation laboratory can make a significant contribution to the success of corresponding development processes. However, it also became clear that, in addition to contributions from Fraunhofer IIS, SCS and JOSEPHS®, contributions from other partners are also needed. As an anchor point, JOSEPHS® already gathers the individual players on a physical platform in Nuremberg’s city center. It has the potential to further promote the innovative ecosystem that has been developed around Fraunhofer IIS during the last years. Against this background, the existing service portfolio must be revised and further developed in the coming years. For example, the current format does not yet offer a joint working space in which employees of the partners can work together and concentrate on one topic for several hours. The research campus of Fraunhofer IIS in Waischenfeld could also be more intensively integrated into the JOSEPHS concept. The campus is a well-equipped retreat area in which work on concrete and complex problems can be carried out undisturbed and far away from all distractions, across company boundaries and for several days.

References Arbeitskreis Smart Service Welt (2015). Smart Service Welt – Umsetzungsempfehlungen für das Zukunftsprojekt Internetbasierte Dienste für die Wirtschaft. Acatech, Berlin. Available at: https://www.digitale-technologien.de/DT/Redaktion/DE/Downloads/Publikation/smartservice-welt-umsetzungsempfehlungen_lang.pdf?__blob=publicationFile&v=3 (Last access February 11, 2018). Becker, W., Eierle, B., Fliaster, A., Ivens, B., Leischnig, A., Pflaum, A., Sucky, E. (Hrsg.) (2019). Geschäftsmodelle in der digitalen Welt: Strategien, Prozesse und Praxiserfahrungen. Springer 2019. Beverungen, D., Müller, O., Matzner, M., Mendling, J., & vom Brocke, J. (2017). Conceptualizing smart service systems. Electronic Markets, published online 17. November 2017. Burmeister, C., Lüttgens, D., & Piller, F. (2016). Business Model Innovation for Industrie 4.0: Why the “Industrial Internet” Mandates a New Perspective on Innovation. Die Unternehmung, 70(2), 124–152. Christensen, C. (1997). The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail. Harvard Business School Press, Boston. Christensen, C.M. (2006). The Ongoing Process of Building a Theory of Disruption. Journal of Product Innovation Management 23, 39–55. Christensen, C.M., Raynor, M., & McDonald, R. (2015). What is Disruptive Innovation?. Harvard Business Review, 93, 44–53. Christensen, M.C., Altman, E.J., McDonald R. & Palmer, J. (2016). Disruptive Innovation: Intelectual History and Future Paths. Harvard Business School. Working Paper. Engels, G., Plass, C., & Ramming, F.J. (Hrsg.) (2017). IT-Plattformen für die Smart Service Welt: Verständnis und Handlungsfelder. Acatech. Berlin. Gunawi, H.S., Do, T., Hellerstein, J.M., Stoica, I., Borthakur, D., Robbins, J. (2011). Failure as a Service (FaaS): A Cloud Service for Large-Scale, Online Failure Drills. Electrical Engineering and Computer

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Sciences, University of California at Berkeley, Technical Report No. UCB/EECS-2011-87, Available at: http://www.eecs.berkeley.edu/Pubs/TechRpts/2011/EECS-2011-87.html. Klötzer C., & Pflaum, A. (2015). Cyber-Physical Systems as the technical foundation for problem solutions in manufacturing, logistics and Supply Chain Management. In: Proceedings of the 2015 5th International Conference on the Internet of Things (IOT). Institute of Electrical and Electronics Engineers (IEEE), Seoul, 12–19. Klötzer, C., & Pflaum, A. (2017). Toward the Development of a Maturity Model for Digitalization within the Manufacturing Industry’s Supply Chain. In: Proceedings of the 50th Hawaii International Conference on System Sciences 2017. Hawaii, 4210–4219. Papert, M., Pflaum, A. (2017). Development of an ecosystem model for the realization of Internet of Things (IoT) services in Supply Chain Management: A Grounded Theory study. Electronic Markets, 27(2), 175–189. Pflaum, A., Gölzer, P. (2018). The IoT and Digital Transformation: Toward the Data-Driven Enterprise, in: IEEE Pervasive Computing, (17-1), 87–91. Pflaum, A.& Klötzer, C. (2019). Digitale Transformation – Von der Pipeline zur Plattformökonomie, in: Becker, Eierle, Fliaster, Ivens, Leischnig, Pflaum, Sucky (Hrsg.) Geschäftsmodelle in der digitalen Welt – Strategie, Prozesse und Praxiserfahrungen, Springer 2019. Pflaum, A. & Schulz, E. (2018). Auf dem Weg zum digitalen Geschäftsmodell – „Tour de Force“ von der Vision des digitalisierten Unternehmens zum disruptiven Potenzial digitaler Plattformen, HMD Praxis der Wirtschaftsinformatik, 55(2), 234–251. Porter, M. E., & Heppelmann, J. E. (2014). How Smart, Connected Products Are Transforming Competition. Harvard Business Review, 93(10), 96–114. Teece, D. J. (2010). Business models, business strategy and innovation. Long Range Planning, 43(2–3), 172–194. Turber, S., vom Brocke, J., Gassmann, O., & Fleisch, E. (2014). Designing Business Models in the Era of Internet of Things. In: Tremblay M C et al. (Hrsg.) Advancing the Impact of Design Science: Moving from Theory to Practice. DESRIST 2014. Lecture Notes in Computer Science 8463. Springer, Cham. 17–31. Zott, C., & Amit, R. (2017). Business Model Innovation: How to Create Value in a Digital World. GFK Marketing Intelligence 9(1), 18–23.

Frank Danzinger, Rebekka Schmidt, Fabian Memmert, and Michaela Pichlbauer

5 Open Lab Functionalities in Offline-Retail – A Step Towards Future Retail? 1 Offline Retail, Open Labs, and the Concept of Co-Creation The retail sector and especially, its traditional offline business is under pressure. Changes in demand of more and more individualized customers (e.g., Grewal et al., 2009; Liedtke et al., 2012), rising competition of new online retailers and new services (e.g., Amazon with same day delivery), and digitalization in general (e.g., through smart products or in-store digitalization) are pushing the existent business model of offline retail to its limits. As a result, offline retailers need to experiment with new functionalities, services, and business models in order to respond to this threat. The current answers to this challenge in practice are manifold: a number of retailers focus on creating a unique shopping experience (e.g., by using personalized marketing), others focus on cross-channel approaches or on stressing their mobile marketing activities (Gutknecht et al., 2014; Gupta, 2017). Strategies in this context often include the deployment of in-store technologies (i.e., payment methods, product displays, information search) to generate customer enthusiasm and unique shopping experience. Retailers also look for strategies to improve their processes (e.g., by reducing waiting times) to increase customer satisfaction (Pantano, Priporas, Sorace, & Iazzolino, 2017; Wittmann, Listl, Stahl, & Seidenschwarz, 2017). Although, we find many experiments and new approaches in retail, there is no new dominant design for future offline retail within sight. Consequently, an increasing number of small and midsized retailers run out of business. Their downtown spaces are either abandoned by retailers or occupied by other businesses, such as coffee shops or bigger franchise chains. It seems that these new interactions, functionalities, and business models in the same location can create and capture more value than traditional retail interactions. Interestingly, we find at the same time a trend in innovation research that strives to create value from inspiring interactions between companies and their (potential) users/customers (e.g., lead users, wisdom of the crowd, innovation communities). Researchers and practitioners have developed in recent years numerous online and offline tools, infrastructures, business models, and even physical spaces Frank Danzinger, Rebekka Schmidt, Fraunhofer IIS – Center for Applied Research on Supply Chain Services SCS, Nuremberg, Germany Fabian Memmert, EOS GmbH, Munich, Germany Michaela Pichlbauer, Günther Rid Stiftung für den bayerischen Einzelhandel, Munich, Germany https://doi.org/10.1515/9783110633665-005

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to provide a platform for such interactions. With respect to the goal of this article, we focus in the following on the offline aspect of this trend in innovation research: Namely, the living lab and open laboratory movement. The basic idea of living labs was taken to a number of fields of application – from health, to mobility, even to retail. The project INNOLAB, for example, lists almost 100 living labs mainly in Germany (http://www.innolab-livinglabs.de) and the European Network of Living Labs (EnoLL) labels worldwide more than 440 spaces and projects with the term “living lab” (http://www.enoll.org). Despite these developments, we find no unified understanding of the term “living lab” so far. For example, Bergvall-Kareborn and Ståhlbröst (2009) define a living lab as “user-centric innovation milieu built on every-day practice and research with an approach that facilitates user influence in open and distributed innovation processes” (p. 357). Other authors stress aspects like the innovation ecosystem and stakeholder integration (e.g., Behrend et al., 2018), the customer-product interaction (e.g., von Geibler et al., 2013), the testing capability for early prototypes (e.g., Baedeker et al., 2014) or the inherent concept of co-creation (e.g., the EnoLL definition). In the context of this article, we want to challenge the value of the living lab concept for retail settings. Subsequently, we will look into settings and functionalities that provide a high degree of accessibility for potential customers and users. Thus, living lab infrastructures with low degrees of openness/accessibility (e.g., simple lead user workshops or on-invitation only infrastructures) are less relevant for our purpose. We highlight this by referring to the term “open lab” in the following. Hence, we observe two trends dealing with a change in user/customer interactions. First, the struggling offline retail business and second, the developing open lab scene. In this context, we ask in this article whether or not offline retail can benefit from embracing open lab functionalities into its operations and business model. The underlying assumption is simple: Offline retailers need to search for new ways to achieve and retain a competitive advantage against their digital opponents (Greve, Martinez, Jonas, Neely, & Möslein, 2016). Thus, deepening and broadening the customer experience and existing relationship in offline retail is vital. On the other hand, studies show that serious open innovation activities have a positive impact on customer loyalty and satisfaction (e.g., Grissemann & Stokburger-Sauer, 2012; Cossío-Silva et al., 2016). To explore our question, we further focus our perspective on the core and value creating elements of retail and open labs: their user and customer interactions. In both spaces, the actors co-create value. In offline retail, the customer, for example, provides own needs as well as selection activities by choosing a product and the retailer contributes inventory or advisory services. If there is a match, value is created for both sides and a traditional products/services-for-money transaction occurs. We also find value creating matches in open laboratories. The user provides his needs, knowledge, experience as well as curiosity and the researcher (or innovating company) provides prototypes, the opportunity to develop own prototypes, open questions and a positive atmosphere. Value is created for both sides if the

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user input provides guidance for further value creating developments and the user conceives an appreciative and curiosity-satisfying exchange with the prototypes. Consequently, we will limit down our focus on co-creation functionalities and respective interactions. The concept of co-creation became popular with the start of this century and is under research in a number of application areas, including co-innovation of products and services (e.g., Nambisan, 2010), customization efforts of users (Franke and Piller 2004), prosumption (e.g., Xie et al., 2008), and even co-creation in retail (e.g., Andreu et al., 2010). Up to today, however, we find little consensus on what “cocreation of value” actually is. For example, Saarijärvi et al. (2013) ask for more clarity on how “co”, “creation”, and “value” are connected and Ramaswamy and Ozcan (2018) demand for more clarification than “just ‘stating’ that value is co-created” (p. 197). We are well aware of the discussion, the plethora of approaches, and the lack of consensus in the theory of “co-creation of value”. We also acknowledge the value of these discussions. However, the practical value for the design of explicit cocreation settings and functionalities is rather low. Consequently, we use this literature as a theoretical background and mirror it with our experience from designing and operating co-creation activities in practice for more than five years and 50,000 co-creation activities/interactions.1 Doing so, we have identified four crucial points for selecting our “theoretical anchor” to answer our research question: – Value is co-created in single interactions. – Interactions in co-creation of value need to be understood in a wider, comprehensive definition. Acknowledging the different concepts interactions include “value in use”, “co-production of value”, or “value in exchange” aspects simultaneously. This allows to evaluate a single interaction through many lenses but also to include multiple actor types (e.g., buyers but also non-buyers in retail or co-creators and passive observers in open labs). – Value co-creation does not follow the traditional linear value chain model (Porter, 1985) but requires interactive systems and platforms that allow multiple and simultaneous forms of interaction. Consequently, any supporting infrastructure and platform needs to be more than a mere, neutral channel or passive mediator of one kind of interaction (e.g., Orlikowski & Scott, 2015). – The separation in a goods-dominant logic and a service-dominant logic is inspiring but rather of theoretical nature. With respect to the coordination of necessary activities to ignite and control co-creation, we again follow Orlikowski and Scott (2015) who argue that co-creation in products and services requires similar activities.

1 Two of the authors have been and still are involved in the development and operation of an open laboratory in a downtown environment for more than 6 years.

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In order to align and structure our findings for designing co-creation-enabling infrastructures in open labs and in retail, we have identified the concept of Ramaswamy and Ozcan (2018) as meeting the listed four aspects above. They define co-creation as follows: “Co-creation is enactment of interactional creation across interactive systemenvironments (afforded by interactive platforms)” (p. 200). Building on the concept of assemblage (Deleuze & Guattari, 1987), they consider the locus of value creation to be interactions. These interactions are enabled by configurations of the components “artifacts”, “persons”, “processes” and “interfaces”. Artifacts can be physical and digitalized things. With respect to our context, it may be articles of trade or prototypes. Processes comprise all conventionalized/existing/analogue processes as well as new/digitized business processes. Persons are individuals in their various roles, for example, as a customer, employee, supplier, or partner. The concept of persons implies that a single individual can have several roles. For example, an employee can also have the role of a customer or a consultant. Finally, the component interfaces contains all “physical and digitalized means by which an entity comes into interaction with another entity” (p. 198). In our context, for example, this means challenging displays or traditional retail touchpoints. The concept (cf. Figure 5.1) understands all four basic components as being ingredients of different configurations (assemblages) that allow for co-creation on interactive platforms (p. 198). In the understanding of Ramaswamy and Ozcan (2018) these interactive platforms (in our case, an open lab or retail space) “engage in enactment of interactional creation of value” (p. 199). By simultaneously stressing the physical and digital nature of the four basic components, they also account for the rising degree of digitalization and connectedness of individuals and things (e.g., IoT-supported processes or smart products as articles of trade). In addition, Ramaswamy and Ozcan (2018) conceptualize “agencing engagements” and “structuring organizations” as enabling or constraining interactions. The first, agencing engagements “refer to the process of actors entering into a constitutive relationship with (the four basic) components of interactive platforms” (p. 199). The authors aggregate all desire-based interactions (e.g., motivations) as well as powerbased interactions (e.g., through hierarchy or market power but also the idea of sharing). The later, “structuring organizations”, is defined “as actor-networked relations in interactive system environments, both in terms of ‘what they can create’ and ‘what creates them’” (p. 199). With this aspect, they introduce the enabling/constraining effects of an organization “around” a co-creation platform, namely the selection of relevant organizational elements, their connection, and alignment towards a shared objective of the organization. Neither agencing engagements nor structuring organizations can be considered as fully distinct aspects, if specific co-creation arrangements are analyzed with this framework. If, for example, a co-creation platform deliberately integrates motivational aspects of potential co-creators into its services, its organizational structure, its network and its overall business model are addressed simultaneously. For this

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Experienced Outcomes

Artifacts

Persons

e.g., trade articles, prototypes

individuals in their roles, e.g., cust., employee, partner

CO-Creation

Engaging Oraganizations

= interactional Creation

Interfaces

Processes

physical/digital, enable interaction e.g., touchpoint

existing/new, analogue/ digital

Resourced Capabilities Figure 5.1: Conceptualization of co-creation in open lab settings according to Ramaswamy and Ozcan (2018).

reason, we simplify the concept by integrating both aspects (agencing engagements and structuring organizations) in one enabling/constraining aspect and name it “engaging organizations”. The engaging organization has a stabilizing and memory effect for co-creation. It strengthens specific, successful configurations of the basic components and can recall negative experiences. Consequently, the enabling/constraining character of engaging organizations develops resources/capabilities for further co-creation interactions. Each co-creative interaction activates a new configuration and creates experienced outcomes, which are again an input for the engaging organizations and all participating actors. With this adapted model of co-creation (cf. Figure 5.1), we will structure and analyze our research question, whether or not offline retail can benefit from embracing open lab/co-creation functionalities into its operations.

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We will apply this model in the following in two studies that we will present in the remainder of this article. First, we will analyze in the next section the results of a focus group on future retail with respect to aspects of co-creation. Second, we will discuss the findings from an offline retail setting into which co-creation elements were embedded.

2 Future Retail – A Question of Co-Creation?! Facing the current challenges in retail, including questions of design, identity, and functionalities, we further specify our research question and ask “Is it possible to integrate the current challenges and solutions for future retail into a framework for co-creation?” and “How can offline-retail benefit from co-creation/open lab functionalities in the future?” In order to answer this question we invited in October 2017 a group of 35 retail and open lab experts (e.g., innovative retailers, trade associations, founders in retail) to discuss approaches to improve the situation in offline retail. The findings led to an open agenda for future retail. The Study: In line with the actor network theory foundations in the concept of Ramaswamy and Ozcan (2018), we embedded the challenges in offline retail into a wider context and connected them to further actors beyond the traditional B2C-/ seller-buyer-dyads. According to the concurrent literature, we derived three major issues to start and structure the discussion: (1) co-creative customers/prosumers, (2) digitalized retail, and (3) interactive town/city centers. The first issue deals with the shift in consumer behavior towards prosumption (e.g., Toffler, 1980). A key question in this issue is, for example: ‚“How should retailers operate if customers act as co-creators?” The second issue focuses on offline retail itself and highlighted aspects of (in-store) digitalization. An exemplary key question for the second issue is, “Which new functionalities can be opened up by means of digitalization?” Lastly, the wider retail context receives the focus, the changes of the downtown areas. Key questions for the third issue for example are: “How will information and products flow in/through our cities in the future? – Or, will there be a flow at all?” Within the workshop 169 theses and 29 solutions have been created. From this input, six fields of actions have been identified. The following paragraph briefly describes the six fields of action and analyses them according to the model provided in Section 1.1. The Findings: The first field of action is technology-driven and titles “Digital technologies require knowledge, real experiences from retail as well as an embedment into retail”. This field deals with three major aspects. First, the fact that a wave of digital technologies and applications is currently hitting the retail sector. The list includes smart products (e.g., smart watches), digital in-store services (e.g., cashless payment, AR/VR), and hybrid and mobile systems for and in retail

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(e.g., click and collect). Second, the information availability on application aspects of these numerous technologies remains unsatisfactory from the retailers’ perspective. Lastly, the retail interaction requires a very high quality of service standard from technologies (e.g., low failure or delay tolerance). The experts group classified three needs in this field of action: (1) A need for better information on retail technologies, containing experience information from other retailers on specific deployments. (2) A need for further developments of central technologies (e.g., VR/ AR) in close cooperation between retailers and technology developers (e.g., startups). And (3) a need for implementation assistance, e.g., how to derive value and a business model from digitalization (e.g., in terms of productivity, customer loyalty). Taking these findings into the given framework of co-creation in offline retail, we find in this field of action all four basic components for co-creation heavily affected. At least a part of the trading goods (artifacts) in retail get smarter and will become more connected. In-store digitalization affects core processes, such as the payment process (processes). Additionally, technology developments demand new partners/roles (persons) in the model and lead to the development or adaption of interfaces with new actors (interfaces). The second field of action is named “Digitalized retail needs put humans into its center”. Digitalization in retail allows for new designs. Within this category the experts subsumed two aspects: (1) Retailers generally need to understand the new role and situation of the customer better. This requires a wider understanding far beyond the narrow transaction process and even traditional customer journey, for example: “At which touchpoints is information through a chatbot or robot supportive/sufficient/ . . . ?”, “Where is customization beneficiary?”, and “Where does the customer wish more personal contact or recommendation?” (2) The education of most employees in retail is not prepared for the new role of the customer and her digital support requirements. A retail employee in the future needs to be more of an enabler, coach, trainer, or even event manager than a traditional shopkeeper or trained salesperson. With respect to the future changes in the context of cocreation in offline-retail, we find in the second field of action especially the component persons affected as the roles of employees and consumers are about to change fundamentally. In a direct consequence, also the component interfaces will be affected as different roles imply a change in touchpoints between retail and its stakeholders. The third field of action is denominated “Retail and cities need to learn about experimentation”. The current environmental challenges hit the core interactions of retail. Thus, the industry needs to renew itself and its tools at a higher speed. This requires (1) that offline retail needs to close its experimentation gap to online retailers. Successful online platforms (i.e., Amazon) made experiments a core element of their operations. This competence needs to be learnt by offliners as well. (2) One major aspect towards experimentation is an increase of the degree of openness of offline retailers (e.g., through new partnerships with tech start-ups, city-logistic-providers, or

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even other retailers). (3) Similarly, the surrounding ecosystem and the responsible bodies (e.g., municipality, advertising associations), the cities and downtown areas also need to understand themselves as living labs. Taking these findings into the given framework of co-creation in offline retail, we find in the third field of action two components to be very prominent. First, the component persons as new interaction partners arise. Second, processes as embedding experimentation into operations will need to change daily routines. This will also affect the role and qualification needs of the component persons. Indeed, experimentation will also challenge the field “engaging organizations”, as successful experiments need to adapt an organization and its “learning circle”. For example, different resources will be required for experimentation (e.g., controlled freedom for professional experiments in daily routines) and the experienced outcomes of these activities need to be analyzed and used by and/or for the organization. The fourth field of action has the label “Retailers need to become teamplayers”. To fight the threat of being marginalized in a more and more global environment or to be left behind in new technological developments (e.g., introduction of cashless payments), cooperation and information exchange of retail need to be increased through: An effective means of inspiration provision for retailers and even more important an effective experience exchange with experimenting retail colleagues (“peer experimentation/learning”). With respect to the possible changes of co-creative interactions in offline-retail, we find in the fourth field of action mainly new interfaces/touch points for retailers. Additionally, new touchpoints will also affect the field persons as new touchpoints also imply new interaction partners. The last thesis for the fifth field is termed “The concept of shopping needs to be revisited”. As for more and more retailers their core interaction (pure sales), does not leave them in a competitive position, new functionalities for retail spaces need to the thought and tested (e.g., co-innovation, experience-shopping, events and information, pick-up opportunities, 24/7 availability). In its core, the value of offline shopping needs to be put to test. With respect to the change of co-creation interactions in offline-retail, we find in the fifth field similar consequences like in the first field, with effects on all for basic components. The Results: Figure 5.2 subsumes the results from the expert group and aligns them with the conceptualization of co-creation according to Ramaswamy and Ozcan (2018). Generally, it is possible to align the results of the expert group on future retail with the co-creation framework. A simple analysis of the affected categories in the framework (cf. Figure 5.2) also allows first conclusions on how co-creation approaches/an open lab lens might contribute to the future of retail. Although the driving force in future retail seems according to the expert group the digital challenge, the majority of impacts can be found in the component “persons”. The component persons is followed by “interfaces” and “processes”. This is an interesting observation since most retailers currently experiment – if they do at all – rather

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Experienced Outcomes

Persons

Artifacts

individuals in their roles, eg., cust., employee, partner

e.g., trade articles, prototypes

CO-Creation

Engaging Oraganizations

= interactional Creation

Interfaces

Processes

physical/digital, enable interaction e,g., touchpoint

existing/new, analogue/ digital

Resourced Capabilities Figure 5.2: Fields of action in future retail with respect to elements of co-creation and open lab functionalities.

with artifact/technology-related issues (e.g., photos of products through Instagram, new products and assortment, marketing, website2). The finding implies that the issues and discussion on roles and persons in the retail ecosystem should be ranked much higher on the retail development agenda. Besides the component level, we also see in Figure 5.2 as well as in the single fields of action the need for new configurations and connections between the basic components of retail in order to create value in and for retail. Interestingly, this is also a general objective of open labs and their methods and tools.

2 These were the most mentioned experiments of retailers during a workshop with 69 participants at the Future Congress of the Rid Foundation in October 2018.

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3 Integrating Co-Creation into Offline Retail – An Experiment at Swisscom In this section we will report the findings from a case study of a retail organization that deliberately experiments with the infusion of co-creation into a telecommunication store environment. Swisscom’s goal within this experiment was to collect information on how open lab functionalities can be integrated into real offline-retail settings. In more detail, Swisscom wanted to receive information how own customers react to an integration of co-creation and open lab functionalities into a retail area in general and specifically how customers perceive their waiting time in a store if they have the opportunity to bridge the time gap with co-creation activities. To explore the process, we conducted interviews with the customers in the store (n=34) and compared the findings to answers that we received in the open lab environment of JOSEPHS® (http://www.josephs-innovation.com). Moreover, we used additional data of the store’s customer satisfaction survey as well as responses of the store’s employees. Similar to the sections above, we integrate our findings into the adapted model of Ramaswamy and Ozcan (2018). The experiment setting at Swisscom: A temporary co-creation space was integrated into a Swisscom store. The design of the co-creation space was inspired by the open lab design and respective co-creative interactions at JOSEPHS®. The design followed a number of assumptions: (1) The introduced innovative products for cocreation interactions were selected from the telecommunication domain in order to keep artifact-based customer confusion low. The customers were asked to test a prototype of a chatbot which is designed to help customers to solve their problems easily at home. (2) As it was not possible to integrate the co-creation activity fully into shop operations for the time of the experiment, the regular complaint, repair and information processes, and the co-creation process were kept separate. The shop floor manager’s process was an exception (see component “persons” below) – she had to connect the retail and the open lab functionality. The general assumption was that the offer to co-create would have the potential to bridge waiting times and, hence, shorten the perceived time spend at the shop. Consequently, an increase of the satisfaction level of customers with the store services was expected. (3) Whereas the regular processes kept their interfaces, the co-creation interface was designed to be very close to a specific usage situation at the customer’s home (living room). (4) The retail store had a typical retail structure with the advisory stuff and a manager. To support the actual store process, we implemented a shop floor manager who was responsible for organizing the waiting line and introducing/explaining the open lab as well as the prototype to the customers. Beyond this role, the persons in charge of regular interactions and co-creation interactions were not mixed. The co-creation interaction and the research design: The shop manager asked all incoming customers to visit the innovation space and to test the prototype. They

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were offered to interact with the prototype whilst their waiting time without losing their place in line. If they agreed to this offer, they entered the co-creation interaction and interacted with the prototype. Afterwards, they received their regular services and were interviewed with a semi-structured interview if they again agreed. We collected data from the interviews with customers (including net promoter scores) as well as further analysis of customer satisfaction data of the shop during the time of the experiment, from interviews with employees, and from own observations during the experiment. The data was triangulated to derive findings and will be reported according to the identified order of importance of co-creation components in chapter 1.2. Findings: The telecommunication (retail) store has a high level of customer traffic. The store’s customers usually visit the store with a special concern or problem about a product. They take advantage of the competence of the store, the advisory staff, and the possibility to test new products (e.g., Apple releases a new iPhone). During the experiment, most of the interviewed customers (47.1%) visited the telecommunication store because of a strong interest in a new product (e.g., cellphone, hardware). Another 23.5% were interested in a new offering (e.g., contract). The remaining 38.2% of the visitors had a problem with their hardware, wanted to exchange a product or to change a contract. Consequently, most of the customers visited the store with their interest in something new, only a minority had an issue or problem. These customers with a specific problem came into the store with a negatively biased perspective. It is important for this category of customers that their concerns are resolved quickly and in their interest. The store measures its success amongst other indicators through the level of customer satisfaction. Due to the “regular customers” who are coming once a month into the retail store, it can be deduced that the overall customer satisfaction is proportionately high. During the experiment the test lab could support the overall impression of the store: The interviewed participants were very satisfied about the test lab with an average of 4.59 (1 very unsatisfied to 5 very satisfied), which indicates a positive connection between the customer satisfaction and their implementation into the co-creation process of the store. To further explore the reasons for this impression, the customers were asked what they liked about the test lab concept: “The open attitude and the integration of the customers opinion” was ranked first with (46.7%), followed by “interesting opportunity to test new prototypes” (43.3%), and “good advice during the test” (43.3%) Interestingly, the shop driven motivations “new opportunity to increase customer satisfaction” (6.7%) and “it is something new and different to other stores” (3.3%) were ranked very low. Customers are influenced by various factors, especially through their own requirements of the retail store. Their preconceived notion must be fulfilled (e.g., repair or exchange of devices), and doubts should be taken in the interactions within the store environment. The assessment of the relation between the reason of visiting and the frequency of visits of the store showed that the majority (35.3%) of the

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participants are visiting the store once a month. From these customers, 75.0% have a strong interest in new products or contracts and only a minority is visiting the store because of problems with their product (25.0%). This constitutes that customers are overall positive affected to the co-creation test environment and see a potential valuable advantage for themselves. Especially the good service during the test and the integration of the customers’ opinion was mentioned. These findings indicate that the test lab specifically addresses very loyal customers and to some extent also “lead users” or “early adopters” of the offers of the store. A minority of the participants gave negative feedback about the test lab. This feedback can be clustered in four categories: “difficult to put yourself in different situations”, “only when I have time left, I would use it”, “it is not valuable for me”, and “it was hardly discernible”. In the co-creation interactions, most participants were interested in the prototype and tried to challenge themselves to create new ideas and opportunities. They mentioned that the theme of the prototype and testing area affects their choice of interacting with the test lab. It should present an added value for the customer and their future interaction with the company (e.g., new product, new way of how to communicate with the company, methods of reducing waiting time, etc.). A positive correlation is indicated between customer satisfaction and the NPS score for the test lab: Customers who were satisfied with the test lab were more likely to recommend it to others. By analyzing this connection, a positive gradient can be determined. With a positive NPS of 41, the customer survey on the test lab indicates that the retail store has a fundamental base of promoters. Consequently, it can be deducted that 50% of the participants were promoters, 41.2% were passively satisfied and only 8.8% were detractors. This constitutes that the customers are potentially more loyal to the store and would recommend the store and the test lab to others. Not surprisingly, the participants mentioned that time is an important factor in their decision whether to use the test lab or to keep staying in line. As the variables of time and losing the place in line plays a crucial role in the decision making of a customer, many were anxious to get their place back after finishing the test lab. There is a general acknowledgment of the respondents that their usage decision depends on their individual trigger/concern/problem and how much time they plan to spend in the store. This can be summarized into a number of observations: (1) If participants are in hurry or have a serious problem, they prefer to stay in line, even if it takes them longer than testing the prototype. (2) If their initial problem is solved and they have to wait (e.g., their phone is repaired or the contract is getting ready) they enjoyed the possibility to co-create. (3) If customers got the confidence that someone takes care of their request afterward, they were likely to test the lab. Customers were even more willing to test the lab when the employees of the store addressed them directly. The shop floor managers had the problem that customers often perceived them as a representative of another company. Generally, the

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unexpected and different environment of the test lab had a positive effect on the customers. It was perceived as an exciting experience, especially in comparison to other stores. The opportunity to test new prototypes and co-create with the company during their stay generally is evaluated as a potential advantage. Customers felt more involved into the companies’ co-creation process. Their interaction with the prototype and the opportunity to contribute their own ideas gave them a feeling of appreciation. The floor managers reported that potential co-creators are very difficult to identify, especially in a highly frequented telecommunication retail environment. Since there is a large number of potential, unspecified co-creators it is difficult to separate the potential lead users. Mostly they are visiting the store only when they have a special concern about a product, or their favorite company launched a new device (e.g., Apple’s iPhone X). Otherwise, there are currently little incentives to visit the telecommunication retail store. Nonetheless, they were positively affected about the integration of the customer into the co-creation process of a company. Thus, the test lab functionality offered an incentive for potential lead users to co-create on new prototypes and to design them according to their ideas. The results from the customer survey of the retail store have shown that there is a positive effect by the integration of open lab functionalities into real offline settings. We find good indications that the integration of the customer into the cocreation process is positively connected with customer satisfaction. Furthermore, the results also showed that customer satisfaction affects customer loyalty to the retail store. Consequently, the data indicates a positive relationship between the customer’s behavior of co-creation and the customer’s loyalty to the store. Due to the above-mentioned results, offline retail can profit from customer co-creation through increased customer loyalty. Additionally, retail stores can draw added value from the co-creation by the customers (Andreu, Sánchez, & Mele, 2010; Grönroos & Voima, 2013; Mahr, Lievens, & Blazevic, 2014). With respect to the research question we reflected the findings with similar observations in the open lab JOSEPHS®. Findings from contrasting the Swisscom Experiment and the JOSEPHS® Open Lab Setting: The open lab JOSEPHS® consists of four basic elements: The atmosphere of the open lab, partners without ‘explanatory barrier’, conference and workshop area and the core open lab itself (Roth, Fritzsche, Jonas, Danzinger, & Möslein, 2014). Further, an open lab is built upon three key principles, the empowerment of the customer, openness, and realism (Bergvall- Kåreborn & Stahlbrost, 2009). In the surrounding of a telecommunication retail store, those principles should also be pursued to ensure a high-quality output of customer engagement. Nonetheless, different environmental circumstances have to be considered during the implementation. (1) Due to the fact that in a high frequented store the percentage of unspecified co-creators is higher than in a research open lab, most customers are visiting the store with a special (artifact-related) concern. The empowerment of the customers is

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therefore necessary/higher to integrate them into the co-creation process in a retail setting. Consequently, the atmosphere contributes a certain proportion to motivate the customer to co-create a product. Also, the customer needs to be addressed directly to motivate him to co-create. This can be achieved by introducing a shop floor management function who is responsible for the coordination between the people who are waiting and the next free employee of the store. (2) The customers in open labs are getting more actively involved and are even coming into the open lab with a clear purpose of “testing”. In offline retail, however, the implementation of an open lab is a challenging task. Especially when the artifact (e.g., tested prototype) requires the customers to set herself into other than the current situation (e.g., their living room setting/behavior). (3) The positioning and visibility of an open lab space within the retail space accounts as an important factor to reduce the personal barrier of the customers and to encourage them to co-create in the retail store. Like also the arrangement of different elements of the open lab JOSEPHS® indicate, this decision has to be actively made in retail settings and represent a decision of configuration. Results: With respect to our research questions, we report the results according to the model in Section 1 of this article and the identified order of the components in Section 2: – Component “Persons”: In retail, it is predominantly difficult to get customers out of the waiting line when they have a special concern in combination with less time. To integrate these customers into a co-creation process and to reduce their perception of waiting time, an effective management system needs to be installed. One possible way for this is to select a shop floor manager who coordinates the customers with the employee of the store and the innovation island. Through this direct contact, the personal barrier can be alleviated and a personal introduction into the open lab function is possible. Because individuals do not have to wait in line and they know that someone takes care of their concerns, they are more ambitious to co-create. This process offers retail stores the opportunity to increase their customers’ satisfaction and reduce the sense of waiting time, especially during the high frequented rush hours. Integrating cocreation into regular retail operations demands a full revision of the role of interacting roles and persons. Multiple roles have to be taken (e.g., by employees), new roles have to be accepted (e.g., floor manager), and new behaviours have to be actively pushed (e.g., the customer’s ideas on new products). For retailers, this indicates that they have to rethink and retrain their most valuable resource, their employees. They need to be trained and motivated to actively involve customers into the co-creation process. – Component “Interfaces”: Implementing an open lab into a telecommunication retail store has a different effect on customers than in an already established open lab environment. On the one hand, this fact holds challenges for creating respective interfaces and innovation atmosphere in a retail setting. On the other hand, retailers can be assured that they have a highly relevant

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co-creation group for their artifacts and that they can design very specific interfaces for them. The touchpoints, interfaces and atmosphere should reduce harm and provide interesting topics of open lab spaces. Therefore, the location of the open lab seems to be the most important aspect for success of the integration of co-creation and open lab functionalities. A co-creation space, as the major interface, has to be visible for every customer and needs to address her motivation directly. A possible way to implement open labs into a retail store is to create stationary and post-stationary innovation islands. Stationary islands are located in one specific area in the retail store, where different topics of the islands take place. Post-stationary islands represent moveable test environments, which can be located somewhere in the retail store (e.g., next to the waiting line, or on the side of the counter). Additionally, the open lab interfaces should provide a new class of incentives for the customer. The touchpoints should be proactive, and the prototypes should spark interest for co-creation, and indicate an own personal gain from the interaction (e.g., the feeling of being technologically up to date after the interaction). – The component “Processes”: For integrating the test lab functionality into the telecom store, the processes had to be adapted. Although regular retail and new co-creation processes were kept mostly separate in the experiment, we find indications that a deep integration requires resigning the overall process landscape. It became especially clear that connecting points between both functionalities (e.g., waiting and co-creation) are crucial for co-creation interactions in retail (e.g., through the introduction or modification of the shop managers processes). The findings also indicate that there are more connecting processes possible as not all co-creators in the Swisscom study considered the test offer as a pure substitution of their given waiting time. Considering the coordination between retail and open lab functionalities and the higher customer frequencies in retail, an integrated system should make use of digital possibilities. The coordination would be more efficient with a real-time, mobile system (e.g., tablet, mobile innovation island) where customers can preselect their concerns and afterwards can be navigated to the test lab and/or the retail functionalities. Referring to the findings, the direct human address can only be supported by these processes and digital means. Or, in other words, the direct contact between employees and customer is a key element in processes to connect retail and open lab functionalities in order to gain high quality output and to increase customer satisfaction. With respect to processes in open lab functionality, several techniques can be implemented, for example, post-its, mind mapping, interactive implementation, surveys, etc. – The component “Artifacts”: Retail customers are willing to co-create with a prototype and want to challenge themselves in a pleasant manner. To attract valuable innovations the customer must be stimulated to think. Therefore, the open lab must address the intrinsic motivation and give an incentive for

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the customer to co-create with a prototype. Consequently, the prototypes and products in the open lab space have to meet the customers’ interests. Thus, the identification of the customers’ attitudes and interests is needed to create a theme that is attractive to customers and possible co-creators. To achieve this, retailers should restrict their open lab activities at least in first experiments to open lab activities that are in line with their wider range of products of their industry (e.g., chatbots and telecommunication products). This ensures that the confusion of the customer and her activation effort remain low. For more sophisticated approaches to determine a good artifact fit, specific customer surveys and preliminary open lab experiments can be carried out in the store upfront. – Engaging Organizations: An implicit driver for retail stores is that they need to keep their customer base and acquire new customers for their services. Returning customers are representing a strong relationship and loyalty towards the retail store (Wong & Sohal, 2003). Moreover, they are more willing to accept internal store changes while maintaining consistent service quality. The integration of open lab functionalities is a new incentive for the (and especially for loyal) customers as well as new customers with an interest in co-creation and interaction. Furthermore, the store can differentiate itself through other competitors. The potential of reducing perceived waiting time is generally given. However, harvesting this potential is influenced by some factors, namely: precise planning, considering different time management frameworks and the motivation of the shop personnel. Generally, retailers who want to integrate such functionalities need to become truly “engaging”. However, this will make their operations much more complex as they have to follow and integrate several value drivers simultaneously. For example, the retailer has to concentrate on selling but also simultaneously on the collection of valuable, commercial exploitable data. This requires clear configuration decisions. The retailers’ overall goal of customer satisfaction integrates both approaches and is a good starting point for the development. However, already on the next level the customers’ perspectives and the stores’ economic perspectives have to be continuously monitored and aligned. This requires detailed planning upfront as well as a continuous learning process of single aspects of the four components and their interplay. Planning and Learning require an ambidextrous approach that fully advocates and constantly clash retail and open lab principles. As the retail principles are deeply rooted in retail operations, it seems to be valuable to integrate a partner from the open lab community in a “challenger-role” in such a process. Otherwise, the risk is very high, that the open lab functionality turns into a neat, sales promoting interaction activity with no further additional value.

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4 Open Lab Functionalities in Offline Retail – Next Steps towards Future Retail The aim of this article is to provide first insights whether or not future offline retail should embrace open lab functionalities into its operations and its business model. Generally, the results of the two presented studies support the notion that an integration is beneficial. On the one hand, the results of the expert panel on the future of retail could be easily integrated into the adapted co-creation conceptualization of Ramaswamy and Ozcan (2018) and allow for further implications on the future of offline retail. On the other hand, the findings from the case study at Swisscom encourage to further experiment with open lab functionalities in offline retail settings. But what should be the next steps if practitioners in retail want to integrate open lab functionalities? Our findings suggest that the integration of open lab functionalities will alter operations and the retail organization itself fundamentally. It is vital to foster changes towards a truly “engaging organization”. This includes modifications in three different areas: – Strengthen the role of experimentation in the future retail model: Figure 5.1 already indicates that value-creating interactions occur in a steady learning cycle on the configuration of co-creating components (persons, processes, interfaces, and artifacts). As there is currently no new dominant or clearly desirable design for offline retail insight, this process of finding new configurations should be called “experimentation”. Experimentation is more than simple “trial-and-error” or “testing a digital customer touchpoint”. Moreover, it is a core element of open labs. It is a controlled and purposeful development process (e.g., Thomke, 2003) and deliberately supports the process of “assemblage” of co-creation components (Ramaswamy & Ozcan, 2018). Embracing experimentation in retail settings requires explicitly to implement and/or to speed up the learning cycle in Figure 5.1. (resourced capabilities → co-creation interactions → experienced outcomes → engaging organizations → resourced capabilities → . . . ). This implies first, that resources and capabilities for open lab functionalities and co-creation have to be built. Resourced capabilities include the need to free up time and space for experiments within the retail setting, the need to change running processes (cf. Section 1.3), the need to familiarize employees and staff with cocreation methods and tools, and above all, the need to install an organizational change process to stabilize positive experiments and configurations. Two aspects are important within this learning cycle. First, retailers define clearly for each experiment as well as the overall process of embracing open lab functionalities what success is and how it should be measured. Second, retailers continuously restart and fuel this experimentation cycle (e.g., Kohavi & Thomke, 2017). – Rethink the role of humans in future retail: One basic idea in the traditional conceptualization of offline retail is that it is “the” customer touch point at the

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end of a linear supply chain that finally “issues the product”. Clearly, the function of the physical touchpoint remains although multiple other digitized platforms in an overall value creation network were and still are added. However, the offline retail space will be ascribed new functionalities in the future – and as it is a vital contact point, most of these functionalities will have co-creative properties. The understanding of the human in open labs could be an interesting pattern for future retail. It requires a reconsideration of the component “persons” in Figure 5.1 or in general, of humans in future retail. On the one hand, the image of the passive customer has to be challenged (e.g., Toffler, 1980). This allows for new co-creative tasks for the customer that become possible in future retail (e.g., login into an offline store) but are also expected by active customers (e.g., multiple digital and non-digital touchpoint). On the other hand, the understanding of humans on the “supply side” of retail also has to be modified. Retailers and their employees need to be empowered to take multiple roles and actively switch them. From the role of service staff, to consultants, to innovators, to customers, etc. This basically demands a digitized thinking and working culture in future retail. The salesperson needs to be empowered to self-dependably switch roles in order to keep service quality on all channels high. Additionally, the staff needs to deal professionally with cocreative contributions of potential customers (e.g., handling of requests, product feedback). For example, in working hours of low utilized capacity, a service person can turn into a digital consultant or she can deliberately take the customers role to improve the customer journey. Similarly, the natural and situated use of digital devices and data-driven work also need to be developed in future retail. If we want to draw an example to other industries, we would describe the shift on the retailers’ side to the development of the German job description of the car mechanic who developed in the last two decades into an automotive mechatronics engineer. – Increase the connectedness in the ecosystem of future retail: Our initial conceptualization was built on the idea of interactions and capacities of interactions in system-environments (Ramaswamy & Ozcan, 2018). Experimentation and a non-linear understanding of the retail supply chain demands also a new configuration of connections within the ecosystem of retail. This includes changes in power-relationships (e.g., the stores can become market researchers for OEMs) as well as tapping into new co-creative formats (e.g., with start-ups). This includes understanding the store area as platform and arena for multiple interactions. On the one hand, this means that future retail needs to adapt and supplement their controlling tools. For example, measures like the media outreach or online response times of a store now become much more valuable. On the other hand, future retail needs to allow connections to happen on its offline platform that go beyond the traditional sales transactions (e.g., through events). Especially with respect to digital challenges, future retail also needs to

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enlarge its ecosystem to digital solution providers and open up their challenges to professional solvers. Again, the operations and eco-systems of open lab environments can be a pattern for future retail. There are more implications from these studies on the component level that will support practitioners in retail to shape their own future. For example, the order of the importance of single components indicated in Figure 5.2 can provide guidance for new developments and investments. The order suggested in Figure 5.2 is: (1) Persons, (2) Interfaces, (3) Processes, and (4) Artifacts. Interestingly, this order is fully concordant with our experience from our own experience in designing and developing open lab infrastructures. However, order often conflicts with the reality in retail. Here, we rather see tests on the level of artifacts (e.g., new goods, technological gadgets) or processes in combination with interfaces (e.g., tablets or apps to support advisory services) than changes and investments into persons. Indeed, even the provided case study has rather put elements into the experiment on artifact and process level rather than persons or the engaging organization. Similarly, we rather find experiments on the artifact level than experiments with new business models (e.g., further revenue opportunities). Of course, this observation often roots in economic risks, which seem too high for single retailers. On the one hand, this fact should encourage retailers to dare new configurations (e.g., 24h-operations through digital tools). On the other hand, it also requires development facilities and spaces deliberately designed to provide support retail experiments. Lastly, our findings also suggest to better deal with “duality”. Retail and also innovation research has a long tradition in distinguishing online and offline activities. Although this separation might be helpful for analysis and research, it seems to impede a fully integrated working mode. Similarly, we see in our case study as well as from observing existing retail experiments that “traditional retail” and “open lab functionalities” could also establish another duality in daily practice. Consequently, it is important for successful next steps to overcome these dualities and develop the configuration of components into a truly integrated working model and business model, for example, with a combined “noline” and “experimentation” mode. We provided in this article insights on how principles of co-creation and the open lab movement can create value in the retail sector. We have further shown that co-creation conceptualizations are applicable in retail contexts and allow for valuable implications for future retail settings. Moreover, we have presented empirical evidence in a specific setting at Swisscom (mainly loyalty and waiting time issues) that open lab functionalities can be embedded into traditional retail settings. The case study has proven positive effects for offline retail. The findings indicate that the central success factor for embracing co-creation and open lab functionalities is that retail organizations can be turned in truly learning and engaging organizations. Central areas of development are experimentation, the human factor, and connectedness in retail. Although we can provide first evidence and motivation to

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embrace open lab ideas into retail, a number of open questions remain. Further insights and guidelines on the integration of various open lab mechanisms remain: For example, insights referring to the core modus of open lab functionalities (e.g., offline innovation competitions vs. a community mode), the analysis of different retail settings (e.g., in connection with other product groups), and the development of new retail business models (e.g., how to create new revenue streams from cocreation) are important avenues for research. From the experience of both studies, we see the need to fill this research gap and want to encourage developing these insights in a truly engaged and co-creative mode: customers and retailers, research and practice, open lab experts and retailers, etc.

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Part II: Managing Innovation in Open Labs

Albrecht Fritzsche

6 The Many Facets of Open Laboratories and Their Implications for Innovation Management 1 Innovation as Part of a Larger Social Endeavour Open laboratories are an exciting topic for innovation management. They can be used in many different ways to support forecasting, ideation, solution design, prototyping, testing and other activities that take place on the path from vision to value generation. It would be wrong, however, to think of open laboratories exclusively as facilitators of innovation. They also serve many other purposes that need to be studied in order to understand what is going on inside the laboratories and how one can benefit from it. The current popularity of open laboratories seems to be related to a general movement to explore new forms of communication, problem solving and social bonding beyond the practices that have dominated life in industrialized countries during the last centuries. With the gradual departure from standardized mass production and unidirectional communication channels, the structural patterns that the industrial revolution has inscribed upon society often appear to be out of place. The challenges that we have to face in the twenty-first century require a different organisational approach, which gives a better account of the personal identities and desires of human beings and creates more opportunities for everyone to engage actively in public decision making processes. Open laboratories can be considered as a step in this direction, with many potential benefits, not only for innovation, but also for strategizing, sense-making, conflict resolution and other common activities in every institution or community. Open laboratories are therefore by far not all about innovation. This, however, is not a disadvantage for innovation management, but instead quite the opposite. In open laboratories, research and development activities take place within the very environment where innovation is supposed to create value. They proceed in combination with other knowledge operations, which creates a huge potential for dynamic adaptations across domains that would elsewhere be impossible to accomplish. Innovation becomes accessible as part of a larger social endeavour with many new degrees of freedom for managerial intervention. For this reason, it makes good sense to have a look at the wider discourse on open laboratories and the

Albrecht Fritzsche, Ulm University, Institute of Technology and Process Management, Ulm, Germany https://doi.org/10.1515/9783110633665-006

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different motivations for people to visit them, before the next chapters of this book start with a detailed investigation of innovation in open laboratories.

2 Laboratory Work and Openness A closed laboratory provides an environment in which experiments can proceed under controlled conditions. It enables scientists to isolate specific effects from external disturbances and to establish replicable conditions for their observations. Interestingly enough, the closed laboratory is in this respect not much different from a factory that is dedicated to specific industrial activity, so that management can focus on the efficient and effective execution of clearly defined operative steps, again and again. The seclusion from the outside ensures a level of transparency and determinacy at work that could otherwise not be achieved. As a result, it is easy to find out if something unexpected is going on. While managers in the factory will most likely want to get rid of such occurrences, however, the researchers and innovators in the laboratory will rather be attracted to it as an interesting phenomenon that needs further attention. The possibility to work behind closed doors has also another important implication for science and engineering. It allows experts to get slowly acquainted with a phenomenon, giving them time to let their thoughts mature before they are communicated to others. In a closed laboratory, scientists and engineers are granted a level of privacy that makes it much easier for them to err and fail on the way to new insights. No one else knows what they do, criticizes their actions or takes their results away from them before they are ready to be made public. While this must considered as an important easement – if not a precondition – for research, it creates a problem in the communication with people who are not involved in the process. The struggle that is necessary to come to terms with a given subject matter is obscured. Even the scientists and engineers themselves are not required to keep track of the thought processes that have preceded their findings and the role of the laboratory environment for their work. Since Latour (1987), these thought processes have received immense attention in science and technology studies. Among others, they have also inspired a new line of management research (Leonardi, 2013; Orlikowski and Scott, 2008). Furthermore numerous studies of laboratory work have been undertaken to uncover the thought processes of scientists and engineers in academia and industry (Knorr Cetina, 1995). During the last decades, it has also become common practice to host special public events where research laboratories literally open up their doors to let visitors observe and participate in experiments. Similar opportunities are created by mockup laboratories in showrooms or at trade fairs and expositions, where laboratory work in not expected to produce any relevant findings, but to result in a better

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understanding of science and engineering. The laboratory thus turns into a public forum, an agora, where science and engineering in general becomes the object of negotiation (Latour, 2005). Actual results of experimentation and product design lose importance. In addition, Schuurbiers et al. (2013) mention another form of public engagement with science and engineering in laboratories. It is based on the intention to create a closer link between research and technology assessment (cf. Berloznik and van Langenhove, 1998). Since the advancement of science and engineering can have a strong impact on the future of economy, society, even the whole planet, the work performed in the laboratory concerns everyone. The involvement of outsiders strengthens reflection among experts about their work or lets them share the burden of responsibility for their actions (Door, 2012; Mitcham, 2003; Guston and Sarewitz, 2002). Where everyone has the opportunity to participate, decisions about research can be taken in a more democratic way. The work in the laboratory earns more legitimacy as a larger social endeavour. Participatory approaches to research are pursued from many different directions (Leach et al., 2005; Wiggins and Crowston, 2011). They also provide the foundation for open source and open data projects and platforms (e.g. Fecher and Friesike, 2014; Gurstein, 2011; Moody, 2002). The role of open laboratories in this context has been highlighted in particular by the living lab movement, which is strongly supported by policy makers who want to involve larger parts of the population in urban planning, environmental protection and numerous different social and cultural enterprises (e.g. Bergvall-Kåreborn and Ståhlbröst, 2009; Almirall et al. 2012; Dell’Era and Landoni, 2014). Opening up the laboratory can, of course, also serve much more mundane purposes that are well known from crowdsourcing. It can help academia and industry to deal with a shortage of talent or funding (Del Savio et al. 2016, Hossain and Kauranen, 2015). If internal experts are not available or too expensive to perform the necessary tasks, outsiders can be invited to join and help. Where problems of technical applications are addressed, it can be particularly helpful to have users involved who can contribute from personal experience (von Hippel, 1994). In a similar way, people who are directly affected by social change may add important insights in the process of discovery.

3 The Claims of the Creative Society People who visit open laboratories, however, do not necessarily have to have a particular interest in science and engineering. Reckwitz (2018) gives another reason for the current popularity of open laboratories on a more general level. He observes an imperative of creativity in society. In the post-industrial era, anyone seems to be

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allowed to do anything, except not being creative. Seeking fulfilment in the accomplishment of an interesting task is not enough anymore. Society expects a deeper engagement with a subject matter to leave a visible mark with expresses one’s own individuality. After the experience of the industrial revolution, arts and crafts have gained an almost romantic quality as personal practices that avoid the estrangement of standardized, recurring work procedures (e.g. Sennett, 2008). Following Böhme (2003), this phenomenon can be related to a growing aesthetic orientation in today’s economy (see also Bourdieu 1984; Haug 1986). It goes along with a shift from satisfying needs to living out desires and tastes, which are not considered in the traditional models of demand and supply. Instead, they draw on thoughts that have already been expressed in other domains such as pedagogics or psychology for quite some time. Kerschensteiner’s (1913) writings, for example, highlight the importance of letting students relate to the work they do, which can be accomplished by engaging them in practical tasks of constructing objects with their own hands, instead of just making them listen to lectures. Steiner (1947) takes an even more radical approach in the establishment of schools for the children in the Waldorf-Astoria factories to ensure a holistic personal development. In comparison to traditional schools, the artistic education of the children is emphasized to strengthen their creativity and to give them means to express their own personality. Many of these ideas correspond with thoughts in Gestalt theory (Koffka, 1922; Köhler, 1929; Ellis, 1938), which turn the attention away from abstract coding of knowledge in formal structures towards broader forms of experience and understanding based on multi-sensual impressions of objects and their symbolic value. Since the middle of the twentieth century, these thoughts have also found their way into design thinking and other creative practices that enjoy enormous popularity today (Brown, 2008). Open laboratories provide spaces in which all this can be addressed. As such, they rather stand in the tradition of reformatory schools or studios of painters, sculptors and designers than workplaces of scientists and engineers. It is worth noting that many movements towards open laboratories (Gershenfeld, 2005; Hatch, 2014) started out in countries that have abandoned the traditions of craftsmanship and vocational education in the course of the industrial revolution. In this respect, open laboratories also seem to have a compensatory function for the absence of the apprenticeship model of professional education, in which the students spend a lot of time in a workshop or similar environments together with their masters. Summing up the aforementioned considerations , open laboratories can be described as spaces for participation in a large number of different contexts – not only scientific research or innovation, but also many other activities that were so far reserved to a small group of experts. They abandon the idea of a clear distinction between production and consumption, which has left its mark on economic models of the industrial age, but also on the traditions of staging the expert in front of a large audience in arts, science or politics. Similar to other movements towards

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openness and participation, they question the idea of the exceptional genius as the protagonist of change. Instead, they turn the focus to the creativity and knowledge of every member of society. It can be argued that there is even more at stake. With the step beyond the work patterns of professionals in closed settings, open laboratories also depart from the narratives that define the identity of an organisation. Participation in the open laboratories does not require a commitment to a shared goal or the acceptance of a specific role to function within a systemic structure. People can visit the laboratory in pursuit of their own interests. They are free to make judgment calls or express their opinion, no matter how well it fits to the expectations of someone else. What happens in open laboratories is therefore not just resource integration. The possibilities to make use of the visitors in any particular function are fairly limited. In comparison with other collaboration platforms on the internet and elsewhere, the activities in open laboratories are much less guided by specific means of communication or predefined protocols of exchange. Being physically present in the same room at the same time allows people to interact in a multitude of different ways. If they want, they can even do so in complete disregard of any technical device in the laboratory that might afford a certain action. An organisation and the projects performed in an organisation unite people in a common undertaking. In the open laboratory, the diversity, ambiguity and paradox of human society as a whole remain present. The visitors do not go through the same kind of preselection according to common interests, needs or emotional attachments to a certain subject as they would in a company or community of practice. They may never find together as a group or reach a shared understanding of any topic. With open laboratories, innovation management consequently ventures into uncharted territory. It reaches out to people who would otherwise not get involved in any corporate or public initiative, not even in an idea contest or an innovation community online. Many of them may not be interested in innovation at all and spend their time in the laboratory for other purposes than ideation or problem solving. This creates a huge challenge for innovation management, but also a unique opportunity to advance in new directions.

4 Perspectives on Innovation Management in Open Labs As part of a larger social endeavour, innovation in the open laboratory can be approached from many perspectives. The following chapters illustrate this variety with four different accounts of the innovation activities that took place under the amazing multi-coloured coat of JOSEPHS® during the past five years, showing how organisations can bridge the gap between traditional innovation practices and

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work in the open lab, as they immerse in the richness of the laboratory environment, but at the same time draw a benefit for their own projects out of it. Greve et al. explain the role of JOSEPHS® as a setting for co-creation activities on the example of different innovation projects that have taken place there. They describe the implementation of the projects in the laboratory, the interaction with the visitors and the learnings for the companies who have initiated the projects. The chapter gives insight in the wide variety of aspects of innovation that are covered in the laboratory and the many possibilities of companies to make use of the contributions of the visitors. Steinmetz and Hübner turn the attention to the treatment of visitors in the laboratory. They relate JOSEPHS® to the tradition of Nuremberg as a city of trade and its markets. To engage the visitors in innovation, staff at the laboratory has to be as flexible as any salesperson in the interaction with people passing by and looking at the goods that are sold. Steinmetz and Hübner characterise different types of visitors and explain from personal experience how to approach them. Wolpert and Perez Mengual focus on the operative steps of planning, executing and reporting innovation projects at the open laboratory. They highlight the importance of JOSEPHS® as a service provider for innovating organisations and discuss the steps that must be taken in order to ensure that the organisations get valuable results in return for their investment. They show how an open laboratory can be run successfully as a business. Engel looks at the open laboratory as a space to advance the development of start-ups. He compares it to Nuremberg’s digital tech incubator and discusses the different forms of openness that are practiced in both institutions. Engel shows that start-ups do not only benefit from innovation activities at JOSEPHS® though an improvement of their value propositions and business solutions, but also through a better understanding of their corporate identity and position on the market. The example of JOSEPHS® thus shows the intricate relationship between innovation in the open laboratory and the wider context of local business activity. Furthermore, it highlights the numerous managerial tasks that are involved in running the laboratory, which can lead to the emergence of new professions in the future.

References Almirall, E., Lee, M., & Wareham, J. (2012). Mapping living labs in the landscape of innovation methodologies. Technology innovation management review, 2(9), 13–18. Bergvall-Kåreborn, B., & Ståhlbröst, A. (2009). Living Lab: an open and citizen-centric approach for innovation. International Journal of Innovation and Regional Development, 1(4), 356–370. Berloznik, R., & Van Langenhove, L. (1998). Integration of technology assessment in R&D management practices. Technological Forecasting and Social Change, 58, 23–33.

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Böhme, G. (2003). Contribution to the critique of the aesthetic economy. Thesis Eleven, 73(1), 71–82. Bourdieu, P. (1984). Distinction. A Social Critique of the Judgment of Taste. Cambridge, MA: Harvard University Press. Brown, T. (2008). Design thinking. Harvard business review, 86(6), 84–92. Dell’Era, C., & Landoni, P. (2014). Living Lab: A methodology between user‐centred design and participatory design. Creativity and Innovation Management, 23(2), 137–154. Del Savio, L., Prainsack, B. & Buyx, A. (2016). Crowdsourcing the Human Gut. Is crowdsourcing also ‘citizen science’? Journal of Science Communication, 15 (3),A03, 1–16. Doorn, N. (2012). Exploring responsibility rationales in research and development (R&D). Science, Technology & Human Values, 37, 180–209. Ellis, W. D. (Ed.) (1938). A source book on Gestalt psychology. London: Routledge and Kegan Paul. Fecher, B. & Friesike, S. (2014). Open science: one term, five schools of thought. In Bartling, S. and Friesike, S. (Eds.) Opening science (pp. 17–47). Cham: Springer. Gershenfeld, N. (2005). Fab: The coming revolution on your desktop – From personal computers to personal fabrication. New York: Basic Books. Gurstein, M. B. (2011). Open data: Empowering the empowered or effective data use for everyone? First Monday, 16(2). Guston, D. H., & Sarewitz, D. (2002). Real-time technology assessment. Technology in Society, 24, 93–109. Hatch, M. (2014). The maker movement manifesto. New York: McGraw-Hill. Haug, W. F. (1986). Critique of Commodity Aesthetics. Cambridge: Polity Press. Hossain, M. & Kauranen, I. (2015). Crowdsourcing: a comprehensive literature review. Strategic Outsourcing: An International Journal, 8(1), 2–22. Kerschensteiner, G. (1913). The Idea of the Industrial School. New York: Macmillan. Knorr Cetina, K. (1995). Laboratory studies: The cultural approach to the study of science. In Jasanoff, S. Markle, G. E., Peterson, J. C. & Pinch, T. (Eds.) Handbook of science and technology studies, 140–167. Koffka, K. (1922) Perception: An introduction to the Gestalt-theorie. Psychological Bulletin, 19, 531–585. Köhler, W. (1929). Gestalt psychology. New York: Liveright. Latour, B. (2005). From Realpolitik to Dingpolitik or how to make things public. In Latour, B. & Weibel, P. (Eds.), Making things public – Atmospheres of democracy (pp. 14–43). Cambridge: MIT Press. Latour, B. (1987). Science in action. Milton Keynes: Open University Press. Leach, M., Scoones, I. & Wynne, B. (Eds.) (2005). Science and citizens. Globalization and the challenge of engagement. London: Zed. Leonardi, P.M. (2013). Theoretical foundations for the study of sociomateriality. Information and Organization 23, 59–76. Mitcham, C. (2003). Co-responsibility for research integrity. Science and Engineering Ethics, 9, 273–290. Moody, G. (2002): Rebel code: Linux and the open source revolution. London: Penguin Books. Orlikowski, W. J. & Scott, S. V. (2008). Sociomateriality: challenging the separation of technology, work and organization. The academy of management annals, 2(1), 433–474. Reckwitz, A. (2018). The invention of creativity: Modern society and the culture of the new. Cambridge: Polity Press. Schuurbiers, D., Doorn, N., van de Poel, I., & Gorman, M. E. (2013). Mandates and methods for early engagement. In Doorn, N., Schuurbiers, D., Van de Poel, I., & Gorman, M. E. (Eds.). Early engagement and new technologies: Opening up the laboratory (pp. 3–14). Dordrecht: Springer.

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Sennett, R. (2008). The craftsman. New Haven/ London: Yale University Press. Steiner, R. (1947). The Study of Man. London: Anthroposophic Press. von Hippel, E. (1994). “Sticky information” and the locus of problem solving: implications for innovation. Management science, 40(4), 429–439. Wiggins, A., & Crowston, K. (2011). From conservation to crowdsourcing: A typology of citizen science. 44th Hawaii international conference on system sciences. IEEE.

Katharina Greve, Julia M. Jonas, Andy Neely, and Kathrin M. Möslein

7 Unlocking Unique Value Through Co-Creation in Open Laboratories 1 Innovating with the User, for the User Companies increasingly reach outside their own organizational boundaries to engage with customers to jointly develop new products and services (Brunswicker and Chesbrough, 2018). Companies turn towards customers for inspiration to develop innovative products and services that better align with customers’ expectations (Gutu, Manuwa and Mbuya, 2018). Indeed, across all industries, firms agree that involving users in the innovation process – to learn from them and work with them – is vital (Westerlund and Leminen, 2011). By allowing customers to become idea generators and co-creators, it is possible to comprehend their latent or unvoiced needs (Kristensson, Matthing and Johansson, 2008). One prominent approach to foster co-creation with customers, that is becoming increasingly popular, are open laboratories (Fritzsche, 2018). These laboratories are closely related to the notion of open innovation, which purposively makes use of knowledge flows across organizational boundaries (Chesbrough, 2003), and user innovation, which turns the focus to the people who engage in the innovation process (von Hippel, 2009). Indeed, companies increasingly integrate their customers in the innovation process by means of open laboratories. For example, companies such as the German software company SAP, establish their own open labs and leverage the power of customer co-creation. In 2007, SAP set-up their own Co-Innovation Lab (COIL) with the mission to provide partners a structured and guided global approach to producing innovative solutions that have a shorter time to market, with reduced risk (Innovation Leader, 2016). Instead, Lego, German Telekom, and other businesses, establish interactive spaces in their store settings to achieve customers engagement in the exploration of new products and services (Roth et al., 2015). While there are significant differences across a range of open laboratories such as FabLabs, TechShops, and Living Labs (Fritzsche, 2018), they can be defined by the physical environment in which people

Katharina Greve, Institute for Manufacturing, Department of Engineering, University of Cambridge, Cambridge, UK Julia M. Jonas, FAU Erlangen-Nuremberg, Chair of Information Systems - Innovation and Value Creation, Nuremberg, Germany Andy Neely, Pro-Vice-Chancellor for Enterprise and Business Relations at the University of Cambridge, Cambridge, UK Kathrin M. Möslein, FAU Erlangen-Nuremberg and HHL Leipzig Graduate School of Management, Germany https://doi.org/10.1515/9783110633665-007

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can create, validate or test products, services or processes through the direct or indirect engagement with an organization. Some open laboratories involve facilitators and therefore can be seen as intermediaries (Almirall and Wareham, 2008) supporting the innovation process between companies and co-creators. The expression ‘co-creator’ is used when referring to co-creation with customers and users (Leminen, Nyström and Westerlund, 2015). By offering an environment that closely resembles the context of the product or service in real-life, open labs can provide as authentic a use situation as possible. Often these labs offer a more reliable market evaluation than test markets and empower users to contribute to the innovation processes (Salter and White, 2013). While companies increasingly utilize open laboratories for innovation purposes (Leminen and Westerlund, 2016), the different types of project objectives, outcomes and the unique benefits associated with such labs have not been extensively discussed thus far. This chapter presents examples of four co-creation projects that have been conducted in an open lab called JOSEPHS® and exemplifies the benefits of such an open approach to innovation.

2 The Case JOSEPHS® JOSEPHS® is an open lab located in the city center of Nuremberg, in the south of Germany. It is a physical space enabling the active involvement of users in the development, introduction and commercialization of new services and products. JOSEPHS® offers four to five islands for co-creation and prototyping, each occupied by an innovation project for the time-span of three months, under one common theme (see Figure 7.1). On these islands, innovators present ideas, early prototypes,

Figure 7.1: Layout of JOSEPHS®.

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or even products and services at a later development stage, in order to receive authentic feedback from users. Visitors are invited to experience and partake in ongoing innovation journeys of established innovators as well as start-ups and research projects. JOSEPHS® was initiated by the Fraunhofer Center for Applied Research for Supply Chain Services [SCS] in cooperation with the Chair of Information Systems I at Friedrich-Alexander University Erlangen-Nuremberg [FAU] as part of the project Service Factory Nuremberg, a research project supported by the Bavarian Ministry of Economic Affairs, Regional Development and Energy (Roth et al., 2015). To facilitate the interactive exchange and co-creation of innovation, JOSEPHS® offers a platform with governance structures and innovation tools that help both parties, innovators and the co-creating public (Beutel et al., 2017).

Innovators at JOSEPHS® Companies, start-ups and research projects can use JOSEPHS® to evaluate, test and enrich physical, as well as digital ideas and prototypes with a diverse, self-selected group of users. The innovators utilizing the open lab for innovation purposes come from a wide variety of backgrounds and sizes, ranging from start-ups in consumer products to technology providers and larger firms (Roth and Jonas, 2018). Not only do business-to-customer firms use this space, but also business-to-business enterprises that would like to explore what the customer-of-their-customers think about their offerings and tailor them accordingly. The motivation to utilize JOSEPHS® range from easy testing, gaining public awareness, gathering experience in presenting or find specific partners (Roth & Jonas, 2018).

Co-creators at JOSEPHS® Co-creators play a critical role in the innovation process in open laboratories (Garcia Robles et al., 2016). Due to its openness and location, JOSEPHS® attracts a wide variety of co-creators that differ in age, gender, education, professions and other traits. It is vital to encourage and enable them to participate in the co-creation process and provide feedback on specific ideas and prototypes. Therefore, to ensure they have a positive co-creation experience, a variety of research tools are applied, and attention is paid to their interests, available time and earlier experience with innovation tools (Perez Mengual et al., 2018). During one 3-months theme, JOSEPHS® has on average about 3,000 visitors of which 1,000 voluntarily actively engage in co-creation.

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Facilitating the Co-creation Process at JOSEPHS® To facilitate the interactive innovation activities between innovating organizations on one hand and co-creating visitors on the other, JOSEPHS®’ staff plays a critical role. They serve as moderators and hosts to the spaces, since the representatives from innovating firms and projects are usually not present at JOSEPHS®. It is the responsibility of the lab facilitator to engage with co-creators and to also regularly communicate the feedback about the prototype back to the innovating organization. As a result, a continuous feedback loop can be established which enables companies to introduce changes to the prototype throughout the test phase and gather further feedback on new versions of their product or service (Greve, 2018).

3 Co-creation Projects at JOSEPHS® In a 12-months period, about 20–25 co-creation projects take place at JOSEPHS®. Greve (2018) analyses projects carried out at JOSEPHS® and identifies eight categories of project outcomes that are achieved. The study of more than twelve innovation projects reveals that companies focus on one to a maximum of four project objectives during their 3 months test phase at JOSEPHS® (Greve, 2018). However, most companies concentrate on two project objectives. As JOSEPHS® devotes a lot of effort in attracting a variety of co-creators, companies consider having access to them as a unique opportunity. There are several reasons why companies value JOSEPHS’® cocreators. First, some B2B firms usually do not have any contact to their end consumers. Second, some companies wish to have a facilitator that enables this interaction without interfering or influencing the co-creation process directly. Third, some businesses, intentionally, want to reach out to an untargeted audience, or even people that lie outside their usual customer segment. Fourth, the space created, and support offered by JOSEPHS®, presents a unique opportunity to engage with co-creators in a relaxed atmosphere that simulates a real-life setting. Utilizing an open lab like JOSEPHS® offers four distinct types of benefits to organizations, which are listed in Table 7.1: “Obtaining contextual information”, “Verifying existing assumptions”, “Deriving unplanned insights” and “Generating new ideas”. The study of more than twelve innovation projects reveals that companies focus on one to a maximum of four project objectives, among the ones listed in Table 7.1, during their 3 months test phase at JOSEPHS® (Greve, 2018). However, most companies concentrate on two project objectives. As JOSEPHS® devotes a lot of effort in attracting a variety of co-creators, companies consider having access to them as a unique opportunity. There are several reasons why companies value JOSEPHS’® co-creators. First, some B2B firms usually do not have any contact to their end consumers. Second, some companies wish to have a facilitator that enables this interaction without interfering or

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Table 7.1: Benefits of Co-creation projects in open laboratories. Section

.

.

.

.

Benefit

Obtaining contextual information

Verifying existing assumptions

Deriving unplanned insights

Generating new ideas

Display and communication in fashion & lifestyle (DCF)

Processes and customer experience evaluation for retail (PER)

Technology evaluation and enrichment of the solution (TEES)

Project Technology Example evaluation & acceptance in automotive (TEAA)

influencing the co-creation process directly. Third, some businesses, intentionally, want to reach out to an untargeted audience, or even people that lie outside their usual customer segment. Fourth, the space created, and support offered by JOSEPHS®, presents a unique opportunity to engage with co-creators in a relaxed atmosphere that simulates a real-life setting. Different to conventional market research methods, innovation research in open labs like JOSEPHS® can derive additional benefits as a result of its unique concept and associated characteristics. By reference to specific projects, four key benefits are discussed in this section which are summarized in Table 7.1.

3.1 Obtaining Contextual Information The innovation project “Technology evaluation & acceptance in automotive” (TEAA) developed a technology and related device for application in a car. At JOSEPHS®, visitors were invited and able to try out the device directly in a car, which offered an authentic use situation (see Figure 7.2). When sitting in the car, the user could test the reliability of the technology by themselves; they were able to see on an accompanying monitor how the technology worked and how its’ application is implemented for the use case in a car. The TEAA innovators wanted to understand the willingness to pay for a device with this technology. The co-creators’ feedback revealed that instead of placing another physical device in the car, people would prefer a phone application. Nowadays, smart phones are integrated in the car for entertainment, navigation and communication and therefore an integration of the additional technology into an already existing device would be the preferred solution. People said that they do not want another device in the car. Many already use their mobile as a navigation device and could imagine using our device if that would be integrated in the mobile. But they do not want to have another device in the car. (IT specialist of TEAA)

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Figure 7.2: The TEAA project allows visitors to test a new technology in the car.

Due to the continuous feedback the open lab provides to the project manager of TEAA, they were able to quickly respond to the newly derived insights and built a mockup, that kind of resembled a mobile phone (. . .) that was one thing that we really didn’t expect, that people tell us here that they don’t want another device in the car. That they do think it is an important technology, but not if you have another device – but rather that it is integrated in something that one already possesses. (IT specialist of TEAA)

The feedback provided by the co-creators provided crucial contextual information that also influences the customers’ willingness to pay and as a consequence the price point for the technology usage.

3.2 Verifying Existing Assumptions The innovation project by DCF is about recycling for fashion and accessories. At trade shows and exhibitions, the company used to present their products in relation to the origins of the materials. The design of the exhibition space required a lot of extra equipment and effort, which in turn was assessed to represent a barrier for retailers to take on the product. Yet, there was the idea that the full range of materials and equipment is not needed to present and successfully sell the products:

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(. . .) it was always the problem at trade fairs, (. . .) our product needs A) an explanation and B) the traders think that if they take our product on board they should decorate in a specific style. And this is one aspect that we want to excavate, because (. . .) that means more work and it makes them rather shy away to take the product on. (Project Manager DCF)

To verify the ideas about how to display the fashion items and accessories, two contrasting exhibition designs were presented at JOSEPHS®. One exhibition space was decorated with related equipment and materials as the innovation team used to, and the second version was presented as a rather neutral retail space. The objective of the co-creation project was to understand what co-creators find more welcoming, appealing, prettier. As a result of the test phase at JOSEPHS®, it became clear that the [second, more neutral] exhibition option (. . .) which we were seeking affirmation for, did indeed do better, much better [Project Manager DCF]. . For the company, the project at JOSEPHS® confirmed, that we now can say to our traders, okay, you don’t need to decorate in an elaborated way. If that is anyway the idea and the retailer has multiple products for display then that’s of course not negative; But if he doesn’t want that, then he can display the products in an ordinary way. We already knew that beforehand, but the project at JOSEPHS confirmed our hypothesis. (Project Manager DCF)

Therefore, the key benefit of carrying out a co-creation project in an open lab for DCF was that their existing assumption was confirmed. Through JOSEPHS, you get rid of your gut feeling and get a rational profound sample size, that you can rely on and that you are able to work with. You no longer have to act blindly, because you know, okay, I have now numbers who confirm this. (Project Manager DCF)

3.3 Deriving Unplanned Insights PER, a technology innovation project in retail by Mifitto, developed an application that allows to measure one’s own feet online. The measurements help to achieve higher accuracy of fit by determining the right shoe size for every foot. In addition to the app, Mifitto also developed a 3D foot scanner that can be installed in shops. The scanner measures both, feet and shoes, and then recommends a suitable shoe for a person’s foot measurements. The idea is that the increased accuracy of fit increases satisfaction with the purchased shoe and reduces the number of returns because the foot measurements of the customer align with the data of the shoe and so virtually the desired shoe is “tried on”. During their co-creation project at JOSEPHS®, Mifitto posed a number of questions related to the two solutions they offer, the customer journey, data privacy and market needs in the Nuremberg area. Aside from insights that address these topics, Mifitto also derived some unplanned insights regarding the material of the foot scanner on-

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Figure 7.3: Mifitto’s 3D foot scanner for shops.

site which was not focus of their enquiry. Co-creators gave broad feedback that could be implemented directly: the self-service interface should be available not only in German language; a stool or seat is required to take off shoes for scanning and more. Further, the co-creators noticed that the plate where the feet are measured on is leaving a print as if the scanned feet were sweaty. This in turn felt embarrassing for the persons who used the foot scanner and potentially discourages them to use it again. Mifitto took this feedback up easily and developed further prototypes with different materials after their innovation project. Figure 7.3 shows, amongst various others, that one key benefit of the innovation project at JOSEPHS® can be the unplanned insights from the “reality check” and insights innovators can derive through direct user interaction and testing of prototypes.

3.4 Generating New Ideas Fraunhofer IIS/ IISB and the initiative LZE presented their project, a Fitness-Shirt in combination with the patent ELECSA at JOSEPHS®. This solution for performance diagnostics was developed for scenarios in sports, combining the sensors and software for the real-time monitoring of i.a. heart rate, activity, breathing rate and ammonia level (exhaustion). The co-creators at JOSEPHS® did not only try the technology and its related software applications, but also gave input about their perceived usefulness, usage scenarios as well as additional functions that would be desired as a future development of this technology-based solution. Some insights in

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this context include data security issues, a configurator for individual combination of sensors, and clearer washing instructions for the integrated sensors. Furthermore, they gave additional input for the further development of the solution: It would be great to show the output of the monitoring with playful elements, like a ball that needs to stay in a specific, healthy area. (Co-creator 47) An emergency function, in combination with the GPS location of runners would be something great! (Co-Creator 74)

More radical and open ideas for the application of the sensor technology include a skin implant for lactate sensing, an application in dating – where increased heart beat should not be pretended but true excitement – or applications in other domains such as automotive or workplace security. With such open input, new use cases and opportunities for business could be developed, as input for the further development of technology in this case. Totally new ideas are illustrated in this case openly, yet a few other researched projects of JOSEPHS® treat the valuable ideas received, e.g. in the context of housing and entertainment, more discrete.

4 Conclusion The cases of the innovation projects PER by Mifitto, TEAA, DCF and the Fraunhofer Fitness-Shirt show that environments like JOSEPHS® provide an open innovation platform that goes beyond the insights traditional market and innovation research methods are able to capture. Open laboratories like JOSEPHS® can offer insights that go beyond the original research question posed by a company and deliver contextual information that can have an impact on the entire offering. Further, existing assumptions can be verified, and completely new ideas can be generated through the interaction with users and potential users. Due to their semistructured data collection approach, open labs can also deliver unplanned insights as presented through the case of Mifitto. Open labs like JOSEPHS® offer a rich practice-based learning experience for innovators that can draw on the expertise of the lab facilitators as well as the feedback from co-creators that are testing their ideas, processes and prototypes in an authentic use situation. The feedback loop that is established in open laboratories like JOSEPHS® presents innovators with the opportunity to improve their prototypes throughout the test phase and continuously receive new feedback on the updated version. While spaces like JOSEPHS® run counter to the secrecy and silo mentality of traditional closed innovation approaches by involving outsiders for inspiration and feedback, it is also important to acknowledge the limitations of this approach. Greve (2018) suggests that projects which focus on products or services that are

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aimed at a niche market face difficulty in obtaining useful feedback from cocreators. While it does not mean that companies with niche products or services cannot benefit from a co-creation project in open labs, these companies need to focus on aspects of their products and services that are suitable for co-creation contributions from a mainstream audience. Indeed, the large number of projects that met their initial project objectives all focused on aspects that a mainstream audience can offer feedback on (Greve 2018). Also, innovators may establish new connections to partners, create a new fan base and be able to practice their presentation in public spaces overall (Roth and Jonas, 2018). Furthermore, due to the nature of this open laboratory, characterized through its openness to the public, and its main focus on qualitative data collection, results are not representative of a specific target segment nor are they statistically generalizable outcomes. However, the experience provides stimuli, insights and pathways for development from self-selected users, non-users and even potential business partners. This must be taken into consideration when companies evaluate the feedback, they receive from JOSEPHS®. While one could consider an untargeted audience a limitation of this particular approach to innovation, however, one of the most important ingredients for creative thinking is diversity (Ollila and Yström 2016). Indeed, JOSEPHS® attracts people diverse in age, and with different professional backgrounds and experiences. Therefore, if companies are looking for creative ideas which are the fuel driving the innovation process (Goller and Bessant 2017, p. 6) and unbiased feedback through an intermediary who is organizing, advising and facilitating the innovation process, open labs like JOSEPHS® can present a unique opportunity to leverage the power of open innovation.

References Almirall, E. and Wareham, J. (2008). Living labs and open innovation: Roles and applicability. The Electronic Journal for Virtual Organization and Networks, 10(3), 21–46. Beutel, T., Jonas, J. M. and Möslein, K. M. (2017). Co-creation and user involvement in a living lab: An evaluation of applied methods. In: Proceedings of the 13th International Conference on Wirtschaftsinformatik, St. Gallen, Switzerland, 1453–1464. Brunswicker, S. and Chesbrough, H. (2018). The adoption of open innovation in large firms. Research-Technology Management, 61(1), 35–45. Chesbrough, H. (2003). Open innovation: The new imperative for creating and profiting from technology. Boston, Massachusetts, Harvard Business School Press. Fritzsche, A. (2018). Corporate foresight in open laboratories – a translational approach. Technology Analysis & Strategic Management. Taylor & Francis, 30(6), 646–657. doi: 10.1080/ 09537325.2017.1380180. Garcia Robles, A., Hirvikovski, T., Schuurman. D and Stokes, L. (2016). Introducing ENoll and its living lab community. Available at: https://issuu.com/enoll/docs/enoll-print. [Accessed on 29.08.2019]. Goller, I. and Bessant, J. (2017). Creativity for innovation management. London, UK: Routledge.

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Greve, K. (2018). Facilitating co-creation in living labs. Doctoral Thesis, University of Cambridge, 1–212. Gutu, C. L., Manuwa, N. R. and Mbuya, J. M. (2018). Customer expectation on service quality in bed and breakfast establishments in Johannesburg metropolitan. International Journal of Industrial and Manufacturing Engineering, 5(1). von Hippel, E. (2009). Democratizing Innovation: The Evolving Phenomenon of User Innovation. International Journal of Innovation Science, 1(1), 29–40. Innovation Leader (2016). Innovation Labs: Getting started, managing them, delivering results. Available at: https://www.innovationleader.com/labsreport/. [Accessed on 29.08.2019]. Kristensson, P., Matthing, J. and Johansson, N. (2008). Key strategies for the successful involvement of customers in the co-creation of new technology-based services, International Journal of Service Industry Management, 19(4), 474–491. Leminen, S., Nyström, A. G. and Westerlund, M. (2015). A typology of creative consumers in living labs. Journal of Engineering and Technology Management, 37, 6–20. doi: 10.1016/j. jengtecman.2015.08.008. Leminen, S. and Westerlund, M. (2016). A framework for understanding the different research avenues of living labs. International Journal of Technology Marketing, 11(4), 399–420. Ollila, S. and Yström, A. (2016). Exploring design principles of organizing for collaborative innovation: The case of an open innovation initiative. Creativity and Innovation Management, 25(3), 363–377. Perez Mengual, M., Jonas, J. M., Schmitt-Rueth, S. & Danzinger, F. (2018). Tools for collaborating and interacting in Living Labs. Service Design Proof of Concept. Proceedings of the ServDes.2018 Conference. Linköping: Linkö-ping University Electronic Press. Roth A., Jonas J.M. (2018) Dienstleistungsentwicklung im offenen Innovationslabor – Ein Blick durch die Unternehmensbrille. In: Bruhn M., Hadwich K. (eds) Service Business Development. Springer Gabler, Wiesbaden Roth, A., Jonas J.M., Fritzsche, A., Danzinger, F. and Moeslein, K.M. (2015). Spaces for Value Co-Creation: The Case of “JOSEPHS® – The Service Manufactory”. In: European Academy of Management. Warsaw, Poland. Salter, R. and White, S. (2013). Collaborative research in the real world: review of living labs. Living Lab Framework Project, CRC for Lowa Carbon Living. Westerlund, M. and Leminen, S. (2011). Managing the challenges of becoming an open innovation company: Experiences from living labs, Technology Innovation Management Review (October), 19–25.

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8 Working in the Open Lab – Mediation, Trading and Translation 1 Innovation in the City Centre Nuremberg is known today as an industrial centre where many highly innovative products, services and business models are developed and manufactured. This has a long tradition. Centuries ago, Nuremberg was already known across all of Europe as a hotspot for innovation and commerce (Diefenbacher and Endres, 2000). Nuremberg could gain this reputation because of its location at the intersection of two important trade routes, ranging from Portugal and Spain in the West to Austria, Hungary and the neighbouring countries in the East, and also from Scandinavia in the North to Italy in the South. Nuremberg brought people and ideas together. It was a place the most diverse goods were traded and knowledge from different cultures was exchanged (see Figure 8.1). In the age of industrial mass production, trade may seem like a simple transaction. Goods are standardized and available in large quantities. There are precise descriptions of the product characteristics, whose compliance is checked again and again. That was not the case in earlier centuries. Standards were mostly locally defined. Each city had its own rules for determining lengths, measurements and weights. Old churches keep the memory of this practice, with bells telling time, archives documenting social interaction, and inscriptions of lengths and sizes on the church walls, which people could use to check the compliance of products to standards. Beyond that, however, each object was somehow different. If produced far away, an element of novelty and strangeness could also be connected to it, regarding its origins, production and original purpose. Each product therefore required a close inspection to capture its exact characteristics, explanations about its provenance and discussions about its meaning and utility. Therefore, a medieval market can hardly be compared to a modern retail store. It was not just about the exchange of goods and money. It was also a place for much more fundamental conversations, in which the participants first developed a common understanding of what they were actually trading. The role of the merchant went far beyond the purchase and sale of goods. Gorman et al. take up this notion when they talk about trading zones in the modern world as places, where “people from different perspectives and agencies can work together to define a common goal in a way that would be acceptable to their

Ingeborg Steinmetz, Lisa Hübner, Fraunhofer IIS – Center for Applied Research on Supply Chain Services SCS, Nuremberg, Germany https://doi.org/10.1515/9783110633665-008

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Figure 8.1: Impression of a medieval market by Félix de Vigne (Wikimedia/ public domain).

core communities” (Gorman et al. 2013, p. 157). Such trading zones are emerging today in many different contexts. From an industrial perspective, this can be explained by the individualization of products and the shift towards services that are sold together with the products. Here, one can see again that standards are no longer sufficient to understand goods, as everything is embedded into a different application context according to the personal lifestyle of its users. The computer plays an important role in this development, as it represents a “universal calculating machine” (Newman 1948) that defines itself precisely by the fact that it does not serve as an instrument for a specific operation, but allows for versatile usage. This can be seen on many common digital devices today: smartphones, tablets and smart watches cannot only be used for one single purpose. Even the latest generations of automobiles today are more than just means of transportation. Thanks to digital technology, they serve as an interface for traffic, provide comfort and relaxation, and support drivers in many decision-making processes. Digital objects thus do not have a clearly defined scope of application. They present themselves differently for each user and force the user to a much larger engagement in dealing with the object. This entails a different relationship between consumer and producer than the one described in conventional economic models from the twentieth century. Toffler (1980) coined the term “prosumer” that characterizes people who consume, but at the same time get involved in the production of goods. This involvement extends to the participation in the innovation process, which is nowadays propagated in places like Fab Labs, Maker Spaces or Living Labs.

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The consequence is, as Fritzsche (2016), for example, points out, that a revision of the understanding of the expert working in this area is necessary (see also Neely et al. 2018). So far, expertise is commonly related to the ability to design and construct solutions from a technical perspective. With this chapter, we want to draw attention to another approach to expertise in the tradition of trading. Where product development, production and use merge, this expertise emphasizes the role of mediation between the various groups involved as a key element of innovation. Experts who work at the institutes of the Fraunhofer Gesellschaft are widely known for their expertise in developing and designing technology, but they also mediate between different stakeholders in many ways. Research projects at the institutes are application driven and frequently commissioned directly by industry. The insights which the experts gain through their studies are expected to allow a direct transfer into practice to create marketable products. Therefore questions of pure natural science, engineering, sales and marketing must be considered at the same time. Experts at the Fraunhofer Institutes therefore also have to be mediators between the different interest groups. For the most part, however, this happens in closed spaces, according to the well-established principles of engineering. This work is far from the hustle and bustle of a marketplace where various stakeholders are actively engaging in a trading zone. By using open labs, the situation changes radically. Where closed labs are populated by technical experts to mediate between scientific insight and industrial exploitation of specific devices, open labs require value experts who can step beyond the scope of the devices themselves and consider the whole process of sensemaking that surrounds them – much like retailers in old Nuremberg. With the operation of an open innovation laboratory in the city centre of Nuremberg, the Fraunhofer Institute IIS and Friedrich-Alexander-University ErlangenNuremberg build on the tradition of the marketplace in Nuremberg. The laboratory is integrated into a lively commercial environment of shops, cafés, workshops and offices. Its opening hours are aligned with the general opening hours in retail from Monday to Saturday. Visitors experience the lab from the outside like a regular store. However, when they enter, they find that they can do more in the lab than buying goods or receiving services. The interaction with the staff goes much deeper than in other places. It is more creative and allows much influence on the design of valueadded processes. The following pages describe the role of the staff in the open laboratory in more detail, based on a five year experience in Nuremberg.

2 Laboratory Work with Open Doors When scientific experiments are described in a journal, the focus is usually set on the experimental setup which allows it to provoke certain effects. For the layman, this can easily give the impression that such experiments are unique events that are

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carried out in a short time. However, this is only rarely the case. Normally, a scientific experiment consists of many trial runs, each time measuring a large number of data points. For those who carry out the experiments, there are many routine activities that have to be carried out again and again. This also applies to an open laboratory for innovation in the city centre. The duration of the experiment can be compared with the period of time in which an innovation project is hosted in the laboratory. In our case, this is by standard three months. Data points are provided through the interactions with the visitors, from which new insights into the subject matter emerge. Furthermore, every day can be understood as a new trial run, which requires the consideration of very different framework conditions. Before a trial run can be started in a laboratory, there is already much work to do to establish suitable conditions for data collection. The same is true for a day in the open laboratory. Long before any guests can be welcomed in the lab, the staff must start with preparations. The working day in the lab therefore between eight and half past eight in the morning, long before the opening time at ten. After entering the premises, the first item on the staff’s agenda is a status check. Is everything in the right place? Did the colleagues leave any notes on the previous evening to be observed? Are there other peculiarities that need to be considered today? As online media also continue to work while the lab itself is closed, they also need to be checked to see if something new has come up there that needs attention. All this can take a lot of time. Then the laboratory itself has to be prepared for the guests. Doors have to be opened, signs and posters put out, and furniture moved to the right place. The lab includes a coffee lounge, which is first made available to help guests and staff to find a good start into the day. While the coffee machines are already running, the installations for the innovation projects are put into operation. Depending on the project, different initialization steps may be needed, from starting a video presentation on a screen to laying out pens, papers, notepads, keyboards, and other materials, to turning on Virtual Reality glasses and 3D printers. Then the open laboratory is ready for the day. Unlike a lab behind the thick walls of a research centre somewhere in academy or industry, the activities in the open laboratory are coupled with everything else happening in the city at the same time. The way the city lives and breathes is mirrored by the innovation activities taking place in the lab. While other scientific approaches tend to loosen this coupling, the open laboratory does the opposite: it exploits the coupling to stay close to real market conditions, granting laboratory interaction a special kind of authenticity that is lost elsewhere. The contributions to innovation are not pressed into an artificial framework, but left to their own dynamics. The visitors do not follow a rhythm given by someone else, but stick to what they do at their own pace. In the morning, the beat of the city is different than at noon, afternoon and evening. Also, the beginning of the week has a different taste than its end, and winter is different than summer.

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In the morning, you see many people paying the lab a very short visit, to grab a coffee or snack, for example, or maybe for a short break before the hard parts of the job start. They do not have much time for interaction. They just go through moments of escape from the daily chores, where you can see how the mind wanders as they look at the booths where the innovation projects are set up. As these people work nearby, they can come back at a later point in time, ready to delve more deeply into innovation. That is when the short visits for coffee pay off. The visitors have made their own decision to engage in an interaction and therefore can contribute much more substance than pushing them ad hoc to work on a survey. After lunch, many people have a bit more time than in the morning and are ready for discussion and further exploration of the innovations. While stores in the whole city are open, there are also many other visitors who simply stroll around and curiously look into the open lab to find out what is actually happening there. Depending on their schedule, they stay longer in the lab and get involved in the various projects, or they promise to come back when they have more time and can bring their friends. In addition, from late morning until late in the evening, there are always visitors who enter with the intention of engaging in innovation. Sometimes they were already there before. Sometimes they heard about us in the media. Or we were recommended to them. In addition to the interaction area for the company’s innovation projects, our laboratory also offers a meeting room. It is regularly used for public lectures, panel discussions, and workshops that provide more opportunities every week to get into the open laboratory. In doing so, further aspects of the topics of the current innovation projects are addressed, which creates new opportunities to become more deeply involved. The conference room has also become an attraction for companies and research institutes that book it for the whole day for special purposes. They can work there in an environment that pushes them out of the everyday life routines in their offices. As all these workshops include breaks, they open up many opportunities to engage with the participants, such that valuable contributions for all the innovation projects in the lab can be collected. During the week, it is mainly Friday and Saturday when visitors return to the lab to bring their friends and families. On Saturday, interacting with the innovation projects is often a welcome change during a shopping tour of the city centre. Sometimes, visitors leave part of their family in the lab, while they set out on their own errands that are uninteresting to everyone else. This makes the laboratory a popular location to sojourn, which integrates innovation into the daily lives of visitors. Because of the many events taking place in the city of Nuremberg, there are more peak times for visits to the lab over the year. These include the famous Christmas market, which attracts many thousands of additional visitors, as well as trade fairs, with delegations of domestic and foreign experts visiting the lab. The laboratory benefits from the economic strength and innovativeness of the entire

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Franconian region around Nuremberg. At the same time, it provides itself reinforcement for this strength.

3 Visitor Varieties Working as staff in the open laboratory is not comparable to the work that a researcher takes on when performing a conventional experiment. The changing conditions under which each interaction with the visitors of the laboratory takes place make it necessary to constantly renegotiate between the approach of the companies for the innovation project and the personal situation of the visitors. For a conventional experiment, static physical conditions are produced and external factors are carefully controlled. This is not the case for innovation activities in the open laboratory. Generally speaking, everything is possible and everything is allowed. Therefore, the staff must actively intervene in the setting. Only through individualized interaction, the possibility to gather relevant data from all visitors will emerge and thus open up the full scope of innovation. Where such an interaction does not take place, the lab will only be able to capture contributions of very specific groups: tech-savvy visitors with an empathic relationship to a particular technology, early adopters with a particular interest in novelty or agitators with a special mission to improve the world. These people, however, need an open laboratory probably least of all, because they will find other ways to pursue their interests. The real added value of an open laboratory results from the involvement of people who are otherwise unavailable for innovation. Making this involvement possible is maybe the most important task of the lab’s staff on location. A salesperson assumes a similar role in dealing with customers. He or she must get engaged with the visitors and learn to understand how they can relate to the offerings made in the store. As mentioned before, this is comparatively easy in a world of mass production. Customers have previous experience with products. They know what to expect. Their views are shaped by advertising and influencers. For this reason, commerce can also be conducted on the Internet, where instead of direct human interaction people use forums or live chats for mediation. On an ancient medieval market, this was not the case. The same holds true for an innovation laboratory, because much more effort is needed there to clarify and connect products and customer interests. Of course, the way in which innovation projects are set up in the laboratory plays an important role. With appropriate technology, specific options for interaction can be designed to encourage visitor reactions. Nevertheless: people are physically present and have the full range of human expression available to communicate, when technology is not enough. There is still something more to do that only staff can cover, responding to all factors relevant in the given

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situation. As a consequence, any visit to the open laboratory is a new challenge, as if a researcher had to reconfigure the experimental setup each time. For example, someone who only comes to the lab in the morning for a cup of coffee needs to be greeted differently than someone who spends the lunch break in the lab and wants to be distracted from the job. Individuals need different treatment than groups, and factors such as age or occupational expertise also play a role. Laboratory staff must develop sensitivity to such factors and give account of them in their own behaviour. Of course, staff can also ask direct questions where necessary to learn more about the guests – once the first contact is made and such questions seem appropriate. But, as anyone who has ever worked in retail knows, this initial contact is by no means trivial. Paying attention to one’s counterpart is very important to find out who one is dealing with. Based on five years of experience in our open laboratory, we can distinguish several types of visitors. We try to characterize them with the following terms: – Strangers behave like tourists on sightseeing. They have no personal connection to the concept of the laboratory and experience it primarily as a curiosity. They do not necessarily come from far away. Maybe they live close by, but have preservations to become more deeply involved. They have time to see everything, but do not want to get involved. They require entertainment. – Scouts usually do not have time. They are on a reconnaissance mission, through which they want to get to learn about everything to know what they are dealing with. However, they do not want to expose themselves to a conversation, but first of all only understand what is going on in the lab. Once they do, they decide on their own whether to engage or not. For this, they will often come back another time. – Experts are the ones who already know almost everything. They are mainly interested in sharing their expertise. They consider themselves in the driver’s seat of innovation activity and clearly define the direction in which each interaction should go. What they need is short, clear information, and opportunities to make contributions. They are often very grateful for listeners. – Socializers want to be taken care of. They are less concerned with the innovation itself being dealt with, but with their relevance for their own lives. They want to address the services that they or related people get from innovation. They are open for in-depth discussions, but they also like to drift away from the topics that are at the forefront of innovation projects. In detail, of course, further distinctions are possible, which are also documented in the laboratory on the basis of visitor surveys. To decide how to approach visitors best, however, the four types mentioned above already give a good orientation. Even groups can be described by these types. It is quite possible that the individuals in the group belong to different types, but in the interaction with each other they develop a common character to which the staff can react. Sometimes there are

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opportunities to break up a group and treat each one individually, but this way, a lot of energy is lost. The typology of open laboratory visitors should not be understood as a description of personality traits. Scouts often come back later to immerse themselves as experts or socializers in a deeper exchange. Experts return later as scouts while they are in the city centre to have a quick look what’s new in the lab. Strangers can also evolve over time in different directions. In many cases, the professional background determines how visitors interact and when they are more likely to appear as experts and when as socializers. Imagine, for example, a retired engineer who previously worked in telecommunications. He has an affinity to innovation through his biography. This makes him curious and brings him back to the open laboratory again. If he finds topics related to digital data exchange, he can tackle them as an expert with the professional repertoire he has acquired in his job. When it comes to health services for the elderly, the topic is easier to approach from a personal perspective, which is likely to makes him a socializer. Changes in types were particularly noticeable in the first year when the open laboratory was still new. Media attention was huge and motivated many people to visit the lab. However, a lot of effort was needed to make the intention of the entire facility clear. Of course, this was also the case for the staff members themselves, who had to find their own role in the given setting as well. Over time, a consolidation took place. In the lab, this was most noticeable in the emergence of a core group of visitors that kept coming back. Visitors who were initially attracted to a particular topic as experts became more generally involved, supporting the idea of the open lab as a whole. Strangers gradually established a closer connection to the place with every new visit. This was especially true for the people who live or work in the lab surroundings. For many of them, the place became a favourite spot to hang out, when they lost their initial inhibitions in dealing with innovation and started to appreciate the atmosphere and spirit of free thinking in the lab. In addition, the lab also became part of the wider community of open innovators and user innovators in the region, which also populate the local Fab Labs and entrepreneurship centres, share offices at co-working spaces and like creative interaction.

4 Work in the Open Lab as a Profession Much has already been written about the roles that intermediaries play in open innovation (Alexander and Martin, 2013; Katzy et al., 2013). However, based on our experience, we believe that this does not exhaustively describe the tasks of the staff in an open laboratory for innovation in the city centre. In comparison to the management of innovation an online platform or a workshop with experts, etc., the promotion of deeper interactions in the open laboratory requires a much more active engagement

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from the staff. No one single protocol for interaction with everyone can be established (Fritzsche, 2018). Due to the varying conditions under which the interaction takes place and the specific individuals who are involved, thousands of small details make a difference to the outcome. This begins with facial expression and gestures when visitors enter the lab. It includes the whole process of contacting the visitors and presenting the innovation projects. It also reaches further into the support of the visitors in the implementation and documentation of their contributions. Some people just need someone in the background, who will only jump in when they have questions and apart from that not get involved. Others need a counterpart that they can talk to in order to develop their ideas in a dialogue. Still, others have a clear idea of what they want, but struggle to make a connection to the company perspective on their own. In this case, members of the staff take on the role of a translator or negotiator. Imagine an interaction project where a company has set up a configurator that can be used to design digital business models. Such business models are an important topic for innovation in many industries, including automotive, finance and healthcare. The configurator allows visitors to define different product variants, target groups, communication channels, pricing models or delivery processes that will be digitally implemented in the future. This is a topic with affects virtually everyone. It can therefore inspire lively discussions in the laboratory. Not every visitor, however, is willing to work actively with the provided configurator. Members of the staff therefore need to assist them. They can explain the configurator; they can operate it together with the visitor or take over the operation according to the instructions of the visitors. In addition, they can also offer alternate opportunities to contribute to the topic. A simple example is the provision of adhesive charts, where visitors can leave comments that they consider important, independently from the configurators that have been provided. Again, the staff can intervene to varying degrees. It can simply indicate the availability of such charts or strongly encourage visitors to use them; it can give the visitors the charts and pens; or fill in the charts on behalf of the visitors and put their comments in proper phrases. The staff in an open laboratory is in this sense much more than just an intermediary. It is the catalyst to make innovation happen. In doing so, however, there must be awareness of the risk of becoming too dominant. The more the staff gets involved, the more likely it is that own interests and judgments supersede the content of the visitors’ contributions. Instead of opening innovation to the outside, people in the lab then seize the given project upon themselves and give answers from their own perspective, with little consideration of differing perspectives and opinions among the visitors. In this respect, the staff faces a very similar problem like any other scientist, who pursues interventional approaches in the context of action research or design research. Good training in qualitative research and regular supervision, as is common practice in other human sciences, puts the work in the laboratory on a secure foundation. It is necessary to constantly question one’s own behaviour and to check whether the contributions to innovation that arise in the

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laboratory are actually taken from the visitors or whether they are much more an expression of their own perspective on the projects of the companies. Companies can benefit from the visitors’ contributions and the contributions of the staff. However, both should be passed on separately, so you can see the difference between them. This ensures that the voice of the visitors is always heard. By working in the centre of an old merchant city, the attention is automatically drawn to the many parallels between the interaction in the open laboratory and the trading activities that have been going on in the city for hundreds of years. Traders always had to consider different interests at the same time. It was important for them not just to sell something, but to get into a deeper exchange with the customers to make clear how they could get the best value for their money. With the knowledge they generated along the way, traders themselves could drive innovation and become valuable partners in research and development for the suppliers from whom they purchased their goods. In many ways, we continue this tradition with our open laboratory. In fact, one can also think about further implications of our work for trading in the digital age. At a time when innovation is advancing very rapidly, the exchange we have with visitors may serve as a reference for new models of retail that make the most of personal interaction with customers.

References Alexander, A. T., and Martin, D. P. (2013). Intermediaries for open innovation: A competence-based comparison of knowledge transfer offices practices. Technological Forecasting and Social Change, 80(1), 38–49. Diefenbacher, M. and Endres R. (Eds.). (2000). Stadtlexikon Nürnberg. 2., verbesserte Auflage. W. Tümmels Verlag, Nürnberg. Gorman, M. E., Calleja-López, A., Conley, S. N., and Mahootian, F. (2013). Integrating ethicists and social scientists into cutting edge research and technological development. In: Doorn et al. (Eds.), Early engagement and new technologies: Opening up the laboratory (pp. 157–173). Dordrecht: Springer. Fritzsche, A. (2018). Corporate foresight in open laboratories: a translational approach. Technology Analysis & Strategic Management, 30, 646–657. Fritzsche, A. (2016). Open innovation and the core of the engineer’s domain. In: Michelfelder, D. P., Newberry, B. and Zhu, Q. (Eds.) Philosophy and Engineering: Exploring boundaries, expanding connections (pp. 255–266). Cham: Springer. Katzy, B., Turgut, E., Holzmann, T. and Sailer, K. (2013). Innovation intermediaries: a process view on open innovation coordination. Technology Analysis & Strategic Management, 25(3), 295–309. Neely, A., Fell, S., and Fritzsche, A. (2018). Manufacturing with a big M – the grand challenges of engineering in digital societies. In: Fritzsche, A. and Oks, S. (Eds.), The Future of Engineering: Philosophical Foundations, Ethical Problems, and Application Cases (pp. 191–200). Cham: Springer. Newman, M.H.A. (1948). General Principles of the Design of All-Purpose Computing Machines, Proceedings of the Royal Society of London, series A, 195 (1948), 271–274. Toffler, A. (1980). The Third Wave: The Classic Study of Tomorrow. New York, NY: Bantam.

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9 Professional Leadership as Key to Innovation Projects in Open Laboratories 1 Innovation in the Open Laboratory as a Leadership Challenge Industrial innovation management usually follows the pattern of systematic research and development projects within specialized departments. In the past decades, however, changing competencies, shortened product life cycles and increasing digitization have led companies to spent increased effort on opening up to external actors in order to accelerate the innovation process and ensure market value (Huizingh, 2011). The idea of involving external contributors in the innovation process resonates in many managerial approaches of the past decades (Dahlander and Gann, 2010; Trott and Hartmann, 2009). In fact, the design of the dome of the Milan Cathedral and the development of a substitute for butter during the age of Napoleon are just two examples of historic innovation problems that have been made public, with a prize money for the one with the best solution (Fritzsche, 2016). Similarly, trade fairs and exhibitions have long been used to solicit feedback on prototypes that are currently under development in companies. Nevertheless, with the spread of digital technology, a much wider range of opportunities for participatory innovation processes has emerged than ever before. There are essentially two reasons for this. For one thing, digital technology makes communication across organizational boundaries much easier. Large amounts of data can be easily transferred; place and time of their production lose their importance. Secondly, digital technology enables new product and service architectures that combine many different building blocks from different sources. This also creates new ways of taking external contributions into account in the innovation process. Consequently, open innovation has become an important research field in recent decades, exploring in depth the possibilities and benefits of collaboration beyond organizational boundaries. Drawing of suggestions by Tseng, Huff et al. (2013) distinguish three fundamental elements of open innovation which can be observed in contemporary project activities: a platform that promotes exchange among stakeholders, behavioural norms that determine how everyone can contribute, and leadership that coordinates the actors’ participation and guides them toward a solution. The Internet

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offers a multitude of examples with different kinds of instantiations of these three basic elements. Following Möslein and Fritzsche (2017), the examples can be categorized according to the types of tools that they use for open innovation, starting with hosting innovation competitions, online communities of innovators, marketplaces for exchanges between sponsors and solution providers, design studios and solution development toolkits, and technological infrastructures for innovation activities. The way these tools are implemented on the internet already provides clear structures for platforms, behavioural norms and leadership of innovation processes. In open laboratories, where people meet in person to develop new ideas and solutions together, this clarity vanishes (Roth et al. 2015). Open laboratories create entirely new challenges for the design of the basic elements of open innovation. Based on the experiences gathered during the last five years in the open innovation laboratory JOSEPHS® of the Fraunhofer IIS – Center for Applied Research on Supply Chain Services SCS in Nuremberg, these challenges are briefly described on the following pages. The Fraunhofer Gesellschaft is a world-leading organization of applied research with 72 individual institutes at 238 locations (Fraunhofer-Gesellschaft e.V., 2018). Research and development projects for industry are one of its core competencies. From the perspective of the Fraunhofer Gesellschaft, it is therefore particularly important to integrate open laboratories into the innovation process that true added value for industry can arise. The operation of an open innovation laboratory enables the Fraunhofer Gesellschaft to integrate external contributors in selected development projects and thus to create true added value for customers and industry. Based on the principles of contract research, questions are addressed in the laboratory that are posed by industry and expected to be answered in exchange with external contributors. The multitude of activities that can take place in an open laboratory (Fritzsche, 2018) is therefore strongly constrained. In order to work not only in a divergent, exploratory mode, a clear focus must be set. Crafts, storytelling, etc. do not add value for industry by themselves, unless they are linked to the specific goals of the innovation activity that guide creativity and communication in a clear direction. Of the three fundamental elements of open innovation, leadership is therefore of particular importance. On top of that, the lab can not only be run as a publicly sponsored meeting place where interested people share their knowledge. The investments made in the use of the laboratory must be reflected in the value contribution generated from the results. Laboratory work therefore takes place under more demanding conditions than previously discussed in literature on Living Labs, Fab Labs, Maker Spaces, etc. Greater attention to leadership creates new opportunities to generate value for innovation through working in the open laboratory. At the same time, it opens up links to traditional research and development approaches that make open laboratories more accessible to industry.

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2 Setting the Stage for Joint Project Activities In principle, Open Innovation in Nuremberg’s JOSEPHS® follows the same pattern as any other contract research project of the Fraunhofer IIS (see Figure 9.1). At the beginning, there is a special research interest. It determines the key questions that will inform the work in the laboratory. Based on these questions, a procedure for the use of the laboratory is defined. For the duration of the laboratory usage, three months are standard, which makes it possible to dive into joint innovation activities with several thousand visitors of the laboratory. In addition, the period is long enough to make learning curves visible. Learning curves can be observed on the part of the visitors who return to the lab to continue the activities they started earlier. By doing so, they bring in new insights from everyday life. In addition, they often encourage other people to visit the lab as well. Learning curves also take place on the part of companies whose topics are addressed in the laboratory. They have time to reflect on their approaches, adapt their interests, or even follow agile principles in going through multiple cycles of development activities. Not only the interactions with the visitors play a role in this; through the commitment in open laboratories, the companies themselves go through a development. This effect exacerbates by inviting partners and investors into the lab to discuss their innovation activities there. project selection

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Figure 9.1: Sequence of projects in JOSEPHS® (based on Fritzsche, 2015).

Of course, work in the lab is only the middle part of a longer project activity that can last for a much longer period than three months. Long before it comes to the use of the laboratory, basic initial question must be clarified. For many companies, open innovation processes are still an unusual approach. Their experience in this area is often limited to working with a small number of selected business partners and suppliers and marketing activities that may be called creativity contest, but the

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actual contributions of the participants are less important to the companies than the attention that they attract to new products. In discussions with decision-makers from the company, it becomes clear that they are well aware of the additional potential that open innovation offers. However, they often do not know how to anchor it in the company. To do this, the company must be willing to share more information with outsiders and listen more carefully to what they have to say than they have before. For many larger companies, this is difficult because of simple organizational reasons: it is unclear which department is responsible for such activities. Neither internal research and development nor sales and marketing seem to be well suited to this. Experience shows that strategic departments can connect to open laboratories most easily and respond with great enthusiasm to the future orientation of the approach, as it promises to unveil new business opportunities. At the same time, however, they are often far removed from the specialist content that is discussed in the laboratory. In order to guarantee a successful flow of knowledge, experts from other areas must also be involved. In every company, therefore, very different people take the lead in the use of the open laboratory, depending on the respective organizational structure, task allocation and culture. Business models in the twenty-first century are often based on platforms that bring together many different stakeholders in a common network of complementary activities. The work in the open laboratory can best be assessed against this background. For companies that had nothing to do with the Fraunhofer Gesellschaft so far, a long time goes by before the open laboratory environment can be used. In some cases, it takes years to get such companies off the ground with individual workshops, lab visits, and other collaborative activities. If previous contacts already exist, the process can be accelerated. On top of that, there are also many opportunities to extend the scope of cooperation beyond the open laboratory. For example, advanced engineering projects can be included for the development of new technical components. Such projects then also benefit from using the open laboratory at the same time to get close to the customer in a lead phase.

3 Getting the Ball Rolling Most companies today are very familiar with the conduction of project activities. They know exactly how projects have to be organized and which aspects have to be considered. Nonetheless, they are breaking new ground in many ways when using open laboratories. General considerations about time, budget, and content management, etc. apply for the lab as well as for any other project setting. But the goals, the mode of collaboration, and the steps that must be taken concretely to successfully use an open laboratory are new and unfamiliar. Just as with any other research project, the formulation of a research question helps to clarify the situation.

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Innovation activities in the open laboratory can affect all phases on the way from the idea generation to the market launch. However, very different questions have to be asked in the early stages of identification of possible needs and options than later, when initial prototypes are already available as reference points for exchange with visitors to the laboratory. As usual, the research question has to be adapted to the given working conditions, which depend on the corporate strategy, the topic of the innovation and the interaction formats in the open laboratory (see Figure 9.2). During the elaboration of the research question in the first project meetings, additional insights for all those involved about the value of the open laboratory as a research environment emerge as well. Based on this, the further steps in the project can then be worked out.

Figure 9.2: Planning interactions in the open lab with a model.

As the lab is accessible to everyone, one of the first items on the agenda is the clarification how to deal with intellectual capital. From time to time, the suggestion is made to restrict access to the lab and only to admit people who sign a document beforehand which commits them to secrecy. However, this contradicts the entire concept of the open laboratory. Restricting access to the lab will lead to a preselection of contributors that severely limits the added value of the open laboratory. Thresholds are built up that destroy every spontaneous interaction and put the work in the laboratory in a corset that assimilates it to the conditions in the company itself. In addition, and perhaps more importantly, the conclusion of a nondisclosure agreement creates a moral problem. In an open laboratory, companies

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and customers should act at eye level. Both of them contribute equally to cooperation. Not only companies, but everyone who enters the laboratory has the same right to demand secrecy for his or her own ideas. But if all these demands are fulfilled, the innovation activity has failed before it even started. Collaboration in the laboratory must therefore take place in a spirit of shared appreciation. All parties involved must realize that they are entrusted with something valuable in exchange for each other, which they reciprocate accordingly. Only then is honest interaction is possible and hypocrisy avoided. Companies need to be aware from the beginning that not only the visitors to the lab have to disclose something, but that they have to do so themselves as well. The degree to which this is expected to happen should be made explicit soon enough. In continuation of this topic, the roles of the participants must be clarified. Like any other project in contract research, innovation in the open laboratory extends over a longer period of time. During most of the time, the people involved will work independently. Company representatives are always welcome in the open laboratory, but only in exceptional cases can they be allocated to the lab for several months completely for this purpose. Therefore, it is important to determine how the flow of knowledge between companies, operators of the lab and visitors should occur in the project. It can happen in regular workshops or other events held together in the lab, but also through written reports. The other way around, companies can bring learning back to the lab and urge a change of the settings for interaction with visitors. What must be clear is that knowledge generated in the lab is based on rich information. Structured data sets, such as those that can be obtained from a questionnaire, play only a minor role. In this sense, quantitative research approaches in the open laboratory are wasted. Rather, the actual added value comes from qualitative approaches that do not only provide specific measurements, but also help companies to better understand the topic and to review and develop their own interests and assumptions based on the contributions of the lab’s visitors. Personal reports and detailed narrative representations are therefore more important than tables and charts.

4 Enabling the Visitors Of the three mentioned elements of open innovation leadership has already been discussed in detail. The platform and the codes of conduct that apply to joint innovation activities still need further attention. As already mentioned, the open laboratory, as a place of personal interaction, allows for more variations on contributions and knowledge flows than a customary online solution. Jalowski et al. (2019), for example, show that there are many possibilities of distraction with a negative effect on motivation, exchange and goal orientation. To ensure that the project activities

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do not suffer from this, the creation of the laboratory’s interaction space and the role models for the visitors must be well prepared. Since many companies have experience in presenting their products and services at trade fairs and exhibitions, they usually possess material to professionally present themselves and their offerings. Such material can usually be easily installed as a pop-up environment in the open laboratory. In many cases, however, it is not very well suited for generating new ideas and promoting fruitful interaction with visitors. Even if such material finds its way into the open laboratory, there is need for further elaboration. In the open laboratory we operate, there are five interaction areas, which host each a respective innovation project. There is also a large workspace in the middle of the room, which can be used for all topics. The interaction surfaces have a circular shape. They can be equipped with specially made cabinets, shelves or tables, but can also be used without any additional furniture. If necessary, there are additional possibilities for expansion. The interaction areas are never separated by solid walls, such that several topics remain in the range of vision for anyone approaching, offering visitors many choices to engage in innovation. Of course, this can also lead to distraction once a specific activity has been started. As a result, attractions, such as audio-visual media, must be placed in a way that they are visible from afar, but do not disturb anyone when they are in another interaction space. It therefore can be said that every single company, together with others, takes part in a larger endeavour. As a result, more people are attracted to contribute than those who are only interested in a single innovation project. This reduces the degree of pre-selection of visitors before the interaction even begins. When scheduling projects, their relationship to each other is taken into account, so that projects are made available at the same time that fit together and complement each other. As a place for innovation, the open laboratory differs significantly from a booth at a trade fair or a regular showroom in the city centre. Of course, the laboratory also fulfills the function of a stage on which a company can present itself with its own product range. Likewise, market research can also be carried out in the laboratory. However, what makes the lab stand out from other settings of this kind is something else: the active role that visitors take in it. Visitors are not research objects which undergo a treatment and captured in predefined evaluative structures. They are active participants in the innovation process. This is the unifying feature between all the variants of open laboratories that exist today. The visitors can act independently in the laboratory and decide on their own how they want to contribute. Therefore, the interior of the laboratory should not be a static environment, as is the case in museums where works of art must be protected from access by visitors. On the contrary, everything has to be touchable, tangible and changeable. Fab Labs and Maker Spaces enable this through machines such as 3D printers or laser cutters and the provision of materials and tools to build objects. Companies that pursue specific goals with their innovation activities can also make use of these opportunities. In many cases, however, another approach to interaction is needed,

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which cannot be covered by technical construction alone, but instead focuses on a semiotic approach: It is not about generating new objects, but about changing existing ones and, in particular, interpreting them from different perspectives. This does not necessarily require the presence of large and powerful tools and machines. It is much more a matter of the architecture of the objects themselves, and of being able to take them apart and reassemble, extend, and comment on them.

5 Enabling and Guiding Interaction Objects made accessible in an open laboratory can be divided into two categories. The first category contains objects that are themselves part of an innovation. Prototypes of finished products are the most obvious example. Similarly, a blackboard with drawings of an imaginary service delivery process can also be part of an innovation, just as the elaboration of a business model in a structured framework that shows new ways in which value creation can take place. The second category contains objects that comment on and rate such innovations. A conventional example is an open survey which gives visitors the possibility to express their opinion on a new product. It asks questions which can be answered, for example, with short notes on adhesive paper attached to a product are another example. The same goes for any other form of graffiti, recorded storytelling, evaluation of expressions, and measurements of reactions. Both categories open a variety of possibilities for intervention with the objects by visitors. Prototypes can be used in many ways. Parts of them can be replaced or put together differently. In the case of information systems, data can be provided and parameters can be set. Blackboards and paper, pens and brushes can be made ready for use and carried around the lab to other locations in order to work with them there. But an open laboratory also allows for another kind of interaction, as the results of intervention can be shared. Other visitors can see what has been contributed and respond to it. They can support or disagree with reviews and they can explain or critically question them. Likewise, they can pick up and develop ideas from others, or try out and compare adapted products. Of course, companies can also become engaged and react to contributions, provide explanations and clarifications or use the contributions for the creation of next generation of a prototype which replaces the current one in the laboratory. Through this mutual interaction, an innovation evolves in a collaborative effort and becomes a group achievement. Of course, the role of the intermediary is also important for the Fraunhofer Gesellschaft as a leading institution of applied research. In the open laboratory, this role is filled by qualified personnel, which facilitates the engagement of the visitors. This is done through explanations, encouragement to participate, help with the treatment of objects and the expression of feedback. As a third party in the

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interaction between companies and visitors to the lab, the employee has to act as an independent negotiator, with no specific interest in any particular outcome of innovation activity, apart from the vivacity and richness in which these activities are expected to proceed. Of course, in exchange with the visitors and the representatives of the companies, intermediaries become co-creators in their own way, from the perspective of a consultant who brings in specific experience and expertise. Another important role of the personnel is to make sure that they do not lose the contributions made by users in oral form or made up only of gestures and facial expressions. For this, the personnel keep their own logbook, which also serves as critical reflection. In addition, the personnel are of critical importance for the norm of behaviour in the lab, as they cannot be determined simply by the nature of the objects being used. In communicating with the visitors, the personnel set the mood and propose the social protocol for the innovation activities. Practical experience shows that this is hardly a matter of surveillance and control to avert harmful behaviour. The exact opposite is the case. For most visitors, the personnel must emphasize that exploration and feedback are welcome. From shops, museums and other public places, people are used to keeping their distance and not getting involved. The fact that this is undesirable in the open laboratory needs to be learned, which again shows how important leadership is in this context.

6 True Added Value for Industry Although this chapter has only given a rough outline of project work in the open laboratory, it provides some important insights for innovation. First and foremost, it becomes clear that the possibilities of open laboratories are far from exhausted with the approaches of Fab Labs, Living Labs, etc. Open laboratories offer a much greater potential for research and development, which can make a decisive contribution to the competitiveness of companies. This fact is supported by feedback from industry partners who have made use of our laboratory for their innovation projects. Each partner receives a detailed final report on the project, which will also be presented in a meeting with the sponsors. In it, the results of the activities in the laboratory are presented and commented from the expert’s point of view. In addition, companies will be given follow-up recommendations that may relate to product and service design, marketing, audiences, and other relevant aspects, up to general corporate strategy issues. All of this gives companies insights they would not otherwise have been able to achieve and is instrumental in ensuring the success of their innovation efforts. Many companies express their desire to return to the open laboratory to continue the innovation activities at a later date or address a new topic there. In order to leverage more of the potential of open laboratories, it is

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necessary to apply previous findings on open innovation from other settings to the context of laboratories. What can be deduced from the preceding considerations in this respect is that leadership plays a prominent role. If visitors are left completely alone during innovation activities, the breadth of exploration increases, but orientation towards specific goals is likely to disappear. With the expertise of qualified intermediaries who can draw on a wealth of experience in applied research, this effect can be avoided. Organizing innovation activities in open laboratories as projects helps to create an interface between internal work in companies and the use of the laboratory. This also opens up new possibilities for financing open laboratories and making their application more professional.

References Dahlander, L., and Gann, D. M. (2010). How open is innovation?. Research Policy, 39(6), 699–709. Fraunhofer-Gesellschaft e.V. (2018). Standortkarte. Retrieved June 17th, 2019, from https://maps. fraunhofer.de/fsk. Fritzsche, A. (2018). Spreading innovations: models, designs and research directions. In: Bunde, A., Caro, J., Kärger, J. & Vogl, G. (Eds.), Diffusive Spreading in Nature, Technology and Society, Cham: Springer, 277–294. Fritzsche, A. (2016). Open innovation and the core of the engineer’s domain. In Michelfelder, D. P., Newberry, B. & Zhu, Q., Philosophy and Engineering: Exploring boundaries, expanding connections, Cham: Springer, 255–266. Fritzsche, A. (2015). Communication patterns in open innovation laboratories – a conversation analysis. Fall Communication Readings. Moscow: Rosnou. Huizingh, E. K. (2011). Open innovation: State of the art and future perspectives. Technovation, 31(1), 2–9. Huff, A., Möslein, K. and Reichwald, R. (2013). Introduction to Open Innovation. In: Huff, A., Möslein, K., Reichwald, R. (Eds.), Leading Open Innovation, Cambridge: MIT Press, 3–18. Jalowski, M., Fritzsche, A. and Möslein, K.M. (2019). Applications for persuasive technologies in participatory design processes. In: Oinas-Kukkonen, H. et al. (Eds.), Persuasive 2019, Springer LNCS Series, 11433, 74–86. Möslein, K. M. and Fritzsche, A. (2017). The evolution of strategic options, actors, tools and tensions in open innovation. In: Pfeffermann, N. and Gould, J., Strategy and Communication for Innovation, Cham: Springer, 3–18. Roth, A., Jonas, J. M., Fritzsche, A., Danzinger, F. and Möslein, K. M. (2015). Spaces for value cocreation: The case of “JOSEPHS® – The Service Manufactory”. European Academy of Management, Warsaw, Poland. Trott, P., and Hartmann, D. (2009). Why ‘Open Innovation’ is old wine in new bottles. International Journal of Software Engineering and Its Applications, 13 (4), 715–736.

Sebastian Engel

10 Driven by the Same Spirit – Entrepreneurship, Incubation and Open Labs in the Business Ecosystem of Central Franconia 1 Introduction Good ideas can come up anywhere – not just at work, but also at home, on the commute, while shopping or on vacation. If all of these ideas would have to find their way into the development department of a company in order to be commercialized, then great potential for innovation is lost. Therefore, spaces must be created where new ideas can mature into business solutions even outside existing economic structures. Similar to incubators in medicine or biology which support the growth of new life, such spaces are called incubators. In the widest sense, a lot of different facilities serve as incubators for innovation, including open innovation laboratories as well as entrepreneurship centres (Fritzsche, 2018). Business incubators in a closer sense are dedicated to the support of start-ups in the early stages of their development (Grimladi and Grani, 2005; Bergek and Norrman, 2008). They establish conditions under which entrepreneurial activities can easily unfold (Lai and Lin 2015). Their services go beyond the provision of money by venture capitalists and consultancy by business angels (Carayannis and von Zedtwitz, 2005). Business incubators provide a comprehensive environment for startups, inspiring smooth progress and increasing the survival rate over time (Eshun, 2009). Business incubators can be for-profit organizations or non-profit organizations and they can be run by corporations, public institutions or independently (Gassmann and Becker, 2006). Recently opened incubators also show a tendency for a specific focus on verticals like e.g. “health”, “insur-tech” “mobility”, etc. Furthermore, it is possible to differentiate business incubators from business accelerators which run specific programmes to make startups ready for market entry (Cohen 2013; Miller and Bound 2011). Business incubators usually also address fundamental questions of orientation and sense-making, which play an important role in entrepreneurial activity before the actual solution development begins (Fritzsche and Dürrbeck, 2019). Startups have the opportunity to gain a better understanding of the possible narratives behind their business venture, the novelty of their solution and the markets they can address.

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All this shows that there are many levels of openness in business incubators that have a significant impact on the outcome of the work which is performed there. However, openness in business incubators is pursued quite differently than in open innovation laboratories. On the following pages, we want to show how they can complement each other within a larger entrepreneurial ecosystem. For this purpose, we use the example of our business incubator ZOLLHOF and the open innovation laboratory JOSEPHS®.

2 ZOLLHOF – Tech Incubator ZOLLHOF was founded due to the observed need for infrastructures for digital hightech startups in the metropolitan region of Nuremberg and in course of the Bavarian initiative for the countrywide promotion of entrepreneurship (see Figure 10.1). It is an independently operating, non-profit digital tech incubator based in the region of Central Franconia within its largest city Nuremberg. ZOLLHOF is a joint project of various founding partners, including 4 large companies in the region, a private donor, the city of Nuremberg and the Friedrich-Alexander University of Erlangen-Nuremberg.

Figure 10.1: Impression of ZOLLHOF.

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As a startup incubator, ZOLLHOF operates a whole range of initiatives to promote entrepreneurship in the region. The focus is on a group of startups who, after a highly selective process, have been invited to set up their offices in ZOLLHOF, where they receive special support in all aspects of starting up and developing businesses. This includes assistance with administrative issues, legal issues, financing, marketing, pitching, accounting and the setup of operational structures. Much more important, however, is the substantive support that the startups receive in ZOLLHOF. As most of the selected startups are in an early development stage, most of them have similar questions which have to be answered before they can successfully grow their business, e.g.: – What is the problem that the business venture addresses? – Who are the users/customers and what makes them special? – How to connect to the users/customers? – Which partners are important for implementing the business venture? By answering these questions, startups will be able to carefully plan and develop their own business models. ZOLLHOF also attaches great importance to the scalability of business models. Startups are not just newly established small businesses. They are expected to be innovative and to have high growth potential. Therefore they need to build their business ventures to move quickly and smoothly from small scale solutions to large scale solutions. Especially at this point, the cooperation with JOSEPHS® plays an important role. ZOLLHOF focuses on young entrepreneurs and also welcomes talents who have no previous experience in founding companies and have not been spoiled by previous experience with other professional business activities. Another important factor in selecting the startups invited to ZOLLHOF is their openness to sharing with others. ZOLLHOF does not just want to rent out offices. Rather, it’s about bringing like-minded people together to complement and inspire each other. Business incubation at ZOLLHOF means more than just setting external conditions for the work of startups. It also includes an appropriate matching of the participants, by which all startups themselves become assets for each other. Over time, the need for collaboration and exchange may change in some startups. Some of them may therefore only stay in ZOLLHOF for a short time and then move out to make room for others who may benefit and also add more to the community. Of course, startups still have many opportunities to refer to ZOLLHOF if they have no offices on the ground. The doors of ZOLLHOF are open to anyone who wants to work on innovations and advance new solutions. There are dedicated ZOLLHOF venues that can be hired by anyone to talk about new technologies, economic developments or other interesting topics.

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The Spirit of Entrepreneurship Entrepreneurs at ZOLLHOF want to make progress with their business venture. They want to learn, understand, connect to others and, most importantly, get things done. Their workspaces at ZOLLHOF are arranged in a way that they can see each other in doing so. The rooms are spacious. There are no cubicles with high walls to separate people. If someone needs privacy for an important conversation or a longer telephone conference which would disturb others, he or she can use a soundproof telephone box. But even this box is transparent, so that the person inside still is embedded in the community. Some of the workshop spaces at ZOLLHOF are designed in the same way. Only a few conference rooms are completely separated by their own walls. All this establishes an atmosphere of ambitiousness, expectation and energy which spreads from one person to another. Of course, there are large spaces to get coffee and snacks, eat drink and just hang out, but they also focus very much on bringing people together and engaging them in fruitful conversations. Events hosted by ZOLLHOF are driven by the same spirit. The most important format is a Hackathon like HackBay, which takes place every year and Digital Tech Summit and Digital Health Challenge, both in 2018 and 2019. These large events involve guests from the region as well as from more remote locations. A Hackathon lasts two to three days. In the course of the event, the participants drive innovation in groups. Both, HackBay and Digital Tech Summit focus on different topics, which are usually introduced by industrial partners. Digital Health Challenge, as a hackathon organized by ZOLLHOF and its Digital Health Hub Partners “Medical Valley EMN” and “Health Hackers” has a clear focus on digital health topics. The course of work follows the long-established scheme of design thinking in groups during the early stages of product and service development (see e.g. Boukhris et al., 2017). The participants start with ideation in order to orient themselves, to discuss possible approaches and then to choose a direction. It will be further specified step by step and further developed until the creation of a prototype. At the same time, development is always close to the customer. The result is never just a technical device, but a holistic solution that goes hand in hand with the design of a business case. Food, drinks, sleeping spaces are provided, so that the participants can fully focus on their work. Goal-oriented collaboration means that participants quickly build relationships with each other. They come together as a team and make new friends. The competition between the groups for the best solution leads less to a separation, but only to greater incentive and pleasure at work. The participants feel entangled in the same race, which gives them a common identification point. ZOLLHOF hackathons are not primarily aimed at the ZOLLHOF startups, although the innovation topics often have a connection to them in some places. Interested people are invited from outside, especially from what you might call a local innovation scene: a loose network of people who are working on innovation and entrepreneurship and all somehow know each other. Some know each other well and do so much together that they form their own community. There are

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people who are very focused on technical aspects. Others are more interested in design and art or have concrete commercial interests. Certain event objectives may also attract special groups of participants, such as environmental issues that interest social or political groups. In addition to hackathons, there are also regular lectures, discussion groups and workshops happening at ZOLLHOF, which are announced in online and local media and bring more people to ZOLLHOF and its ecosystem. Event rooms can also be booked by other groups for internal events and are in great demand. In addition to the communities that also attend the hackathons, universities, businesses, public institutions and parties host events at ZOLLHOF. They are all very welcome as they enrich the ZOLLHOF community in a variety of ways. This shows another facet of openness in the concept of our incubator.

3 Connecting the Ecosystem The founding partners of ZOLLHOF, as well as many other corporations that have joined the partner network until today, support our incubator with expertise as well as financially. They do this because of different reasons like support of the local entrepreneurship ecosystem, talent as well as startup scouting and as an important puzzle piece in their very own digital transformation process. Everything is dedicated to the larger goal of “making entrepreneurship happen” by inspiring entrepreneurship in the region [and beyond] and supporting its development as a location for innovative, future-oriented businesses. The startups at ZOLLHOF have all freedom do develop their business on their own, in whatever way they like to do it. Of course, nothing keeps them from joining forces with our industrial partners. Quite on the contrary, the startups are encouraged to build strong ties not only to other startups, but all institutions that play a role in the local economy. As our founding partners and other companies are frequently present at ZOLLHOF, hackathons as well as many other occasions, it is fairly easy to connect to them. Furthermore, some of the companies have created own subsidiaries or spin-offs with offices next door to take in the spirit of ZOLLHOF and bring it into their own organizations. Moreover, they bring their managers and subject matter experts to the incubator on a regular basis to show them how work is done here and to confront them with the business ideas that will shape the future of their industry. This proximity has turned out to be very important for everyone involved. Of course, the companies do not need ZOLLHOF to tell them what is going on in their industry. They are very well aware of new, upcoming solutions and the startup scene which emerged over the last years. However, the way of doing business in an agile organisation as a startup is new and interesting to learn from for established

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organically grown corporate structures. They often have the feeling that no intersection exists. It is either a startup culture – agile, dynamic, fast moving and risky – or a traditional corporate culture – stable, reliable, slow and predictable – but nothing in-between. It is probably one of the most important functions of ZOLLHOF to create an interface between both, get people out of their trenches and allow them to engage with each other and find a common ground on which all of them can create synergies. In many respects, ZOLLHOF can be understood as what Star and Griesemer (1989) describe as a boundary object on an institutional level (see also Wenger, 1998). It allows incumbent corporations, startups, but also universities and public administration, to get involved in a common endeavour. Without losing their own identity, they can all contribute as different stakeholders to innovation in the business ecosystem and drive the development ahead (Jonas et al. 2018). Interestingly enough, the subject of their interaction does not seem to be a boundary object itself. It is not fixed, but constantly changing as a result of the activities that are performed. This includes, as Fritzsche (2017) has observed, the practices of engineering in the development of solutions, but also, and probably even more importantly, the operative decisions from a business perceptive: the arrangements of resources, definition of objectives and negotiation of contracts. Having such a space available for stakeholder engagement also opens up many other paths for exchange. This includes the professional biographies of the people who are involved. ZOLLHOF is a place where incumbent companies can look for new talent, both among the entrepreneurs on location and the guests who participate in the different kinds of events. The other way around, experts from the companies can use ZOLLHOF to pursue new goals and take a step out of the corporate culture, either to stay in the startup environment or to bring back valuable experience to the former company later on. Furthermore, a lot of effort in ZOLLHOF goes into the support of students who are interested in entrepreneurship. Together with Friedrich-Alexander University and other universities in the region, special programs have been established to get promising graduates acquainted with the startup culture and the possibilities to create new business ventures on their own.

4 ZOLLHOF and Open Labs Various open labs are part of the ecosystem around ZOLLHOF. This includes, for example, the Fab Labs of the local maker community in Nuremberg and Erlangen (Gershenfeld, 2008). They are mostly driven by technical interests, from people who want to explore the possibilities of new technology, repair broken devices instead of buying new ones, or just play around. The Fab Lab in Erlangen is also closely connected to the engineering faculty of the Friedrich-Alexander University

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and plays a role in professional education. Furthermore, there is Leonardo, for example, a centre for creativity and innovation run by Nuremberg’s academy of fine arts and the universities of applied sciences and music. While still in development, Leonardo seems to follow the pattern of a Living Lab driven by fine arts and design, which is focused on inspiring public discourse (see e.g. Mulder, 2012). Both the Fab Lab and the Living Lab approach play an important role in the ecosystem. They contribute significantly to the exploration of new technical solution spaces and the generation of competence in engineering on the one hand and to the reflection of possibilities and the societal legitimation of innovations. Nevertheless, ZOLLHOF has found out that it needs its own open lab in addition to those already existing. It will be established in the basement of the ZOLLHOF building and contain various kinds of technology that are needed by startups to understand technical possibilities, validate their ideas, build prototypes and demonstrate solutions to investors and partners. The hardware in ZOLLHOF’s own lab will cover many parts of what Möslein and Fritzsche (2017) describe as innovation technologies. It will also be made available for visitors, especially at events where solution generation is on the agenda. There will most likely be a certain overlap between the hardware in our lab and the hardware in the local Fab Labs. However, a clear distinction can be made concerning the conditions of its usage and the intentions behind it (Fritzsche, 2018; O’Hern and Rindfleisch, 2008). Due to regular exchange with regional Fab Labs, the overlap in hardware is kept to a minimum by referring to partners for specific needs that cannot be fulfilled. Work in the open lab will proceed in the same spirit as any other activity in ZOLLHOF. It will be driven by clear entrepreneurial ambitions, with a clear direction towards the goal of getting business ventures under way. In this respect, ZOLLHOF has also strong ties to the other open lab in the local ecosystem which has so far not been mentioned: JOSEPHS®. While open to the public for contributions in many different ways, JOSEPHS® is driven by clear commercial interests by the sponsors who invest in the usage of the lab for good reasons. They consider learnings from JOSEPHS® as valuable insight for their innovation activities. It can help them to better understand potential markets and the perception of their business model in public, or to gather actionable evidence about preferences and possibilities to meet specific demands by customers. All this is also highly important for startups at ZOLLHOF. They are therefore encouraged to bring their own innovations to JOSEPHS® to collaborate with the public. Doing so has already proven to be highly valuable for quite a few of them. Furthermore, it became clear that using JOSEPHS® also helps to build trust with investors, especially since it is related to the Fraunhofer Society and Friedrich-Alexander University and can therefore benefit from their excellent reputation. Even if startups from ZOLLHOF do not have an opportunity to use JOSEPHS® for their own innovation activities at the current point of development, they can benefit a lot from the institution. JOSEPHS® shows them how important it is to interact

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with potential customers and how much they can learn from it. All our entrepreneurs are strongly recommended to go to JOSEPHS® and observe the interactions that take place on the premises. No matter what topic is addressed in JOSEPHS®, the mode of exchange between the staff on location and the visitors from outside is informative regarding the questions that are asked and the feedback that is documented. Furthermore, the entrepreneurs adopt the spirit of innovation at JOSEPHS®, which is similar to the spirit of ZOLLHOF in so many ways.

5 Conclusion Openness has many different shades and colours. No single institution can implement all of them, for the simple reason that an institution defines itself by certain dedicated rules. ZOLLHOF as a business incubator for startup in the early phases of their development follows different rules than JOSEPHS® as an open innovation laboratory. Therefore the forms of openness in ZOLLHOF are in certain aspects different from the forms of openness in JOSEPHS®. In order to support startups in their development, a clear agenda has to be set. Startups have to learn, grow and expand their network. The have to find valuable partners and investors. Everything that would distract them from these tasks does not make much sense in a business incubator and therefore defines the limitations of openness to the outside. Within these limitations, however, the spirit of the startup scene demands openness in the exchange with others, the willingness to share and help, and the immersion in the local ecosystem with all its different players. All this is part of the DNA at our business incubator. Open innovation laboratories are dedicated to the development of technical or economic solutions to certain problems. They invite people with different backgrounds to join in and contribute from their various perspectives to the process. Without the clear dedication of a specific business venture, the activities at open labs can diverge in many directions. This becomes particularly visible in Fab Labs and Living Labs, as they can be observed in the Nuremberg region. It also affects JOSEPHS® in many ways. Companies or larger consortia coming to JOSEPHS® must be ready to be surprised, to learn things that do not fit to their own interests. While this might at first seem confusing, it can still help them in the long run and inspire future business ventures, spin-offs and joint activities with others which go far beyond the scope of the start-up scene. Both perspectives come together, of course, when the common ecosystem in which they all operate is studied as a whole. By seeing ZOLLHOF, JOSEPHS® and all the other institutions involved in the ecosystem as part of a larger endeavour to support innovation and economic development, it becomes clear how much all the

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different stakeholders can enrich their common effort with each single contribution. The Nuremberg region can serve as a guiding example of how this is done.

References Bergek, A., and Norrman, C. (2008). Incubator best practice: A framework. Technovation, 28(1–2), 20–28. Boukhris, A., Fritzsche, A., and Möslein, K. (2017). Co-creation in the early stage of product-service system development. Procedia CIRP, 63, 27–32. Carayannis, E. G., & von Zedtwitz, M. (2005). Architecting gloCal (global-local), real-virtual incubator networks (G-RVINs) as catalysts and accelerators of entrepreneurship in transitioning and developing economies: lessons learned and best practices from current development and business incubation practices. Technovation, 25(2), 95–110. Cohen, S. (2013). What Do Accelerators Do? Insights from Incubators and Angels. Innovations: Technology, Governance, Globalization, 8(3–4), 19–25. Eshun, J. P. Jr. (2009). Business incubation as strategy. Business Strategy Series, 10(3), 156–166. Fritzsche, A. (2017). Open Innovation and the Core of the Engineer’s Domain. In Michelfelder, D. P., Newberry, B. and Zhu, Q. (Eds.), Philosophy and Engineering (pp. 255–266). Springer, Cham. Fritzsche, A. (2018). Spreading innovations: models, designs and research directions. In Bunde, A., Caro, J., Kärger, J. and Vogl, G. (Eds.), Diffusive spreading in nature, technology and society (pp. 277–294). Springer, Cham. Fritzsche, A. and Dürrbeck, K. (2019). Technology before engineering: How James Bond films mediate between fiction and reality in the portrayal of innovation. Technovation, https://doi. org/10.1016/j.technovation.2019.05.006. Gassmann, O., and Becker, B. (2006). Towards a Resource-Based View of Corporate Incubators. International Journal of Innovation Management, 10(1), 19–45. Gershenfeld, N. (2008). Fab: the coming revolution on your desktop-from personal computers to personal fabrication, New York: Basic Books. Jonas, J. M., Boha, J., Sörhammar, D., and Moeslein, K. M. (2018). Stakeholder engagement in intraand inter-organizational innovation: Exploring antecedents of engagement in service ecosystems. Journal of Service Management, 29(3), 399–421. Möslein, K. M. and Fritzsche, A. (2017). The evolution of strategic options, actors, tools and tensions in open innovation. In: Pfeffermann, N. and Gould, J. (Eds.), Strategy and Communication for Innovation (pp. 61–76). Cham: Springer. Mulder, E. (2012). Living Labbing the Rotterdam Way: Co-Creation as an Enabler for Urban Innovation. Technology Innovation Management Review 2(9), pp.39–43. O’Hern, M., and A. Rindfleisch. (2008). “Customer co-creation: a typology and research agenda.” Review of Marketing Research, 6: 94–106. Star, S., and Griesemer, J. (1989). “Ecology, ‘Translations’ and Boundary Objects: Amateurs and Professionals in Berkeley’s Museum of Vertebrate Zoology, 1907–39”. Social Studies of Science. 19 (3): 387–420. Wenger, E. (1998). Communities of Practice: Learning, Meaning, and Identity. Cambridge: Cambridge University Press.

Part III: Co-creating Value with Open Labs

Julia M. Jonas

11 Co-creating Value with Open Labs 1 Introduction To create a room and an environment for diverse actors to work together and to co-create unique value for themselves and others is one of the tasks of open labs. The role of the lab can be to serve as a platform and intermediary for industry, universities, start-ups and other organizations such as administration, interest groups and NGOs. Open labs and related structures have the potential to serve as a third place (Bessant, 2019), where interests can be moderated and aligned, methods and tools for collaboration can be made available and projects can be driven outside tight structures. Furthermore, open labs can take the role as a consultant for new technologies, innovation methods and tools for collaborating with other stakeholders (Bessant & Rush, 1995). Emphasizing different aspects and potentials of open laboratories, the forms of open labs in practice are diverse. As Roth et al. (2014) put it, open labs may be time-specific or continuous, location-specific or event-based, and are thereby influenced by the service scape, the organizing party and the overall theme (Leminen et al., 2012; Greve et al. 2016; Roth and Jonas, 2018). Next to the aspect of being an intermediary and third place, the notion of platforms is strongly connected to the concept of open labs (cf. Dell’Era and Landoni, 2014; Gawer and Cusumano, 2014). The idea of open labs as platforms highlights the aspect of networks: Connecting different actors, open labs are serving as platforms for interactions in established or growing ecosystems i.a. within industries, across industries or for a common regional goal. They allow for (intermediated) communication between users, i.e. co-creators, and innovators, they facilitate easy contact between innovating organizations of different size and background as well as they enable conversations on specific innovation topics, attracting experts and enthusiasts with their overall theme. To make these ways of intermediation possible, the individual actors have to join the platform. They have to approach it – physically or virtually – and get engaged with the platform to create value for themselves and even others (Jaakkola & Alexander, 2014; Storbacka, Brodie, Böhmann, Maglio, & Nenonen, 2016). Engagement, as defined in the sphere of service-dominant-logic school, is broadly understood as a dynamic and iterative process where actors behave in a cocreative way, invest resources, share or participate in experiences with other actors (Brodie, Hollebeek, Juric, & Ilic, 2011; Brodie, Fehrer, & Jaakkola, 2019).

Julia M. Jonas, FAU Erlangen-Nuremberg, Chair of Information Systems - Innovation and Value Creation, Nuremberg, Germany https://doi.org/10.1515/9783110633665-011

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2 Contributions The first chapter in this section takes up this perspective of open labs as platforms for engagement in the context of entrepreneurship. Fehrer and colleagues introduce the concept of engagement platforms to allow actors related to start-up businesses and processes to help innovative ideas to solidify, grow and get ready for the market. This contribution highlights the aspect of networking in open labs, where new connections are made and first loose connections to similar entrepreneurs, experienced managers in relevant or totally different industries, financers or influencers will be created. As an engagement platform, open labs support new connections and have the potential to bridge structural holes (Burt, 2009) in entrepreneurship ecosystems around universities, start-up incubators and entrepreneurship supporting parties. Whilst in this context, the physical space plays an outstanding role to close structural holes and to enable new connections between diverse participants of a platform (e.g. Nambisan, 2009), the work by Nyström, Barner-Rasmussen and Kaartemo looks at open labs as a facilitating structure (Adner, 2017) and moderator (Bessant and Rush, 1995). They analyze a long-term initiative as an open lab – as an intermediary for projects, where the collaboration and co-creation of new value between diverse actors needs to be orchestrated, moderated, and administered to work for a common goal. Starting with a pre-defined number of core actors, the Finnish network around 5G industry needed to be enabled to work together and innovate together. Taking a deep-dive into how a service system for collaborative innovation activities can be developed, the third chapter introduces LESSIE – an approach for collaborative service systems engineering with a focus on digital services. Especially for smart services, the integration and alignment of partners has a special touch to it. Data ownership, data security, hosting and diverse innovation cultures come together in such a setting. This is why Meyer, Haertwig and Anke proposes a step-bystep approach to move forward in a regional attempt to create new digital service systems amongst diverse partners, based on their deep fundament in German service engineering and their experience with open lab project approaches in the past few years. The last chapter of this section takes a productivity perspective on collaborative efforts in open labs. As platforms joint innovation, open labs are developing unique value propositions amongst multiple partners. Yet, in the long run, they are dependent on a sustainable business model like any other business. Open labs are allowing not only for great ideas and the implementation of methods for collaboration, enabling new connections and interfaces for value co-creation with users and other stakeholders – they also need to be well organized and working productively. The earlier chapters in this book showed, how the open lab JOSEPHS® in Nürnberg is organized and how different challenges of multiple roles, team work and continuous improvement in shift work or project management with diverse stakeholders

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were approached in this unique open lab. Daiberl and Roth look at networked productivity, a very relevant but not yet as prominent topic in the context of joint efforts in labs or projects. When delivering an offering together, all partners of the network are part of the value creation process and need to work on their own service quality and productivity at the same time as the joint service and its’ productivity and excellence. Since this is a complex and critical process, they propose three tools to work on service productivity jointly; an improvement process, a service modeling approach and a feedback application. With their approach, the multiple actors and their activities can be orchestrated when improving the overall productivity of the network in an open lab. With these four articles, the following chapter will show how open labs as physical spaces in the sphere of entrepreneurship enable value creation for startups and their innovation ecosystem; it illustrates how open labs can foster technology-based innovation, to co-create new offerings across industries as an intermediary providing a mind-set, tools and processes; and it proposes an approach for creating smart service systems value with open labs. And, it operationalizes the orchestration of service productivity on open labs as networked value creation by proposing a tools for joint improvement.

References Adner, R. (2017). Ecosystem as Structure : An Actionable Construct for Strategy, Journal of Management 43(1), 39–58. Bessant, J. (2019). Creating Innovation Spaces. Retrieved from: http://johnbessant.org/2019/06/ 29/creating-innovation-spaces/ (2019-10-15) Bessant, J., & Rush, H. (1995). Building bridges for innovation: the role of consultants in technology transfer. Research Policy, 24, 97–114. Brodie, R. J., Hollebeek, L. D., Juric, B., & Ilic, A. (2011). Customer Engagement: Conceptual Domain, Fundamental Propositions, and Implications for Research. Journal of Service Research, 14(3), 252–271. Brodie, Roderick J, Fehrer, J. A., & Jaakkola, E. (2019). Actor Engagement in Networks : Defining the Conceptual Domain. Journal of Service Research, 1–16. Burt, R.S. (2009). Structural Holes: The social Structure of Competition, Harvard University Press, Cambridge, MA. Dell’Era, C., & Landoni, P. (2014). Living Lab – A Methodology between User-Centred Design and Participatory Design. Creativity and Innovation Management, 23(2),137–155. Gawer, A., & Cusumano, M. A. (2014). Industry platforms and ecosystem innovation. Journal of Product Innovation Management, 31(3),417–433. Greve, K., Martinez, V., Jonas, J.M., Neely, A., & Möslein, K.M. (2016). Facilitating co-creation in living labs – The JOSEPHS study, http://cambridgeservicealliance.eng.cam.ac.uk/resources/ Downloads/MonthlyPapers/ 2016MayPaper_FacilitatingCoCreationinLivingLabs.pdf (Last access November 2,2017). Jaakkola, E., & Alexander, M. (2014). The Role of Customer Engagement Behavior in Value CoCreation: A Service System Perspective. Journal of Service Research, 17(3),247–261.

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Leminen, S., Westerlund, M.,& Nyström, A.G. (2012). Living Labs as open-innovation Networks. Technology Innovation Management Review, 2(9),6–11. Nambisan, S. (2009). Platforms for Collaboration. Stanford Social Innovation Review, Summer 2009, 44–49. Roth, A., Fritzsche, A., Jonas, J., Danzinger, F., & Möslein, K. M. (2014). Interaktive Kunden als Herausforderung: Die Fallstudie „JOSEPHS® – Die Service-Manufaktur“. HMD Praxis Der Wirtschaftsinformatik. Roth A., Jonas J.M. (2018). Dienstleistungsentwicklung im offenen Innovationslabor – Ein Blick durch die Unternehmensbrille. In: Bruhn M., Hadwich K. (eds) Service Business Development. Springer Gabler, Wiesbaden. Storbacka, K., Brodie, R. J., Böhmann, T., Maglio, P. P., & Nenonen, S. (2016). Actor engagement as a microfoundation for value co-creation. Journal of Business Research.

Julia A. Fehrer, Roderick J. Brodie, Valtteri Kaartemo, and Maximilian Reiter

12 The Role of Engagement Platforms in Innovation Ecosystems 1 Introduction One of the most central and most critical processes for new ventures and incumbents is the commercialization of innovation. Innovation according to Schumpeter (1947, p. 151) refers to “the doing of new things or the doing of things that are already being done in a new way” and thus, is at the core of all market-oriented activities. Innovation is the way to stay successful in rapidly changing marketplaces. However, managers and entrepreneurs alike often set the wrong course in innovation projects leading to insufficient adoption in the market. While there is considerable research devoted to the fuzzy front end of the innovation process, for example, the co-creation of ideas with customers on open crowdsourcing platforms and the importance of integrating customers and their needs in the design process through, for example, design thinking, the complexity of the structure of innovation ecosystems received limited attention to date. The focus of innovation in many companies is very firm-centric with a rather narrow focus on established innovation processes and the platforms. This is problematic, because innovating within an established community – even if this is an open community – often leads to the creation of redundant knowledge and ideas. Thus, we argue that it is important for firms to understand the broader structure and in particular, the structural holes and engagement platforms in their innovation ecosystems. Structural holes are missing contacts between networks, for example, between different industries, different communities or different knowledge fields (Burt, 1992). Bridging such structural holes gives new ventures and incumbents access to ‘fresh’ ideas, new resources and knowledge regarding new markets and application fields. Understanding structural holes can make a difference in decision-making processes. Entrepreneurs, for example, can choose to span structural holes to gain additional market insight and effectively increase risk-taking behaviors inherently important for entrepreneurial decision-making (Martinez and Aldrich, 2011). Incumbents on the other hand can use structural holes to increase the heterogeneity of knowledge and thereby inform creative thinking in the organization in order to find new solutions for old problems (Rodan, 2010; Bentley, 2018). Julia A. Fehrer, University of Auckland Business School, Auckland Central, New Zealand Roderick J. Brodie, Auckland, New Zealand Valtteri Kaartemo, University of Turku, Turku School of Economics, Rehtorinpellonkatu, Turku, Finland Maximilian Reiter, University of Bayreuth, Bayreuth, Germany https://doi.org/10.1515/9783110633665-012

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We propose that it is through engagement platforms in innovation ecosystems, that entrepreneurs and incumbents can bridge structural holes. While it is important to have engagement platforms functioning as hubs (i.e., platform to connect, engage and establish a community) in innovation ecosystems, it is equally important to have engagement platforms as boundary spanners (i.e., crossing points to new previously unconnected networks). We use insights from three innovation systems in North Bavaria, New Zealand and Finland to illustrate roles, designs and value co-creation mechanism of engagement platforms to highlight the importance of these platforms for setting the right course in the innovation and commercialization process leading to a higher market adoption. This book chapter proceeds as follows. First, we point out the characteristics of engagement platforms and why they are growing in relevance in contemporary business environments. After illustrating the role, anatomy, design and value co-creation mechanisms of engagement platforms we embed them in a broader innovation ecosystem perspective to draw attention to the complex interplay of the platform structure and the way stakeholders engage on platforms. We end with a discussion of engagement platforms in different innovation ecosystems and how entrepreneurs and incumbents best position on these platforms using examples from across the world.

2 Engagement Platforms The concept of platforms has a long-standing history in systems, innovation and technology management literatures (Eckhardt, Ciuchta and Carpenter, 2018). More recently – with the growing importance of the so coined ‘sharing’ or ‘collaborative economy’ – the discourse on ‘engagement platforms’ has also gained momentum in the marketing and broader management disciplines. Firms like Uber, Airbnb or Kickstarter are seen as disrupters of their industries (Breidbach and Brodie, 2017). These platform businesses constitute the majority of the fastest growing organizations in the global economy. In today’s networked age, strategic benefits are increasingly generated over platforms, which allow various stakeholders to engage with one another. Platform business models extend beyond the “unicorns”-companies and tech start-ups. An increasing number of mature incumbent organizations in a variety of industries are part of ecosystems in which they either operate as or are governed by engagement platforms (Altman, 2015; Fehrer et al., 2018a). Ramaswamy and Gouillart (2010) describe engagement platforms using four criteria: transparency, access, dialogue and reflexivity. Transparency implies that interactions between stakeholders (for example, between customers and firms) are visible to a wider audience through engagement platforms, such as for instance social networking sites. Accessibility means that stakeholders are enabled to integrate their resources (such as their knowledge and skills) into the engagement platform, for example, by

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adding or sharing content. These resource integration processes of all involved stakeholders continuously change the nature and characteristics of engagement platforms. For example, by adding a calendar function to events posted on Facebook the platform acts not only as a platform to connect with friends, but also as a personal organizer for planning your weekends and leisure time. Engagement platforms facilitate the dialogue and knowledge exchange amongst multiple stakeholders, which consequently cocreates value for all these stakeholders in the ecosystem (Prahalad and Ramaswamy, 2004; Ramaswamy and Guillard, 2010). Developer platforms, for example, allow software developers to work with open codes created by other software developers and thereby extending the functionalities of the software to increase its application fields in the market. Finally, reflexivity implies that engagement platforms are adaptive and a result of continuous change. Based on these four criteria Breidbach and Brodie (2014) define engagement platforms as ‘physical and virtual touchpoints designed to provide structural support for the exchange and integration of resources, and thereby co-creation of value between actors in a service system’ (page 596). In order to unpack this rather complex definition, we address its main components: role, anatomy, design types and the value creation mechanisms of engagement platforms more in detail. Role of engagement platforms: Engagement platforms allow various stakeholders to connect and engage with one another. This is possible, because they provide the structures and rules as to how to connect. They can be considered as the basic artefacts to facilitate engagement among different stakeholders (Breidbach et al., 2014). Engagement platforms are embedded within broader ecosystems, centred on service-for-service exchange and innovation (Lusch and Nambisan, 2015; Adner and Kapoor, 2010). Anatomy of engagement platforms: Engagement platforms are not constrained to digital environments, nor do they exist or act in isolation from their engaging stakeholders or other platforms within the ecosystem (Breidbach and Brodie, 2017). Nenonen et al. (2012), for example, provide a classification for engagement platforms referring to digital spaces, physical spaces, processes and activities. Engagement platforms act as a crossing points for stakeholders within and across these spaces. Platform design types: There are various different ways of categorizing different types of engagement platforms. We differentiate between platform designs that facilitate different mechanisms of stakeholder engagement. Most commonly, platforms are described as passive intermediaries in the literature. Intermediaries provide the structure for specific services. For example, an online banking platform provides a standardized way for customers to handle their monetary transactions. The platform in this case is a passive enabler of service exchange between the bank and their customers. Technology creators are open or closed ‘container’ platforms to create ideas and in particular, intellectual property within a certain structural framework. Examples are developer platforms such as Unity an open source programming framework supported by Microsoft for developing augmented reality applications or crowdsourcing platforms. Matchmakers are discussed in the context of connecting one, two- or multi-sided

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markets (Parker et al., 2016). Matchmaker platforms are based on a brokerage logic. That is, the platform acts as a broker between supply and demand. The Airbnb platform, for example, matches hosts and guests (two-sided market), Kickstarter brings together entrepreneurs and funders (multi-sided market), Tinder matches individuals with the same dating intentions (one-sided market). Finally, platforms can be designed as centreless ‘living’ structures, which continuously change. A recent example for such platforms are decentralized autonomous organizations, that function based on blockchain technology. Compared to the previously described platform designs, this platform type has no one single point of power that governs the network of stakeholders. Coordination in such decentralized autonomous platforms is provided based on shared goals and shared values supported through incentives and the self-executing blockchain. An example for a living structure is ShareRing (https://sharering.network/en), a decentralized marketplace supported by blockchain technology designed for sharing absolutely everything – from storage space to tools, clothes, jewellery, food, or even your cooking skills. Small local service providers, as well as superstores, can enter the network with low entry barriers, such as service fees. Through creating their own crypto currency (SharePay), ShareRing significantly reduces the costs and effort of international trades including bank transfers and currency risks. ShareRing provides a secure way to pay for sharing services anywhere in the world, thus opens the global marketplace for very rare and fragmented services (Fehrer et al., 2018b). Value co-creation on engagement platforms: Engagement platforms by nature co-create value through connecting various stakeholders effectively and efficiently and by allowing these stakeholders to collaborate. Fehrer et al. (2018a) identified different value co-creation mechanisms of engagement platforms: direct and indirect network effects, reduced transaction costs, creation of synergies and the use of underutilized resources. Positive network effects refer to effects market actors derive from using services, depending on the number of other market actors using the same services (Katz and Shapiro, 1985). For example, Facebook only provides value for individuals if their social circle uses the platform as well. Uber only works, if there is a sufficient number of drivers and customers in a focal city. Indirect network effects on the other hand refer to value creation based on the diffusion of a certain standard (e.g. Apple iOS). More specifically, the higher the diffusion of a standard, the more services and applications will be provided that are compatible with this standard. Buying an iPhone, for example, creates value because of access to the app universe available at the app store. Positive network effects create incentives to ‘herd’ on a certain engagement platform and thus create lock-in mechanisms and high switching costs. Further, engagement platforms reduce transaction costs and thereby increase efficiency. Coase (1937) explains ‘why firms exist’ based in their function to reduce transaction costs. He refers to three specific transaction costs: search costs (resources and time for finding what you need), contracting costs (resources and time for negotiation) and coordination costs (coordinate activities among dispersed stakeholders). Engagement

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platforms reduce search costs, for example by offering smart search mechanisms (e.g., Google search). They also reduce contracting and coordination costs through engagement practices established on the platform (e.g., review mechanisms). The elimination of redundancies and thus, the creation of synergies is another very central value co-creation mechanism of engagement platforms (Zott et al., 2010). Synergies can be achieved through platforms on three levels: between services (e.g., bundling of service offerings on one platform), between technologies (e.g., programming standards or frameworks on the platform) and between activities (e.g., aligned engagement practices on the platform). Finally, through engagement platforms it is possible to access resources of other stakeholders, without owning them. For example, Microsoft can draw on the knowledge of their Unitiy developer community to further develop the Unity software, without having to employ these developers. Airbnb guest can rent the beach house, tree house or house boat from private owners, which potentially had been empty (underutilized) before the Airbnb platform existed. Figure 12.1 gives an overview of the characteristics of engagement platforms.

3 Engagement Platforms in Innovation Ecosystems As previously discussed, engagement platforms are embedded within broader ecosystems. More specifically, they have an important role for the emergence of innovation in ecosystems. Although considerable research has been devoted to understanding the role of the platform providers in the innovation process, most of this research is focused on the platform as the unit of analysis. For example, crowdsourcing and the role of crowdsourcing platforms for the ideation and innovation process of big corporates is studied extensively. We broaden this dyadic and firm-centric view of the innovation process to a broader more holistic view of stakeholder relationships in innovation ecosystems, which allows for understanding the network structures that are important for entrepreneurs and corporates to drive innovation.

3.1 Interdependencies in Innovation Ecosystems The innovation ecosystems concept draws on established literature on business ecosystems (e.g. Moore, 1994), and moves from a narrow firm-centric and dyadic perspective, to a much broader view, including the interdependencies of multiple stakeholders (Chandler et al., 2019). More specifically, innovation ecosystems are described as networks of interdependent relationships of versatile stakeholders. Further, the concept of innovation ecosystems relates to the metaphor of biological ecosystems, which offers additional insights regarding their structures and dynamics

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Platform role

Facilitation of engagement: – Allows various stakeholders to connect and engage with one another – Provides structures as to how to engage with each other Digital and physical platform configurations:

2. Platform anatomy

– Engagement platforms connect stakeholders within and across different environments, such as digital spaces, physical spaces, processes and activities Different designs to enable engagement:

3.

Platform design types

– Intermediaries: passive enablers of service exchange between stakeholders (e.g., online banking platform, CRM platform) – Technology creators: open or closed platforms to create ideas and in particular, intellectual property within a certain structural framework (e.g., Unity supported by Microsoft, R, Unix) – Matchmakers: connect one-, two-or multi-sided markets (e.g. Tinder, Facebook, Airbnb, Kickstarter) – Living structures: dynamically changing platform structure, enabled through e.g. blockchain technology (e.g., Bitcoin, ShareRing, WindingTree) Value cocreation mechanisms through platforms:

4. Platform value creation

– Direct network effects: Creating market power – Indirect network effects: Providing standards – Transaction costs: Reduction of search, coordination and contracting costs – Complementarities: Creating synergies in the system – Capacity utilization: Creating access to new, underutilized and fragmented resources

Figure 12.1: Characteristics of engagement platforms.

(Breidbach and Brodie, 2016). Iansiti and Levien (2004) identify four characteristics of biological ecosystems: First, ecosystems consist of large numbers of loosely interconnected participants that depend on each other for their performance and survival. Second, participants are mutually dependent, meaning that outcomes can only be influenced and not controlled. Third, the system is subject to continuous change. Stakeholders continuously enter and leave the system, thus innovation ecosystems have ‘porous’ boundaries. The continuous flow of stakeholders gives innovation ecosystems a complex structural dynamic. Stakeholders not only engage or disengage they also continuously change roles and positioning in the system (Adner and Kapoor, 2010). Finally, innovation ecosystems are governed through hubs of engaged stakeholders (Iansiti and Levien, 2004). Each stakeholder in the innovation ecosystem has multiple ties to other stakeholders, which are in turn also interconnected. This interweaved web of connections forms the structure of the innovation ecosystem. Engagement platforms function as hubs in such network structures. As previously described, engagement platforms

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provide the structures and rules as to how to connect and thereby allow various stakeholders to engage with one another and consequently co-create value through increased efficiency and effectiveness in the ecosystem. However, the more connections and engagement practices in innovation ecosystems become established, the more likely it is to create redundant information and knowledge. This in turn may slow down the innovative power in the ecosystem (Burt, 1992; 2004). Thus, engagement platforms in innovation ecosystem have a second, very central function of bridging structural holes. According to Burt (1992) structural holes exist in networks when there is a lack of direct contact or tie between stakeholders (Burt, 1992) (see Figure 12.2). Stakeholders that operate near structural holes have the greatest chance of developing innovation (Burt, 2004). This is because these stakeholders are less immersed in the established way of thinking within a certain community and more exposed to other networks. Engagement platforms, as illustrated in Figure 12.3 function as boundary spanners to overcome structural holes and enable inter-network information flows (Burt, 1992). Bridging structural holes gives all stakeholders on the engagement platform access to new ideas and resources. Further, it is a way to mediate knowledge transfer between disconnected networks. Thus, innovation ecosystems need the dynamic interplay of both engagement platforms as hubs and as boundary spanners for the co-creation of innovation.

Innovation ecosystem

Porous boundaries Structural hole

Figure 12.2: Structural holes in innovation ecosystems.

New ventures often begin their business with family members and close friends, because this way they can reduce initial costs in compensations and they are surrounded with a network, they trust (Martinez and Aldrich, 2011). However, due to strong ties

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Innovation ecosystem Engagement platform as

Porous boundaries Engagement platform as

Figure 12.3: Function of engagement platforms in innovation ecosystems.

and closed network formations, new ventures can easily fail caused by the lack of innovation or the focus on too narrow markets. Recognizing this issue, engagement platforms in innovation ecosystems provide the new connections needed to create nonredundant, fresh knowledge. This can be crucial at times of growth, when new ventures try to find and shape the markets for their new products and services. Further, these new connections my lead to pivoting and change the initial ideas towards new application fields with higher chances to succeed in the market. In what follows we illustrate the critical functions of engagement platforms using examples from three innovation ecosystems in New Zealand, Finland and North Bavaria to give entrepreneurs and incumbents an idea as to how to position and engage on such platforms.

3.2 Insights from Three Innovation Ecosystems When analysing the structure of the three innovation ecosystems in New Zealand, Finland and North Bavaria, we found several engagement platforms with different functions and in different ‘spaces’ in their respective ecosystem (see Figure 12.4). We identified five important spaces, that these engagement platforms connect: the entrepreneur space, the university space, the corporate space, the funding space and the market space. Engagement platforms act as a crossing points for stakeholders within and across these spaces and thereby function as hubs and boundary spanners. SparkUp Turku for example is an engagement platform in Turku (Southwest Finland) that brings together organizations and activities of the Turku Science Park Oy, Boost Turku (student entrepreneurship society), Creve (an incubator for businesses

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FUNDING SPACE (INTERNATIONAL) MARKET SPACE

Crowdfunding platforms hubs

hubs

boundary matchspanners makers

Business angle & VC platforms boundary spanners

matchmakers

Accelerator platforms hubs

ENTREPRNEUR SPACE Corporate labs

living boundary structures spanners

hubs

technology creators

Open labs Incubator platforms hubs

technology creators

hubs boundary technology spanners creators

UNIVERSITY SPACE

University labs hubs

technology creators

Figure 12.4: Complex structures of innovation ecosystems.

in the creative industries), and Junior Achievement Finland (an organization that advances entrepreneurial attitude and an active lifestyle among Finnish youths), companies working within its premises, other companies, as well as local universities. The function of SparkUp Turku is two-fold. First, SparkUp Turku is a hub that offers a community space as well as training and mentoring programs for new ventures and established businesses with the aim to support networking and exchange of fresh ideas. There are specific training and acceleration programs that match each phase: StartingUp (for idea development), TeamingUp (for finding partners), BusinessUp (for getting started), and LevelUp (for market and international growth). While there are other actors in the region that help new businesses, SparkUp Turku focuses on matching people with ventures that have a potential to scale in international markets. In other words, the hub creates synergies in the local innovation system. Second, SparkUp Turku serves as a boundary spanner bringing together students, businesses and other organizations through educational services and entrepreneurial activities. Thus, SparkUp Turku links different industries, communities and knowledge fields. For instance, immigrant entrepreneurs get linked to the Finish innovation ecosystem via SparkUp Turku. Immigrant entrepreneurs offer underutilized

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resources that are beneficial to the local community providing international networks and business opportunities. The Velocity engagement platform in Auckland, New Zealand similarly acts as a boundary spanner for different spaces in the New Zealand innovation ecosystem and as a hub for students, entrepreneurs, investors and New Zealand businesses to connect. Velocity is an engagement platform established through the University of Auckland. The platform encourages interdisciplinary and entrepreneurial work between different faculties and supports in particular entrepreneurial ideas of students. Velocity as a living structure has grown from 800 members in 2014 to 2.400 members in 2018. One central activity of the platform is the annual idea competition for students, where the best student idea wins NZD 100.000 seed funding. Velocity links entrepreneurs (in particluar university spin-outs) with incubators and business angle networks to provide mentorship and financial support for early stage ventures. UniServices another boundary spanning engagemet platform within the New Zealand ecosystem works closely with Velocity and supports new ventures in particulary in the area of intellectual property (IP) protection. Further, UniServices accomodates an investment committee with members including successful New Zealand entrepreneurs, New Zealand investors, business angels, student and academic representatives. This committee provides funding and links to national and international platforms (e.g. to the Silicon Valley) for futher funding, mentorship and access to international markets. The JOSEPHS®’ engagement platform in Nuremberg, North Bavaria represents another important platform type within innovation ecsystems – an open lab. Labs in innovation ecosystems typically function as technology creators bringing together knowledge and resources from different stakeholders to create new solutions. The JOSEPHS®’ acts as a hub and connects corporates and entrepreneurs with potential customers and offers an open test environment for co-creating, prototyping, testing and experimenting with new services and products. Engaging in such open labs increases the chances to succeed with new ideas in the market, because new services and products are developed in continous iterations together with potential customers. Further, the JOSEPHS®’ spans boundaries to other engagement platforms in the North Bavarian and German innovation ecosystem, such as the ZOLLHOF, a technology incubator.

4 Conclusion Taken together, by highlighting different roles of engagement platforms in different innovation ecosystems, it becomes clear that for innovation to get sucessfully commercialized and adopted in the market it needs both: innovation hubs, connecting, engaging and establishing a community of versatile stakeholders and boundary

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spanners as crossing points to new, previously unconnected networks. Thus, entrepreneurs and incumbents need to understand the structure of their innovation ecosystem and the engagement platforms embedded within these systems. Engagement platforms are designed as matchmakers, technology creators or living structures and can support during different maturity levels of the innovation and commmercialization process. (Open) labs create environments for testing, prototyping and experiementing, incubator platforms provide technological support and training for entrepreneurs and innovators, and accelerator platforms create linkages to new application fields, funding and new markets. Using engagement platforms thoughtfully as hubs to engage with an inoovation community and boundary spanners to operate close to structural holes offers a promissing strategy for the commercialization of innovation in co-opetition with other players in the market.

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Anna-Greta Nyström, Wilhelm Barner-Rasmussen, and Valtteri Kaartemo

13 B2B Vertical Collaboration and Open Innovation – The Case of 5G in Finland 1 Introduction In recent years, new approaches have emerged that align the user of products and services with the producer in the innovation process. A growing part of the knowledge economy is based on the input of users and communities to solve technological and organizational problems (Harhoff & Lakhani, 2016). Innovations are increasingly the result of activities beyond the boundaries of a single organization. Chesbrough (2003, 2006) coined the term open innovation, denoting innovation processes in which firms interact extensively with their environment to experiment with business models. This, in turn, leads to the exploration and exploitation of external information. This open innovation model has been shown to improve user value (Almirall & CasadesusMasanell, 2010) and lead to better innovation performance (Chiaroni et al., 2010). Firms maintain knowledge outside of their organizational boundaries over time, which according to Chesbrough (2006) as well as Grant and Baden-Fuller (2004) signals that interorganizational relationships are, in a sense, an extension of the firm’s internal knowledge bases. This view is at the very core of the open innovation literature. Open innovation involves knowledge exploration and exploitation inside and outside an organization’s boundaries throughout the innovation process (cf. Chesbrough, 2003; Grant & Baden-Fuller, 2004). Open innovation processes combine internal and external ideas into platforms, architectures, and systems (cf. Bogers et al., 2017). However, internal activities are also critical to open innovation processes. Recently, Bogers et al. (2018) discussed two kinds of open innovation, namely outside-in and inside-out (also referred to as inbound and outbound) open innovation. The former focuses on external inputs and contributions to the firm’s innovation activities, whereas the latter emphasizes the sharing of firm ideas with actors outside the firm. Bogers et al. (2018) regard the latter, inside-out or outbound open innovation, as less well understood by academics as well as practitioners. In this chapter, we aim to highlight the inside-out perspective of open innovation based on a case study of an evolving topic, namely fifth generation (5G) mobile communications technology in Finland, one of the world’s leading countries in terms of technology development. In communication technology there is a long tradition of Anna-Greta Nyström, Wilhelm Barner-Rasmussen, Åbo Akademi University, School of Business & Economics, Vänrikinkatu, Turku Valtteri Kaartemo, University of Turku, Turku School of Economics, Rehtorinpellonkatu, Turku, Finland https://doi.org/10.1515/9783110633665-013

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joint development over country borders through international working groups (e.g., ITU, 5GPPP), industry-research collaboration, and various projects. However, 5G is envisioned to integrate into new industry areas, and therefore extensive collaboration between technology developers and deployers is required. This explains the existence of national industry-research projects such as the one studied here, with the aim of technologically advancing 5G within different industry areas (“verticals”) in which wireless connectivity is not yet a core feature. The project, WIreless for VErticals (WIVE), was funded by Business Finland, the Finnish funding agency for innovation, in 2017–2018 as one of several concurrent national projects advancing 5G and deploying established 5G test networks. We explore how this consortium, consisting of organizations representing various industry areas, engaged in open innovation collaboration and value co-creation in order to jointly create pilots and technical testing sites, in which 5G connectivity was designed to enhance value-creation in specific industry areas. We regard the case project as an “open lab”, in which the participants jointly aimed at value cocreation through collaboration over traditional industry borders. This empirical study of an outbound open innovation process in the context of B2B vertical collaboration contributes to open innovation literature by illuminating the importance of joint expectations for the process, and the crucial role of facilitators in bringing actors together to develop use cases and pilots. In addition, we discuss how verticals can combine forces to jointly develop markets and business models with the help of an external facilitator. This can be replicated in other countries and around other emerging technologies that shape markets across industry boundaries.

2 Open Innovation Through Collaboration and Co-creation With digitization, innovation processes have undergone a radical transformation from the traditional, closed approach (Chesbrough, 2003; 2006). Open innovation, rather than being a coherent theory, covers several approaches. At its simplest, it suggests open and diffused boundaries between corporations and their environments (Chesbrough, 2006). There are several types of open innovation networks and operational models available for organizations to participate in collaboration networks, including crowdsourcing (cf. Estellés-Arolas & González-Ladrón-deGuevara, 2012), innovation communities (cf. Pisano & Verganti, 2008), innovation contests (cf. Bulligner & Moeslein, 2013), and open source (cf. Bonaccorsi et al., 2006; Lakhani & von Hippel, 2003; Raasch et al., 2013). Gassmann & Enkel (2006) identified three forms of open innovation processes (see also Bogers et al., 2018): (1) Outside-in refers to the use of external sources of innovation. This involves the transfer of knowledge from customers, suppliers, partners or even universities

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and competitors. In other words, the firm “opens up” its innovation process (Bogers et al., 2018). (2) Inside-out is when internally developed ideas are out-licensed to external partners, and specifically to allowing and encouraging other actors to use these ideas in their own revised business models (Bogers et al., 2018). Key reasons to do so lie in distribution partnerships, collaborative development, or to profit from a developed technology that is not used internally. (3) Coupled process combines outside-in and inside-out, but rather than just sharing resources and expertise, companies collaborate closely, e.g., in a joint venture. Open innovation with customers has mainly been regarded as an outside-in process, in which ideas for new products or services are sourced from customers (cf. Bogers et al., 2010). This perspective has shifted towards consumer-centricity, meaning that rather than exploiting the knowledge of the users, knowledge is cocreated with users. Co-creation (Prahalad & Ramaswamy, 2000) can thus be viewed as a coupled process in which users or customers generate ideas for new products and services, test prototypes, and remain attached to the company beyond the development process (Brunoe et al., 2014). Co-creation is a “form of market or business strategy that emphasizes the generation and ongoing realization of mutual firm – customer value. It views markets as forums for firms and active customers to share, combine and renew each other’s resources and capabilities to create value through new forms of interaction, service and learning mechanisms” (Minghetti, 2014, p. 14). However, organizations tend to lack the ability to exploit external resources and attempts at outbound innovation due to poor strategic planning (cf. Chesbrough, 2003). The question thus arises how firms should go about exploring outbound open innovation, with what operational model, and with which strategic goals in mind. In 2000, Tschirky and colleagues suggested that firms were learning to employ external exploitation in a strategic and systematic way instead of making ad-hoc decisions on technology exploitation case-by-case, but clearly this remains a major challenge today. It therefore remains pertinent advice that firms should take a strategic and systematic approach to collaboration patterns, partnerships, value networks, and to defining the value of open innovation patterns. Our study is an attempt to clarify how this can be done.

3 The Context: 5G Technology in the Making The context of our research, 5G mobile communication systems, is a crucial component of the paradigm shift that the telecommunications sector, like many other fields and industries, is undergoing. 5G is designed for very low latency applications, high user

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travelling speeds, high accuracy in, e.g., determining user location, higher data rates, and lower energy consumption. It is aimed to serve a wide range of industries and business areas, not only mass consumer markets. A novel feature of 5G is the integration of vertical industries, including energy, media and entertainment, health, factories, and the automotive industry (5G-PPP, 2016). This is expected to cause a transformation as specialized companies will be able to provide services and establish new positions in markets and value networks, bringing major change to environments currently dominated by bilateral relationships between mobile operators and their customers. 5G is therefore expected to change actor roles, network positions, business models, and business ecosystems in multiple vertical industries. One of the aims for 5G is to create ecosystems that can meet new technical needs emerging in these vertical industries. This will allow for innovative business models and services as well as new collaboration constellations and networks. Thus, a major challenge for the ICT industry as well as the verticals is to identify application areas and deployment opportunities that different stakeholders can exploit to co-create value. However, as 5G technology is still in a process of development and standardization, scenarios and assumptions are currently shaping the expectations of business opportunities, 5G services, and actor roles. In addition, the notion of the Internet of Things (IoT) is fueling expectations by painting a world in which devices and machines communicate and visions of automation, connectivity, and mixed reality become reality. Developers and utilizers of 5G connectivity are thus racing to discover and unlock the potential of new services, connected devices, sensors and tracking, critical communications applications and so forth.

4 Research Approach The WIVE project is one of three national 5G development consortia funded by Business Finland during 2017–2018. Alongside technology and network developers, the project consortium consists of actors from different vertical industry sectors including media, energy, and logistics. Industry partners are Nokia Bell Labs, Telia Company, ABB, Kalmar Cargotec, Teleste, Digita, YLE, Magister Solutions and Nordic Semiconductor. The aim set for WIVE is to build the foundation for innovative wireless solutions in the spirit of open innovation by bringing new experiences to end-users, and to advance 5G by focusing especially on developing machine type communication (MTC) and ultra-reliable low latency communication (URLLC). Eventually, the consortium seeks to contribute to developing IoT, which largely depends on fast and reliable mobile technology networks. This emerging mobile communications system is fruitful to study from an open innovation perspective, as there are high expectations and assumptions associated

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ICT/Telecommunications “enablers”

Logistics: Kalmar Cargotec

Powergrid/smartgrid: ABB

Vertical industries “utilizers”

Media industry: YLE, Digita, Teleste

with faster and more reliable 5G wireless technology, related not only to technical performance but also to functionality and value creation in various industries (see Figure 13.1). 5G wireless infrastructure entails improved features and performance over current systems, enabling new services and devices such as simple, efficient sensors able to communicate with each other. Concurrently, it enables innovation and value to shift from smartphones towards other types of devices such as connected cars and machines, wearables and gadgets, home automation, and industrial internet. 5G is not only about faster data rates but is rather an enabling system for creating user value by deploying wireless connectivity to new fields and areas.

Hardware manufacturers and software developers: Nokia Bell Labs, Magister solutions, Nordic semiconductor, Telia Company, research units (Helsinki, Tampere, Turku, Oulu)

Figure 13.1: The ICT/telecommunications industry and vertical industries represented in WIVE (Source: WIVE material).

Data Collection and Analysis To pursue the case study on outbound open innovation, primary data were collected through participant observation and interviews. Two of the authors were also participants in WIVE, where their tasks was to facilitate inside-out open innovation processes and develop new knowledge of IoT and 5G in project-internal and public workshops; support the other project participants in developing use cases, scenarios, and business models; and conduct research on certain issues relevant to the project (e.g., changing media consumption and behavior, and emerging business models and markets). This provided ample opportunities to observe other open innovation project participants, who were middle managers and experts working with technological issues related to 5G, both in and between project-related events. We took written notes related to the discussion topics, questions raised, and ideas or concerns expressed in relation to future 5G services, the market and ecosystem, as well as business opportunities. The workshop themes were largely based on participants’ interests and needs. Meetings and workshops were used to facilitate

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discussion between the industry actors; to share and compare thoughts, not only concerning technology and regulation, but also concerning business opportunities, actor network constellations, actor roles, scenarios, and value creation. These data were supplemented by interviews. One co-author worked closely with all industry partners in the consortium to identify and define use cases of important business areas and processes in which 5G could create value for the organization. In 2017 we also conducted two rounds of in-depth interviews, one in February focusing on identifying use cases (n = 8), and the other in August focusing on 5G business opportunities (n = 15). We also collected secondary data including industry reports and white papers, and one co-author visited sites or observed physical areas in which 5G will be deployed by the participating industry actors. In total, we build on ca 130 hours of participant observation and 15 hours of interviews. The interviews and discussions at two workshops on business opportunities and business models respectively were recorded and transcribed verbatim. The transcripts were analyzed for words and narratives related to outbound open innovation process, such as: collaboration, value co-creation, partnerships, and value networks. The written notes on observations were analyzed with the same codes.

5 Three Phases of an Inside-out Open Innovation Process in the Context of B2B Vertical Collaboration: Unfolding Collaboration and Innovation Patterns This section presents our findings, divided into three phases.

Phase 1: Starting the Project and Calibrating Expectations At the start of the project, one co-author interviewed representatives from the partner firms regarding their expectations on the project and 5G in general. All partners stressed the importance of articulating clear and concrete aims for the project and highlighted the development of new service concepts utilizing 5G connectivity, as well as testing these in real-life settings or available test beds (performance measurements and validation). The partners were also interested in exploring ecosystems and business models related to 5G but were not able to specify which these would be. A secondary expectation related to the establishment of collaborative and interactive relationships with other project partners. As reasons to participate in the consortium, those partners that were engaged in 5G technology development (e.g., Nokia, Teleste) specifically mentioned understanding

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customer needs and emerging business ecosystems. Those partners who were participating in order to explore the value of deploying 5G connectivity in their own industry area (e.g., the cargo handling equipment and automated terminal solutions provider Kalmar Cargotec) wanted to understand how 5G can support internal development, what advantages it might offer them, and ultimately the role they might play in the development of 5G on a more general level. Use cases and business opportunities were also highlighted as topics to explore during the project period. Based on these interviews, the lead author compiled a list of the partners’ expectations, subsequently used to structure workshops that she facilitated during phases 2 and 3. The researcher thus facilitated the articulation of partners’ expectations, increasing their awareness of their partly different and partly overlapping goals and interests. This made it easier to identify areas of alignment and common interest for subsequent discussion. This role was supported by the researcher acting as a conduit of projectrelated information among partners in the course of regular project coordination work. During this stage, the researcher also helped draft a first version of Figure 13.2, which was an important touchstone of the project in terms of articulating different actors, technologies, and potential use cases subsequently covered in the project. The researchers thus acted as ‘innovation intermediaries’ in the open innovation scene (Chesbrough, 2006); a role related to facilitating innovation, transferring knowledge, and negotiating between parties.

Figure 13.2: Defined use cases in the WIVE project (Source: WIVE material).

Phase 2: Deploying 5G through Building Use Cases and Piloting Activities During this phase the partners developed descriptions of potential areas or services to be developed further using 5G. These descriptions were in fact use cases, created for

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further evaluation as to which technical features or enablers would be suitable to test and pilot during the project. The partners suggested a total of 21 use cases, which were categorized into three themes by the researchers, namely (i) extreme mobile broadband (eMBB), (ii) ultra-reliable low latency communication (URLLC), and (iii) massive machine type communication (mMTC). The use case descriptions were evaluated jointly by researchers and company representatives (mostly in terms of technical issues and parameters related to the test networks). Some use cases were then combined, and others were deemed to fall outside the scope of the project. The three themes were then specified further with regard to pilot case features. For example, eMBB-pilots demonstrated high data rates, low latency, virtual and augmented reality services as well as services related to public authorities; how to distribute media content using 5G became a particular focus here. URLLC pilots were characterized by high data rates, high reliability, high availability and ultra-low latency, and were connected to industry and factory areas, such as harbors. mMTC demonstrated predictive maintenance metering, wearable activity meters, low power consumption, high device density, low cost, tracking and roaming, and related foremost to powergrid and the emerging smartgrid area. The consortium jointly agreed on six specific pilots to be carried out; one in the harbor area led by Kalmar Cargotec, one related to smartgrid led by ABB, three related to media led by Digita, Finnish national broadcaster YLE, and Teleste, and one pilot related to mMTC led by Digita and Telia. Research partners were involved in all piloting activities. Concurrently with the pilots, this stage of the project also comprised four workshops coordinated and facilitated by the first co-author, partly with the support of the second co-author and other researchers. These events served as platforms during which the partners were able to assess progress made in the other themes and pilots, and jointly make sense of the learning journey they were engaged in. During the workshops, participants were also able to exchange views about issues topical to 5G, which at this time was receiving increasing public interest, and make sense of public and media reactions to 5G-related news and initiatives. During these events the researchers contributed by sharing their own findings from the interview rounds, which provided a further base for discussion.

Phase 3: Results and Realized Collaboration The six pilots were successfully executed, with public demonstrations and joint press releases. Nokia, ABB, and Kalmar Cargotec showcased one of the first realworld applications of time-critical 5G applications on electricity grid and harbor automation in two jointly conducted piloting activities. ABB and Kalmar Cargotec represent industry areas where wireless connectivity is not a core competence. Similarly, the other partners executed their joint piloting activities during the project, receiving validation and results from technical trials on the use of 5G in respective pilot case.

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As the technical trials ended, business model viability came more strongly to the fore, for example whether a particular partner firm should attempt to move to a service-focused business model with network partners providing parts of the solution. At this phase, business models were further elaborated and developed with facilitation by the researchers but were not yet put into practice by the partner firms. Another dimension of facilitation by the researchers was summarizing findings from prior phases.

6 Discussion: Projects as Open Labs for Collaboration and Outbound Open Innovation The case study on outbound open innovation shows that the following aspects are important for the success of open lab collaboration: – Articulate joint expectations among partners – Communication and collaboration are key in order to understand other partners’ needs, motives, and expectations – Outbound open innovation activities shift focus from innovation activities internally to platforms, ecosystems and innovation networks – Structured facilitation of the open innovation activities and tasks is important for reaching joint goals We contribute to open innovation literature by illuminating the dynamics of an outbound open innovation process in the context of B2B vertical collaboration. Our case emphasizes the importance of articulating joint expectations for “open lab” collaboration, and the benefits of building use cases and pilots in the next phase of the process. The WIVE use cases were presented and discussed numerous times among the project partners until consensus was reached concerning which to develop into pilot cases. This process allowed project partners to develop a joint preliminary understanding of other partners’ requirements, needs, and goals of deploying 5G technology in their business areas. The motives for B2B companies to partake in open innovation processes may differ significantly from those of consumers and individual people, meaning we know very little about them. As the focus of business is shifting towards platform economies and ecosystems, the need to study open innovation processes from the perspective of B2B verticals is increasing. The B2B sharing economy shifts the businesses mentality from ownership to access (Cusumano, 2014; Puschmann & Alt, 2016), which requires a new approach to value networks and interorganizational collaboration. In short, innovating in an open lab manner where firms seek knowledge outside their organizational boundaries, exploring and exploiting external information, is becoming the new normal. It is imperative for businesses to develop the skills needed to manage these processes and derive maximum benefit from them.

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Working together in an open lab environment can be challenging if expectations differ. Therefore, a common understanding of the joint goals and potential gains must be developed already at the very early stages of such collaboration. The process is by definition somewhat ambiguous, and our research suggests that companies do recognize several different potential roles in new markets. But further research is needed to understand how these different roles influence the activities of vertical companies in an open innovation process, and when they coalesce into positions that can be more difficult to change. In particular, we need to learn more about how a common understanding of expectations is formed, and the processes through which ideas, hopes, fears, and incomplete understandings can be articulated and concretized into use cases and pilots that bind partners together in outbound open innovation activities. Interfirm collaboration has frequently been associated with geographical proximity (cf. Knoben & Oerlemans, 2006). In this case, while most of the participating firms are internationally active, the dynamics of the case are largely restricted to the specific geographical area and jurisdiction of Finland. When a specific local context acts as a laboratory for activities related to an emerging technology, open innovation may be facilitated by the absence or minimization of, e.g., cultural, temporal, legal, and geographical barriers to knowledge sharing (see e.g., Riege, 2005). We believe that outbound open innovation in specific geographical areas, where innovations to be deployed globally are created locally, should receive more attention by researchers. Our study also emphasizes the importance of facilitating the outbound open innovation process. Our interpretation of this in-depth case study is that structured facilitation was important in focusing the activities within WIVE and articulating what kind of outputs the partner firms could realistically aim for given available resources, while still keeping up a high level of ambition in terms of novel insights and handling inevitable frustrations associated with unknown end results and partly unaligned partner interests. An active facilitating role was carried out by researchers who were full-fledged project participants in terms of receiving funding and contributing to outputs, but still neutral “outsiders” in the sense that they were not technology experts and lacked potential business interest conflicts with any partner firms. While the findings suggest that it is important to have this kind of facilitators in the open lab, there is still room for further research to validate this insight and find out how to support outbound open innovation process effectively.

7 Summary and Conclusions Outbound open innovation allows firms to share ideas with external actors and encourages these to use and develop the ideas further in their own business models. In the WIVE project, technology developers were actively sharing ideas with firms

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from other industries in order to better understand the needs and deployment areas of 5G within those industries. Concurrently, vertical industry actors were seeking to understand the potential contribution of 5G to their industry areas, organization, and internal processes in terms of flexibility, efficiency, and value creation. Outbound open innovation is difficult to manage as partners may have very different expectations. It is therefore important to establish interaction patterns where collaboration is facilitated by partners that, for instance, represent academia or another third-party actor. Firms are of course fully capable of negotiating collaboration patterns but participating in large open lab-type projects may strain the resources of individual experts and require significant input to articulate joint goals and determine partners’ roles in reaching them. Facilitators can be important in moving the collaboration forward from plans to execution and help systematize the collaboration experience. In WIVE, the partners jointly created use cases, but in a process facilitated by researchers. This shows how the industrial interorganizational relationships and collaboration patterns crucial to outbound open innovation can benefit from the role of a facilitator or innovation intermediate. Project-based collaboration thus becomes a viable model for outbound open innovation, in which firms explore information outside their organizational boundaries and third-party actors such as researchers act as facilitators of the open lab and its processes. Acknowledgements: This work was supported by Business Finland under the project Wireless for Verticals (WIVE) and the Academy of Finland (315604).

References 5G–PPP. (2016). 5G empowering vertical industries, White paper, 5G Infrastructure Public Private Partnership, European Commission. Available at: https://5g-ppp.eu/wp-content/uploads/ 2016/02/BROCHURE_5PPP_BAT2_PL.pdf Accessed December 9, 2017. Almirall, E., & Casadesus-Masanell, R. (2010). Open versus closed innovation: A model of discovery and divergence. Academy of Management Review, 35(1), 27–47. Bogers, M., Afuah, A., & Bastian, B. (2010). Users as innovators: a review, critique, and future research directions. Journal of Management, 36(4), 857–875. Bogers, M., Zobel, A-K., Afuah, A., Almirall, E., Brunswicker, S., Dahlander, L., Frederiksen, L., Gawer, A. et al. (2017). The open innovation research landscape: established perspectives and emerging themes across different levels of analysis. Industry and Innovation, 24(1), 8–40. Bogers, M., Chesbrough, H., & Moedas, C. (2018). Open innovation. Research, practices, and Policies. California Management Review, 60(2), 5–16. Bonaccorsi, A., Giannangeli, S., & Rossi, C. (2006). Entry strategies under competing standards: Hybrid business models in the open source software industry. Management Science, 53(7), 1085–1098. Brunoe, T. D., Nielsen, K., Joergensen, K. A., & Taps, S. B. (Eds.) (2014). Twenty years of mass customization – Towards new frontiers. In: Proceedings of the 7th World Conference on Mass Customization, Personalization, and Co-Creation (MCPC 2014), Aalborg, Denmark, February 4th-7th, 2014. Springer Science & Business Media.

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Bullinger, A.C., & Moeslein, K. (2013). Innovation contests: Systematization of the field and future research. In S. Dasgupta (Ed.), Studies in virtual communities, blogs, and modern social networking: Measurements, analysis, and investigations (pp. 1–14). Hershey, PA: IGI Global. Chesbrough, H. (2003). The era of open innovation, MIT Sloan Management Review, 44(3), 35–41. Chesbrough, H. (2006). Open innovation: A new paradigm for understanding industrial innovation. In Chesbrough, H., Vanhaverbeke, W. and J. West (Eds.). Open innovation: Researching a new paradigm (pp. 1–2). Oxford: Oxford University Press. Chiaroni, D., Chiasa, V., & Frattini, F. (2010). Unravelling the process from closed to open innovation: Evidence from mature, asset-intensive industries. R&D Management, 40(3), 222–245. Cusumano, M.A. (2014). How traditional firms must compete in the sharing economy. Communications of the ACM, 58(1), 32–34. Estellés-Arolas, E., & González-Ladrón-de-Guevara, F. (2012). Towards an integrated crowdsourcing definition. Journal of Information Science, 38(2), 189–200. Gassmann, O., & Enkel, E. (2006). Open innovation. Zeitschrift Führung+Organisation, 75(3), 132–138. Grant, R. M., & Baden-Fuller, C. (2004). A knowledge accessing theory of strategic alliances. Journal of Management Studies, 41, 61–84. Harhoff, D., & Lakhani, K. R. (Eds.) (2016). Revolutionizing innovation: Users, communities, and open innovation. MIT Press. Knoben, J. & Oerlemans L. A. G. (2006). Proximity and inter-organisational collaboration: A literature review. International Journal of Management Reviews, 8(2), 71–89. Lakhani, K. R., & Von Hippel, E. (2003). How open source software works: “free” user-to-user assistance. Research Policy, 32(6), 923–943. Minghetti, M. (2014). Collaborative intelligence: Towards the social organization. Cambridge Scholars Publishing. Pisano, G. P., & Verganti, R. (2008). Which kind of collaboration is right for you? Harvard Business Review, 86(12), 80–86. Prahalad, C.K., & Ramaswamy, V. (2000). Co-opting customer competence. Harvard Business Review, 78 (1), 79–87. Puschmann, T., & Alt, R. (2016). Sharing economy. Business & Information Systems Engineering, 58(1), 93–99. Raasch, C., Lee, V., Spath, S., & Herstatt, C. (2013). The rise and fall of interdisciplinary research: The case of open source innovation. Research Policy, 42(5), 1138–1151. Riege, A. (2005). Three‐dozen knowledge‐sharing barriers managers must consider. Journal of Knowledge Management, 9(3), 18–35. Tschirky, H., Escher, J.-P., Tokdemir, D., & Belz, C. (2000). Technology marketing: a new core competence of technology-intensive enterprises. International Journal of Technology Management, 20, 459–474.

Kyrill Meyer, Jörg Härtwig, and Jürgen Anke

14 An Innovation Network for Collaborative Engineering of Smart Service Systems – The LESSIE Approach 1 Smart Service Ecosystems and their Potential Service systems, in general, are defined as a “value-co-creation configurations of people, technology, value propositions” (Maglio and Spohrer, 2008, 18) and as such are social-technical systems (Böhmann et al., 2014). Connecting organization and information they “create and deliver value to providers, users and other interested entities, through service” (Barile and Polese 2010, 22). Such systems become “smart” through the integration of layers of “connected intelligence that augment the actions of individuals and organizations, automate processes, transform data, and incorporate digitally empowered systems” into the lives of people and with that, increasing the “insight into and control over the tangible world” (Demirkan et al., 2015, 734). More technically, those systems will require information technology (Beverungen et al., 2017) to generate input including autonomous data acquisition using sensors, means of communicating data using different networks, aggregation and interpretation of data using software and providing value through services that connect to the consumer in the real world (Figure 14.1). Therefore, in many cases, there will also be an integration with physical products (Porter und Heppelmann, 2014) as an external factor. For some time, value creation in service ecosystems is part of the scientific discussion (Kaartemo et al., 2017). Service ecosystems are defined as “relatively self-contained, self-adjusting system(s) of resource-integrating actors connected by shared institutional arrangements and mutual value creation through service exchange” (Vargo and Lusch, 2016, 10–11), while at the same time being influenced by internal and external factors (Chapin et al., 2011). Smart services within service ecosystems contribute to this view in different aspects: First, smart services are the result of complex interactions in (inter-)organizational workflows and business processes with a high degree of digital interaction, increasingly relying on technologies such as the Internet of things (IoT), Internet of value (IoV) or blockchain to realize data interchange and build trust among parties. Therefore, they are providing a certain degree of self-containment and self-governance. Second, smart services facilitate value co-creation improving collaboration within the desired context using the internet as well as offer the Kyrill Meyer, Jörg Härtwig, Institute for Digital Technologies IFDT, Leipzig, Germany Jürgen Anke, HTW Dresden, Germany https://doi.org/10.1515/9783110633665-014

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Smart Service Ecosystems Service Layer: Applications, Services and Combinations

Software Layer: Data Management, Cloud Services, Algorithms

multiple providers

Technical Layer: Sensors, Networks, Devices

multiple consumers (B2B/ B2C)

Figure 14.1: Layers of smart service ecosystems.

possibility for innovation through the adjustment to the value propositions on the different layers of the system. Smart services within such ecosystems can take on different forms. Existing services of providers can be transformed into digital services (e.g. location-based shuttle services using an app instead of a taxi stand), the servitization of products (e.g. not selling the machine, but selling the availability together with a set of further services that can gradually enhance the usability over time) or building completely new smart services (e.g. blockchain-based trading of energy between households). Different dimensions for the potential of smart service ecosystems have been named so far in the scientific literature. They can be differentiated between social/ecologic and economic benefits if compared with a status quo (Table 14.1, list not conclusive). Table 14.1: Potential benefits associated with smart service ecosystems. Social and ecologic benefits

Economic benefits

Location-based use through networks allow consuming the service where needed (e.g. Anke et al., ; Schäfer et al., ; Allmendinger and Lombreglia, )

Cost reduction and scalability through automation and multiple uses of functionality (e.g. Anke et al. ; S. J. Clement et al., ; Allmendinger and Lombreglia, )

Context-dependent adaptation of service provision (Schäfer et al., )

Possibility for new markets, customers and business models or alternative value generation (e.g. Anke et al. ; Zolnowski and Böhmann ; Valamuri et al. )

Changes in customer orientation, participation, and networking between the participants of the ecosystem (Anke et al., )

Adaptability of the services due to changing parameters of the market (Paluch, )

Higher sustainability score (Doualle et al., )

Possibility for shorter innovation cycles

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2 Challenges for Engineering and Innovation of Smart Services Providing value through a smart service as part of ecosystems is not a trivial task. The systematic engineering of smart services requires knowledge about the structure of the ecosystem and the roles of the participants, the different services within the ecosystem and their value proposition, and a precise understanding about the components in the different layers that help execute the services and make them smart. Innovators will require knowledge or building blocks for smart service innovation from different sources internal or external to the ecosystem of the services. It is becoming apparent that in many cases a key role will be played by those companies that succeed in building, operating, controlling and continuously developing Smart Service Ecosystems (Deutsche Akademie der Technikwissenschaften, 2015), even though more decentralized approaches such as e.g. decentralized autonomous organizations (DAOs, see Chohan, 2017) could provide alternatives. Accordingly, an approach for the development of smart services in ecosystems needs to address different aspects: – A general understanding of the setting of the ecosystem needs to be formed to develop an understanding of where it can provide innovative solutions and add value to someone. – The way and form to connect the different partners is to be designed and will require the value propositions, ways of collaborating, communication channels and interaction relationships as well as coordination mechanisms and roles within the ecosystem (Schäfer et al., 2015). – Understanding, developing and integrating the necessary technical expertise in the form of hardware and software. This will require sensors and actuators, embedded and networked devices as well as data analytics, big data and cloud solutions as well as software frameworks to provide e.g. blockchain or AI-components. – Designing the interaction in the social-technical system, especially the humancomputer interaction as well as considering social aspects for the workforce that should be supported through the use of IT and not be overwhelmed (Meyer et al., 2018). In general, a number of challenges can be identified for the systematic development to establish and maintain Smart Services (Anke et al., 2018): – Interdisciplinary cooperation: The competencies of different disciplines and experts must be coordinated for the development of the individual components on the different layers in order to realize the ecosystem. – Integration of innovation and development processes: Smart services as innovations provide value but come with high uncertainty about customer needs, willingness to pay and market acceptance. Therefore, setting up a complex ecosystem can

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easily result in a failure as there is a lack of evidence-based design knowledge for smart services (Satzger et al., 2010) and the ecosystems they form. – Different lifecycles of the individual components: While software components and cyber-physical sensors/actors can be changed relatively quickly, larger physical components, e.g. a machine, remain in operation for a long time. Likewise, organizational processes are often not customizable quickly because of the effort required for change management. Since smart services consist of elements of these categories, they also influence the time to market.

3 The LESSIE Approach: Collaborative Smart Service Innovation To address the challenges discussed in the previous chapter, any approach will have to consider the cooperative and interdisciplinary facets smart service systems are associated with. With the continuing importance of services in most developed economies over the last decades, significant efforts can be observed in research on how to understand and develop services systematically. With these, a general distinction between aspects of conceptual design and technical implementation has emerged. Those aspects can also be considered as relevant for the development of smart service ecosystems. While the conceptual focus is primarily on the logic of value creation, business models and service development from a marketing perspective, the technical implementation considers the hardware and software required and, if necessary, their integration into physical systems. Several methods for the design and engineering have been proposed and can contribute towards the engineering of smart services (see examples in Table 14.2). They guide in the systematic design of services in one or more aspects and are mostly generic, meaning they can be applied to different types of services in a variety of industries and markets. From a user’s perspective, their existence will not necessarily lead to an easy application and adoption for smart service systems: Certain required aspects may not be covered and an easy start in a network of actors may not be found quickly. The key question that the authors have been concerned with as part of their work with different parties in the development of such services is the following: How to find a lightweight entry point to explore potential applications and opportunities of smart services and set up the ecosystem step-by-step using use suitable methods. For this, the authors have been working with an approach called LESSIE in pilot use-cases and intend to explore the possibilities further. LESSIE is focused on the interaction between businesses as part of smart service systems (B2B), integrates technical and business expertise and also considers contributions from research institutions. As a methodological approach, LESSIE is a mediator that considers existing

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Table 14.2: Examples for methods that can be applied for smart service engineering. Design/conceptual aspects of (smart) services

Technical aspects of (smart) services

System/Ecosystem aspects of smart services

Service Design & Engineering (Richter and Tschandll, ; Meyer and Böttcher, ); New Service Development (Daun and Klein, )

Integration Engineering (Thränert, ISE – Methodology ) (Kett et al., )

Business Process Modeling (Reijers, )

Requirements Engineering (Kotonya and Sommerville, )

Motion (Sehmi and Schwegler, )

Design Thinking (Brown, ) and Innovation management

Software Engineering, especially agile approaches such as SCRUM (Schwaber, )

Smart Service Canvas (Pöppelbuß and Durst, )

Business Engineering (Österle and Winter, )

Service-oriented Migration and Reuse Technique (Lewis et al., )

System Engineering for CPS (Marilungo et al., )

Product Service System Engineering (Cavalieri and Pezzotta, )

Service-Oriented Analysis and Service-oriented Design (Zimmermann et al., ) Modeling and Architecture (Arsanjani, )

Business Model Innovation for Product-Service Systems (Barquet et al., )

methodologies feasible to develop the components on the different layers of the smart service ecosystem. Fundamentally, it is based on the idea of bottom-up-innovations in a region (Meyer und Thieme, 2010) with application fields and experts in fields such as mechanical engineering, connected and embedded devices, sensors, software engineering, data analytics, business development, design, marketing. These disciplines are available in general but not connected in the necessary form. As a mediation structure, the LESSIE network is the basis for cooperation and provides access to a “regional innovation pool” in which ideas can be discussed, explored and opportunities for cooperation can be tested (Figure 14.2). It is important to note that with this structure, there is only a light commitment to engage in open innovation necessary on behalf of the participants, making the approach lightweight from an engagement perspective. The LESSIE approach is working with several steps to develop smart service ecosystems (Figure 14.3). It is important to note that these steps do not constitute a simple linear process – instead they form the basis for an iterative and agile development model that works through the different layers of the ecosystem and will require different specialists at different times of the development. While the process

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New or improved services

– Technical Expertise – Market Knowledge – Customers – Ideas – Problems – Demands

Business Partners: Companies, Experts

Exploring Innovation in Smart Service Ecosystems

LESSIE Network/LAB

Regional Innovation Pool

LESSIE Management

Systematic Knowledge Development

– Methods and Tools – Service Engineering Requirements – New technologies – Ideas

Scientific Partners: Universities, Research Institutions

Figure 14.2: LESSIE as mediator in smart service ecosystems.

Creating awareness for Smart Service Ecosystems

Idea scouting and intake of impulses

Lightweight exploration of processes and resources

Evaluation and conceptualization

Develop and stabilize the layers of the ecosystem

Evaluate sustainability and adapt

Figure 14.3: Steps of the LESSIE approach.

is intended to be very open at first, it can remain that way during the further stages or transform into a more closed process controlled through one or a few participants when it comes to the implementation of vital components for the ecosystem. The different steps can be explained in short as follows: 1. Creating awareness for Smart Service Ecosystems: Every innovation process needs to start with some preparation. For Smart Service Ecosystems it is vital that before engaging into any development activities, the management of a company or a group of participants forms a common understanding about what smart services could mean in their respective context and get an idea of the opportunities or risks in forming business models within an ecosystem. In addition, it is necessary to talk about responsibilities and resources: Who should (backed by the management) engage in the development activities and what initial resources can be allocated? While at this stage this does not mean detailed financial planning, it makes clear that every innovation activity will require time and money as well as engaged personnel.

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Idea scouting and intake of impulses: Within this stage, it is all about generating ideas and identifying potential innovations. While this is facilitated in workshops for the generation of new ideas using creativity methods, in many cases it can also be about changing the perspective on the current business and rethinking value propositions. Often hunches about what could be done or explored to improve the current business linger around and can be taken in as impulses for a next exploration step: What technology could support, what information is needed, how could processes be improved? 3. Lightweight exploration of processes and resources: This stage is the most important step of the LESSIE approach. It uses explorative settings such as hackathons (Briscoe, 2014), thinkathons and service jams (Knoll, 2018) as a testing stage for the requirements of the smart services on the different layers. Depending on the idea, it can contribute to the clarification of processes and dependencies and be a first step in the definition of the roles different participants will need to take on. At the same time, it provides an early stage for the necessary infrastructure and software and helps to better understand the technical components of the system. 4. Evaluation and conceptualization: The next step in the engineering of smart service innovation works with the results of the exploration process to evaluate the feasibility, provide a more specific concept and the timeline for a realization and estimates again the resources and efforts that different participants are able and willing to provide. It acts as a stage-gate and can result in stopping the process. Also, it can be decided to continue the exploration process for a different aspect of the existing idea, meaning that other components within the layers of the system are taken into more careful consideration. 5. Develop and stabilize the layers of the ecosystem: Given the development timeline and resources, during this step the actual implementation of the ecosystem takes place. The different required components on the layers of the system are implemented and linked and the service offerings are launched to the market. While the goal needs to have the complete ecosystem ready for the service, it might be easier to develop an initial ecosystem setup and continue exploring different value propositions or technological aspects. As a social-technical system is being set up, efforts will not only include the implementation, e.g. the software engineering but will also require elements of change management and market preparation as a means to stabilize the ecosystem. 6. Evaluate sustainability and adapt: The last step of the approach is aimed at the continuous improvement and use of an ecosystem after its initial implementation. It consists of an overall evaluation of the implementation process with respect to the initial goals and will help with adapting the system using the mechanisms provided as part of the engineering. 2.

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Overall, the LESSIE approach provides the possibility to explore aspects of smart services at an early stage and guides towards an agile process for evolutionary engineering. While the initial steps 1–3 can be conducted within a timeframe of only a few weeks per iteration, the other steps of the process will in many cases require more effort or time. Overall, the level of sophistication will increase with each iteration that takes place (Figure 14.4).

Smart Service Ecosystem Development

Service Layer

Exploration

Awareness/Ideas

Exploration

multiple providers

Software Layer

Further exploration

Technical Layer

multiple consumers (B2B/ B2C)

Figure 14.4: Steps of the LESSIE approach.

4 First Experiences and Further Work So far, the LESSIE approach has been used within the exploration of smart service opportunities using the blockchain technology. Supported by external advisers, different companies have been forming ideas as challenges for exploration within hackathon events. In groups with heterogeneous skillsets, these ideas were explored, and solutions were implemented on service, software, and technical layers. The result has then been presented to the companies for further development. This included companies which employed the exploration teams further within their internal engineering process and others that returned with different aspects for exploration in an upcoming event. Some of the still early observations include that many companies have a good understanding as to where in the context of their business smart services could be implemented and would provide value. In many cases, technology exploration and aspects of cooperation are an important part and the partners are more than willing to provide the resources for this kind of lightweight exploration. At the same time, setting the stage to form an understanding regarding the efforts needed to develop successful smart services and act within an ecosystem seems to be rather challenging. Further efforts will go into the structuring of a local innovation pool to employ the LESSIE approach and provide the necessary expertise to different industries.

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This might also be supported through competence matching platforms, which help to identify relevant expertise for a given project or innovative idea. In doing so, the connection between the different stages of the LESSIE approach can be strengthened as well. Smart services, in general, provide many opportunities for providers of such services as well as companies helping them to build and run these. At the same time, many companies are unaware of these potentials or unsure of how to exploit them. Therefore, it is important for the LESSIE approach to provide orientation and guidance for the various roles and required competencies in smart service systems. To further support the collaborative innovation for smart service engineering, we also see the need for accompanying research to measure economic benefits for participants. As a general trend, we can observe that many companies struggle with translating possibilities resulting from new technological and digital solutions into business opportunities. For smart services, a number of methodologies can support to some extent but have not found their way into use with the companies that would profit from their application. Here, the LESSIE approach will provide support.

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Reijers, H.A. (2003). Design and control of workflow processes. Business process management for the service industry. Berlin: Springer (Lecture Notes in Computer Science, 2617). Available at: http://www.loc.gov/catdir/enhancements/fy0817/2003045661-d.html. Richter, H.M., & Tschandl, M. (2017). Service Engineering – Neue Services erfolgreich gestalten und umsetzen. In: Bruhn, M. and Hadwich, K. (Eds). Dienstleistungen 4.0: Konzepte – Methoden – Instrumente. Band 1. Forum Dienstleistungsmanagement. Wiesbaden: Springer Fachmedien Wiesbaden, 157–184. Available at: https://doi.org/10.1007/978-3-658-17550-4_7. Satzger, G., Ganz, W., Beck, R., Benkenstein, M., Bichler, M., Bienzeisler, B. et al. (Eds.). (2010). Auf dem Weg zu einer Service Science – Perspektiven, Forschungsthemen und Handlungsempfehlungen aus der Sicht einer interdisziplinären Arbeitsgruppe. Empfehlungen an die Taskforce Dienstleistungen im Rahmen der Forschungsunion Wirtschaft–Wissenschaft. Stuttgart: Büro der Taskforce Dienstleistungen. Available at: http://web.achive.org/web/ 20140912202811/www.3sresearch.de:80/wp-content/uploads/weg_service_science_191010_ web(1).pdf. Schäfer, T., Jud, Ch. & Mikusz, M. (2015). Plattform-Ökosysteme im Bereich der intelligent vernetzten Mobilität: Eine Geschäftsmodellanalyse. HMD, 52 (3), 386–400. Schwaber, K. (2004). Agile project management with Scrum. Microsoft press. Sehmi, A. & Schwegler, B. (2006). Service-oriented modeling for connected systems-part 1. The Architecture Journal 7 (1), 33–41. Thränert, M. (2009). Integration-Engineering. Zugl: Leipzig, Univ., Diss., 2008. LIV, Leipzig. Velamuri, V.K., Bansemir, B., Neyer, A.K., & Möslein, K.M. (2013). Product Service Systems as a driver for business model innovation: Lessons learned from the manufacturing industry. International Journal of Innovation Management, 17 (01), 1340004. Vargo, S.L., & Lusch, R.F. (2016). Institutions and axioms: an extension and update of servicedominant logic. Journal of the Academy of Marketing Science, 44(1),5–23. Zimmermann, O., Krogdahl, P., & Gee, C. (2004). Elements of service-oriented analysis and design. IBM developerworks. Available at: https://www.ibm.com/developerworks/library/ws-soad1/. Zolnowski, A., & Böhmann, T. (2013). Veränderungstreiber service-orientierter Geschäftsmodelle. In: Böhmann, T., Warg, M. and Weiß, P. (Eds.). Service-orientierte Geschäftsmodelle. Erfolgreich umsetzen. Berlin: Springer Gabler, 31–52.

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15 Driving Service Productivity of Open Innovation Labs 1 Introduction Following the paradigm of open innovation, organizations should harness ideas and knowledge from users and other relevant actors to successfully develop new or improved products and services (Bogers, Chesbrough, & Moedas, 2018; Chesbrough, 2003; Huff, Möslein, & Reichwald, 2013). In this regard, open innovation can be considered as “a distributed innovation process based on purposively managed knowledge flows across organizational boundaries” (Chesbrough & Bogers, 2014, p. 17). Increasingly, these knowledge flows are realized by means of dedicated Open Innovation Laboratories (OI-Labs) like JOSEPHS® that serve as a physical platform connecting different actors for collaborative research and development (R&D) initiatives (Roth, Fritzsche, Jonas, Danzinger, & Möslein, 2014; Schmidt & Brinks, 2017). Often, these initiatives are offered as knowledge-intensive business-services connecting different actors to carry out development activities such as prototyping and testing, technical validation, or system integration (Grotenhuis, 2017). Moreover, these services are networked. From the perspective of the customer (i.e., an organization seeking innovation-related insights), two or more entities (e.g., the operator and individuals providing feedback) are required for delivering the connected, overall service (Tax, McCutcheon, & Wilkinson, 2013). Focusing on service interactions in the context of OI-Labs, current questions of service management arise. For instance, practitioners may ask themselves how to systematically measure and enhance the productivity of networked service delivery – a theme which is considered critical for sustained operational success but so far lacks conceptual clarity and operational approaches (Ostrom, Parasuraman, Bowen, Patricio, & Voss, 2015). The contribution at hand aims to support practitioners in this regard. First, it provides a conceptualization of productivity in the context of an OILab like JOSEPHS®. Subsequently, it presents several insights derived from a design science research (DSR) study addressing the systematic improvement of productivity. Finally, managerial implications are highlighted.1

1 This contributions adopts the findings derived throughout the dissertation project of the first author Christofer Daiberl. It summarizes key results and translates them into the management of an OILab. A more detailed, empirically-based elaboration and presentation of the contents discussed can Christofer F. Daiberl, JOSEPHS GmbH, Nuremberg, Germany Angela Roth, FAU Erlangen-Nuremberg, Chair of Information Systems – Innovation and Value Creation, Nuremberg, Germany https://doi.org/10.1515/9783110633665-015

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2 What is Service Productivity of an Open Innovation Lab? In general, service can be considered as a special type of interactive production process relying on customer inputs for execution (Sampson, 2010; Sampson & Froehle, 2006). In this regard, customers are the individuals or entities that decide if one or more providers should receive a monetary or non-monetary compensation for service delivery (Sampson, 2012; Sampson & Froehle, 2006). For example, in the context of JOSEPHS®, the customer (i.e., an innovating organization hiring JOSEPHS® to gain innovationrelated knowledge) has to provide inputs (i.e., tangible and/or intangible resources) such as capital, background information to the R&D project, research questions, prototypes, ideas. Depending on these inputs, the intermediary (i.e., the JOSEPHS® staff) supports the particular prototyping and testing initiative and potential users (i.e., visitors of JOSEPHS®) provide feedback on these prototypes. In doing so, all of these actors interactively influence process outputs (e.g., user feedback, monetary compensation). Against this backdrop, service productivity is often considered as an efficiencyfocused concept and defined as the ratio of service outputs to inputs over a particular period of time (Johnston & Jones, 2004; Parasuraman, 2010). However, this definition is rooted in industrial productivity thinking and it remains unclear how to assess service inputs and outputs in an objective manner (Biege, Lay, Zanker, & Schmall, 2013; Djellal & Gallouj, 2013; Yalley & Sekhon, 2014). Yalley and Sekhon (2014) have proposed to overcome an exclusive focus on objective measures and focus on service outcomes as a more relevant consideration point for service productivity management. Service outcomes encompass the direct and indirect effects of service delivery which can be measured by stakeholder satisfaction at a given point in time (Yalley & Sekhon, 2014). Moreover, the authors substantiate previous claims that in order to be a meaningful concept, service productivity should encompass both efficiency and effectiveness dimensions (Bessant, Lehmann, & Möslein, 2014; Grönroos & Ojasalo, 2004; Yalley & Sekhon, 2014). Whereas efficiency relates to how well inputs are utilized for a production process, effectiveness relates to the degree of goal attainment from a particular actor’s point of view (Tangen, 2005). Whereas most service productivity models and frameworks are concerned with the perspective of a focal provider and/or the customer, recently it has been highlighted to consider a network perspective for service productivity research (Ostrom et al., 2015). Following the concept of the service delivery network (SDN), services are increasingly delivered by two or more co-providers (Tax et al., 2013). Thus, it is not enough to consider productivity from a monadic (i.e., a single provider)

be found in the related conference papers (Daiberl, Naik, & Roth, 2018; Daiberl, Roth, & Möslein, 2016a; Daiberl et al., 2016b) as well as the dissertation itself (Daiberl, 2020). Some parts remained verbatim and unchanged.

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or dyadic perspective (i.e., provider and customer). As highlighted above, the results and thus the success of each R&D project at JOSEPHS® is, among others, dependent on interactions between the customer, the staff of the intermediary, and visitors of JOSEPHS®. Each of these actors has its individual productivity perspective which is dependent on its service-related interests, values, resources, understanding and experience, as well as their service-specific expectations (Daiberl, Roth, & Möslein, 2016b). Considering the importance of collective performance for sustained operational success (Gummesson, 2007; Hillebrand, Driessen, & Koll, 2015; Verleye et al., 2017), it is vital that service delivery is productive for the focal provider, customers, and relevant co-providers alike. Following these arguments, the productivity of an OI-Lab is conceptualized as a subjective, dynamic, and multi-level phenomenon integrating both efficiency and effectiveness considerations, as presented in Figure 15.1. In order to stress the networked nature of interactions, it is coined networked service productivity (NSP). On the individual level, NSP is defined as a particular actor’s satisfaction with effects of the OI-Lab (e.g., innovation insights, experience, financial gains, marketing results new business partners) at a given time considering its individual inputs (e.g., capital, time, effort, knowledge). A particular actor’s individual NSP influences NSP on the aggregated level addressing the perceptions of all relevant actors required to successfully realize the open lab in the long term (Daiberl, 2020).

Figure 15.1: Service productivity of an OI-Lab (based on Daiberl, 2020).

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Having proposed the concept of NSP as a performance measure for the productivity of an OI-Lab, next, insights from a DSR study aiming for its systematic enhancement in are presented (for a more detailed description of the study, the developed artifacts, and their design see Daiberl, 2020).

3 Improving Networked Service Productivity of an Open Innovation Lab: Insights from Design Science Research Study For building and evaluating an approach that is suitable for systematically improving NSP in the context of an OI-Lab, a design science research study (DSR) was conducted (Daiberl, 2020). Being prominent in the field of information systems, DSR is an iterative process to design and evaluate socio-technical artifacts that not only solve practical problems but also contribute to theory (Gregor & Hevner, 2013; Hevner, March, Park, & Ram, 2004; Peffers, Tuunanen, Rothenberger, & Chatterjee, 2007). Artifacts are broadly defined and can address a broad range of designed objects such as specific software products (i.e., instantiations), techniques and algorithms (i.e., methods), semantics (i.e., models) or abstract design theories (Gregor & Hevner, 2013). For developing an artifact, the researcher draws on existing theoretical foundations (Hevner et al., 2004). This research on networked service productivity follows the DSR process of Peffers et al. (2007) and included the following activities: Initially, (Step 1) the problem had to be delineated and the value of a developing a solution had to be highlighted (Peffers et al. 2007). As described above, there is still a lack of approaches that can support the intermediary operating an OI-Lab to enhance the productivity of networked service delivery. Considering the increasing prevalence of OI-Labs and other instances of networked service delivery (e.g., Tax et al., 2013), this can be considered as an important issue which should be addressed by new solutions. Second (Step 2), the objectives of the solution had to be defined (Peffers et al., 2007). In order to solve the problem, the solution should support the intermediary of an OI-Lab to systematically and iteratively identify and analyze opportunities to enhance aggregated NSP (Objective 1). Next, the solution should integrate the OILab operator, customers, and relevant co-providers into the identification and analysis of these opportunities (Objective 2). In doing so, it shall be ensured that their individual perspectives on NSP are adequately considered. Moreover, the solution should support the operator of the OI-Lab to systematically and iteratively intervene in order to enhance aggregated NSP (Objective 3). Third, (Step 3) the solution had to be designed and developed. For enhancing aggregated networked service productivity, it is considered necessary to holistically

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model the current situation of networked service delivery both from the customers’ and from an operational point of view. Afterward, these models can be analyzed and suggestions for future improvements can be derived. For this purpose, complementary approaches from service design, information systems, and engineering were synthesized and extended as summarized next: – For modeling the customer’s point of view on networked service delivery, the solution draws on the customer journey modeling language (CJML) (Halvorsrud, Haugstveit, & Pultier, 2016; Halvorsrud, Kvale, & Følstad, 2016; Halvorsrud, Lee, Haugstveit, & Følstad, 2014). In CJML, touchpoints, i.e., discrete interactions between the customer and the different co-providers, represent the basic unit of analysis. They are illustrated as a circle labeled with a temporal identifier (T0, T1, etc.). In this context, the boundary color of each circle visualizes its initiator and within each circle a symbol is applied to represent the physical or virtual channel mediating the touchpoint (Halvorsrud, Haugstveit, et al., 2016; Halvorsrud et al., 2014). – To model the operational perspective on networked service delivery, a modified version of the work system snapshot (WSS) is adopted (Alter, 2013). A work system is “a system in which human participants and/or machines perform work (processes and activities) using information, technology, and other resources to produce specific products/services for specific internal and/or external customers” (Alter, 2013, p. 75). In this regard, a working system that is only applied for service delivery can be coined a service system (Alter, 2012). Typically, a WSS of a service system would summarize its (1) customers, the (2) services generated for their benefit, as well as the (3) processes and activities that are carried out for this purpose. Furthermore, it details the human (4) participants, (5) technology, and (6) information necessary to realize these processes and activities (Alter, 2013). For the proposed solution, such snapshots are created to illustrate the service system underlying each touchpoint. In order to foster a more holistic operational understanding, besides the system elements mentioned above, (7) other critical resources (e.g., money, physical goods) required for service delivery are depicted as well. – In order to analyze respective models, a modified version of the failure modes and effects analysis- (FMEA) based portfolio approach to service productivity improvement is applied (Geum, Shin, & Park, 2011). The approach extends traditional FMEA from the field of engineering to service research. For this purpose, initially, a flowchart is prepared to illustrate the different steps of the overall production process. Afterward, each step is analyzed to identify, assess, and eliminate failure modes, i.e., occurrences that may reduce service productivity. Moreover, the process is analyzed to identify and assess innovation modes, i.e., opportunities to enhance service productivity. For evaluation, a formalized portfolio approach is adopted. Whereas each failure mode is analyzed according to its “severity” and probability of “occurrence”, each innovation

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mode is assessed according to its expected positive “impact” and “feasibility” of implementation (Geum et al., 2011). So far, this approach has only been demonstrated in dyadic interactions between the customer and one provider. For the proposed solution it is extended to a networked context by considering failure and innovation modes for all actors required to realize service delivery. Finally, (Step 4) the solution had to be evaluated. This contribution presents the findings of an artificial evaluation to assess its efficacy (Venable, Pries-Heje, & Baskerville, 2016). In doing so, the goal is to establish how well the solution can theoretically achieve its goals, without applying it to a real situation (Prat, Comyn-Wattiau, & Akoka, 2015). For this purpose, a criteria-based analysis was conducted in the form of logical reasoning (Prat et al., 2015; Sonnenberg & vom Brocke, 2012), Next, the proposed solution is introduced before presenting the results of its artificial evaluation.

4 The Networked Service Productivity Improvement Technique Following the DSR process described above, the networked service productivity improvement technique (NSPIRET) was developed in an iterative manner (this chapter summarizes the beta version of NSPIRET as presented by Daiberl, 2020). It comprises three interrelated artifacts: (Aritfact 1) A process of improving aggregated NSP called “NSPIRET Navigator”, (Artifact 2) a dedicated modeling approach called “NSPIRET Snapshotting”, and (Aritifact 3) a web-based feedback tool called “NSPIRET Shoutbox”. Next, these artifacts are depicted in more detail, considering the intermediary of an OI-Lab as the focal provider.

NSPIRET Navigator: Process of Improving Aggregated NSP As depicted in Figure 15.2, the NSPIRET Navigator comprises four iterative phases: First, throughout the (1) facilitation phase, supportive activities are conducted. Together with the decision-makers of the intermediary, an NSP improvement team is set up to determine objectives, responsibilities, as well as procedures (F1). Moreover, team members set-up and maintain the web-based feedback tool (F2) and invite respondents of the different network actors to share their productivityrelated feedback (F3). Second, throughout the (2) (re-)modeling phase, team members collect required data, e.g., by means of interviews and observations, and harness NSPIRET Snapshotting to depict the customer journey (M1). The latter is visualized as a series of expected touchpoints of the OI-Lab customer with the focal provider and other

Figure 15.2: Conceptual structure of NSPIRET navigator (Daiberl, 2020).

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co-providers (Halvorsrud, Kvale, et al., 2016). Next, for each touchpoint, the underlying service system is depicted (M2) (Alter, 2013). Furthermore, for each actor, expected benefits are summarized to support the NSP improvement team when planning productivity-related interventions (M3). Third, throughout the (3) discovery phase, opportunities to improve aggregated NSP are mutually identified and evaluated. For this purpose, representatives of the different network actors contributing to the OI-Lab utilize the NSPIRET Shoutbox to communicate failure and innovation modes identified and rate their individually perceived severity and impact (Geum et al., 2011) (D1). Afterwards, the team members use the NSPIRET Shoutbox to review, clarify, and edit the failure and innovation modes and evaluate their likelihood of occurrence and feasibility respectively (D2). Finally, throughout the (4) intervention phase, the results of the discovery phase are utilized and team members decide upon purposeful interventions after reflecting upon their effects on aggregated NSP (I1). Subsequently, team members initiate re-modeling and intervene according to the responsibilities and procedures defined in the facilitation phase (I2). Respective activities are recorded in the NSPIRET Shoutbox to ensure traceability (I3) and results of interventions are communicated with the actors affected (I4).

NSPIRET Snapshotting: Modeling of Networked Service Delivery For modeling networked service delivery from both the customers’ as well as from an operational point of view, the NSPIRET Snapshotting approach is proposed (see Fig. 15.2). This approach represents in its first row which period of the customer journey is considered for the snapshot. In the second row, the touchpoints of the respective period are illustrated, building on the CJML. Within an NSPIRET Snapshot, several alternative channels can be depicted within a dotted frame. In doing so, the goal is to make modeling more flexible as for each touchpoint a range of different channels may be relevant. For instance, the customer of an OI-Lab may initially learn about the possibilities of prototyping and testing (i.e., touchpoint) via a dedicated website (i.e., digital channel) or in a personal discussion with representatives of the operator on-site (i.e., physical channel). Next, from row three onward, the service systems underlying the different touchpoints are summarized, adopting a modified version of the WSS introduced above. For this purpose, first, the particular service is highlighted. It summarizes what the respective service system intends to deliver for the customer under consideration. Afterward, the corresponding processes and activities, participants, information, technologies and other critical resources (i.e., operational inputs for networked service delivery) are listed. Finally, the expected benefits of the different network actors contributing to networked service delivery are summarized. An exemplary excerpt of a snapshot developed for an OI-Lab is illustrated in Figure 15.3. It depicts two

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Figure 15.3: Excerpt of an NSPIRET snapshot developed in the context of an OI-Lab (see also Daiberl, 2020).

touchpoints of the core service period and parts of the corresponding service systems. For illustrative purposes, the representation purposefully remains on an abstract level and may be described in greater detail depending on the contextual demands.

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NSPIRET Shoutbox: Web-based Feedback Tool As described above, the NSPIRET Shoutbox is a tool to foster the participatory identification and evaluation of opportunities for enhancing aggregated NSP. It is instantiated as a responsive web-application. In the following, the tool is briefly described according to the interactions carried out by the representatives of the different networks actors as well as by the NSP improvement team: – Representatives of the OI-Lab operator, customers, visitors and other co-providers access the NSPIRET Shoutbox via an internet browser. On the landing page, as depicted in Figures 15.4 and 15.5, they can report and rate any occurrences reducing NSP (i.e., failure modes) and/or ideas for improving NSP (i.e., innovation mode) from their point of view. For rating the “severity” of failure modes and “impact” of innovation modes, team members select the smiley best reflecting their individual perceptions (Geum et al., 2011). – The NSP improvement team accesses the admin panel of the NSPIRET Shoutbox using an individual log-in. The responsible team member reviews and edits the innovation and failure modes which includes the correction of typos and selection of corresponding service system elements (i.e., sources of failure and innovation modes) and touchpoints. Moreover, individual contributions are grouped in case they describe the same problem or opportunity to enhance NSP. Subsequently, the responsible team member assesses a failure mode’s likelihood of future “occurrence” and an innovation mode’s “feasibility” of implementation resulting in respective portfolios as visualized in Figures 15.5 and 15.6. Within the portfolios, failure and innovation modes are visualized as color-coded dots, whose diameter increases the more individual contributions are grouped together. The higher the overall ratings for severity and occurrence in the context of failure modes and impact and feasibility in the context of innovation modes, the higher the priority to intervene (Geum et al., 2011). Depending on the priority, the NSP improvement team plans an intervention within the admin panel of the NSPIRET Shoutbox.

5 Theoretical Efficacy For evaluating the theoretical efficacy of NSPRIET, a criteria-based analysis was conducted in the form of logical reasoning (Prat et al., 2015; Sonnenberg & vom Brocke, 2012). In doing so, the objectives of NSPIRET are compared to the corresponding considerations of its design. As a result, design boundaries are made explicit which point out potential limitations and thus need to be taken into consideration for realworld application in the context of an OI-Lab (see also Daiberl, 2020).

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Figure 15.4: Screenshot of sharing a failure mode using the NSPIRET shoutbox (see also Daiberl, 2020).

The first objective of NSPIRET is concerned with the systematics of iteratively identifying and analyzing opportunities for improving aggregated NSP in the context of an OI-Lab. For this purpose, the NSPIRET Navigator proposes the facilitation, (re-)modeling and discovery phase each comprising a set of operational activities. However, whereas these activities serve as a general orientation for application, it is still necessary that each NSP improvement team specifies internal processes in line with the context-specific demands of a particular OI-Lab.

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Figure 15.5: Screenshot of sharing an innovation mode using the NSPIRET shoutbox (see also Daiberl, 2020).

Concerning modeling, NSPIRET Snapshotting integrates the CJML and the WSS to derive a structured representation of the expected touchpoints and the respective service systems. Such a visualization serves as a “common denominator” which supports rigor and formalism in the following analysis activities (Halvorsrud, Kvale, et al., 2016). However, NSPIRET Snapshotting does not focus on individual customer journeys. Hence, not all possible interactions are depicted. Furthermore, for each application the appropriate level of detail on which service system elements are described needs to be identified. For the identification and evaluation of productivity improvement opportunities, the NSPIRET Navigator follows the general procedure and rating scales of the FMEAbased portfolio approach which are transferred into the design of the NSPIRET Shoutbox. Resulting portfolios of failure and innovation modes are utilized by the NSP improvement team to inspire purposeful changes to the current service systems

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Figure 15.6: Screenshot of an exemplary portfolio of failure modes depicted in the NSPIRET shoutbox (see also Daiberl, 2020).

Figure 15.7: Screenshot of an exemplary portfolio of innovation modes depicted in the NSPIRET shoutbox (see also Daiberl, 2020).

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in order to enhance service productivity for the different network actors. In this context, portfolio management can support managerial focus and rational decision making in terms of resource allocation. However, in case conflicting demands of the different network actors are identified, the NSP improvement team has to reflect upon the multisided effects of possible interventions as trade-offs are inevitable. The second objective of NSPIRET is meant to ensure that the individual productivity perspectives of network actors are actually considered by the NSP improvement team. For this purpose, the different relevant network actors identify and evaluate failure and innovation modes themselves. NSPIRET Snapshotting depicts both, the customer and the operational perspective on networked service delivery. A challenge in this context is that that successful application requires the NSP improvement team to enable and motivate network actors to explicitly share their productivity-related feedback with respect to the OI-Lab. In order to facilitate contributions, the NSPIRET Shoutbox is implemented as a web-based tool improve the efficiency of interactions (Sampson, 2012) and overcome any temporal and spatial restrictions (Awazu et al., 2009; Dahan & Hauser, 2002). In this context, it is vital to provide high levels of ease of use (Davis, 1989) and understandability (Prat et al., 2015) which is likely to require context-specific adaptations. In line with the third objective, i.e., fostering systematic and iterative interventions, the NSPIRET Navigator proposes the intervention phase and related operational activities. The NSPIRET Shoutbox supports the NSP improvement team by ensuring transparency and keeping track of what has been conducted so far. However, as any systematic and iterative improvement initiative, NSPIRET requires long-term organizational commitment and resource investments for effectively applying the technique (van der Wiele, van Iwaarden, Dale, & Williams, 2006). Besides having to set-up and maintain the technical infrastructure the necessary additional effort in daily operations must not be underestimated.

6 Conclusion This contribution presents a conceptual model of service productivity in the context of an OI-Lab. Moreover, NSPIRET, a technique that can support its systematic enhancement, was introduced. The latter was developed following DSR (for the empirical results underlying this contribution see Daiberl, 2020). NSPIRET synthesizes and extends existing contributions from service design, information systems, and engineering and its current design was evaluated in an artificial-formative manner following a criteria-based analysis. The first insight of this study is, that driving service productivity of an OI-Lab is reasonable and theoretically feasible. Following, the conceptual model proposed, it

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is about enhancing actors’ satisfaction with the effects of the OI-Lab at a given time considering their resource contributions for reaching these effects. Second, especially in the context of an OI-Lab, it was highlighted that the managerial focus should not be on the productivity of a single actor, but on networks of actors. Consequently, this shows the importance of a networked approach and highlights the necessity to overcome a monadic perspective and consider productivity for all actors relevant for continuously operating an OI-Lab. Third, for integrating the different perception of relevant actors into the productivity management of an OI-Lab, participatory approaches such as NSPIRET might help to deal with the challenges of a networked perspective. They help to gain a better understanding of the different perspectives and help to sense and seize opportunities to enhance aggregated NSP. Forth, NSPIRET takes the touchpoints of the customer journey as the basic unit of analysis and as a foundation for enhancing aggregated NSP. Thus, the focus is on the main interactions of the customer with the different co-providers, providing a pragmatic approach to decide about which actors to include into productivity analysis and ensuring that improvements are driven by an in-depth customer understanding which is essential in a service context. The study also faces some limitations. Initially, the design boundaries explicated above need to be considered. Moreover, the focus on the customer journey, may also fade out other important issues for enhancing productivity for the different actors of an OI-Lab. Therefore, NSPIRET should be considered as a complementary approach to other tools and techniques for improving the effectiveness and efficiency of interactions in the context of an OI-Lab. For future research it would be of interest to learn about how the number and diversity of stakeholder groups within OI-Labs influences service productivity. It is also worth to analyze how tools from service systems engineering literature are suitable as complementary approaches to improve aggregated NSP (Höckmayr & Roth, 2017).

References Alter, S. (2012). Metamodel for service analysis and design based on an operational view of service and service systems. Service Science, 4(3), 183–294. Alter, S. (2013). Work system theory: Overview of core concepts, extensions, and challenges for the future. Journal of the Association for Information Systems, 14(2), 72–121. Awazu, Y., Baloh, P., Desouza, K. C., Wecht, C. H., Kim, J., & Jha, S. (2009). InformationCommunication Technologies Open Up Innovation. Research Technology Management, 52(1), 51–58. Bessant, J., Lehmann, C., & Möslein, K. M. (2014). Service productivity and innovation. In J. Bessant, C. Lehmann, & K. M. Möslein (Eds.), Driving Service Productivity: Value creation through innovation (pp. 3–15). Cham: Springer.

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Biege, S., Lay, G., Zanker, C., & Schmall, T. (2013). Challenges of measuring service productivity in innovative, knowledge-intensive business services. The Service Industries Journal, 33(3–4), 378–391. Bogers, M., Chesbrough, H., & Moedas, C. (2018). Open innovation: Research, practices, and policies. California Management Review, 60(2),5–16. Witten, Berlin: Universität Witten/ Herdecke. Chesbrough, H. (2003). Open innovation: The new imperative for creating and profiting from technology. Boston: Harvard Business School Press. Chesbrough, H., & Bogers, M. (2014). Explicating Open Innovation: Clarifying an Emerging Paradigm for Understanding Innovation. In H. Chesbrough, W. Vanhaverbeke, & J. West (Eds.), New Frontiers in Open Innovation (pp. 3–28). Oxford: Oxford University Press. Dahan, E., & Hauser, J. R. (2002). The virtual customer. Journal of Product Innovation Management, 19(5), 332–353. Daiberl, C. F. (2020). Driving networked service productivity. Wiesbaden: Springer Gabler. Daiberl, C. F., Naik, H., & Roth, A. (2018). Proposing the NSPIRE technique: Improving productivity of networked service delivery. In R&D Management Conference. Daiberl, C. F., Roth, A., & Möslein, K. M. (2016a). Approaches for enhancing productivity of networked service delivery: A review and assessment. In 26th Annual RESER Conference. Daiberl, C. F., Roth, A., & Möslein, K. M. (2016b). Conceptualizing productivity within a service network: The case of JOSEPHS®. In 23rd EuROMA Conference. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. Djellal, F., & Gallouj, F. (2013). The productivity challenge in services: measurement and strategic perspectives. The Service Industries Journal, 33(3–4), 282–299. Drucker, P. F. (1974). Management: Tasks, responsibilities, practices. New York: Harper & Row. Geum, Y., Shin, J., & Park, Y. (2011). FMEA-based portfolio approach to service productivity improvement. The Service Industries Journal, 31(11), 1825–1847. Gregor, S., & Hevner, A. R. (2013). Positioning and presenting design science research for maximum impact. MIS Quarterly, 37 (2). Grönroos, C., & Ojasalo, K. (2004). Service productivity: Towards a conceptualization of the transformation of inputs into economic results in services. Journal of Business Research, 57(4), 414–423. Grotenhuis, F. D. J. (2017). Living labs as service providers: From proliferation to coordination. Global Business and Organizational Excellence, 36(4), 52–57. Gummesson, E. (2007). Exit services marketing – enter service marketing. Journal of Customer Behaviour, 6(2), 113–141. Halvorsrud, R., Haugstveit, I. M., & Pultier, A. (2016). Evaluation of a modelling language for customer journeys. In 2016 IEEE Symposium on Visual Langauges and Human-Centric Computing (pp. 40–48). Halvorsrud, R., Kvale, K., & Følstad, A. (2016). Improving service quality through customer journey analysis. Journal of Service Theory and Practice, 26(8), 840–876. Halvorsrud, R., Lee, E., Haugstveit, I. M., & Følstad, A. (2014). Components of a visual language for service design. In Proceedings of ServDes (pp. 291–300). Lancaster. Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105. Hillebrand, B., Driessen, P. H., & Koll, O. (2015). Stakeholder marketing: Theoretical foundations and required capabilities. Journal of the Academy of Marketing Science, 43(4), 411–428.

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Höckmayr, B., & Roth A. (2017). Design of a Method for Service Systems Engineering in the Digital Age. Proceedings of the 38th Internationcal Conference on Information Systems (ICIS). Seoul, South Korea. Huff, A. S., Möslein, K. M., & Reichwald, R. (2013). Introduction to open innovation. In A. S. Huff, K. M. Möslein, & R. Reichwald (Eds.), Leading open innovation (pp. 3–18). Cambridge: The MIT Press. Johnston, R., & Jones, P. (2004). Service productivity: Towards understanding the relationship between operational and customer productivity. International Journal of Productivity and Performance Management, 53(3), 201–213. Ostrom, A. L., Parasuraman, A., Bowen, D. E., Patricio, L., & Voss, C. A. (2015). Service research priorities in a rapidly changing context. Journal of Service Research, 18(2), 127–159. Parasuraman, A. (2010). Service productivity, quality and innovation: Implications for servicedesign practice and research. International Journal of Quality and Service Sciences, 2(3), 277–286. Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45–78. Prat, N., Comyn-Wattiau, I., & Akoka, J. (2015). A taxonomy of evaluation methods for information systems artifacts. Journal of Management Information Systems, 32(3), 229–267. Roth, A., Fritzsche, A., Jonas, J., Danzinger, F., & Möslein, K. M. (2014). Interaktive Kunden als Herausforderung: Die Fallstudie „JOSEPHS® – Die Service-Manufaktur“. HMD Praxis Der Wirtschaftsinformatik, 1–13. Sampson, S. E. (2010). The unified service theory: A paradigm for service science. In M. Paul P., C. A. Kieliszewski, & J. C. Spohrer (Eds.), Handbook of Service Science (pp. 107–131). New York: Springer. Sampson, S. E. (2012). Visualizing service operations. Journal of Service Research, 15(2), 182–198. Sampson, S. E., & Froehle, C. (2006). Foundations and implications of a proposed unified services theory. Production and Operations Management, 15(2),329–343. Schmidt, S., & Brinks, V. (2017). Open creative labs: Spatial settings at the intersection of communities and organizations. Creativity and Innovation Management, 26(3), 291–299. Sonnenberg, C., & vom Brocke, J. (2012). Design science research evaluation. In K. Peffers, M. Rothenberger, & B. Kuechler (Eds.), DESRIST 2012, LNCS 7286 (pp. 381–397). Berlin, Heidelberg: Springer. Tangen, S. (2005). Demystifying productivity and performance. International Journal of Productivity and Performance Management, 54(1), 34–46. Tax, S. S., McCutcheon, D., & Wilkinson, I. F. (2013). The service delivery network (SDN): A customer-centric perspective of the customer journey. Journal of Service Research, 16(4), 454–470. van der Wiele, T., van Iwaarden, J., Dale, B. G., & Williams, R. (2006). A comparison of five modern improvement approaches. International Journal of Productivity and Quality Management, 1(4), 363–378. Venable, J., Pries-Heje, J., & Baskerville, R. (2016). FEDS: A framework for evaluation in design science research. European Journal of Information Systems, 25(1), 77–89. Verleye, K., Jaakkola, E., Hodgkinson, I. R., Gyuchan, T. J., Odekerken- Schröder, G., & Quist, J. (2017). What causes imbalance in complex service networks? Evidence from a public health service. Journal of Service Management, 28(1), 34–56. Yalley, A. A., & Sekhon, H. S. (2014). Service production process: Implications for service productivity. International Journal of Productivity and Performance Management, 63(8), 1012–1030.

Part IV: Open Labs as Innovation Spaces

Kathrin M. Möslein

16 Understanding Open Labs – The Challenge of Place and Space 1 The Role of Place and Space for Innovation Place and space are mainly white spots when looked at from the perspective of business school scholars. The discipline of management and organization research for a long time has simply neglected their existence throughout the life of a public or private organization or the process of innovation and commercialization. Spatial decisions have long been seen as constitutive decisions in organizations that are made when an organization is first set up (Weber, 1909 see also Roscher, 1872 and Weber, 1914). They might only be reconsidered when the organization has to adapt to labour or consumer markets over time. Economic theory on the other hand has been a timeless and placeless discipline at its core and abstracted from those irrelevant realities. These abstractions helped a lot to simplify reality and focus on what seemed important for a long time. Paradoxically, however, with the advent of the internet and the creation of global digital spaces the expected “death of distance” (Cairncross, 1997) turned into a discovery of distance: suddenly place and space started to matter. This holds true for the broader arena of management, organization and economics research. For innovation research, Eric von Hippel early on pointed to the important role of “Sticky Information” and the “Locus of Problem Solving” for innovation (v. Hippel, 1994, 1998). He focused on the need to combine the right pieces of knowledge from different domains in order to solve a problem related to innovation. As knowledge quite often is “sticky” – i.e. hard to codify and transport – the locus of effective problem solving is often bound to the locus of the knowledge needed. Digitization today has created more and better ways to capture and transport knowledge: we create and transmit rich visualizations, use real-time collaboration tools or invest in virtual or augmented reality to overcome the burden of distributed knowledge. Still, however, we cannot overcome the “stickiness” of many crucial pieces of knowledge that really matter. They are often bound to knowledgeable people. In innovation those innovators matter most who are able to bridge across knowledge domains, connect previously unconnected pieces of information and creatively combine what has never been part of an integrated solution. This is how problems are solved, how solutions are created, how innovations arise. Place and space, thus, truly matter for innovation. It, therefore, does not come as a surprise that we all look at innovation ecosystems, innovation regions, innovation

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clusters or innovation hot spots as places where innovation is most likely to happen. For sure, we constantly aim to replace real-word places by digital spaces. We hope to create clouds and hubs, platforms and networks in the digital world that have the potential to support and unleash similar innovation power as the good old workshops, coffee shops, saloons. In many ways those innovation spaces can be extremely helpful. Industrial data spaces allow engineers to connect and collaboratively create models, prototypes and ultimately products and solutions in the virtual world. Online education spaces allow joint learning experiences on a global scale. Digital health platforms bridge between specialists to create the best possible solutions for patients across distance. Thus, in many cases we can overcome distance and create shared digital spaces for exploration, innovation and co-creation of new solutions. In many other cases, however, the digital bridges we create and heavily use are not able to overcome the challenges of distributed knowledge. This is the case when “sticky knowledge” is key. When organizing for open innovation these challenges often arise. Open innovation, understood as based on mechanisms of broadcast search and selfselection (Lakhani 2006), knows many productive implementations on digital platforms: online innovation contests, innovation markets or innovation communities flourish and provide wonderful innovation results (Sawhney et al. 2005; Möslein & Fritzsche 2017). In fact, they are a key driver of the positive connotation and standing that open innovation currently enjoys. However, we also have to understand that in those settings the innovation question is usually already well formulated as a starting point. Where the question is clear, the matching of innovators and knowledge pieces that would have otherwise never met across wide distances can create wonderful and often surprising innovation solutions. Finding the question, however, can be an innovation challenge in itself. In many cases, an immense amount of sense-making and translation between different stakeholder groups is necessary before the question can be put into words (Fritzsche & Dürrbeck 2019; Rohrbeck & Gemünden 2011). And often even well understood questions cannot easily be solved by simply matching people and knowledge in online spaces. These are the cases where it still matters to meet in real places, to put heads together, to touch and feel the problem and parts of solutions, to explore, test and fail not just with bits, but bricks (Roth & Jonas, 2018). These are the cases were we are reminded of the historic importance of coffee shops, saloons or city markets as spots where knowledge has always been exchanged and ideas were created. The more we realize this need to meet and the promise of place for ideation and exploration, innovation and co-creation, the more we become aware of the vast range of different places that are currently created (Fritzsche, 2018a): techshops and makerspaces, fab labs, living labs, innovation arenas, adaptive labs and garages, and many more. Those places are often seen as different realizations of open innovation laboratories. In all those lab contexts firms are acutely confronted with spatial challenges of access and separation of co-creators or the richness and reach of their communication

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activities. These challenges, however, can no longer be dealt with by traditional location theories. Neither can innovation be contained in specific departments of a firm, nor does it spread in a simple diffusion pattern on the market (Fritzsche, 2018b). What we see in e-business, digital transformation and open innovation settings truly differs from the traditional picture. Today, “sticky knowledge” determines spatial decisions for innovation and problem solving often on a daily basis, virtual spaces and real places take the role of platforms where innovators and co-creators meet (Reichwald et al., 2001) and the design of these platforms turns out to be crucial for open innovation strategies (Bullinger, 2012; Huff, Reichwald, Möslein, 2013). For about two decades open innovation initiatives have experimented with the options which these virtual and real platforms can provide and the design parameters that specific open innovation strategies can build on. Still, research on the spatial aspects of organizing for open innovation is scarce and better design knowledge is needed: How to design virtual and real spaces as open innovation platforms? How to bridge between the virtual and the real in these spaces? How to integrate spaces for open innovation in larger innovation ecosystems? How to institutionalize such spaces in global innovation networks?

2 Researching Open Labs as Innovation Spaces In the following chapters, selected international researchers with strong interest and experience in the questions raised, will shed light and illustrate important aspects of our “white spot” in the understanding of place and space. In their chapters they will focus the role of open labs from different research angles. By doing so, they hopefully will inspire you to dig deeper, will create curiosity and channel energy towards those important topics. John Bessant draws the attention to the careful planning that hides behind the seemingly spontaneous emergence of creativity and collaboration in open labs. He explores how successful lab operators set conditions for the possibility of innovation in open labs in a variety of ways. The findings are summarized in three main elements which need to be considered: boundary objects, boundary agents and boundary spaces. Susanne Ollila and Anna Yström pick up the idea of boundary spaces in a deeper analysis of “in-between spaces” that allow for an interaction between different stakeholders in innovation. Drawing on several lines of research from social theory, they characterise such spaces as multiplex, in-becoming, recursive and translative. Inbetween spaces are permanently re-arranged and newly constituted. Open laboratories provide an environment where this is possible to happen. Jan Mehlich and Mitchell Tseng discuss how innovation activities in open labs can take advantage of flexibility regarding specific features of new products and

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services. Drawing on the notion of tolerance ranges from engineering, they show that an open interaction with customers can help firms to make better use of the solution space for new designs. To illustrate their thoughts, Mehlich and Tseng use experiences from innovation workshops held by members of the JOSEPHS® team in Taiwan. Pramoth Kumar Joseph, R Srinivasan and Sandeep Lakshmipathy further expand the investigation to virtual and real spaces for open innovation from the perspective of strategic management. They identify nine archetypes of open innovation capabilities in reference to the focus of the innovation activities and the resources that are deployed in such spaces. Online platforms can increase the potential for open innovation in a place such as JOSEPHS® with complementary opportunities for interaction, wherever the underlying strategy asks for it. Altogether, the four chapters show the breadth of spatial questions that can be asked about open labs. They hold important implications for the future of management and organisation research. As the possibilities to arrange resources and implement value creation processes have multiplied in the digital age, innovation, but also other business functions can nowadays take place in a large variety of different forms. Open labs can play a key role in the quest of a better understanding of this variety and its implications for economic development. They offer spaces that can dynamically be adapted to different needs for exchange and interaction. The more we know about open labs, the better we will also be able to handle the increasing variety of digitally supported business operation in general. Furthermore, the chapters in this part of the book suggest highly interesting research agendas for the study of open labs, which cannot only lead to many important insights, but also allow for a deeper exchange between innovation scholars with different academic backgrounds. Engineering, sociology, history and many other disciplines can add inspiration to management and organisation studies and make a rich field of study even richer.

References Bullinger, A.C. (2012). IT-based Interactive Innovation. Habilitation thesis, Friedrich-Alexander University Erlangen-Nuremberg 2012. Cairncross, F. (1997). The Death of Distance: How the Communications Revolution Will Change Our Lives. Harvard Business Review Press: Cambridge MA. Fritzsche, A. & Dürrbeck, K. (2019). Technology before engineering: How James Bond films mediate between fiction and reality in the portrayal of innovation. Technovation, https://doi.org/10.1016/ j.technovation.2019.05.006. Fritzsche A. (2018a) Corporate foresight in open laboratories–a translational approach. Technology Analysis & Strategic Management 30(6): 646–657.

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Fritzsche, A. (2018b). Spreading innovations: models, designs and research directions. In: Bunde, A., Caro, J., Kärger, J. and Vogl, G. (Eds.), Diffusive Spreading in Nature, Technology and Society (pp. 277–294). Cham: Springer. Huff, A.S., Möslein, K.M. and Reichwald, R. (Eds.) (2013). Leading Open Innovation. MIT Press: Cambridge MA, pp. 69–85. Lakhani, K. (2006). Broadcast Search in Problem Solving: Attracting Solutions from the Periphery, PICMET 2006 Proceedings, 9–13 July. Istanbul, Turkey. Möslein, K. M. and Fritzsche, A. (2017). The evolution of strategic options, actors, tools and tensions in open innovation. In: Pfeffermann, N. and Gould, J. (Eds.) Strategy and Communication for Innovation, Cham: Springer, 3–18. Reichwald, R., Möslein, K.M. and Piller, F.T. (2001). Reinventing Place: The new role of location in electronic business, SMS 21st Conference 2001, October 21–24, San Francisco, CA. Rohrbeck, R., and H. G. Gemünden. (2011). Corporate Foresight: Its Three Roles in Enhancing the Innovation Capacity of a Firm. Technological Forecasting and Social Change 78 (2): 231–243. Roth, A. and Jonas, J.M. (2018). Dienstleistungsentwicklung im offenen Innovationslabor – Ein Blick durch die Unternehmensbrille. In M. Bruhn & K. Hadwich (Eds.), Service Business Development (pp. 66–82). Wiesbaden: Springer. Roscher, W. (1872). Studium über Naturgesetze, welche den zweckmäßigsten Standort der Industriezweige bestimmen. Leipzig. Sawhney, M., Verona, G. and Prandelli, E. (2005). Collaborating to create: The internet as a platform for customer engagement. Journal on Interactive Marketing, 19 (4), Autumn (Fall) 2005, pp 4–17. v. Hippel, E. (1994). ‘Sticky Information’ and the Locus of Problem Solving: Implications for Innovation, in: Management Science, 40(4), pp. 429–439. v. Hippel, E. (1998). Explorations of the Impact of „Sticky“ Local Information on the Locus of Innovation. In Franke, N., v. Braun, C.F. (Eds.) Innovationsforschung und Technologiemanagement (pp. 275–284). Springer, Berlin, Heidelberg. Weber, A. (1909). Über den Standort der Industrien, 1. Teil: Reine Theorie des Standortes. Tübingen. Weber, A. (1914). Industrielle Standortlehre. Tübingen.

John Bessant

17 Creating the Creative Open Lab 1 Introduction If you’re in the innovation game you probably ought to meet Madame de Geoffrin. Anyone working at the fuzzy front end or trying to find ways to break out of the box and open up new pathways could probably benefit from a few minutes of her time. She has a wealth of experience around how to enable this sort of thing. It wouldn’t be easy – you’d have to build a time machine to enable you to visit but it would repay the effort. Back in the eighteenth century she hosted what was arguably the most famous salon in Europe. People came from far and wide to meet her and the guest list was impressive – kings and queens, writers, painters, sculptors, wealthy patrons of the arts – in fact anyone who was interested in cultural life at the time. But it wasn’t simply a good place to meet, eat and drink. The key to her success as a salonierre was to enable people to share ideas, to build on those ideas, to help shape and develop them – in short, to innovate. This wasn’t a short session either; one of her personal innovations was to shift the timing of her events from a late evening supper to lunches which allowed all afternoon and beyond for discussion, presentation and elaboration. What she really understood was that this kind of creative encounter doesn’t just happen – it takes careful construction, co-ordination and management. It’s a lesson which would be well taken today when innovation labs and similar ventures are becoming increasingly popular. Whether you call them innovation hubs, maker-spaces, fab-labs, accelerators or hotspots you can hardly turn a street corner or a magazine page before you bump into another example. The names may vary but the underlying idea is the same – a place where people can meet to get inspired and supported by each other, to articulate and co-create. An environment in which ideas can be explored and played with. Right now it seems like everyone is jumping on the bandwagon. Companies looking to reinvent themselves no longer set up corporate venture units – they establish their own Silicon Valley style start-up garages and lofts.1 City and regional governments rebrand their incubators as innovation hubs and build lab-style environments with support facilities to allow a new generation of entrepreneurs to realise their dreams (and hopefully deliver local economic growth as a by-product). Fab-labs and maker-spaces abound, run down old warehouses and industrial buildings are being

1 https://www.cbinsights.com/research/corporate-innovation-labs/ John Bessant, University of Exeter, Exeter, UK https://doi.org/10.1515/9783110633665-017

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reinvented as shells within which new forms of entrepreneurial life can flourish. In the social innovation world new ideas are being worked on in dedicated places designed to bring together stakeholders and enable them to create new ways of dealing with old challenges. Even in the extreme conditions of the humanitarian field innovation labs now take their place as part of the essential resource kit for helping mitigate natural and man-made disaster. All of these ventures are built on the belief that innovation (particularly of the radical, game-changing variety) needs somewhere to incubate and flourish, ideally well away from the busy day-to-day mainstream. Spaces where ideas can grow, be prototyped, experimented with and ultimately taken to scale. But like science parks before them there is a risk that many of these labs are being set up because it is the fashionable thing to do. A study by consultants Capgemini in 2016 suggested that new labs were being opened at a rate of ten per week; however failure rates of these labs were often as high as 90%.2 Expectations run high but the very ease with which they can be established means that it is also simple to close them down again. Just as Madame de Geoffrin’s salons were more than comfortable surroundings and good catering so innovation labs and spaces need to be more than a chillout space with some beanbags on the floor and whiteboards on the walls. They need to move beyond simply adopting tools and techniques like lean start-up and hackathons and work to avoid what Steve Blank has termed ‘innovation theatre’ – essentially putting on a performance without the underlying understanding of how innovation actually works. So what can we learn, not only from Madame de Geoffrin but also from the growing body of research on successful innovation labs and spaces? The first lesson is that they involve much more than just lucky encounters; they have structure, process and sustainability. Like their predecessors in the last century, the great corporate R&D labs belonging to companies like Philips, Bell, Xerox or Corning, successful innovation labs are made, not born. In this chapter we’ll look a five key principles which underpin successful innovation labs and spaces, drawing on a growing body of research and an increasing number of carefully researched case studies (Groves & Marlow, 2016; Dodgson & Gann, 2019). These five themes are: – Enabling creative collisions – Proximity, diversity and interaction – Experimentation – Prototyping and boundary objects – Management and facilitation

2 https://www.capgemini.com/news/despite-mass-investment-innovation-centers-are-not-makingorganizations-more-innovative/

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2 Creative Collisions Creating meeting places for innovation isn’t a new idea. Back in the 17th century places like Oxford were full of coffee-houses, sometimes called ‘penny universities’ because that was the price of admission including coffee. But it wasn’t the hot beverage which drew people so much as the opportunity to mix and exchange ideas – a place where the ‘normal’ rules of society governed by status and economic position were left aside and people could meet and explore new possibilities on an equal footing. And they weren’t just about talking; in 1680 Edward Lloyd’s premises hosted a mixture of ship owners, captains, merchants and others with links to the maritime world. New ventures were explored and support for them secured – an early version of today’s venture capital pitching. Today’s towering Lloyds building has its roots in that start-up meeting place. A few blocks away Jonathan’s Coffee House became the favoured meeting place for another group of potential investors and entrepreneurs – the foundation of the London Stock Exchange. Isaac Newton was a fan of the Grecian coffee house where experimental scientists liked to gather – and where he once dissected a dolphin on the table! And today’s branch of Starbucks on Russell St is on the site of Button’s coffee house where, in 1712 poets, playwrights and journalists gathered around long wooden tables drinking, thinking, writing and discussing literature into the night. We might think ‘open innovation’ is a new idea but it was alive and very much kicking three hundred years ago! And, as we’ve seen It wasn’t only coffeehouses; similar hotspots for innovation could be found in the swish drawing rooms of Paris, St Petersburg and Milan. Under the careful management of women like Madame de Geoffrin such salons became home to progressive ideas and creative conversations, incubators of new thinking in music, visual arts, theatre and science. Innovation spaces flourish in the most unlikely places. For example Gordon French’s garage in Menlo Park, California in the mid-1970s was home to the Homebrew Computer Club, an informal group of electronic enthusiasts and technically minded hobbyists who gathered to trade parts, circuits, and information about DIY construction of computing devices. One of the regular members was Steve Wozniak who credits this as the place where the Apple 1 was born. Walker’s Wagon Wheel tavern in the 1970s has a particularly important place in recent innovation history. Its name provides a great description of its role – like spokes on a wheel people and ideas converged on its centre and on a Friday night the air was full of conversation. Ideas flew around the place, colliding and often crashing in flames on the floor. But some of them fused, became something bigger, began conversations which carried on over the coming weeks and grew into new businesses. Its location was also important – Mountain View, San Francisco, close to the emerging technology cluster of start-ups, big electronics firms like Fairchild Semiconductor, the sprawling campus of Stanford University. Silicon Valley as it was to become.

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What these all have in common is that they are more than simply meeting points. And they work in part because they help provide a crucible within which ideas at the ‘fuzzy front end’ of innovation can emerge (Susan E Reid & Ulrike de Brentani, 2004). In this very early stage it’s not clear what the landscape here actually involves – navigating it is like stumbling through thick fog while trying to move forward in a direction which you think is the right one. For example, when an artist first starts to work with materials there is often a long period of problem exploration, one in which ‘discovery orientation’ is an important skill. Picking up and playing around with the objects, sketching and drafting, doodling are all exploratory strategies. Jazz musicians improvising around a clutch of chords will do something similar, probing and testing by tossing out phrases and seeing whether they work or not. Actors and directors explore different versions of characters and interplay, looking for something beyond the words on the page, hunting for a way of bringing them to life. Entrepreneurs rarely start with ‘the’ definitive version of their new venture. They may have a broad vision, a sense of direction, but their progress towards it is one of probe and learn, trying out different things, learning through failure and feedback and pivoting around the core idea until they arrive at their solution (Sarasvathy, 2008). And established organizations operate partly in ‘fuzzy’ mode. Whilst their mainstream innovation offerings can be updated and incrementally improved along clear strategic pathways, finding radical solutions, breakthrough products and services requires approaches which allow for experimentation, failure and fast learning. That’s where innovation spaces like the Wagon Wheel come in. The old bar has gone (closed in 2003) but the role it played is as important as ever. At this early stage it’s critically important to have conversations, explore possibilities, make connections between different worlds of knowledge – networking is the name of the game. Innovation spaces matter, not simply as coffee shops and bars but for what they represent – meeting points where knowledge intersects.

3 Proximity and Interaction Just bringing people together may not be enough, even if you get the right mix. We need to understand the ways in which creative collisions can happen and be nurtured – and that comes down to several things Making knowledge connections isn’t simply joining the dots in mechanical fashion. Research has repeatedly shown that we need to look at the role of brokers, people who straddle the boundaries of different knowledge worlds and enable traffic to flow across them (Fleming & Waguespack, 2007). These days we talk knowingly about social capital and the importance of building up networks – ‘its not what you

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know, but who you know’ – but this idea owes much to sociologist Roland Burt and his research in the 1990s (Burt, 2005). The core of his theory is that where two ‘knowledge worlds’ possess different, ‘non-redundant’ information (they know something you don’t) then there is a ‘structural hole’ between them (Burt, 1992). Brokers provide the bridge between these and are central to effective flow of knowledge across them. These days some of the new knowledge technologies can provide ways of amplifying and even automating some aspects of this. (Think about Facebook’s ability to find ‘friends’ you might like to connect with – and about the potential application of ‘knowledge friending’ in terms of moving knowledge around organizations and building relevant networks). But the importance of brokers even in this virtual space remains significant. A second key point in enabling effective innovation spaces is the need to promote diversity but also to retain focus and coherence. Social networks around knowledge aren’t all the same – back in the 1970s Mark Granovetter showed that they varied in terms of their connectivity (Granovetter, 1973). Much of the time they involve dense connections or people sharing similar and complementary information – something he called ‘strong ties’. But for new knowledge to move between networks we need much looser links between different worlds – what he called ‘weak ties’. (For example this is the challenge currently facing players in the auto industry – their world of strong ties may not be enough to help them connect to the very different knowledge worlds they will need in the emerging mobility industry. They need the equivalent of the chance encounters offered by the Wagon Wheel bar or its 2016 equivalent!) Once again we are in the broker’s territory – there is a need for people or mechanisms to help cross these knowledge worlds, to act as boundary spanners. Tom Allen’s pioneering work in the 1970s gave us some powerful insights into the ways this happens – for example through technological gatekeepers who are able to see the relevance of external knowledge but who also have the internal social connections to enable the right person to connect to it (Allen, 1977). One of the functions which innovation spaces can provide is as a forum where cross-sector innovation can take place (Bessant & Trifilova, 2017). Many problems are essentially similar in nature when abstracted to a high enough level – for example enabling more efficient utilisation of operating theatres in a hospital can benefit from approaches developed for pit stops in Formula 1 motor racing or turnaround time reduction in low cost airlines. Bringing these two worlds together and enabling ‘recombinant innovation’ depends again on brokerage skills and extensive access to multiple networks (Hargadon, 2003). Linked to this is the growing understanding of the value in engaging with stakeholders as early as possible in the innovation process. Users in particular are a powerful source of ideas – indeed research suggests that they are often responsible for initiating a significant proportion of ideas which then go on to become major

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innovations (Von Hippel, 2016). This goes far beyond using them as passive commentators in focus groups; instead they are increasingly being seen as potential cocreators of products and services. Their value is not simply in increasing the idea variety at the front end of the innovation process; they are also key agents in ensuring rapid and widespread diffusion. Adoption depends on innovations being perceived as ‘compatible’ – fitting in to the user’s world. So by gathering user insights – often difficult to articulate – about the context in which innovations will operate helps improve the chances of widespread acceptance. All of this argues for innovation spaces with a wide-open access approach, drawing in users and facilitating co-creation with them. Once again this requires a ‘boundary space’ and facilitation to enable it to happen; examples include the children’s ‘labs’ being designed into Lego theme parks and the Lab Campus project of Munich Airport in which the significant stream of travellers passing through the airport will be offered the chance to engage in innovation activities as part of their journey. There is also a spatial component to this; research and experience show that physical proximity matters. Finding ways to enable ideas and people to collide includes creating an architecture to support it. That’s a contribution which Steve Jobs made when in exile from Apple; his layout ideas for Pixar Studios made it impossible for people not to bump into each other and spark conversations. And thinking about engineering interactions was at the heart of much of Tom Allens’ research and it led to the development of what has come to be known as the ‘Allen curve’. This shows that there is a strong negative correlation between physical distance and frequency of communication between people. It offers a powerful design principle which Pixar have benefitted from – as have BMW which uses the same principles in the underlying architecture of its futuristic R&D Centre in Munich (Allen & Henn, 2007). This highlights another role for innovation spaces. Digital innovation tools allow for extensive collaboration in virtual space but there is much which requires face to face interaction, particularly (as we will see later) when the process of shaping and developing ideas into prototypes begins. Finding a place in which these two worlds can intersect, where on-line and off-line innovation can meet is another important role for innovation spaces – and requires the same input of brokerage, enabling translation and connection between these worlds. The nature of such interactions will vary – for example on-line retailing and co-creation with users is a rapidly growing field but still requires some point where the clothes and jewellery and furniture configured and imagined in virtual space can be experienced in a physical way. The role of innovation spaces like Josephs (see elsewhere in this book) is that they provide an intersection point for these different innovation perspectives.

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4 Experimentation Experimentation lies at the heart of innovation, something which Leonardo da Vinci recognised five hundred years ago. Experiment is the interpreter of nature. Experiments never deceive. It is our judgment which sometimes deceives itself because it expects results which experiment refuses. We must consult experiment, varying the circumstances, until we have deduced general rules, for experiment alone can furnish reliable rules.

Creating boundary spaces in which people and ideas can creatively collide and where different worlds can be spanned is of considerable value – but if all that gets transacted there is talk then it may not help much. Innovation is like an omelette – it can’t be made without breaking eggs. So another key component is having a safe space in which to allow the extensive egg-breaking associated with learning something new to take place. And that is the essence of a laboratory – somewhere to play around safely. Innovation research and practice is increasingly clear about the important role of experiment and ‘play’ and the need for a discovery orientation in exploring new possibilities (Thomke, 2002; Schrage, 2000; Dodgson, Gann, & Salter, 2005). This includes accepting that failure is an inevitable part of the process. But for most organizations, public and private sector, the emphasis is on reliability and repeatability, not on play and experimentation. Total quality requires conformance and adherence to standards, again effectively driving out the variation which comes from play. So there is a role for innovation spaces as environments which offer a safe playground in which intelligent failure is seen as a legitimate activity. (This becomes an issue of especial relevance in fields like healthcare and humanitarian work where there may be serious ethical problems around using agile/ experimental approaches with vulnerable users. In these situations a safe ‘quarantined’ space where such activity might be explored is an important part of the innovation repertoire) (Bessant, Richards, & Hughes, 2010). As the psychologist Amy Edmondson points out in her work on psychological safety, creativity flourishes in a culture where there is perceived support (Amy C. Edmondson, 2003; Goller & Bessant, 2017). It’s no coincidence that methodologies used in innovation labs closely follow the lean start-up/agile approach in which experiment and learning from intelligent failure is part of the process (Ries, 2011; Morris, Ma, & Wu, 2014). But the underlying assumption is that the context in which this takes place is supportive rather than judgmental, one which accepts failure as part of a learning process.

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5 Prototyping and Boundary Objects Innovation begins with ideas locked up in someone’s head. Creative interchange and shared exploration can give those ideas shape and energy – but sooner or later there is a need to make them real. Extensive research on innovation in a variety of different worlds highlights the value of problem exploration – the kind of playing with ideas which we saw in the previous section. So how do we go about problem-exploring? How do we move from vague notions, hunches, half-formed ideas towards something more workable? Not by a single leap but by a series of stepping-stones, bridges, scaffolding – essentially playing with ideas about the problem. The ‘creative’ artists in Csikszentmihalyi and Getzels famous study picked up objects, weighed them, turned them around in their hands, looked at them from different angles (Csikszentmihalyi, 1988). Entrepreneur James Dyson’s method is the same – create a prototype to help focus the exploration because “ . . . prototypes allow you to quickly get a feel for things and uncover subtle design flaws . . . ” (Dyson, 1997) The clue is in the name – proto-type. It’s not about the finished object but a stepping-stone, a test-bed for learning, some way of exploring in laboratory/experimental mode. Children do this naturally – from the moment they can start to hold and examine an object they begin to explore it, trying out all its possibilities. And when they play together they multiply the possible options in inspiring fashion – a humble cardboard box can become a spaceship, a shop, a stage, an article of clothing, and it can change its identity with impressive speed! Prototyping offers some important features to help us in problem exploration: – It creates a ‘boundary object’, something around which other people and perspectives can gather, a device for sharing insights into problem dimensions as well as solutions. – It offers us a stepping stone in our thought processes, making ideas real enough to see and play with them but without the lock-in effect of being tied into trying to make the solutions work – we can still change our minds. – It allows plurality – we don’t have to play with a single idea, we can bet on multiple horses early on in the race rather than trying to pick winners. – It allows for learning – even when a prototype fails we accumulate knowledge which might come in helpful elsewhere. – It suggests further possibilities – as we play with a prototype it gives us a key to open up the problem, break open the shell and explore more deeply. – It allows us to work with half-formed ideas and hunches – enables a ‘conversation with a shadowy idea’ . . . – It allows for emergence – sometimes we can’t predict what will happen when different elements interact. Trying something out helps explore surprising combinations.

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Prototyping has always been an important part of innovation – even when the solution trajectory is clear there is plenty of room for using test pieces to refine the product and get the bugs out. But rather than seeing this as a late stage tool to help polish a solution we should look more closely at its role at the very fuzzy front end of the innovation process. Prototypes can take many forms – physical models, sketches, simulations, even stories which can be adapted and retold. Their key feature is that they provide some interface around which different stakeholders can explore and contribute – they enable co-creation. Once again there is a clear role for innovation spaces to provide the context within which prototyping can take place. But there is particular value in doing so in a context where there is also diversity of commentary and input, engaging with prototypes and helping to refine them. This is easier to do when the prototypes are sketches but as we move towards more accurate representations and simulations so there is a need to engage technological support. This is one of the powerful arguments behind linking innovation labs to ‘maker spaces’ and ‘fab-labs’ – they become environments in which prototyping can extend deep into the development process.

6 Management and Facilitation of Innovation By now it should be clear that successful innovation spaces are far more than physical environments. And just as Madame de Geoffrin’s salon depended on her skill in organizing and managing the events so todays labs and spaces need hands-on management. This is not simply a matter of operations and logistics; there is skill in brokerage, in coaching and supporting nascent ideas, in planning events, in focusing activities. Successful innovation spaces don’t just happen, they are created and managed. There are several dimensions to this: – Convening – bringing people together and publicising the space to attract interesting participation – Combining – ensuring sufficient diversity without losing focus and then providing enabling mechanisms to help bridge between different worlds – Capability-building, supporting the use of methods and processes like lean startup to equip players in the lab with ways of translating their ideas into value – Coaching – providing support and mentoring to help guide and steer nascent entrepreneurs and ideas – Co-ordinating and connecting – enabling networking and links inside the community and beyond to external institutions – Community-building – creating a supportive peer group and a context which enables co-operation and sharing

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Two other features are worth mentioning – the first is around the leadership qualities involved in delivering such a context. There are many examples in history of successful innovation spaces – Thomas Edison’s Invention factory, Boss Kettering’s ‘Barn Gang’,3 Kelley Johnson’s Skunk works’ and others (Axelrod, 2008; Rich & Janos, 1994). What they share is an approach to strategic leadership which (while delivered in different styles) provided the above mix of enabling support and direction. And the second concerns building ‘dynamic capability’ (Teece, 2009). Innovation management is about embedding key behavioural routines which enable the process to happen repeatedly – but it is also about having the additional capacity for stepping back and reviewing those routines. Continually asking the questions around which routines to maintain and strengthen, which to pull back on or even eliminate, and which new ones to add to the repertoire. In other words organizations need a capacity for ‘innovation model innovation’. It’s the same with innovation labs and spaces; whilst much has already been learned a key skill will be to build dynamic capability in order to upgrade the ways in which such spaces operate.

7 Summary To summarise, innovation spaces depend on three interacting elements supported by strategic leadership: – Boundary spaces in which creative collisions can happen, bridges built across diverse people – The opportunity to develop and play with boundary objects – prototypes – and use this information to drive learning and convergence – Brokers as boundary agents, ensuring connections can happen Laboratories are play spaces in which experimentation can take place safely – and where the prototype provides the focus for such learning. As a boundary object it has the potential to engage many stakeholders and enable co-creation between them. But prototypes alone aren’t enough – to work effectively with them we also need boundary agents – someone to bring relevant people together and enable them to have useful conversations. They are ‘boundary spanners’ – able to operate in different networks but also to see relevance and make connections between them. Which brings us back to boundary spaces – where does all of this happen? And can we create spaces which actively stimulate and enable innovation? As we’ve seen research suggests that it’s much more than a simple physical space – just like a theatre we can provide the basic stage but it is the particular arrangements of scenery, lighting, properties and above all actors and directors which bring it to 3 ‘Boss Kettering and the Barn gang’, 2018.

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life. There is a growing body of experience around configuring such boundary spaces and there are a number of useful examples reported elsewhere in this book. Part of the story is in seeing boundary spaces as prototypes in themselves – places where we can learn more about the approach. This book gives a number of examples and their beginnings of a theoretical framework which we can use as scaffolding to ensure future spaces are well-constructed and sustainable. There are many lessons still to be learned but it would be nice to think that if Madame de Geoffrin were to hitch a ride back with us in our time machine she would feel comfortably at home in some of them.

References Allen, T. (1977). Managing the flow of technology. Cambridge, Mass.: MIT Press. Allen, T., & Henn, G. (2007). The organization and architecture of innovation. Oxford: Elsevier. Amy C Edmondson. (2003). Framing for learning: Lessons in successful technology implementation. California Management Review, 45(2), 34. Axelrod, A. (2008). Edison on innovation. Chichester: John Wiley. Bessant, J., Richards, S., & Hughes, T. (2010). Beyond Light Bulbs and Pipelines: Leading and Nurturing Innovation in the Public Sector. Sunningdale Institute, National School of Government. Bessant, John, & Trifilova, A. (2017). Developing absorptive capacity for recombinant innovation. Business Process Management Journal, 23(6), 1094–1107. https://doi.org/10.1108/BPMJ-102016-0215. Boss Kettering and the Barn gang. (2018, November 23). Retrieved 28 June 2019, from Managing innovation website: http://johnbessant.org/case-studies/boss-kettering-and-the-barn-gang/ Burt, R. (1992). Structural holes: Thesocial structure of competition. Cambridge MA: Harvard University Press. Burt, R. (2005). Brokerage and closure. Oxford: Oxford University Press. Csikszentmihalyi, M. (1988). Motivation and creativity. New Ideas in Psychology, 6(2), 159–176. Dodgson, M., & Gann, D. (2019). Playful Entrepreneur. Yale University Press. Retrieved from https://yalebooks.yale.edu/book/9780300233926/playful-entrepreneur Dodgson, M., Gann, D., & Salter, A. (2005). Think, play, do: Technology and Organization in the Emerging Innovation Process. Oxford: Oxford University Press. Dyson, J. (1997). Against the odds. London: Orion. Fleming, L., & Waguespack, D. (2007). Brokerage, Boundary Spanning, and Leadership in Open Innovation Communities. Organization Science, 18(2), 165–180. Goller, I., & Bessant, J. (2017). Creativity for innovation. London: Routledge. Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360–1380. Groves, K., & Marlow, O. (2016). Spaces for innovation. London: Frame3. Hargadon, A. (2003). How breakthroughs happen. Boston: Harvard Business School Press. Morris, L., Ma, M., & Wu, P. (2014). Agile Innovation: The Revolutionary Approach to Accelerate Success, Inspire Engagement, and Ignite Creativity. New York: Wiley. Rich, B., & Janos, L. (1994). Skunk works. London: Warner Books. Ries, E. (2011). The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses. New York: Crown.

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Sarasvathy, S. (2008). Effectuation: Elements of Entrepreneurial Expertise. Cheltenham: Edward Elgar. Schrage, M. (2000). Serious play: How the world’s best companies simulate to innovate. Boston: Harvard Business School Press. Susan E Reid, & Ulrike de Brentani. (2004). The Fuzzy Front End of New Product Development for Discontinuous Innovations: A Theoretical Model. The Journal of Product Innovation Management, 21(3), 170. Teece, D. (2009). Dynamic capabilities and strategic management. Oxford: Oxford University Press. Thomke, S. (2002). Experimentation matters. Boston: Harvard Business School Press. Von Hippel, E. (2016). Free innovation. Cambridge, MA: MIT Press.

Susanne Ollila and Anna Yström

18 Open Laboratories as “In-between Spaces” 1 Open Laboratories There is a global trend of customers and consumers increasingly co-creating solutions and experiences in a more distributed world. Relationships and partnering have become even more critical for innovation to happen and the distribution of power shift as individuals become increasingly networked, tapping into the wisdom of friends and communities. The focus of relationships changes from one-to-one to many-to-many. Distributed innovation is becoming the norm, with important implications for organizations and management looking ahead. Realizing creative and ground-breaking solutions thus require a touch-point to connect people with different experiences and knowledge. Open laboratories provide a setting for companies to integrate customers in their activities in an offline environment. There are various types of open laboratories such as FabLabs, TechShops and LivingLabs. Some open laboratories are organized within the boundaries of one firm, others as a joint open laboratory for e.g. a consortium or ecosystem of actors and yet others are open for the public. Some may make the assumption that such a lab should be open to everyone but this choice of organizational design i.e. if the open laboratory is connected to one or several organizations or is public, has implications for the degree and kind of openness as well as how the laboratory operates and is managed. Some open labs that target for example public citizens, typically exhibit a different kind of openness compared to an open laboratory organized within the boundaries of the firm, where a selected number of partners might be invited to share data or work on joint projects. In addition to having various organizational designs open laboratories are also equipped differently to support their purpose through e.g. easy prototyping, the construction of technical artefacts, testing of early solutions, open exchange of knowledge or networking. Engaging people in shared activities seems to be a general notion in open laboratories independent of type of organizing, i.e. open laboratories are to a great extent about interaction both social as well as human-machine interaction. This interaction can lead to predetermined or emergent contributions and it can be designed and led by users, producers, consumers or other types of stakeholders. Susanne Ollila, Chalmers University of Technology, Department of Technology Management and Economics, Gothenburg, Sweden Anna Yström, Linköping University, Department of Management and Engineering, Linköping, Sweden and Chalmers University of Technology, Department of Technology Management and Economics, Gothenburg, Sweden https://doi.org/10.1515/9783110633665-018

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Often “. . . open laboratories facilitate a transgression of industrial boundaries in the practice of innovation, which forces professionals to abandon accustomed procedural patterns, role models and hierarchical structures” (Fritzsche, 2018: p. 648). Thus, a critical role for open laboratories is to invite actors to leave their durable field positions – that is, their legitimate identities in a field, including their formal roles, and to propose new positions to them as co-creators or co-learners that enables and supports interaction with actors from different fields.

2 Introducing Space Research on organizational spaces has proposed that the “collaborative community”, such as those emerging in Open laboratories, is becoming more important (Adler, 2015) and the implications are potentially significant for our understanding of the organizing and managing of innovative work. As a result of transcending traditional organizational patterns, roles and norms, Open laboratories can be described as both “no man’s land” and “everyone’s land” – a territory that challenge us to further understand what characterize such space. Our notion of space is informed by Henri Lefebvre’s idea of social space: “[Social] space is not a thing among other things, nor a product among products: rather, it subsumes things produced and encompasses their interrelationships in their co-existence and simultaneity”. (Lefebvre, 1991: p. 73). Spaces are social products each with their own mental, physical, ideological and cultural realm (Lefebvre, 1991). Hence, the Open laboratory through its design serves as a mediator between mental activity (invention, the thinking) and social activity (realization, the doing) and it is deployed in space. When entering a space (physical or virtual) we are engaging in the “production of space”, which according to Lefebvre is the relations between different kinds of spatial experience: the conceived, perceived and lived (Delbridge and Sallaz, 2015). By walking into the Open laboratory and interacting with the equipment and other people we “produce the Open laboratory” but, according to Lefebvre (1991) each space has its own spatial practice ensuring continuity and some degree of cohesion and symbolism, and it “permits fresh actions to occur, while suggesting others and prohibiting yet others” (p. 73). Accordingly, through the interaction taking place in the Open laboratory we are simultaneously creating the lab and being created by the lab in the sense of what we think we can do and not do.

3 In-between Space Building on the notion of space and the claim that Open laboratories are about providing new positions enabling and supporting interaction such as co-creation and

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co-learning we introduce the concept of “in-between space” as a way of making sense of Open laboratories. An in-between space is a space in-between actants (including both individuals and objects) with its own character and value. It is a space which is “both-and” as well as “neither-nor”. It is a space of transformation, embracing disorder and ambiguity with the potential of destruction as well as becoming. Our concept “in-between space” builds on ideas from e.g. anthropology, psychology and behavioural science. We elicit the core characteristics of such a space to be multiplex; in becoming; recursive and translative.

3.1 Multiplex A core characteristic of an in-between space appears to be how it not only embraces multiplicity, but in fact is constituted by it. The different perspectives that people bring when engaging in interaction in the in-between space are respected and valued, while at the same time, they form the foundation of something new that is created jointly. This new something is constituted by contributions of each interacting individual, making it recognizable and familiar in one sense. Still it is something other than the mere sum of each contribution. Harvard Professor Bhabha’s work on post-colonial theory describes this as a culture in-between which is the contaminated yet connective tissue between . . . bafflingly both alike and different (Bhabha, 1996: p. 54). Bhabha’s work on Third Space theory furthermore explains a space where individuals can hold multiple identities and ideas intact and draw from each at different times. This suggests that in-between spaces enable and invite individuals to act as “hybrids”, capable of holding different sets of traditions, practices, norms and values, e.g. from multiple institutional settings, in their hearts and minds. It is not about choosing one over the other, it is about understanding and experiencing nuances, grey zones, ambiguity and the in-between space becomes a bridge that enables individuals to keep their multiple identities separate but knowing when to use them. These spaces allows plurality and difference – convergence without coincidence (Clegg and Kornberger, 2006). The in-between space becomes a connective tissue between multiple cultures, identities, communities and times. In Open laboratories, the mix of individuals and experiences is essential to the type of outcomes that can be produced. Depending on whether the open laboratory is designed within the boundaries of one organisation, as spanning over several organizational boundaries or as a public open laboratory, the mix of perspectives needs to be ensured in different ways. For example, in an open lab within an organisation, spontaneous external participants would most likely be rare in comparison to a public open laboratory where access is more generous and open. The multiplexity furthermore implies that individuals are not necessarily reduced to only play their institutional role, but instead invited to bring their full range of identities and cultures with them, in order to have the widest possible palette to choose from when interacting in the Open

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laboratory. Some individuals engaged in the open laboratory might have several affiliations such as one to a university and one to an industrial actor, and in such cases the open laboratory needs to enable this individual to sustain both these positions and maybe even utilize this individual multiplexity as a boundary spanning role supporting the sensemaking in-between e.g. the perspectives of science and business.

3.2 In Becoming Another core characteristic relates to the emergent nature of in-between spaces. Such spaces cannot be deliberately designed and implemented at full scale from day one. Rather, it is a space in becoming, continuously evolving through interaction and learning and essentially never reaching a complete state. As such, in-between spaces could be said to share features with liminal spaces (Van Gennep, 1908), a notion originating from anthropology. Liminality describes the ambiguity or disorientation that occurs in the middle period of rituals, when individuals no longer hold their pre-ritual status but have not yet begun the transformation to the status they will hold when the ritual is complete. During a ritual’s liminal stage, individuals “stand at the threshold” between their previous way of structuring their identity, time, or community, and a new way, which the ritual establishes. It can be described as a sort of “social limbo” that shares few attributes with either the preceding or the subsequent state. Still, this state is critical for developing a more nuanced understanding of both states, i.e. the origin and the future. In the context of in-between spaces, this implies a space where the individual can adopt a behaviour unrestrained by the mundane classifications of everyday life by “suspending ordinary social structures”. As such, the individual is “betwixt and between” conventional and everyday structures, which creates a certain freedom to operate in a different way. Furthermore, being in transition or in becoming suggests a space where the lines between reality and imagination are blurred. Winnicott (1971/2005) describes this as a transitional space and a way to think about creative imagination and potential, from a psychological point of view. It is a place where things can exist and not exist at the same time, challenging the dichotomy of reality and imagination, which are often treated as binary terms with connotations of external and internal worlds, characterized by objectivity and subjectivity respectively. But the transitional space is “. . . a resting place for the individual engaged in the perpetual human task of keeping inner and outer reality separate yet interrelated” (Winnicott 1971/2005: p. 3). As such, the in-between space becomes a space where the most authentic and creative aspects of our individual and collective existence can come to life, including e.g. artistic, scientific, and religious expression. By considering an in-between space as a transitional space, it adds a playful character to it, offering the individuals engaging in interaction the potential to continuously negotiate what is and what could be. In the setting of Open laboratories,

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this implies that actors and individuals engaging in such a space can leave their previous institutional positions behind in order to be more flexible and adopt new positions enabling learning together. In Weick’s (1996) terminology this would be to “drop your tools” when entering the Open Laboratory. It can be argued that individuals who are attracted to and thrive in the Open Laboratory are not seeking predictability, structure and clarity but are rather curious, eager to learn and to make something of the potential of such a space. To “drop your tools” in a setting like this could imply a certain risk of exposing vulnerability in terms of knowledge or power and would require a certain degree of confidence in professional identity, skills and competence. Still, Winnicott also noted that an in-between space could never replace inner and outer worlds. It appears that the potency of its potential lies specifically in that we cannot stay in this space of creative possibility and transformation forever. As such, the surrounding context and the organisational design of the Open Laboratory influence how emergent such a space can be. When organized within as well as across organizational boundaries, it is necessary to consider the institutional forces that inevitably strives to maintain and reinforce status quo. Naturally, grappling with those forces can cause tensions in the Open Laboratory as it counteracts the emergent nature of the in-between space, which could be particularly noticeable in Open Laboratories existing within the boundaries of an organization. In Open Laboratories organization transcending organizational boundaries, tensions can be expected in relation to balancing and bridging the diversity and divergence of institutional forces within each participating organization.

3.3 Recursive The in-between space is also a recursive space (Wood, 2012) as it is continuously reconfigured through the participation of both the designers of the materialized space and the actors. The capacity to interact is enabled through a certain organizational design but the recursive perspective focuses on the interaction between space as a structure that shapes action and action that re-shapes and reinforces space, suggesting that it is the interplay that acts as a driving force behind both (Hernes et al., 2006). A recursive view of space implies that we see space as existing through its production and reproduction. It implies a repeated procedure in which the outcome of each action is defined by the results of previous actions. As such, the in-between space exists through feedback between the state of the materialized space (physical or virtual) and the state of the actors i.e. their knowledge, mood, motivation. In other words, interacting with e.g. the design, layout and equipment in an inbetween space is a process of creating space, and this process of creation is not without history. Hence, already produced space can be decoded into new space. Space is actively created when an actor becomes entangled with the materialized

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space and its possibilities. Through this entanglement, the actor encounters space in two different modes; through sensing the represented space and through generating space by means of output. This means that the actor not only enters the materialized sensory space but also changes it, i.e. the space and the actor mediate one another. Thus, the in-between space invites the actors to engage in a space defined by the organization of objects, and simultaneously create that space by altering the organization of objects. Once it is produced, it can only be reproduced through actions and interactions. For the Open laboratory, this means that while space is what shapes action and interaction, it is also reshaped by actions and interactions in turn. It becomes no more or no less than what the actors or individuals interacting in the space make of it. As such, engagement can lead to reinforcing positive interactions, which will influence individuals’ motivation. On the other hand – non-actions can also have a significant effect, dampening motivation and incentives to engage, causing a negative spiral not conducive to the goal of producing creative solutions. The recursiveness of in-between space implies a certain playfulness, inviting individuals to test and experiment, and the outcomes of such experimentation reinforce the cycle of perpetual evolution of the space. This should not be considered as random, serendipitous exploration, but rather purposeful, skillful and thorough investigation to continuously utilize and expand the potential of the space. While it may be tempting to consider an Open Laboratory as something that can be designed and implemented based on a pre-set template, in practice this does not appear to be a realistic strategy. Whether existing within or across organizational boundaries, the critical influence of individuals actions on what the space becomes cannot be disregarded.

3.4 Translative The final characteristic of an in-between space that we bring up in this chapter is how such a space functions as a translator as it gives individuals an opportunity to be something “other”. In such translative spaces the traces of other voices are given space to resonate and hidden agency can become present (Spivak, 1988). This translative aspect helps to explain and make sense of explorative activities in the inbetween space, allowing us to cope with uncertainty and ambiguity. Our use of language and practice is culturally embedded and the work of a translative space is to connect cultures: what has different meanings in different cultures can be related by giving other voices and agency. There is also a continuous translation between the present and the future which implies probing and imagining possibilities, and where there is no right answer to be found. With a cultural translation perspective on in-between spaces, we seek to understand how language and practice within different cultures can resonate with each other and be the genesis of new language and practice.

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A parallel can be drawn to so called interstitial spaces (Furnari, 2014) where individuals temporarily break free from existing institutions and experiment collectively with new activities and ideas, which can, in turn, constitute new practices. These are small scale settings, reminiscent of open laboratories or e.g. makerspaces where individuals positioned in different fields interact occasionally and informally around common activities to which they devote limited time. It is suggested that such spaces are fruitful because they are informal and occasional, and institutionally diverse. At the core of interstitial spaces is the everyday situations of social interaction between fields, which are not initially expected to be consequential but can offer important opportunities for the emergence of new practice. Nonetheless, the very same characteristics can also be problematic in achieving the desired outcomes. Furnari (2014) argue that successful interaction rituals and the presence of catalysts sustaining others’ interactions and assisting the construction of shared meanings, is needed for such spaces to fulfil their translative potential. We do not argue that this translative aspect of in-between spaces is free from power and political expression, but it presents an opportunity to create shared meaning and practice. But in-between spaces can also be disruptive, as Foucault (1971) infers when he labeled spaces in which translation takes place as heterotopias, i.e. worlds within worlds, both mirroring and disturbing what is outside. It is a term used to describe spaces that have more layers of meaning or relationships to other places than what immediately meets the eye, things that can be disturbing, intense, incompatible, and contradictory. Thus, the in-between space becomes a place where contrasts and contradictions between different societal or personal interests and perspectives can be enacted (Fritzsche, 2018). For Open laboratories, this implies an awareness of the translation going on in the in-between spaces among the actors, and that it might trigger a need to develop new language to reflect shared understandings. There is also a translation from the inside of the space, to that which is outside, and vice versa. Individuals active in the space need to develop skills and routines for describing, characterizing, motivating and legitimizing what goes on in the space to “outsiders”, as well as bring valuable insights, knowledge and experiences from the outside to make work in the in-between space interesting and relevant. Furthermore, translation is not a rational linear process with a defined start and end point, but rather an uncertain, ambiguous social process emerging through interaction. As individuals move between spaces and cultures, they develop a cultural sensitivity that can be highly useful in in-between spaces. This must also be reflected in the organizational setup of the space, its coordination and management. Such sensitivity can be encouraged in the design of the open laboratory for example by establishing routines and practices for knowledge sharing across projects, positions, areas of expertise or organizational affiliation. The practice of continuously being exposed to different experiences and perspectives appears useful to develop cultural awareness and ultimately sensitivity. From a coordination or management point of view, this

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implies avoiding creating silos within the organizing of the open lab or if work is distributed among several individuals or organizations, to ensure appropriate cross-talk.

4 Concluding Reflections: Organizing In-between Spaces In this chapter, we have introduced the concept of in-between space and outlined four core characteristics of such a space – multiplex, in-becoming, recursive and translative. We suggest that the concept is particularly relevant to make sense of Open laboratories. Still, the question of organizing space remains a relatively under-developed but nonetheless important for understanding processes and practices of organizing according to Clegg and Kornberger (2006). They claim that we can easily get caught up in only considering phenomena in cognitive, immaterial spaces of organization, and not really consider the implications in the material space. Thus, thinking about organizing Open laboratories as in-between space implies connecting the immaterial space (characteristics and values) and cognitive with material space (physical space, work stations, equipment). This allows for a fuller picture of what interaction is needed to enable people to co-create creative solutions. Taking seriously the four characteristics of the in-between space, we can understand that supporting interaction for innovation activities in such a multidimensional space would require orchestration. It challenges us to consider what such orchestration could mean in an in-between space with these characteristics that may render traditional modes of top-down control quite ineffective. Rather it requires delicate orchestration capable of dealing with the inherent uncertainty and ambiguity that characterize the in-between space (Ollila and Yström, 2017); facilitating interaction, making sense of the space and bringing a tactical awareness of how to enable actions among participants. We argue that participants must pay heed to all four characteristics in an integrated manner. This means the diversity inherent in the multiplex characteristic; the flux and change of the in-becoming characteristic, the mutual shaping and being shaped of the recursive characteristic, and finally, the social interaction processes of the translative space. Along with this is a need to embrace values connected to the co-existence of multiple cultures and identities, of playful behaviour outside of the everyday, of sensing and acting, and of connecting across differences. It is such values that pave the way for learning and creativity desired in Open laboratories. But it must also be acknowledged that living the four characteristics can also result in tensions – between participants or even within participants themselves – it is not without complication to engage in in-between spaces, and perhaps not all individuals will enjoy it or thrive in such a setting.

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As we have described, the in-between space it is indeed a social space, created and recreated through interaction among the participants. Thus it makes sense to reaffirm how indispensable participants are in the organizing of Open laboratories (Ollila and Yström, 2016). We believe that the concept of in-between space and its four characteristics points to new forms of interaction where power dynamics are shifted or broken down to enable new relationships between participants to emerge over time. While the variety and diversity of institutions into which the individuals have been socialized can make social interactions in Open laboratories challenging, such conditions are also potentially conducive to the emergence of innovative hybrid practices combining their different perspectives. These situated interactions between fields can facilitate the emergence of new practices such as freely sharing knowledge and intermingling to shape a new technology. Engaging in an in-between space is to let go of fixed ideas of “how things should be” and “how things should work”. This is an explorative space where people leave their baggage at the door, metaphorically and, quite likely, literally. Stepping into this challenging cognitive space, participants may feel that everything they hold dear about routines and preferences for getting things done are now put into question. It is through starting from this position in a different space, stripped of formal roles, that participants interact with people with different experiences, expertise and knowledge to set out together on shaping ground-breaking solutions through their joint activities. In the in-between space they interact to shape a physical space that meets their purpose and in turn are shaped by that space in their activities. The physical space is a part of creating social spaces and their form provide implicit answers to questions of e.g. legitimacy, power, control, what actions are possible, and which are not in this particular space. To achieve a fruitful Open laboratory, it seems imperative that there is consistency between the verbal messages and the nonverbal messages expressed through e.g. architecture and design. It is this mutually reinforcing relationship between the material and the cognitive that needs to be carefully understood to actualize the potential of inbetween spaces.

References Adler PS. (2015) Community and innovation: from Tönnies to Marx. Organization Studies 36(4). Bhabha HK. (1996) Cultures In-Between. In: Hall S and du Gay P (eds) Questions of Cultural Identity. Thousand Oaks: Sage Publications. Clegg S and Kornberger M. (2006) Space, organizations and management theory: Liber Oslo. Delbridge R and Sallaz JJ. (2015) Work: four worlds and ways of seeing. Organization Studies 36(11): 1449–1462. Foucault M. (1971) Orders of discourse. Social science information 10(2): 7–30. Fritzsche A. (2018) Corporate foresight in open laboratories–a translational approach. Technology Analysis & Strategic Management 30(6): 646–657.

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Furnari S. (2014) Interstitial spaces: Microinteraction settings and the genesis of new practices between institutional fields. Academy of Management review 39(4): 439–462. Hernes T, Bakken T and Olsen PI. (2006) Spaces as Process: Developing a Recursive Perspective on Organisational Space. In: Clegg S and Kornberger M (eds) Space, organizations and management theory. Oslo: Liber. Lefebvre H. (1991) The production of space: Oxford Blackwell. Ollila S and Yström A. (2016) Exploring Design Principles of Organizing for Collaborative Innovation: The Case of an Open Innovation Initiative. Creativity and Innovation Management 25(3): 363–377. Ollila S and Yström A. (2017) An investigation into the roles of open innovation collaboration managers. R&D Management 47(2): 236–252. Spivak, G.C. (1988) Can the subaltern speak?. Can the subaltern speak? Reflections on the history of an idea, pp. 21–78. Van Gennep A. (1908) The Rites of Passage London: Routledge & Kegan Paul. Weick, K.E. (1996) Drop your tools: An allegory for organizational studies. Administrative science quarterly, June 1, pp. 301–313. Winnicott DW. (1971/2005) Playing and Reality, London: Routledge. Wood A. (2012) Recursive space: Play and creating space. Games and Culture 7(1): 87–105.

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19 Navigating in the Vastness – Making Sense of the Dynamics of Consumer Choices 1 Introduction Open innovation (OI) and its subchapters open design, open manufacturing, etc. aim at exploiting input delivered by potential and actual consumers, customers, users, but also suppliers, contractors and other stakeholders as knowledge for value co-creation. Their feedback on prototypes of innovative products and services supports the alignment between needs and utility, between demands and offer, between consumers and providers. Competencies in the fields of engineering, design, manufacturing, management and marketing are enriched by establishing communication platforms and feedback loops with users and appliers (see Huff, Möslein and Reichwald, 2013). The underlying idea is that innovation can be more effective, in terms of quality or speed, when many stakeholders get involved in and contribute to the process, especially – besides designers and developers – the providers and consumers. Effective, here, is evaluated in terms of value co-creation which includes environmental impact, response to market, user acceptance, and other factors. What is of value is elaborated in direct exchange with stakeholders – those for whom something is at stake – constructively accompanying the innovation process from early stages onwards for an apt innovation- and design-related overview (see Darbellay, Moody and Lubart, 2017; for an entrepreneurial perspective see Kaufmann and Shams, 2015). This allows the incorporation and realization of value preferences and interests in a non-technocratic deliberative fashion. Open Lab and Open Innovation platforms like JOSEPHS® are established in order to explore fruitful feedback loops at an early stage of the innovation process. The interaction between innovators, product and/or service developers, providers, and potential consumers and customers allows insights into needs and desires of appliers, their functional requirements, preferences and capabilities. The effectiveness of such a process depends not only on the quality of acquired knowledge concerning intents of participants, but even more on the concern of the latitudes of such intents. The knowledge that is created using OI tools has previously been considered tacit. Thus, it requires careful analysis and refinement on a firm methodological and theoretical foundation. After all, expressed needs, desires and preferences need to withstand critical scrutiny and must – in case of value conflicts and ambiguities – be weighed

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and set into perspective with the larger framework of co-created value. In this context, it is of great interest not only to know stakeholders’ preferences and needs but also to gain insight into their latitudinal dimensions: the dynamic process of preference formation, margins of acceptability of product or service parameters, ranges of flexibility and interchangeability concerning particular features, and possible indifferences towards certain aspects. It has been observed – and we may all confirm this with our own experiences and consumption behaviour – that preferences are seldom fixed but underlie a complex process of dynamic decision-making in interplay with factors like trade-offs, re-considerations and alignment with varying conditions and knowledge thereof (see Foxall, 2010 and references within). With other words: Customers of products and services show a certain degree of tolerance concerning functional requirements. Tolerances of functional requirements in the physical domain are well accepted in academia and industry. Originally recognized as inevitable imprecision of manufacturing and measuring processes, it evolved into an active approach of tolerancing as a strategy to mitigate risks of performance, schedule, development cost, technology, market and business in a wider context of uncertainty management (Morse et al., 2018). Thus, tolerances of technical specifications – in simple contexts denoted with the ± symbol, in more complex systems using the ASME or the ISO standards – allow for versatility, flexibility, robustness and reliability as parameters of performance stability in engineering and manufacturing. Well-established knowledge of product domain tolerances, backed up by more than a century of practical experience and scholarly attention, has evolved into a sophisticated set of tools for viable co-creation of value, predominantly in the sense of utility, functionality and profit. Tolerances in the customer domain received comparably little attention to date. In disciplines like economic psychology (Foxall, 2016; Kühberger and SchulteMecklenbeck, 2018), decision science (Hamilton, 2016; Cox Jr., 2015; Roy, 2016), or neuromarketing (Daugherty and Hoffman, 2017), it is widely acknowledged that preferences and decision factors in purchase considerations are seldom clearly determined but exhibit ranges of acceptability, undergo changes, or even tend towards indifference. Yet, exploiting this knowledge for design and engineering is a relatively new idea. While several approaches to eliciting knowledge on customer domain TFR and its translation into design, manufacturing and marketing strategies are in place (for example, machine-based deep learning approaches to analyse consumer´s feedback on e-commerce platforms; Wang, Mo and Tseng, 2018; see also: Lin and Tseng, 2018), the formalization of the concept and its application in design and business have not been well developed yet. In this essay, the three themes introduced here – open innovation, value cocreation, TCFR – are interwoven and set into perspective. Experiences from three collaborative JOSEPHS®-ISTM workshops will serve as starting point of the subsequent reflections. The insights extracted from the participants’ reports on their customers’ purchase and usage patterns allow for an illustrative qualitative description of TCFR and how elicited knowledge of it has the potential to enrich OI processes.

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It will be shown how the theoretical underpinning of TCFR knowledge creates space for more meaningful interpretation and analysis of consumer input, thus identifying a pattern in the complexity of the issue, or, with other words, enabling the epistemic and conceptual navigation in the vastness of user intents.

2 Results from the ISTM-JOSEPHS® Workshops Between 2015 and 2017, three interactive workshops were held at the ISTM at Fengchia University, Taichung, Taiwan, in cooperation with JOSEPHS® and the FAU ErlangenNuremberg. While varying in details, especially in their choice of case studies, their connecting thread is the practical approach to open innovation, digitalization trends, and the impact of systems thinking on design, innovation and value creation. Participants – approximately 30 in each workshop – were entrepreneurs, innovators and businesspeople from Taiwan, as well as students of ISTM (see Figure 19.1).

Figure 19.1: Presentation of ideas for constructive deliberative discourse.

2.1 December 4th–9th 2015 – Industry 4.0 In this workshop, the participants gained a profound overview of the aims, methods and strategies of Industry 4.0 as a specific approach to mass customization in the

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age of digital systems. In a mix of lectures, practical courses and team project work, the close interaction between lecturers, instructors, students and external collaborators stimulated discussions and reflections on synergetic value creation through connection of the physical with the cyber world. Participants were asked to conceptualize wearable products that support or enable industry 4.0 applications. In FCU’s labs and workshops, prototypes could be made using 3D printing, laser cutting, and other techniques. The underlying current theme focused on the realization and manifestation of value rather than on the sophistication of the designed product itself: How can the values of one’s ideas be identified, framed, realized and exploited?

2.2 October 7th–9th 2016 – Digital Transformation This workshop elaborated further on the impact of digitization on the innovation process and the prospects of cyber-physical platforms for the shift from product/ service- to system-oriented business models (see Figure 19.2). By the example of a bike-sharing service, the participants were asked to design and develop alternative and innovative ‘digitized’ ideas that advance the bike-sharing experience for both customers and providers. A special focus was put on value analysis and capturing, employing concepts and methodologies developed at JOSEPHS®.

Figure 19.2: Explorative deliberative prototyping.

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2.3 May 7th–9th 2017 – Innovative Entrepreneurship The digital transformation of industry and society forces companies to explore new directions for innovation (see Figure 19.3). With cyber-physical systems and the internet of things, organizational boundaries lose importance and new forms of value creation involving actors and resources from inside and outside a company become possible. The participants of this workshop experienced how companies can make better use of the business ecosystems in which they and their R&D activities are embedded. In a practical design process for a prototypical solution of healthcare equipment, they paid special attention to external actors and resources and learned strategies of engagement with external contributors to innovation and value creation.

Figure 19.3: Mapping and discussing innovation space.

2.4 Connecting Theme The workshops were designed and conceptualized as facilitators of critical reflection on purposes, goals and implications of innovation processes; in short: the cocreation of value as the result of innovation activity. The close collaboration between

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the academic and the industrial realm – university scholars, researchers, students and companies, corporations, entrepreneurs – enabled the sharing of practical business experiences in combination with recent trends in the methodological development of open innovation, industry 4.0, and mass customization. Amongst other aspects, a striking insight was the notorious difficulty to know and to understand customers and consumers, their preferences and decision-making strategies, their needs and desires, their skills and technical competences and capabilities, and the influential factors that cause all of these to be dynamic and variable. Yet, knowing their related degrees of flexibility, necessity, and sensitivity concerning functional requirements and prize is of outstanding significance for entrepreneurs and product/ service providers. For example, a local fitness equipment manufacturer reported experiences with customers who purchase individualized and optimized treadmills with digital training programs, initially highly motivated to keep fit and healthy. Yet, after a few months of enthusiasm, they fall back into inactivity, with the treadmills being neglected and misused as laundry racks. Knowing these behavioural patterns may encourage the provider to develop and implement technical solutions that support the customer with maintaining a regular training routine, for example by reward systems or app-based training guides and reminders. Innovative solutions may co-create value in the sense that consumers harvest the optimal benefits from the purchased device, appreciate the producer’s efforts on personalized assistance in the form of strong commitment to the company’s products, and exploit the device’s full potential provided by the embodied functions at the main stage of its designed life cycle. The learning outcome of the workshop activities – design and prototyping under consideration of value co-creation along multi-stakeholder perceptions – has been greatly supported and somewhat confirmed by the participating entrepreneurs’ and innovators’ experiences and occasional doubtful reflections on how to deal with the complex issue of consumer behaviour and consumer needs. Wearable products require careful analysis of user’s technical capabilities and the consumer-friendliness of the interface. Advanced digitalized mobility services like bike- and car-sharing must incorporate customer demands like flexibility, convenience, usage types, trade-offs in comparison with alternatives, amongst others. Healthcare technologies are embedded in complex networks of actors, demands, laws, regulations, markets, and social trends and developments. These examples illustrate the importance and practical significance of understanding consumer choice, preferences, and needs. Moreover, they show that not only the pinpoint knowledge of functional requirements as fixed parameters is of interest, but also the latitudinal dimensions of that knowledge that understand intents (preferences, desires, needs, capabilities) as subject of various degrees of tolerance.

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3 TCFR and Open Innovation One of the primary goals of Open Innovation is the incorporation and alignment of capabilities, needs and interests of various stakeholders in the innovation process. The source of knowledge of these parameters has changed throughout the last decades. The academic expert approach – researchers and scientists study the consumer and model consumer choice and behaviour – has been extended by direct involvement of the general public as stakeholder of innovation processes. This democratization of innovation (von Hippel, 2005) is best exemplified by the JOSEPHS® innovation lab in which the public has the chance to experience and evaluate early phase innovation projects. Innovators, then, hope to gain valuable insights into the expectable acceptance and impact of their innovations so that refinements and improvements can be implemented with comparably low effort at early development stages. All in all, Open Innovation provides an effective way of eliciting knowledge on consumer/ user preferences and aligning it with interests and values of other stakeholders such as developers, manufacturers, providers, etc. So far for the theory. The novelty of the Open Innovation concept implies that hermeneutic frameworks for the empiric assessment of the acquired user feedback may lack theoretical foundations and approved methodologies. Thus, it faces difficulties in receiving wide acceptance because of its lack of empiric-scientific rigidity (repeatability, replicability, reproducibility) which is required for meaningful decision-making. Especially, the analysis of data from open innovation projects must treat any normative content such as expressions of desires, needs, interests, values, etc. carefully if it intends to turn it from tacit into viable orientational knowledge. Evaluative categories are, for example: – Desirability – from must-haves to nice-to-haves to indifference to aversion/ avoidance; – Availability – constraints of acquisition, the price to pay (in terms of environmental impact, scarce resources, exploitation, logistics, etc.); – Affordability – cost-performance-ratio, the willingness to pay for the expected utility; – Social norms and values – moral conscience and ethical judgments. Knowledge on preferences concerning functional requirements, in this context, is just as important as knowledge on tolerance levels of such preferences. With other words: Not only the states of users’ intents (preferences, needs, desires, but also capabilities and skills) need to be looked at, but also the latitude of such intents. Whereas some product or service parameters must fulfil expected functional requirements rather strictly in order to be accepted by appliers and users, other features may allow for wider ranges of acceptability. Knowing these tolerance levels enables innovators to trade off interests without losing potential customers and without missing opportunities to create utility.

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3.1 Navigating in the TCFR Space Understanding knowledge of consumers and their needs and capabilities in a latitudinal dimension changes the conceptual idea of where and how to identify overlaps and shared preferences of various users. Understanding consumer functional requirements (CFR) as trajectories of dynamic and time- and condition-dependent factors, junctions may or may not occur (Figure 19.4(a)). Extending these trajectories into a latitudinal direction (the move from CFR to TCFR, Figure 19.4(b)) creates a space in which the overlap of acceptability ranges is of important significance for the designer, provider or seller since this is the space innovators are looking for. In this respect, extending the elicitation of CFR towards dynamic and flexible ranges does not make the issue more complicated and inoperable, but visualizes patterns in the vastness that enable a more effective navigation towards value co-creation as the goal of innovation activities.

(a)

(b)

Figure 19.4: Customer functional requirements as conditional and dynamic trajectories. (a) Modelled as fixed and static; (b) Modelled as dynamic and flexible within ranges of tolerance (2D projection to be imagined as 3D).

In view of participative value co-creation as the goal of Open Innovation, modelling TCFR as static with fixed quantitative ranges of acceptability would miss an important point: the possibility of trade-offs, compromises or argumentative discourse. This kind of active tolerancing, in analogy to tolerancing as uncertainty management in the physical domain (see above), requires a dynamic model that captures the possibility of qualitative change of the acceptability range by incentives or other condition alterations. This insight is significant for the design and operation of OI platforms. When providers such as, for example, exhibitors of innovation projects seek to gain an understanding of consumer and user needs and requirements, they

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let them test their product or service and record their feedback, for example, as a survey or audio-visual recording. This methodology allows for statistical data analysis, but it is limited in a sense that it can only capture static moments. Instead, conceptualizing consumer intents as margins spun up by dynamic tolerances evokes the theme of navigating in an unknown space full of obstacles and surprises. In view of Figure 19.4 (B), this navigation process, ideally, leads to the identification of the overlapping space. The TCFR knowledge supports the navigator – a decision-maker involved in the design, development, production, marketing or distribution of a product or service – in making choices that bring her closer to the overall goal of value creation. As follows from these considerations, when the goal is to stir a constructive deliberative discourse with stakeholders including consumers, users, or the public in general, then both the design of the OI setup and the acquisition and assessment of feedback would have to be different. Innovators may employ scenario and simulation tools to present their products and services in different contexts and raise awareness among testers (visitors of the OI platform) for hidden decision factors, for example, environmental, social, legal, or ethical considerations. Evaluations, including emotional responses, face expressions, rational argumentations, and success with trial operations while applying or using the tested product or service need to be recorded in such a way that it allows the identification for trade-off pathways and incentive potentials. An example: A medical technology start-up company exhibits a new approach of a hospital service robotics unit. Aware of the uncanny valley (patients reject robots with features that are sufficiently human but still clearly machine-like, comparable to the response to zombies, see Mori, 1970), it is designed without too human features but in a way that it looks reliable and trustworthy rather than like an industrial assembly bot. The feedback of platform attendees reveals that among younger healthy visitors, the acceptance is very high and only a few design-related suggestions are made, whereas among older participants the doubts and negative evaluations prevail. The exhibitor extents his presentation by the visual experience of a 360° cinema, or virtual reality goggles, that enables simulations of future situations and scenarios, for example a development of the health care system, lack of qualified care personnel, or the differences between robot-aided care and humanaided care in terms of comfort and safety. Now, visitors can compare and evaluate a range of options. This reveals that also the rather critical elderly people can accept service robots in healthcare when certain conditions are given. Besides this ‘positive’ example – creating acceptance by widening the tolerance levels with convincing arguments – there may also be ‘negative’ approaches in the sense that a high acceptance of or affinity for particular technological developments such as surveillance technologies, data storage, renewable energy, etc., is confronted with critical arguments like adverse environmental impact, high risk of societal externalities, etc., in order to stimulate reflection and constructive deliberation on goals,

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ends and values. This approach, as a side effect, avoids the OI platform simply being used for advertisement or campaigning. Moreover, in current setups, OI is mostly employed for incremental refinement and alignment of existing and more or less progressed innovations. Yet, in order to fulfil the deeper intention of OI, it would be necessary to involve consumers at earlier stages of innovation processes so that discontinuous and disruptive developments are constructively assessed and enabled. For that purpose, the identification of consumers’ sweet spots as the state of their preferences and desires and the dynamic dimension of it (TCFR, the latitude of intents) gains particular significance. In practice, an OI platform that allows for systematic analysis of the latitude of preferences provides important service for innovators that aim at successful management and implementation of risky and more radical innovations without losing ground. In any case, shifting the focus from consumer intents – needs, interests, preferences and capabilities – to latitude of intents – tolerance, ranges of acceptance, alterability of preferences by arguments and/or incentives – allows for a systematization and consolidation of the assessment tools. When stakeholders’ feedback – not only from consumers but also from other players – is collected in terms of ranges, margins and trade-off potentials, it is more telling and insightful than statistical analysis with standard means and deviations.

3.2 TCFR and Value Co-creation The evaluative claims made above (‘more insightful’, ‘important’, ‘need to be looked at’, etc.) require a normative premise so that they can be accepted. Therefore, the connection between TCFR knowledge and value co-creation as its normative call needs to be drawn. As Figure 19.5 illustrates, value is interpreted differently in different domains (inspired by Kaihara et al., 2018, yet significantly extended). It may be summarized as representing the value ascriptions from the viewpoints of engineering, economy, society at large, individual people (insightfully represented by psychology), and – as an outstanding discipline covering all areas – normative considerations such as ethics and law. The engineering value of a product is seen in its function and safety, while the economic value is its price and profitability. For individuals, a product’s value is mirrored in the satisfaction level of the user, often measured as its utility. On the social level, satisfaction is not sufficient to represent the value of things. Instead, we may look at the fulfilment of the interest in integrity (physical, mental, ethical). Last but not least, in normative sciences, the value of something is seen in it being good/bad or right/wrong. The design, production and dissemination of goods and services deserves the label sustainable when value is created and maintained in all five fields, that means when functionality serves certain purposes and desires, when economic profit is generated, when consumers are satisfied, when societal integrity is ensured, and

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Society: Value = Integrity

Economics: Value = Price

Co-Creative Value: SUSTAINABILITY

Engineering: Value = Function

Psychology Value = Satisfaction

Ethics: Value = Norm (Good/right)

Figure 19.5: Sustainability as value co-creation.

when ethical or legal norms are supported and/or protected (see Shelley, 2017; Russ, 2018; van den Hoven Vermaas and van de Poel, 2015). It is obvious from these considerations that the satisfaction of consumers’ and providers’ needs and interests is not sufficient for business transactions to count as sustainable. Therefore, eliciting consumer needs and implementing this knowledge in innovation processes has clear limits concerning the realization of sustainability. Examining and exploiting TCFR has a much greater potential of reaching this goal. Interchangeability and flexibility as results of tolerances in user preferences allow innovators to identify options of compensating unpopular but otherwise valuable product and service features with efficient incentivizing. Individual and by trend self-interested value preferences can be aligned with environmental, economic, societal and other interests. Several positive impacts of considering TCFR knowledge in the design, manufacturing and market domain can be imagined: – Supply chain management – Providers use TCFR insights to get rid of stock of undesired items before ordering new items by providing incentives to increase acceptance of the unpopular ones. This saves resources and supply capability. Moreover, in the same manner, recycling strategies may become a viable alternative to using new materials (Wang, Wang, Mo and Tseng 2017a+b). – Logistics management – Providers who know the TCFR of their clients can optimize transportation, schedule delivery efficiently, and exploit storage capacities profitably. – Resource management – Enabled by proper TCFR-based design, producers may use cheaper, easily available materials even though customers prefer high quality but scarce materials.

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– Manufacturing and production efficiency – Studies on TCFR allow effective choices of selected variances of one type of product with one production line rather than various types with many cost-intensive production lines. – Consumption – TCFR elicitation pays off in various consumption-related benefits, for example, balancing out unsustainable consumer behaviour by setting incentives for consuming eco-friendly and ethically without decrease in consumer satisfaction; fulfilling needs while constructively questioning them. This short list of potentials and scenarios shows that TCFR – if managed well – may contribute to environmental protection, economic profitability, social integrity and consumer satisfaction at the same time. Understanding the consumer, thus, turns from a profit- and efficiency-driven motivation towards an integrative sustainabilitydriven approach.

4 Summary and Outlook The concept of TCFR has been introduced and laid out as a tool for knowledge acquisition that serves the goals of value co-creation. Expanding consumer needs and preferences into stakeholder value propositions and tolerance ranges of capabilities has the potential to incorporate normative trade-offs and sustainability considerations into the design and innovation process. Open innovation platforms provide opportunities for collecting stakeholder views and feedback as valuable input data for TFR assessment. This non-tacit normative orientational knowledge allows for its implementation at early stages of innovation projects, thus becoming constructive and value-co-creative. Conceptual and methodological obstacles need to be addressed by scholarly support, tightening the interdisciplinary collaboration of public sector academia and private sector industry. University- or academy-based institutions like JOSEPHS®, thus, serve as a suitable location for such paradigmatically innovative approaches of (open) innovation. The abovementioned benefits of TCFR knowledge stand and fall with the successful elicitation and assessment of it. If, by any method – for example, analysis of ecommerce databases with deep learning algorithms, or collection of user feedback on an OI platform – TCFR knowledge has been elicited, its translation into viable and operable product and service development objectives requires the elaboration of a theoretically well-founded heuristic framework for its assessment. On the basis of such a theoretical backbone, consumer data acquired in open innovation setups can be analysed fruitfully and in a methodologically rigorous fashion. Profound insights in decision theory (Steele and Stefánsson, 2016), social choice theory (List, 2013) and game theory (Ross, 2019) may serve a great purpose for that. With the formalization of latitudinal consumer parameters such as tolerances, ranges of acceptability, interchangeability of

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options, dynamic re-consideration of preferences, etc., a theoretical framework of knowledge conceptualization in interdisciplinary and open innovation contexts would progress greatly. To be clear, TFR does not only play a role in the consumer domain but – in view of alignment of value interests – also in other downstream domains of production. The ultimate goal is to identify and exploit flexibilities and effortless process and goal alteration potentials to increase the overall value creation that meets the interests of all involved stakeholders and third parties. Further research is needed to elaborate viable business models and knowledge management strategies to unleash the full capability of the TCFR approach.

References Cox Jr., L.A. (2015). Breakthroughs in Decision Science and Risk Analysis, Wiley, Hoboken, USA. Darbellay, F., Moody, Z. and Lubart, T. (2017). Creativity, Design Thinking and Interdisciplinarity, Springer Nature, Singapore. Daugherty T. and Hoffman, E. (2017). Neuromarketing: Understanding the Application of Neuroscientific Methods within Marketing Research. In: Thomas, A.R., Pop, N.A., Iorga, A.M. and Ducu, C. (Eds.), Ethics and Neuromarketing. Implications for Market Research and Business Practice, Springer Intl. Pub., Switzerland. Foxall, G.R. (2010). Interpreting Consumer Choice, Routledge, Abingdon, UK. Foxall, G.R. (2016). Perspectives on Consumer Choice. From Behavior to Action, from Action to Agency, Palgrave Macmillan, London. Hamilton, R. (2016). How You Decide. The Science of Human Decision Making, The Teaching Company, Chantilly, USA. Huff A.S., Möslein, K.M. and Reichwald, R. (2013). Leading Open Innovation, MIT Press, London, UK. Kaihara T., Nishino, N., Ueda, K., Tseng, M.M., Váncza, J., Schönsleben, P., Teti, R. and Takenaka, T. (2018). Value creation in production: Reconsideration from interdisciplinary approaches. CIRP Ann. Manuf. Technol., 67, 791–813. Kaufmann, H.R. and Shams, S.M.R. (2015). Entrepreneurial Challenges in the 21st Century. Creating Stakeholder Value Co-Creation, Palgrave Macmillan, Basingstoke, UK. Kühberger, A. and Schulte Mecklenbeck, M. (2018). Theories of Economic Decision-Making: Value, Risk and Affect. In: Ranyard R. (Ed.), Economic Psychology, Wiley, Chichester, UK. Lin, E.M.H. and Tseng, M.M. (2018). Tolerances of customers’ requirements: a review of current researches. Procedia CIRP, 72, 1208–1213. List, C. (2013). Social Choice Theory. In: Zalta, E.N., (Ed.), The Stanford Encyclopedia of Philosophy (Winter 2013 Edition). Available at: https://plato.stanford.edu/archives/win2013/entries/ social-choice/. Accessed April 19, 2019. Mori, M. (1970). Bukimi no tani. Energy, 7(4), 33–35 (Originally in Japanese). Morse, E., Dantan, J.-Y., Anwer, N., Söderberg, R., Moroni, G., Qureshi, A., Jiang, X. and Mathieu, L. (2018). Tolerancing: Managing uncertainty from conceptual design to final product, CIRP Ann. Manuf. Technol., 67, 695–717. Ross, D. (2019). Game Theory, In: Zalta, E.N. (Ed.), The Stanford Encyclopedia of Philosophy (Spring 2019 Edition). Available at: https://plato.stanford.edu/archives/spr2019/entries/game-theory/. Accessed April 19, 2019.

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Roy, S. (2016). Decision Making and Modelling in Cognitive Science, Springer India, New Delhi, India. Russ, J. (2018). Sustainability and Design Ethics, 2nd Edition, CRC Press, Boca Raton, USA. Shelley, C. (2017). Design and Society: Social Issues in Technological Design, Springer Intl. Steele, K. and Stefánsson, H.O. (2016) Decision Theory, In: Zalta, E.N. (Ed.), The Stanford Encyclopedia of Philosophy (Winter 2016 Edition). Available at: https://plato.stanford.edu/ archives/win2016/entries/decision-theory/. Accessed April 19, 2019. van den Hoven, J., Vermaas, P.E. and van de Poel, I. (2015). Handbook of Ethics, Values, and Technological Design, Springer, Dordrecht, Netherlands. von Hippel, E. (2005). Democratizing Innovation, MIT Press, London, UK. Wang, W., Wang, Y., Mo, D. and Tseng, M.M. (2017a). Component reuse in remanufacturing across multiple product generations. Procedia CIRP, 63, 704–708. Wang, W., Wang, Y., Mo, D. and Tseng, M.M. (2017b). Managing component reuse in remanufacturing under product diffusion dynamics. Intl. J. Prod. Econ, 183, 551–560. Wang Y., Mo, D. and Tseng, M.M. (2018). Mapping customer needs to design parameters in the front end of product design by applying deep learning. CIRP Ann. Manuf. Technol., 67, 145–148.

Pramoth Kumar Joseph, Srinivasan R, and Sandeep Lakshmipathy

20 Innovating in the Open lab – Archetypes of OI Strategies and Capabilities 1 Introduction Open innovation (OI) and related business models have captured the imagination of large and small corporations alike in the recent few decades. Open innovation is “the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation, respectively” (Chesbrough, 2006, p.2). While most firms look at OI as involving external partners in their innovation processes and decisions, it is in effect, much more than that. It could be seen as an external form of governance, including partnerships, alliances, markets and contracts, contests, platforms, and user/ community involvement in innovation (Felin and Zenger, 2014). Such a broad perspective allows for firms to leverage OI and its possibilities to access a larger base of knowledge to solve a broader range of problems, both within the firm as well as its interface with its stakeholders. In this chapter, we argue that firms need specific capabilities to engage in OI. Depending on the capabilities firms possess/ acquire, they adopt distinctly different OI strategies. We evolve a framework containing nine archetypes of OI strategies and capabilities, based on the firms’ innovation focus areas and resource deployment decisions. Over the past few decades, OI as a practice has gained significant momentum, thanks to a variety of factors: (a) development of technologies that have lowered the costs of communication across organizational boundaries; (b) assembly of alternative figurations of communications, incentives and property rights; and (c) the increasing importance of linking macro-level aspects in problem solving by managers (Felin and Zenger, 2014). The adoption of OI has helped firms transform their organizational boundaries into semi-permeable membranes, that allow for easy movement of innovation between the organization’s internal innovation processes with the external environment. Research on OI has consciously moved the phenomenon from the traditional economic arguments of vertical integration and outsourcing to exploration of internal and external sources of business opportunities, and the exploitation of these

Pramoth Kumar Joseph, IIIT, Bangalore, India Srinivasan R, IIM, Bangalore, India Sandeep Lakshmipathy, BITS, Pilani, India https://doi.org/10.1515/9783110633665-020

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opportunities through multiple channels (West and Gallagher, 2006). For firms to manage the process of OI effectively, they need to not just perform make-or-buy decisions (as in vertical integration / outsourcing decisions), but be capable of managing both exploration and exploitation (Dittrich, 2007). For an organization to successfully adopt OI, it needs to possess and manage three kinds of innovators– core inside innovators, peripheral inside innovators, and outside innovators (Möslein, 2013). Only when an organization is able to manage the duality of exploration and exploitation using conscious innovators and innovation processes, will OI provide it with commensurate benefits. Such balanced OI helps firms overcome organizational inertia, positively influences business model innovation, resulting in superior organizational performance by enabling free flow of ideas within and between organizations (Huang, et al., 2013). Researchers have enumerated a variety of OI processes, including: outside-in, inside-out, and the coupled process (Enkel and Gassmann, 2009). Outside-in process refers to the firm absorbing ideas from the external environment to enrich their own internal innovation / new product development activities. Such processes enrich the firm’s own knowledge base through integration of suppliers’, customers’ and other external sources’ knowledge with the firm’s stock of knowledge, and increase its innovativeness (Enkel and Gassman, 2009). This process helps firms leverage innovation existing outside the boundaries of the firm (Gassman and Enkel, 2004; Chesbrough and Crowther, 2006). Inside-out process refers to the process by which firms commercialize their ideas or technologies through external entities or firms, rather than their in-house R&D units. Such a process also helps in exploiting a firm’s ideas in different markets, including licensing (or even selling) intellectual property and multiplying technology by channelizing ideas to external (sometimes new) markets (Gassman and Enkel, 2004; Chesbrough and Crowther, 2006; Enkel and Gassman, 2007). This inside-out process helps the firms exploit their existing stock of knowledge and innovation faster than they could have done internally. Coupled processes integrate both inside-out and outside-in processes to create superior value and innovation outcomes. Such opening up of the firms’ business models to leverage outside-in ideas, while at the same time, allowing for internal intellectual property to be commercialized by external entities will enable firms to create and capture superior value (Chesbrough, 2007). For instance, upstream innovations in science-based industries are made patentable so that they can be utilized by technology and/ or digital enable partners downstream for commercialization, thereby creating superior value (Gambardella and McGahan, 2010). Such balances between outside-in and inside-out OI processes have been largely made possible by technological advances that have enabled digitalization of intellectual assets that could be codified and traded within and outside the firms. We argue that for such superior value to be generated by using coupled innovation processes, it is imperative that firms develop conscious innovative capabilities as well as organizational ambidexterity. In this chapter, we emphasize that innovation is a

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specific organizational capability, and for firms to benefit from their OI activities requires specific intent and resource configurations. Using two cases (one online and one offline) of open innovation platforms, we highlight why and how organizational ambidexterity is a critical organizational resource that enable firms to generate maximum value out of their innovation programmes.

1.1 Open Innovation as an Organizational Dynamic Capability We argue that OI is an organizational dynamic capability (Teece, 2007) and not every firm can/ should use it. Dynamic capability refers to the “firm’s processes that use resources – specifically the processes to integrate, reconfigure, gain and release resources – to match and even create market change. Dynamic capabilities thus are the organizational and strategic routines by which firms achieve resource configurations as markets emerge, collide, split, evolve, and die” (Eisenhardt and Martin, 2000, p. 1107). For firms to develop such dynamic capabilities, they need to develop absorptive capacity, or the ability of the firm to assimilate knowledge and information from outside the firm. It is the “ability of a firm to recognize the value of new, external information, assimilate it, and apply it to commercial ends” (Cohen and Levinthal, 1990, p. 128). The concept of absorptive capacity posits that higher the stock of a firm’s prior knowledge, the more it can develop absorptive capacity. For a firm to possess truly dynamic capabilities, it should be able to (a) sense and shape opportunities and threats from the external world (outside-in); (b) seize opportunities by deploying its existing resources (inside-out); and (c) maintain competitiveness through a conscious mix of sensing and seizing opportunities (coupled). Such a portfolio of activities would help firms in enhancing their innovative performance (Teece, 2007). Applying the dynamic capabilities framework in the context of managing a firms’ knowledge, Lichtenthaler and Lichtenthaler (2009) identified six knowledge capabilities – inventive, transformation and innovative (at the intra firm level); and absorptive, connective, and desorptive (at the inter firm level). Dynamic innovation capabilities include a firm’s stock of innovation knowledge routines and should result in transformation of a firm’s innovation knowledge resources and routines (Cheng and Chen, 2013). It is not just sufficient for a firm to have appropriate resources to undertake OI, there needs to be explicit intent to deploy those resources appropriately (Bate, 2010). Strategic intent and dynamic capabilities together influence firm resource allocation decisions and have a positive influence on firm performance. We conceptualize open innovation as an organization’s dynamic capability. For a firm to leverage and maximize the benefits arising out of open innovation, it should be able to (a) sense, collect, collate, and integrate outside knowledge into the firm; (b) assess the importance of, and evaluate the value of such knowledge; (c) convert such valuable knowledge into market opportunities; and (d) leverage

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these for superior organizational performance (Teece, 2007). Using the dynamic capability framework (DCF), we analyze open innovation as a firm-specific dynamic capability that leverages on its absorptive capacity and the ability to ambidextrously deploy its existing resource configurations as well as explore new resources from outside the firm boundaries. We use a case study approach to evolve a typology of innovation processes, with a special emphasis on how multi-sided platforms contribute to the development of open innovation capabilities.

2 OI Capability Archetypes Based on the firms’ innovation focus and their resource deployment decisions, we can classify firms’ innovation processes into a matrix (see Table 20.1) based on their OI capabilities. True open innovation occurs when firms shift towards building a system of complementary knowledge supported with thriving communities of practice (Brown and Duguid, 2001). In such a context where the stock and flow of knowledge drive organizational change and transformation, there is no choice between exploration and exploitation. Firms need to focus on both – a conscious, dynamic and strategic choice of ambidexterity in managing innovation processes (Lavie, Stettner, and Tushman, 2010). Firms that focus on leveraging external knowledge primarily, lose focus of their core asset base of knowledge; whereas firms that focus on exploiting its existing knowledge run the risk of firm obsolescence (Levinthal and March, 1993). Exploitation and exploration enhance each other, irrespective of whether the firms had their own innovation unit at all (Blindenback-Driessen and van Den Ende, 2014). Based on the firms’ innovation focus (internal or closed innovation; external or open innovation, or both), and their resource deployment decisions (exploitation of existing resources, exploration of new resources, or both), we argue that firms would need different capabilities. In the following paragraphs, we will elaborate on the capabilities required for each of these contexts. Table 20.1: OI Capability Archetypes with Closed innovation internal focus.

Closed innovation Internal focus

Exploitation

Exploration

Both

Knowledge management systems

Top-down and bottom-up innovation programmes

Ambidexterity

When the firm’s intent is to exploit its existing resources through closed innovation processes, the primary tools facilitating such processes would include robust knowledge management systems. The knowledge-based view (KBV) of the firm

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elaborates on how developing specific innovative capacity within the firm helps increase its capability to identify, assimilate, and utilize business opportunities, leveraging on its internal resources (Lichtenthaler, et al., 2009). Such focused innovative capacity enhancing processes helps firms sustain interest and maintain internal research capability, builds strong cross-functional collaboration, and promotes top-down and bottom-up innovation within organizations (Salge, et al., 2012). As firms move from exploiting existing resources to exploring new opportunities outside the firm, it needs different capabilities. Most knowledge exploration processes begin with identifying its current resource configurations and the search is conducted around existing knowledge. Internal inventions and their integration into the current knowledge base, therefore, forms the basis of developing inventive knowledge (Lichtenthaler, et al., 2009). Such inventions and integration within the firm requires the organization to be responsive and promote free flow of ideas within the firm, especially across vertical hierarchies as well as horizontal business divisions. Specific investments in innovation programmes that promote such organization-wide inventive capacities would help firms explore new knowledge from within the firm. However, it is also important that firms explore new knowledge from outside the firm. Firms also need to sense and seize opportunities from the environment and possess the ability to integrate it with its existing stock of internal knowledge, in the form of absorptive capacity (Cohen and Levinthal, 1990; Zahra and George, 2002). For increased market competitiveness, most firms would want to integrate exploitation of their existing resources as well as explore new knowledge from outside. Such firms are known as ambidextrous organizations and they possess the “ability to simultaneously pursue both incremental and discontinuous innovation . . . from hosting multiple contradictory structures, processes, and cultures within the same firm” (Tushman and Reilly, 1996, p. 24). These ambidextrous firms maximize existing product innovation competencies through exploitation and minimize other negative effects by replacing them with new competencies using exploration. The mix of how much to relatively emphasize on exploitation and exploration depends on how radical the innovations tend to be: the more radical the innovation, high competence exploitation is coupled with low competence exploration, and vice versa (AtauheneGima, 2005). Ambidexterity places a high emphasis on the firm’s ability to integrate knowledge from internal and external sources, especially in key organizational activities such as customer relationships, corporate venturing and strategic alliances (Raisch, et al., 2009). In all of the aforementioned innovation programmes (see Table 20.1), the agency of innovation is still within the firm. However, there are firms that understand the limitations of entirely anchoring their innovation within the firm, and start looking at open innovation, seeking to actively engage with innovators from outside the firm boundaries.

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For such outside-in innovation processes, when the intent is of engaging stakeholders outside the firm to exploit existing resources, the firm is typically looking to receive feedback on their own ideas, maybe a partially developed product/ service prototype or a business model (see Table 20.2). The inside-out innovation process requires transfer of intellectual property and technology to external sources, apart from the ability to absorb knowledge from outside to validate and provide feedback on the innovation process. Such stakeholder engagement in innovation processes could be explained using transaction cost economics, complementarity of assets, and absorptive capacity in the context of dynamic capabilities framework (DCF), in evolving alliances and networks (Rasmussen, 2013).

Table 20.2: OI Capability Archetypes with Open innovation external focus.

Open innovation External focus

Exploitation

Exploration

Both

Stakeholder engagement in innovation processes

User/ community innovation

Lead-user engagement and rapid prototyping

However, in the context of turbulent markets, involvement of the suppliers and/ or customers in the innovation process with the intent of seeking new resources from outside the firm (exploration) would be critical to reduce risks (Schweitzer, Gassman and Gaubinger, 2011). Such engagement in innovation processes could be done using well-thought out alliances with their current and familiar partners in their collaborative innovation efforts (Dittrich and Duysters, 2007), as well as those who are totally unrelated to the firm but are interested in contributing to the innovation process (true open innovation). Such user/ community innovation would allow the firm to continuously explore new knowledge from outside the firm from a larger community base for a variety of different sources. Most firms use open innovation in order to sense and seek opportunities from outside that would also fit with their existing resources, allowing them to both exploit current knowledge as well as explore new knowledge simultaneously. In order for firms to do this, they need to engage with lead-users and early adopters for rapid prototyping and feedback. Such engagement will help firms “develop the right technology” by enabling organizational openness and sustained user engagement (Baden-Fuller and Haefliger, 2013). Such processes would also help firms with robust business model choices (Jansen, Van den Bosch, and Volberda, 2006). As we have seen so far, the division between open and closed innovation is quite difficult to achieve, and a lot of firms iterate between the two, or use both of them simultaneously. Some of the innovation processes allow for continuous balance and/ or seamless transition from closed and open innovations (see Table 20.3).

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Table 20.3: OI Capability Archetypes with both internal and external innovation focus.

Innovation focus (both internal and external)

Exploitation

Exploration

Both

Knowledge integration

Boundary spanning

Embedded innovation

When firms adopt both closed and open innovation for exploiting existing resources, the firms integrate their knowledge with what is available in the external environment. Such knowledge integration allows for firms to continuously adapt their firmspecific competencies to needs of the external environment (Teece, Pisano, and Sheun, 1997), and such integration provides firms with much needed competitive advantage (Drechsler and Natter, 2012). Such firms may even be willing to share their intellectual property with alliance partners and their knowledge networks (Fosfuri, 2006). When organizations view innovation as a form of search for distant knowledge or new capabilities that are unfamiliar (Li, Vanhaverbeke, and Schoenmakers, 2008), the role of boundary spanning as an activity becomes critical. These boundary spanning roles ensure that organizations can continuously explore (Greve, 2007) by being both within and outside the firm continuously and enabling two-way transfer of knowledge. When done efficiently and effectively, such capabilities positively influence product innovation and market performance (Yalcinkaya, Calantone, and Griffith, 2007). There are firms that wish to integrate both modes of innovation for both exploration and exploitation. Such firms are able to define robust processes for business model innovation, service product innovation and service process innovation (Wang, et al., 2015). These firms are engaged in embedded innovation, where they engage with multiple communities of stakeholders within and outside the firm – communities of interest, communities of affinity, communities of science and communities of practice (Hafkesbrink and Schroll, 2011). Embedded innovation is defined as “the fundamental ability of a firm to synchronize organizational structures, processes, cultures with open collaborative learning processes in surrounding communities, networks, and stakeholder groups so as to ensure the integration of different external and internal knowledge, i.e., competencies or technological capabilities, and to exploit this knowledge to commercial ends” (Hafkesbrink and Schroll, 2011, p. 69). Putting all these together (see Table 20.4), we evolve a table that highlights nine different innovation strategies.

3 Method and Data We use a qualitative inductive case study method to elucidate our framework on innovation strategies. We use data from two different contexts, HackerEarth

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Table 20.4: OI Capability Archetypes. OI dynamic capability

Innovation focus

Resource deployment decisions Exploitation

Exploration

Both

Internal

Knowledge management systems

Top-down and bottom- Ambidexterity up innovation programmes

External

Stakeholder engagement in innovation processes

User/ community innovation

Both

Knowledge integration Boundary spanning

Lead-user engagement and rapid prototyping

Embedded innovation

(https://www.hackerearth.com) and JOSEPHS® (www.josephs-innovation.com). A subset of the authors collected data from these two organizations for writing detailed case studies (Srinivasan, 2016; Srinivasan, Lakshmipathy, and Koride, 2018). These case studies were written based on in-depth interviews of multiple stake holders, multiple discussions with the teams behind the platforms and data was triangulated from secondary sources such as journal articles, newspaper content and other published sources. These two organizations – HackerEarth and JOSEPHS® – represent online and offline contexts respectively of open innovation laboratories, engaged in helping firms embrace embedded innovation.

3.1 HackerEarth HackerEarth is a popular developer community and platform that brought together two key sides of the software development process – enterprises and software developers. Started as a recruitment platform, HackerEarth facilitated enterprises to host coding contests to assist in recruiting of talented coding enthusiasts in the burgeoning Indian IT marketplace. HackerEarth used its learning to develop and perfect its code evaluation engine that would help enterprises automatically evaluate the quality of code written by its potential recruits. Leveraging the same two sides, HackerEarth realized the opportunity to enlarge its services to enterprises beyond pure recruitment – helping them solve complex product (software) development challenges. This enabled enterprises access external developers, most of them independently engaged in problem-solving, for solving their challenges. Such open innovation programmes provided breakthrough ideas and innovation opportunities for enterprises, and diverse/ complex innovation challenges for independent software programmers. Furthermore, HackerEarth extended its platform to provide enterprises with internal innovation challenges (only for internal employees) for

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solving critical problems and/ or evaluating their own employees. HackerEarth realized the utility it could bring for the firm’s internal innovation engine by bridging together engineers/ developers from diverse divisions of the enterprise to participate in solving product issues through lowering of intra firm boundaries.

3.2 JOSEPHS® JOSEPHS® has been designed to operate as an open innovation laboratory, set up in the Nuremberg city center. This is a partnership between the Friedrich Alexander University of Erlangen-Nuremberg (FAU), Fraunhofer IIS, and industry partners. The intent is to attract a wide range of walk-in customers, who would see it as an extension of a retail store. JOSEPHS® was built on the core belief that when customers engage with a product or service innovation at its earliest possible stages, true customization and value addition was possible. The earlier the customer interaction in a product development/ innovation lifecycle, as in pilot testing of prototypes takes place, the more value addition can be expected. This was especially true in the context of services, where customer engagement can shape the way businesses design their products, services and business models by leveraging early interaction with real customers. JOSEPHS® has been designed to look and operate like a retail store and a workshop (werkstatt) area, both at the same time. The workshop design was achieved with a special focus on the ambience and interiors that signaled a certain degree of seriousness and informality, promoting innovative and creative behavior. The visitor interaction area (the manufactory) has therefore, been designed to look similar to a workshop with a high table and spaces for documentation of feedback and interactions. A coffee shop at the entrance helps attract random visitors into JOSEPHS®; a space for organizing meetings/ events (denkfabrik, or a thought factory) attracts scientists and technically oriented visitors; spots for housing tenant firms attracted entrepreneurs and firms for solving specific research questions; and the workshop area helped showcase specific technologies as well as collect data from visitors and firms.

4 Discussion For true embedded innovation to happen, it is imperative that they work with OI enablers such as HackerEarth and JOSEPHS®. These two platforms representing online and offline multi-sided platforms are architected in a manner that provides firms much-needed capabilities to engage in coupled open innovation (open and closed, simultaneously) as well as develop ambidexterity in innovation.

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Firms could become ambidextrous by pursuing (i) sequential ambidexterity as firms shift structures over time, (ii) simultaneous or structural ambidexterity as firms create separate subunits for exploration and exploitation but at the same time integrated to utilize resources and capabilities, and (iii) contextual ambidexterity as firms create processes or systems that enable and empower individuals to make their own decisions on how best to contribute to exploration and exploitation (Tushman and O’Reilly, 1996). Achieving a balance between exploration and exploitation also helps develop beneficial complementarities (Chen and Katila, 2008). Within the firm, presence of strong values, manager rotation and internal trainings have been known to promote ambidexterity (Markides and Chu, 2009). A diversified firm with independent business units that were provided with operational autonomy with centralized strategic and financial control had greater success with achieving ambidexterity and innovation (Markides and Chu, 2009). Chiu, et al (2011) outlined the concept of innovation ambidexterity and highlighted openness, strategy and innovativeness as key dimensions of ambidexterity. They argued that for firms to excel in innovation via ambidexterity, they need to balance exploitation and exploration for radical transformation (Chiu, et al., 2011). For an ambidextrous firm to adopt coupled open innovation (both inside-out and outside-in), it is imperative that appropriate integration mechanisms are adopted. Such integration mechanisms may include feedback loops and interactions between co-creators; processes that promote tight integration of knowledge received from outside through open innovation activities into the internal R&D to extract complementarities; and mechanisms for developing and sustaining firm absorptive capacity. In order to successfully assimilate external knowledge through collaborations, internal R&D organizations may be organized into varying degrees of openness – totally closed, integrated, specialized, and totally open (Lazzaroti and Manzini, 2009). Such absorptive capacity and synthesis of external knowledge and internal knowledge is critical for commercialization (West and Bogers, 2014).

4.1 Embedded Innovation at HackerEarth and JOSEPHS® As a cloud-hosted platform, HackerEarth made it seamless for external and internal innovators to participate in open and closed innovation activities that an enterprise could leverage to leapfrog itself in the market. By bringing together open and closed innovation on the same platform, HackerEarth helped overcome the challenges to using proprietary IT tools and processes that would have otherwise been a hinderance to seamless innovation. HackerEarth facilitated the discovery of talented developers for enterprises (recruitment based on code evaluation engine), matched the needs of specific problems to the external innovators (open innovation) as well as internal challenges (closed innovation). The platform architecture helped HackerEarth exploit its internal resources (internal developers solving firm-specific

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problems) as well as explore new resources from outside (independent developers solving enterprise-defined problems). The four dimensions of JOSEPHS®’ architecture ensured that it operates a multi-sided platform with significant same-side and cross-side network effects across all sides of the platform – the random visitors, tenant firms, Fraunhofer IIS/ JOSEPHS® staff, University researchers, and workshop participants. This architecture ensured that tenant firms engaged in open innovation by engaging with random visitors and workshop participants, as well as closed innovation through deep engagement with FAU researchers and Fraunhofer IIS staff. The architecture of JOSEPHS® helps firms exploit their internal resources (prototyping and product development capabilities) as well as exploring new capabilities from outside the firm (through leveraging the strengths of random visitors and technical expertise available at JOSEPHS®).

5 Archetypes of Innovative Firms Having seen how OI platforms such as HackerEarth and JOSEPHS® enable embedded innovation, let us also explore the kinds of firm capabilities that are required to engage in each of the nine innovation strategies. We aver that each of these innovation strategies require specific capabilities, which we map out in this concluding section. We describe each of the capabilities with an example. a) Knowledge management systems: Consulting and professional service firms such as McKinsey have invested heavily in developing robust knowledge management systems to enable every front-line employee of the firm have access to entire firm’s knowledge. Within McKinsey, it is labelled as “One firm” concept (Maister, 1985). b) Top-down and bottom-up innovation programmes: Traditional innovation departments in mature firms like Corning (www.corning.com) that keep moving from one new innovation to another (exploration) over a period of time thereby establishing its credentials as a focused and innovative firm. c) Ambidexterity: Autonomous operating business units (BU) with sufficient authority to make innovation decisions at the operating level, with corporate new ventures business unit in global corporations such as the P&G ventures (https://pgventuresstudio.com/whoweare) enable both exploitation (by the business unit R&D unit) and exploration (by the corporate new venture unit) at the same time. d) Stakeholder engagement in innovation processes: Firms such as Eli Lily have created an open innovation community of scientists to advance innovation in biomedical science (Narsalay, et al., 2017) that have enabled seamless stakeholder engagement in the firm’s innovation processes.

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e) User / community innovation: Technology firms competing in hyper-competitive markets like PrecyseTech (www.precysetech.com) have engaged with solution providers across the globe using online platforms such as Innocentive), to develop new products faster to the market. f) Lead-user engagement and rapid prototyping: Mature firms such as Microsoft have invested in establishing innovation opportunities at the periphery of the firm, like Microsoft for Startups (https://startups.microsoft.com). Such organizational structures help firms engage with disruptive technologies without the risk of cannibalizing their core, enabling OI for ambidextrous firms. g) Knowledge integration: Open data projects such as Data World (https://data. world/datasets/uber) provide opportunities for collating and sharing of data on firms (for example, rides data sets on Uber and RideAustin). Such projects allow for firms to throw open their internal data for broad analysis by the external community of innovators and subsequently utilizing the analysis within the firm. h) Boundary spanning: In order to enable exploratory research with both open and closed innovation, it is critical for multiple actors to come together. For example, the concept of Medical Valley EMN (http://en.medical-valley-emn.de/), where a variety of innovation organizations including government, large mature organizations, universities, as well as innovative startups collaborate and co-create new ideas and concepts as well as technologies. i) Embedded innovation: Specialized OI laboratory platforms such as HackerEarth and JOSEPHS® help firms engage in embedded innovation through simultaneously engaging in open and closed innovations, as well as develop ambidexterity. Typical examples are firms that seek to innovate their service design and delivery processes – MyBoshi (https://www.myboshi.net). Table 20.5 integrates these examples into the nine innovation strategies elucidated above.

6 Conclusion Open Innovation (OI) has caught the attention of managers across the world to push the boundaries of the firm’s innovative capabilities. In this chapter, we elucidate nine different strategies for the practice of open innovation strategies and provide directions for which OI strategy to choose under what conditions. We evolve a two-dimensional framework – firms’ innovation intent and resource configurations. Using a case-study approach (a pure online and another offline lab), we demonstrate what open innovation laboratories need to add, in order to help firms to develop ambidexterity as well as exploit OI capabilities.

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Table 20.5: Strategies for OI capability archetypes. Exploitation

Exploration

Both

Closed innovation Internal focus

Knowledge management systems “One firm” concept in consulting firms like McKinsey

Top-down and bottom-up innovation programmes Traditional innovation departments in firms like Serengiti Corning

Ambidexterity Autonomous business units with central R&D teams in diversified corporations

Open innovation External focus

Stakeholder engagement in innovation processes OI community of scientists at Eli Lily

User/ community innovation Technology firms like PrecyseTech innovating using OI platforms like Innocentive

Lead-user engagement and rapid prototyping Mature firms such as Microsoft Accelerator using crowdsourcing or engaging with incubators/ startups for innovation

Innovation focus (both internal and external)

Knowledge integration Open data projects that house data from firms such as Uber

Boundary spanning Traditional scientificacademic partnerships, such as the Medical Valley, EMN

Embedded innovation OI in service design and delivery using platforms such as HackerEarth and JOSEPHS®

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Part V: New Frontiers for Open Labs

Albrecht Fritzsche

21 Open Labs as Islands of Reason in the Digital Age 1 Innovation Management and the Digital Age The digital transformation of business and society has brought about a golden age of innovation management. With the ubiquitous availability of interconnected digital devices, seemingly endless new possibilities for innovation have opened up, which require organisation, guidance and control. Big data allow a detailed analysis of industrial operation, an optimisation of process planning and predictive maintenance (Lee et al. 2013; Chen et al., 2016; Gölzer and Fritzsche, 2017). Integrated models of physical and informational objects create new potential for business activities, where agency can be assigned not only to human actors, but also to advanced decisionmaking algorithms that respond dynamically to changing status information (Atzori et al., 2010; Oks et al., 2017). The algorithms can either be implemented in specific physical objects or become accessible through new forms of smart services that draw on different resources at the same time (Lee et al., 2014, Boukhris and Fritzsche, 2019). As a consequence, radically new patterns of value creation emerge that are expected to provoke a fundamental change of industrial structure (Porter & Heppelmann, 2014; Skog et al., 2018). All this is addressed by a new notion of digital innovation that has lately gained increasing popularity among many researchers (Nambisan et al., 2017; Yoo et al., 2012). Digital technology has had another effect on innovation management as well. Worldwide communication networks, online communities, virtual design studios and digital manufacturing techniques like 3d-printing have also changed the settings in which innovation takes place, the people it involves and the interactions it permits (Möslein and Fritzsche, 2017). They have turned the attention of innovation management to the importance of users as contributors in all phases of the innovation process and higher degrees of self-organisation in all activities that it includes (Chesbrough et al., 2006; von Hippel, 2005). During the last years, immense efforts have been spent in academia and industry to find out how innovation can proceed under such circumstances (Bogers et al., 2017; Huizingh, 2011). From these efforts, a lot has been learned about the possibilities of innovation management in the digital age. In view of these developments, the concept of an open laboratory as it was discussed so far in this book may look like a strange anachronism. A place where people meet in person to discuss innovation seems to miss out on everything that has

Albrecht Fritzsche, Ulm University, Institute of Technology and Process Management, Ulm, Germany https://doi.org/10.1515/9783110633665-021

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lately happened in the field, and the question arises how open laboratories can go on to provide added value for innovation management in the digital age. The final part of this book assembles five chapters that give very different answers to this question. As an introduction, the following pages contain a draft for a general understanding of open laboratories as “islands of reason” in the digital age, a term that is taken from Weizenbaum and Wendt (2015). It highlights the importance of open laboratories as spaces where innovation can unfold without being dominated by any predefined structure and objective, fostering a form of technological activism that avoids the pitfalls of misleading visions and supporting a sustainable social and economic development.

2 Technological Evangelism and the Pitfalls of Self-Referentiality Innovation brings novelty to the world. While a lot has been written about innovation, novelty has received much less attention, and the few papers that discuss it in more detail choose very different approaches (e.g. Roth, 2009; Fritzsche and Dürrbeck, 2019). Novelty is essentially a negative term. It refers to something that is not there yet, something that is noticeably different from anything we already know. Therefore, by bringing novelty into the world, innovation always takes a step into the unknown. It changes the ability of an organisation to act and opens up new directions for its development. Existing knowledge is expanded, products and services are replaced, customs and routines are changed and markets are redefined. With Schumpeter (1994), innovation is often described as creative destruction. Its consequences are very hard to predict. Unsurprisingly, organisations are often reluctant to engage in innovation activities that force them to abandon well-accustomed structures and expose them to uncertainty (Christensen, 1997). Phenomena like the digital transformation, however, make it very clear that change is inevitable. The only question is who takes the lead in its pursuit and who falls behind. Insofar as organisations have to rely on vague assumptions instead of solid facts about the consequences of their innovation activities, they constantly need to reassure themselves that they are on the right track. In addition to the actual solution development, innovating organisations therefore have to explore so-called “techno-visionary futures” (Grunwald, 2018): anticipations of the outcome of innovation during the nest years. Techno-visionary futures are shaped by many different factors, including foresight activities (von der Gracht et al., 2010; Daheim and Uerz, 2008), storytelling in popular culture (Perkowitz, 2016; Fritzsche and Dürrbeck, 2019), but also the active interference with public opinion by technological evangelists (Kawasaki, 1991). Technological

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evangelists advocate certain directions of technical progress and raise expectations about potential benefits (Maher, 2015). Many organisations employ their own evangelists to influence public opinion and gain support for their activities (Mankevich et al., 2018; McInerney, 2007). Professional evangelists are usually experts with a lot of knowledge and experience in engineering. It allows them to extrapolate from their own work in the anticipation of the future and gives them credibility in public. Early examples of evangelism in the context of digital technology date back to the second half of the twentieth century, when Weizenbaum (1976) and others studied the discourse in the field of artificial intelligence (see also Weizenbaum 1992; 1978). Based on examples from the treatment of his own software applications, Weizenbaum shows that technological evangelism is hardly affected by the results of science and engineering projects. Even if the evidence from practice points into completely different directions, the underlying techno-visionary futures can remain the same. In line with Horkheimer (1974), Weizenbaum interprets this phenomenon as a failure of reason. Due to the prophetic nature of the predictions that are made, arguments against them on the basis of hard evidence remain unsuccessful. Technological evangelism seems to adhere exclusively to the patterns of an economy of attention (c.f. Franck, 2019; Goldhaber, 1997). It is concerned with media coverage, the attraction of funding for project activities and similar incentives that do not relate to the outcomes of innovation activities, but only their acceptability as efforts that correspond with a plausible narrative of progress. As a consequence, situations can arise in which techno-visionary futures are much more robust to change than the actual solution development, as they adhere to publically acceptable or purposively challenging narratives which are not up to date. This is illustrated, for example, by the portrayal of future technology in popular culture, where the devices and their functions are highly innovative, while the purposes for which they are used and the problems that the protagonists have to face remain very old-fashioned. Weizenbaum’s work has strongly contributed to the development of professional ethics in computer science and related disciplines (Capurro, 2017). It also resonates in the efforts that are recently undertaken to promote responsible innovation (Guston et al., 2014). At the same time, however, the digital transformation has created conditions under which technological evangelism can expand in new directions that have little to do with professional expertise. And without any further intervention or ethical grounding, the emerging structures of innovation management in the digital age do very little to discourage it. Crowdsourcing approaches to innovation, for instance, take advantage of the fact that global communication networks make it always possible to find someone somewhere who likes one’s ideas and supports them with time, effort and money for a while. Even the craziest project can get some positive response, without having to bother with concerns that others might raise. Online communities of technological enthusiasts work in a similar way, as they define themselves by the interest in a topic

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and most of the time also by specific sets of moral values in the treatment of technology. Individuals who do not share these values are not expected to be part of the community and there are many ways how they can be excluded – explicitly or by subtle social pressure. Digital technology does not actively create filter bubbles around such activities (Pariser, 2011), but it provides conditions under which it becomes very easy for the people who are involved to filter out anyone and anything they do not want to address. To a certain extent, the flourishing start-up scene shows comparable patterns, as it incentivises new ideas and concepts with financial support and public attention before they are exposed to actual market pressure. Most entrepreneurial exercises in educational institutions also miss the final steps of the development process that would prove whether the students have spent their time on valuable ideas or just practiced visionary thinking in a sandbox. Furthermore, entrepreneurship usually proceeds exclusively in a project mode, which unites a small group of people in pursuit of a common endeavour. Critical questions regarding the goal of the project activities challenge the identity of the whole group and delay their work. Project teams can therefore be expected to show little tolerance for such questions. Due to the enormous numbers of people who are pushed to engage in innovation and entrepreneurship in the digital age, there are, of course, a few who will come up with valuable results. For all the others, however, the situation looks rather bleak. Except for the personal experience they gain, there is fairly little that will remain from their activities and even less that can be shared. In line with Horckheimer’s (1974) original analysis, innovation is reduced to an unreflected evolutionary process that dismisses all the opportunities to gain insight from critical discourse and to progress towards a better understanding of the directions that innovation should take in the digital age.

3 Technological Activism and the Open Laboratory As an alternative to technological evangelism, Weizenbaum (1992) recommends a form of technological activism that takes the exact opposite approach in dealing with critique. It is dedicated to the same principles of falsification as experimental scientific research. Technological activism in Weizenbaum’s sense exposes itself willingly to the risk of being wrong. It actively seeks out experiences of failure as a chance to learn and gain a better understanding of the subject matter. Alongside with this approach goes a much more cautious treatment of grand stories of progress (cf. Latour, 2009) and a deliberative reasoning process (cf. Habermas, 1984). Instead of forcing visions of technical progress upon others, it takes one step at a time in the development of new devices and patterns of value creation to understand what solutions are actually possible and to turn the efforts of innovation into the right direction.

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After all that has been said so far about open laboratories in this book, it seems clear that they provide a suitable setting to support this form of technical activism. The heterotopic character of open laboratories ensures that grand stories of progress remain in an unfinished and debatable state. As part of a larger social endeavour, innovation in the open laboratory is subject to constant negotiation, which gives insight into its correspondence with actual needs and desires of the people who are involved. It remains exposed and vulnerable, which, as Joseph(sic) Weizenbaum may have put it, would send shivers down the spine of every evangelist seeking reaffirmation and authority. Open laboratories can lay the groundwork for a more sustainable and less self-referential form of innovation in the digital age, which stays closely connected with social discourse. In this sense, they can become what Weizenbaum and Wendt (2015) describe as islands of reason in the cyberstream: spaces where innovations and innovators can mature at the same time. Although the following chapters do not necessarily share the points of view that are expressed here, they all can be related in one way or another to Weizenbaum’s notion of technological activism and the open laboratory in the digital age. Steven Rader and Amy Kaminski discuss the concept of the open laboratory as a means to support innovation at NASA. There is probably no other institution on earth that depends on a responsible treatment of innovation as much as NASA. The development of solutions for space travel requires technical excellence, diligence, but also public engagement and support for the ambitious projects of the next years. For a long time, NASA has used open innovation in various ways. Rader and Kaminski give an overview of these activities and relate them to the idea of a virtual lab, which allows all stakeholders to work together. David Sarpong and Amit Rawal turn the attention towards the idea of a do-ityourself lab. They describe work in the do-it-yourself lab as an expression of citizenship in the pursuit of innovation without interference from large organisations. Sarpong and Rawal discuss the conditions under which value can be captured from the activities in the lab and their implications for management. Anna Krefting and Hanan Prince discuss the role of the open laboratory in cultures that have been marginalised by the industrial revolution. Using the example of the Nubian region in southern Egypt, they explain how an open laboratory can enable new forms of innovation based on traditional patterns of interaction that are otherwise supressed. Based on personal experience, they list a variety of success factors for the implementation of an open lab under such circumstances and its productive operation. Max Jalowski concludes the book with an exploration of the possibilities to support work in the open laboratory with smart devices. Drawing on recent experiences with the digital transformation of industry, he develops the concept of a cyberphysical lab, where the physical interaction of the innovators is accompanied by information processing in digital devices to enable more efficient participatory design activities. Jalowski develops a framework to assess the added value of smart

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devices for participatory design and applies the framework to various different kinds of artefacts. The four chapters show very clearly that research on innovation in open laboratories has so far only covered a small fraction of the numerous aspects of the whole phenomenon. Despite all the insights that have already been gained, there is still a lot to learn about the open laboratory and its role in the dawning digital age. Open laboratories require the attention of many different scientific disciplines, including management studies as well as sociology, engineering and design theory. Furthermore, open laboratories can be places where these disciplines find new ways to interact and share their experience to expand innovation research to new horizons. This could be the beginning of a wonderful friendship.

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Steve Rader and Amy P. Kaminski

22 A Virtual Laboratory for Open Innovation in Space Exploration: The NASA Tournament Lab 1 Introduction NASA, the U.S. civil space agency, has long been known for its innovations that have enabled more than 60 years of space exploration including rovers to Mars, probes around our solar system, and humans to the Moon (Launius and McCurdy, 2017). Historically, these capabilities have resulted from the talent that the agency has recruited and hired or has accessed through its contractor base and academic research network. However, more recently, NASA has begun to move outside its traditional workforce to find and develop innovative solutions. These efforts started in 2005 after NASA received authority from the U.S. Congress to conduct and fund prize competitions open to the public as a way to access and develop innovative solutions to some of NASA’s hardest problems. Inspired by the X Prize’s success in using prize purses to incentivize teams to form and compete to meet audacious technology goals, this new authority led to the formation of NASA’s Centennial Challenges prize competitions (Byko, 2004; Davidian, 2005). This initiative continues today and has posed nearly two dozen challenges that have resulted in significant aerospace technology advances and has provided a funding multiplier to research and development efforts. NASA now has a robust prizes and challenges portfolio that includes not only the Centennial Challenges but also the NASA Tournament Lab, the International Space Apps Challenge (the world’s largest hackathon), and a number of studentfocused engineering design challenges (Gustetic et al. 2015, Kaminski et al., 2016). The agency also supports a robust set of citizen science projects. Together, these initiatives bring NASA technical problems and mission needs together with a global pool of potential problem solvers, in effect creating virtual marketplaces or laboratories for open innovation to take place. NASA has used the virtual collaboration that crowdsourcing platforms and communities provide in a wide range of applications. Through prize competitions, challenges, freelance crowdsourcing, microtasking, and hackathons, NASA researchers and technologists have tapped into new fields of expertise and skill sets and have identified solutions to various technical

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problems, found new or emerging technologies with space applications, developed or improved technical and creative products, such as graphics, videos, or user interfaces. Citizen science platforms and communities have enabled NASA to extend its cadre of observers and analysts, in turn broadening the ability to gather data and accelerate research in the space and Earth sciences. All of NASA’s crowdsourcing approaches have a secondary contribution to public and student engagement with science, technology, engineering, and mathematics. This chapter will focus on how one of NASA’s crowdsourcing initiatives, the NASA Tournament Lab (NTL), which is operated through the Center of Excellence for Collaborative Innovation (CoECI) at NASA’s Johnson Space Center, serves as a virtual open innovation lab, connecting NASA technology and other needs with problem solvers around the world. We will show that NTL provides an organization and a framework for promoting, enabling, facilitating, and evaluating the use of crowdsourcing communities to fulfill NASA’s mission to explore space. We offer examples that illustrate NTL’s effectiveness in serving its purpose and share insights about the value of open innovation spaces and virtual laboratories as they might be useful for other fields and industries.

2 The NASA Tournament Lab: Tools and Effectiveness In 2009 President Obama shared his Open Government Initiative, promoting a vision to make U.S. Government departments and agencies more transparent, participatory, and collaborative in their dealings with the American public. Around that time, the Johnson Space Center’s Space and Life Sciences Directorate had begun to experiment with using crowdsourced challenges to support technology developments when the program’s budgets began to shrink. The White House Office of Science and Technology Policy took note of NASA’s success with these approaches and urged the agency to establish CoECI in 2011. This office was tasked with promoting and facilitating these new open innovation techniques across the NASA organization as well as in support of the needs of other U.S. federal agencies. CoECI set up a virtual laboratory that it branded the NASA Tournament Lab (NTL) to bring various commercial crowdsourcing platforms on contract. To date, the NTL has used multiple contract mechanisms to access 16 different crowdsourcing platforms to run more than 350 challenges/projects for NASA and 15 other U.S. federal agencies. The basis of NTL is the NASA Open Innovation Services (NOIS) contract, a fiveyear, multi-vendor contract. The current NOIS contract includes 10 companies that specialize in crowdsourced challenges. These companies include Topcoder, Kaggle, Innocentive, NineSigma, HeroX, Luminary Labs, Common Pool, OpenIDEO, Tongal, and Patexia. Each company specializes in particular types of challenges; Topcoder,

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for example, focuses on computer coding challenges while Tongal supports video development and creative content competitions. Each has its own “curated community” of participants registered to its website to participate in challenges it supports. The ten NOIS vendors represent access to almost 40 million people from around the world. NTL uses these platforms to run challenges to solve hard technical problems using challenges that are looking for ideas, conceptual designs, detailed designs, algorithm improvement, software development, videos, or even working prototypes for NASA and other U.S. federal agencies. The NTL has been used to execute over 100 challenges for NASA and other federal agencies. NTL challenges have been solved 48% of the time with positive progress on 92%. For 73% of these challenges, it is estimated that the traditional approach to finding a solution would have been more expensive. To date, these challenges have saved an estimated $20M. NTL challenges have been used to solve difficult technical problems. For example, the NASA Earth and Space Air Prize challenge (NASA Earth & Space Air Prize, 2017) resulted in two working prototypes for significantly improved affordable aerosol sensors. Challenges have resulted in algorithms with improved performance like NASA’s Asteroid Data Hunter challenge (NASA, 2014), that delivered an algorithm that can detect 15% more asteroids than the previously existing capability could. NTL challenges have even generated highquality software applications like the Food Intake Tracker (NASA, 2013), an iPad app for the International Space Station astronauts. NTL surveys indicate that 98% of the users within NASA and other federal agencies that have run an NTL challenge report a positive experience and that they would use challenges again. NTL also includes a contract with Yet2, a company that specialized in searches for technologies targeted at technical problems. This tool uses crowd networks instead of crowd-based challenges and is very effective in finding emerging technologies that can be applied to a specific need. This capability has been increasingly important due to the exponential growth in the technology development and startup community. Yet2 has also been very successful with a 93% success rate and has resulted in over $1M in cost savings. These technology searches have successfully found multiple previously unknown technologies for solid-state power amplifiers, landing system LIDAR, heads-in displays, and jet engine controllers. NTL has also pioneered and facilitated the use of low-cost challenges and freelance work as a way to virtually collaborate with the public in contributing to NASA’s mission. NTL has used the U.S. Government’s “purchase card program,” which is limited to procurements under $10,000 to engage with crowdsourcing platforms like GrabCAD, Freelancer, cOutsource, and Amazon Mechanical Turkto develop mechanical designs, graphics, videos, software, and machine learning data sets. These low-cost challenges or freelance projects have been very effective with a solve rate of 73% and a positive outcome 100% of the time. The micro-purchase challenges and projects show a cost savings 92% of the time over traditional methods with the average savings of 58%. Additionally, this method provides an

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affordable way for NASA projects to “try out” this new virtual collaboration toolset while giving the public lots of opportunities to engage and contribute to NASA’s mission. These challenges have been used to develop a mechanical design for 3-D printable handrail clamps for the International Space Station (ISS), a smartwatch application for ISS astronauts, robotic arm designs for a free-flying robot for the ISS, and even a training course for developing Delay/Disruption Tolerant Network (DTN) protocol-based software. CoECI also has established an internal NASA community of employees and contractors that collaborate to solve posted challenges. NASA’s internal employee crowd or virtual collaboration space is called NASA@work and currently is hosted on the ideaScale platform. About 24,000 of NASA’s 55,000 government and contractor workforce participates on NASA@work. CoECI posts 2–4 challenges a month to the NASA@work site and the members respond with solutions to those challenges while other members comment and collaborate around those solutions. CoECI has posted over 150 challenges and 93% of those challenges have reported a positive outcome. Of those challenges where cost estimates were provided for how much a similar solution would have costed using traditional methods, 84% of NASA@work solutions were less expensive with the average cost savings being an astounding 82%.To date, the NASA@work program, which is free for NASA projects to use, has saved NASA an estimated $8.3M. This platform has been best for finding and sharing existing design knowledge across NASA while accessing the incredible creativity, expertise, and innovation of the NASA employee and contractor community. Significant outcomes from these challenges include finding an existing prototype for a desired method for measuring astronaut urine output in micro-gravity, finding new ideas and technologies for sensing biomarkers, and developing novel operations concepts for future Mars settlements.

3 Lessons Learned NTL has served as a virtual laboratory in the sense that it has facilitated the pursuit of open innovation; in addition, through CoECI’s long-standing relationships with the Harvard Business School and the Massachusetts Institute of Technology (MIT), NTL is a site of research into the mechanisms and effectiveness of open innovation and how collaboration with crowds work. The experience of using the NTL for collaborating with crowdsourcing communities has revealed and/or validated the following learnings about open innovation. Diversity is a powerful force in innovation. Diversity in the crowd provides a distinct advantage to finding the novel innovation that can make a significant advance in the capability or even solve unsolved problems. This was demonstrated clearly in one of NTL’s early challenges on the Innocentive platform called “Data

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Driven Forecasting of Solar Flares” which aimed to increase NASA’s ability to predict solar flares from 2 hours to 4 hours in order to better protect astronauts on the ISS. The winning solution actually provided a working algorithm that could predict solar flares 8 hours before they occurred. However, the solution did not come from the heliophysics community but rather from a semi-retired communications engineer who repurposed the mathematics used to extract radio signals from noise to the solar flare prediction problem. One study out of MIT (Jeppesen and Lahani, 2009) examined successful challenges on the Innocentive platform and found that 70% of the solutions came from individuals that were outside of the technical domain of the challenge owner. This pattern has proven itself repeatedly in NTL challenges where solutions to space suit waste management problems have come from laparoscopic surgical tools and space communications security solutions have come from synchronous computing techniques. The virtual collaboration that comes from large diverse crowds very often brings in approaches or insights that are not visible to the domain experts that have been working on a problem for a long time. Passionate communities of practice are a powerful force. A number of crowdsourcing communities are built around a domain or passion. Tongal is a community of 100,000 film-makers. Topcoder is a community of 1.4 million software developers and data scientists. Kaggle is a community of 1.6 million people that are passionate about machine learning. GrabCAD is a community of 5 million mechanical engineers and designers. Many of the people from around the world that participate in these communities are there to learn and connect. For many, challenges posted on these platforms are opportunities to learn, collaborate, and show off one’s talents. As a result, the challenges on these platforms have some fairly amazing outcomes. NASA’s initial challenge on GrabCAD to design a 3D-printable handrail clamp for the ISS resulted in almost 500 CAD designs that were quite impressive. A recent challenge that the NTL ran for the U.S. Department of Homeland Security on Kaggle to improve the threat detection algorithm for millimeter-wave scanners in airports resulted in a 98% accurate algorithm that was far superior to the current state of the art. A NASA challenge on Topcoder to improve the detection of asteroids using telescope imagery resulted in a 15% improvement in the detection algorithm. The combination of collaboration among the crowd and the competition between the crowd using a challenge has proven to be a very powerful approach to solving hard problems and getting very high-quality results. Opening up projects to public participation can provide a win-win. Virtual collaboration labs like the NTL afford the opportunity for the public to participate and contribute to an organization’s mission. NASA benefits from identifying unknown solutions to its problems as well as from receiving very positive public support from around the world; concomitantly, many people are interested in making a meaningful contribution to its mission. For NASA projects that have used micropurchase challenges and projects to obtain a graphic, a design, or a video using the crowd, they find that they get a high-quality product at a very low cost. As NASA

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projects are very cost constrained, this is very helpful to projects while engaging the public in a meaningful way. Moreover, this same “drive to contribute” is not limited to NASA. Innovation has to start with understanding the problem. While the goal of this virtual collaborative lab was to find innovative solutions, the most valuable, high-value problems were actually hidden in the organizations that sought to use a challenge approach. For many organizations, it turns out that it takes a process to really frame the most important problems where solutions will have the most impact. This process basically requires understanding the goals, establishing a “gap”, and then decomposing the problem. The first step is establishing the overarching goals for the system or process. For most NASA systems, these goals come down to minimizing mass, power, and volume while increasing performance (thrust, sensing fidelity, computation, reliability, etc.). The best goals are ambitious “giant leaps” that seem to be on the edge of possibility. Understanding the target values of the design goals is important (½ the mass, 1/10th the power, 25% performance increase, etc.) because it is then possible to measure the distance between the current system performance and the goals. This is the “gap” and establishes the bigger problem to solve. The next step is to decompose the problem to find those areas with the smallest scope and the largest impact on closing the “gap.” For example, if one is trying to reduce mass by ½, then she can examine what the heaviest components are and create a challenge around reducing mass in those components. Community curation is an important component of getting ongoing highvalue results from any crowdsourcing platform. For commercial crowdsourcing platforms, it is helpful when finding and selecting platforms to understand how each curates its crowd. This is also important for organizations implementing internal crowdsourcing platforms, like NASA@work, since then the organization is the one having to curate the crowd. Probably the most important crowd curation function is communicating expectations. Crowd members join and participate in communities effectively when they understand what they are doing and how the process works. Communities tend to become ineffective and even have negative impacts if they have unmet expectations (for example, they thought the challenge owner was going to implement their winning idea and didn’t). For many communities, members are participating as a way to connect with others around the community’s goals. The program and platform should facilitate conversations, social connection, and a sense of community to engage the participants. These types of features should also be used to drive users to regularly engage in the platform and community. The bottom line with crowd curation is that one usually gets out of a community what one puts into it. If one establishes a community that draws in participation, one has a much better chance of getting that community to provide value. Communities can be very different. Innovation or problem-solving communities tend to be very diverse in their technical backgrounds, which, as discussed

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previously, is key to discovering innovations that must come from outside the challenge owner’s discipline. However, specialty communities tend to have lots of people that are passionate about a certain technical area and thus provide access to very high levels of expertise or skills. Since incentives to participate are not always intuitive and vary by platform type, the best platforms understand their unique community and what motivates members to do work on the platform and engage in challenges or projects. Incentives are an art and a science. Part of crowd curation is also understanding what incentives work with the community and to offer incentives that mobilize their participation. In crowdsourcing, incentives are often broken down into 4 “G”s: Gold, Guts, Glory, and Good. The most obvious and visible incentive that many crowdsourcing platforms use, and especially for the “challenge” model, is monetary (“gold”). Most all challenges have some monetary prize for the winning solution(s). However, the amount of those monetary awards can vary greatly between communities, and Harvard has found in some of its research that it is possible to over-fund a prize and actually drive participants away from a challenge (Boudreau, Lacatera and Lakhani, 2011). The second motivation is “guts,” which involves tapping into people that have a passion for the challenge. Sometimes it is what the challenge is about, but more often, it is simply how interesting or challenging the problem is. This is seen especially in the data science area where participants will often comment on how they were drawn into a challenge because of how interesting the problem was. Note that this can include using a challenge as a learning experience. “Glory,” or prestige, is also a key incentive. This is many times implemented in some sort of gamification on a given platform, like earning a virtual badge. This is particularly in play in expert or discipline focused communities where “standing” is measured within the community. Glory can also relate to the client name brand like NASA. The prestige of winning a NASA challenge actually affects monetary prize amount and, on average, NASA challenges can offer about 30% less prize money and get the same results as a commercial company. The final incentive type is “good,” or altruism (Phillips, Tang and Rader, 2018). Many community members are driven to make a difference in the world. That incentivizes them to participate and even spend resources in pursuit of contributing to a greater good. NASA is actually often seen as an institution that contributes to the common good. Most community members’ participation is driven by more than one type of incentive, and so it is important for the platform to offer multiple incentives in parallel to maximize crowd participation and collaboration. Finally, it should be mentioned that there can be disincentives as well. The intellectual property (IP) position of the winner’s solution can drive participation, especially of companies who may have business models that rely on long-term revenue from IP development. If a challenge owner asks for ownership of the IP, he may eliminate the very groups he needs to encourage to participate.

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4 Conclusion The NTL, one of NASA’s several crowdsourcing initiatives, is a unique example of a virtual innovation space that serves as a marketplace that connected large online crowdsourcing platforms and communities to NASA problems in search of solutions. Engaging with these large communities to find innovative solutions to hard problems has provided NASA and others with a tool set that has been quite effective, supplementing more traditional forms of innovation such as the use of an inhouse workforce, contracts with private companies, or grants to academic research institutions. The rate of technology advances across disparate industries continues to increase and is being driven by low-cost access to powerful technology building blocks and tools such as additive manufacturing, machine learning, open software interfaces, nano-technology, CRISPR, and blockchain to name a few. As the quantity and diversity of knowledge, technology, and skill increases, these new crowdbased collaboration tools are emerging as the best ways to find and match problems with solutions. While these open innovation tools have matured over the past 15–20 years and are used by many institutions in the private and public sectors, they are still considered new and are not well-understood by many organizations. At NASA, CoECI remains committed to promoting the workforce’s adoption and use of the NTL and crowdsourcing approaches as an important and powerful component of NASA’s innovation toolkit.

References Boudreau, K.J., Lacetera, N. and Lakhani. (2011). An Empirical Analysis of Innovation Contests Management Science, Articles in Advance,INFORMS, 1–21. Available at: https://www.nasa. gov/sites/default/files/incentives_and_problem_uncertainty.pdf Byko, M. (2004). SpaceShipOne, the Ansari X Prize, and the materials of the civilian space race. JOM Journal of the Minerals, Metals and Materials Society, 56(11), 24–28. Davidian, K. (2005). Prize competitions and NASA’s Centennial Challenges program. International Lunar Conference (pp. 09–08). Gustetic, J. L., Crusan, J., Rader, S., & Ortega, S. (2015). Outcome-driven open innovation at NASA. Space Policy, 34, 11–17. Jeppesen, L.B. and Lakhani, K.R. (2009). Forthcoming. Marginality and problem-solving effectiveness in broadcast search. Organization Science, 20. Kaminski, A., Buquo, L., Roman, M., Beck, B., and Thaller M. (2016) NASA’s Public Participation Universe: Why and How the U.S. Space Agency Is Democratizing Its Approaches to Innovation. AIAA Space 2016 conference. Launius, R. D., & McCurdy, H. E. (Eds.). 2017. NASA Spaceflight: A History of Innovation. Cham: Springer. NASA Earth & Space Air Prize. (2017). Available at: https://www.earthspaceairprize.org/ (accessed July 24, 2019).

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NASA. (2013). International Space Station – Food Intake Tracker. Available at: https://www.nasa. gov/content/international-space-station-food-intake-tracker (accessed July 24, 2019). NASA. (2014). Asteroid Data Hunter Challenge. Available at: https://www.nasa.gov/content/ asteroid-data-hunter-challenge-0 (accessed July 24, 2019). Phillips, P. Tang, S. Rader, S. (2018). Surprising Results from Large Crowds Using Micro-Purchase Challenges – Using Contests on Freelancing Communities to Source Innovative, Impactful and Cost-Effective Solutions. Paper for the Laboratory for Innovation Sciences at Harvard (LISH) for the Crowd Academy, September 2018. Available at: (https://innovationscienceguide.org/re sources/surprising-results-from-large-crowds-using-micro-purchase-challenges-usingcontests-on-freelancing-communities-to-source-innovative-impactful-and-cost-effectivesolutions).

David Sarpong and Amit Rawal

23 From Open Labs to DiY Labs – Harnessing ‘the wisdom of crowds’ for Innovation 1 Introduction Tapping into the ‘wisdom of the crowd’ to support innovation has come to dominate contemporary discourse on managing innovation at the firm level. Organised under the rubrics of open innovation (Chesbrough, 2006), this new turn to innovation management helps organisations to sense, explore, and exploit distributed knowledge, ideas, and insights, located beyond the boundaries of the firm to support organisational innovation efforts. Open innovation has become popular amongst organisations, since it enables them to delve into ideas outside of the silos of their corporate research and development [R&D] labs at a reasonable cost, as well as reduce pressure to use internal resources (Asakawa et al., 2010). Beyond these benefits, leveraging open innovation has almost become imperative for firms embedded in high-velocity environments to achieve a competitive edge (Ahn et al., 2017). In this regard many organisations have distinctive strategies, programs, and organising processes specifically designed to helping them to delve into the ‘wisdom of the crowd’ to support their innovation. Pushing past the boundaries of the firm, some organisations have gone as far to create spaces for their customers, suppliers, and other value network partners to interact with products and services, with the goal of them helping to create new products and services (Piller, Ihl and Vossen, 2011). Organised around the logic of co-creation or co-innovation (Wikhamn and Styhre, 2019; Leminen et al., 2018), these quintessential corporate innovation hubs, have become the focal point of attention of innovation research on open laboratories. Several companies have developed diverse open laboratories that employ diverse methodologies to create and tap into novel and useful ideas located beyond their internal R&D laboratories (Fritzsche, 2018). Xerox, Apple, Google, and Cisco, for example, have physically set up innovative workshop centres within their firm (Berger and Brem, 2016). BMW, alternatively, partners with another lab organisation, Maker Space, which enables their staff to make use of Maker Space workshop facilities (Troxler, 2016). Others, such as DHL, host workshops for customers, to discover, discuss and co-create services to aid customers across the world (Fournier, 2017). Companies have also embarked in forming a ‘digital’ lab, which entails online sites for customers to discuss ideas in a forum style (Möslein and Fritzsche, 2017).

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For example, Starbucks has its own customer co-creation site, ideas.starbucks,com (What’s your Stabucks idea, 2019). Customers can submit, talk through and refine innovative ideas as well as amend prototypes of new products on their dedicated site; they are then awarded for their contributions to encourage further engagement (Sigala and Chalkiti, 2015; Welch and Buvat, 2015). At the extreme end of the open lab continuum is what has come to be known as DiY Labs. These are community hub independent labs, they involve conducting basic to advanced experiments with new scientific technologies in private settings, more aptly known as ‘hackspaces’ (Downes et al., 2013; Halfacree, 2004). These hackspaces can be set in atypical lab environments, including for example, museums or private homes (Ellis and Waterton, 2005; Meyer, 2013). DiY Labs are also created by professionals for anyone, irrespective of their educational background and skillset (Gonlinelli and Ruivenkamp, 2016). They are based on an open-sourced principle meaning that they can involve an unlimited number of research activities (Wolinsky, 2009). Some of these labs have focussed on basic molecular biology investigations, to advance recombinant DNA technology and gene-editing (Sleator, 2016; Wolinsky, 2009; Revill and Jefferson, 2013). While recent research on open innovation has extended our knowledge about open labs and how they may contribute to firm level innovation (Cruickshank, 2016; Toupin, 2014; Berger and Brem, 2016), our integrated understanding of how organizations could contribute and benefit from DiY Labs remains impoverished. In this regard, we know far less about the activities of DiY Labs and how the context within which they operate could potentially serve as sites for the identification of opportunities for innovation. In response, this paper seeks to extend our understanding of the emergence and significance of DiY Labs to the identification of distinctive opportunities for innovation and how organizations could contribute and benefit from them. The paper contributes to the existing broad literature on open innovation in two ways. First, in addition to exploring the potentialities of DiY Labs to democratising science and innovation, we shed light on the emergence of new forms of organizing that may lead to the identification of opportunities for innovation. Second, we go further to suggest some potential ways of profiting from DIY Labs and calls managerial attention to new in ways to exploring and exploiting opportunities otherwise overlooked by their competitors. The remaining paper is structured as follows. First it provides an overview of open laboratories as strategic contexts for harnessing ‘the wisdom of crowds’ for innovation. Next is a cursory overview of the rise and organizing logics of DiY Labs as community-based innovation hubs. Following this, we delineate the potentialities of DiY Labs serving as a context and opportunity for the identification of opportunities for innovation. We then propose some potential ways organizations could profit from DiY Labs, as demonstrated with a Heuristic Framework before we conclude.

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2 Open Laboratories as Innovation Hubs Open laboratories offer a range of benefits for the organisation they form part of, primarily enabling the firm to have access to complementary technologies, knowledge, and ideas to support their innovation activities (Thestrup and Robinson, 2016). Open laboratories could potentially help firms to make strategic use of their external environment to capture ideas and also monitor how their internal inventions are utilised by their customers and other external actors (Chesbrough and Crowther, 2006). In this regard, open laboratories promote inbound open innovation, through engaging back and forth with customers and other organisations, to trigger the development of product innovations (von Hippel, 2009; Chesbrough, 2006). Open laboratories do so by putting staff and customers together to collectively align on new concepts, crucially empowering customers to work with firms to find and develop novel solutions to their problems (von Hippel, 2009; Cruickshank, 2016). Hatch (2014) reports that firms partnering with workshop organisations like Fab Labs are able to provide customers tools to directly produce prototypes and solutions for free or at an affordable price during their open laboratory workshops, bringing their innovative solutions to life. From a wider perspective, firms can also enhance their commercial awareness as they probe and evaluate social trends, open-ended futures, and inarticulate and unconscious societal aspirations that are discussed within open laboratory workshops (Toupin, 2014). Existing evidence suggests that many firms have benefited from these innovative open laboratories. DHL for instance, noted that their workshops with customers significantly improved their rate of customer satisfaction (Fournier, 2017). Accenture has also developed its own Innovation Hub which acts as a valuable tool for the corporation to showcase their innovations to partners, clients and other stakeholders (Accenture, 2019). Elsewhere, Starbucks engaging their customer base through their ‘online’ open laboratory have been soliciting for new ideas to support their innovation processes (Ståhlbröst et al., 2013). Open laboratories grant organizations the power to vary the approach they take to manage conversations and the entire innovation discourse they seek to orchestrate (O’Hern and Rindfleisch, 2010). Such control over the operation of the open laboratory workshop, organizations can obtain stakeholder perceptions of a service they are developing, gathering feedback in stages, and extending the organizations understanding of the conditions under which their innovations could potentially be speciated (Chesbrough and Crowther, 2006). As an extension of outbound innovation, Fritzsche (2018) noted the influence of open laboratories on how organizations could cultivate and nurture their corporate foresight potential so they could survive better in their business environments frequently characterised by change, ambiguity, and complexity. Providing opportunities for organizational members to subject their assumptions to scrutiny, and question the viability and sustainability of their business models vis a vis competing alternatives (Fritzsche, 2017; Rene, 2010). Akin to the DiY movement of the late 20th century (e.g. Toffler 1980), community hub

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independent labs conducting basic to advanced experiments with new scientific technologies are gaining popularity. We argue that these communal labs, oftenreferred to as DiY Labs, which are popping up in cities around the world (Kuiken, 2016; Rognoli, 2015), could potentially serve as sites for the identification of opportunities for innovation.

3 Do-it-Yourself (DiY) Labs Organised around open-source principles, DiY Labs are independent communitybased science research hubs, often set up by Scientists and Science Enthusiasts to learn, experiment and get involve with the world of Science, technology and innovation [STI] advancement. These ‘citizen laboratories’ are flourishing because they are attracting volunteers, communities, groups, and venture capitalists, making them alternative homes for talent located within and beyond the theoretical boundaries of universities and organizations keen to open up the processes of science, technology, and innovation to the public (Hecker et al., 2018; Sleator, 2016; Landrain, 2013). In carrying out basic and often advanced experiments in private buildings often labelled as ‘hackspaces’, DIY Labs are challenging the near monopoly of traditional institutions such as universities and private organizations as the fundamental locus for practicing science (Downes et al., 2013; Halfacree, 2004). They do this by providing context for people to meet at unconventional settings and locations to try their hands on scientific experiments, discuss and share knowledge on emerging technological trajectories, and alternative potential means to push scientific frontiers further. Providing scientific educational outreach and putting tools into the hands of those who want to learn, DiY Labs have come to represent a platform for science and engineering innovation at the grassroots level. Thus, the new turn to DiY Labs promise to demystify and democratise STI by enabling amateurs to conduct basic and surprisingly complex experiments (Sleator, 2016; Meyer, 2013), and fostering citizen science in areas such as molecular biology, recombinant DNA technologies, bioinformatics, genetic engineering and gene editing [e.g. CRISPR/Cas9] technologies. Recent concerns and apprehensions about the operations and regulations of DIY Labs (Ferretti, 2019; Wolinsky, 2009), their ethical implications (Fiske et al., 2019; Wexler, 2016), and the ambivalences of their hazards in fostering responsible science (Tanenbaum, et al., 2013), have put these labs in the spotlight. Critics have gone further to argue that DiY Labs pose security threat to public health and environmental safety as they often operate free from rules and regulations that oversee the operations of the well-established firms and universities (Gorman, 2011). Their unregulated experiments, conducted in rudimentary facilities including kitchens and garages, often breach international laboratory protocols, and as argued by Revill and Jefferson (2013), might accidentally or intentionally unleash devastating

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consequences on human life. Beyond these concerns, we see DiY Labs as a natural extension of open laboratories whose potentialities in challenging organizations and universities as the only place to do serious research, offers an alternative model for the search and identification of opportunities for innovation (Seyfried et al., 2014). The starting point for organizations to profiting from these labs, we argue, is to commit to the new turn to democratising innovation, engaging with DiY Labs, and treating them as independent open-ended innovation crucibles, whose activities and practices could start or disrupt existing innovation and technological trajectories.

4 DiY Labs as Open Innovation Crucibles While many DiY Labs tend to specialise in particular innovation and technological domains, they frequently have no limits on the ‘research’ and innovation activities they can undertake. Conceptualised as innovation crucibles (Gonelli and Ruivenkamp, 2016), we argue that the open source principles on which DiY Labs operate qualifies them as quintessential sites for the identification of limits and the generation of opportunities for innovation that could be tapped by organizations in three distinct ways. Firstly, these laboratories provide unprecedented spaces for people from diverse occupations to collaborate and explore scientific problems in the same environment. DiY Labs therefore serve as a locus to sharing and mobilizing differential, competing, and often disparate visions of individuals to pushing further the frontiers of science, technology, and innovations that have the potential to change lives (Sarpong and Maclean, 2012). Second, free from bureaucracy, protracted funding, and the trappings of the traditional publishing systems, DiY Labs are able to respond to the discontentment of formally organized research communities to explore place emphasis on alternative ways of ‘doing’ science and prioritise free open access channels to ‘communicating’ science (Wolinsky, 2009; Nicholson, 2012). In so doing, DiY labs have come to represent the centres of excellence that conduct basic and, mostly, blue-sky research that might otherwise not be funded by research councils because value cannot be readily captured from their current applications (Griffiths, 2014; Ferreti, 2019). In exploiting new technologies such as 3D printing to help them induce cheap and cost-effective lab equipment for their experiments, DiYLabs are not only becoming viable centres to pursuing ‘serious’ science outside universities. They are gradually becoming much more accessible to the public as the cost of running them keep going down (Tanenbaum et al, 2013). Third, many DiY Labs tend to host volunteer-organised ‘hackathons’ where technical people and laymen are invited to work together to find creative ways of “overcoming the inherent limits of a system, improving, re-appropriating or

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subverting it beyond the original intentions of its creator” (Patterson, 2017). Such innovation festivals, we argue, provide a playful environment for creative individuals to assemble, wind-tunnel their ideas, and build collaborative projects in safe spaces.

5 Capturing Value from DiY Labs Beyond the hope and hype, the ownership structure of most DiY Labs, the cost of running them, and the open source principles on which they operate presents challenges for the management of their intellectual property and patent rights (Landrain et al., 2013; Gorman, 2011). How could organizations potentially create and capture sustainable value from DiY Labs? In responding to this lacuna, we present a heuristic framework that could provide organisations with relevant insight into their own ways of organising and managing their innovation activities that can prepare them to capture sustainable value from DiY Labs to support their innovation activities. Our heuristic framework is organised on the premise that the focal firm has a R&D function embedded within their organisation. As shown in the Figure 23.1, the dotted arrows indicate the continuous flow of boundary spanners or personnel joining DiY Labs. We delineate key ways organisations could capture value from DiY Labs along three lines of attention. First, by incentivising employees to join DiY

DiY Lab 3

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Collaborative Partnerships

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Focal firm Figure 23.1: Capturing value from DiY Labs.

Sponsoring DiY Labs Activities

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Staff participation in DiY Labs

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Labs so they may be exposed to new ideas and insights located beyond the confines of firms’ R&D labs. The second involves the orchestration of strategic partnerships with DiYs so they can access their potentially complementary knowledge and ideas to support their organizing processes. Finally, they stand a greater chance of capturing sustainable value from DiY Labs by sponsoring their Hackathon tournaments that have come to represent independent strategic creative forecasting sites.

5.1 Incentivising Staff to Join DiY Labs Joining a DiY Lab is the first step to gaining first-hand access to their praxis and the discourse shaping their everyday doings and organizing. In this regard, the starting point for organizations seeking to profit from DiY Labs is to encourage their employees to join DiY labs in their communities. In particular, organization can create and develop specific incentives schemes aimed specifically at their R&D employees to get them jump onto the DiY bandwagon. Following their participation in DiY Lab events, R&D teams can organise periodic internal meetings, for staff to discuss and share their experiences in participating in DiY Labs and potentially what the organization can learn from them. In order to obtain buy-in to such scheme, organizations may have to proactively endorse the advantages of these DiY labs to their employees, highlighting the possibility of sharing their skills with other professionals, and contributing to improving the public understanding of science (Sleator, 2016; Meyer, 2013). Bring employees into contact with customers, DiY Labs provide a platform for communicating and scrutinizing new ideas (Songwu, Riqi and Anping, 2009; Seyfried et al., 2014; Berger and Brem, 2016). Providing context for employees and customers to interact in real time, continuously reflect on their “doings”, share their thoughts, and describe their interpretive schemes, they serve as sites for enacting new technostructures and wind-tunnelling ideas in safe environments. This rich, socially embedded know-how can be contextually captured to improve customer capabilities, business models, and in turn sustainable value creation and capture (von Hippel, 2009 Chesbrough, 2006). DiY laboratories thus have the power to not only develop the talent of the focal firm, they also put staff in the driving seat to accelerate innovative changes within the firm.

5.2 Establishing Strategic Partnerships with DiY Labs Beyond encouraging their employees to join DiY Labs, we encourage organizations, particularly, their R&D departments, to go further to establish strategic partnership with DiY Labs within and beyond their immediate geographical location. The ultimate objective here is to unearth exactly what actors involved in DiY Labs actually

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do, thereby bridging the gap between the theory and practice of innovation as something people do. These partnerships could involve supporting the research that DiY Labs undertake and backing them as genuine research hubs. However, these strategic partnerships should be based on two caveats, the first being that the firm’s R&D employees and other employees can freely access these DiY Labs with ease, thus facilitating our first recommendation to incentivise staff to join DiY Labs. The second being that to a certain degree, the research undertaken at these DiY Labs should in the broadest sense, be ethical, near cutting edge, and if possible be of interest to the focal firm. This could involve projects in search for commercial applications and strategic fit between technology and markets aimed at enhancing product portfolio (O’Hern and Rindfleisch, 2010). These strategic partnerships with DiY Labs may also entail the building of a series of alliances with other businesses, universities, and institutions that may also sought to build bridges with with DiY Labs. Such initiatives, we surmise, do not only have the potential to mobilise different actors within an innovation eco-system focus on exploring specific nascegeneric technologies with broad potential applications (O’Hern and Rindfleisch, 2010). They also legitimise the new research model promoted by of DiY Labs, support their growth in the community, and in turn, contribute to the new turn to democratising innovation (von Hippel, 2009; Chesbrough, 2006), and support the research performance of the focal firm (Asakawa, et al.,2010).

5.3 Sponsoring DiY Labs and their Hackathon Tournaments As an extension of the strategic partnerships forged between firms and independent DiY Labs, we propose that firm’s as part of the R&D investment can support DiY Labs and their creative forecasting events. These events which frequently take the form of Hackathon tournaments do not entail the search for technical precision; neither do they require just technical skills for people to take part. Rather, it’s about usefulness and the identification of viable area(s) of application following the development of a technology (Joe and Fiona, 2018; Simon, 2015). Thus, Hackathon tourneys provide opportunities for the study how people interact with technology in their daily practices and the resultant enacted structures [rules and resources instantiated in recurrent social practice], are (re)constituted in their recurrent engagement with the technologies at hand. Sponsoring such events, we argue, gives the organizations the incentive and leverage to uncover new layers of meaning and stake their claim in framing the initial boundaries of emerging technologies. At a time when scarcity of highly skilled talent in the technology world is becoming a real challenge, having access to such events provide opportunities for winning the race to identifying and recruiting talent otherwise overlooked by competitors to supporting the focal firms in-house innovation activities.

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6 Conclusion In this paper, we have sought to examine the organizing logics of open innovation, focusing on DiY Labs, and how organisations could potentially profit from them. Conceptualized as innovation crucibles, we unpack how these Labs have come to represent a strategic site for the identification of opportunities for innovation. Organized along three strategic lines of attention, we delineate three mutually inclusive ways of organizing that could potentially support organizations to benefit from the activities of DiY Labs. These includes incentivising staff to join local DiY labs, the active building of strategic partnerships with DiY Labs, and committing to sponsoring DiY Labs and their Hackathon events. Departing from the essentialist view of prescribing rules of engagement, to placing emphasis on what organizations can do to exploit the potentialities of DiY Labs, our heuristic framework on capturing value from DiY Labs, we surmise, could be leveraged by organisations’ to harness and exploit ideas from DiY Labs to support their innovation activities in ways otherwise overlooked by their competitors.

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24 Tapping into Cultural Richness – Open Labs in Nubia 1 Beyond Abstraction and Formalization Innovation is a term mostly discussed in business and engineering, but it has undoubtedly deeper roots in human life and its development over time. References to innovation can accordingly be found in many disciplines, from psychology to history, which all draw on more basic philosophical thought. Here, German Idealism of the early nineteenth century plays an important role, as it shifted the attention of philosophy from being towards becoming (Hegel, 1807). Scholars of German Idealism started to study the development of individual or collective intellect over time and the emergence of reason (Schelling, 1800), thus providing a foundation for everything that is now discussed in the field of innovation. The basics are rather simple: In their native state, human beings experience themselves as a unity with the world. Then, they gradually develop a distance to objects in the environment. This distance allows reflection about means and ends, which eventually leads to new insight. This philosophical model has been further developed and abused in different ways. On the one hand, it has found its way into historical and economic theories through the works of Marx (1887). On the other hand, it has also left many traces in works on other topics such as educational psychology, where the generation of distance also is considered as a foundation for development. Scholars in the early twentieth century described ontogenetic as well as phylogenetic human development as a process of abstraction, leading towards the ability to think in formal terms (Piaget, 1950) and decontextualized concepts (Vygotski, 1962). Indeed, there is evidence that western civilisations have gone through this kind of a development. Krämer (1988), for example, finds increased abstraction in the development of language and thought from ancient Greece to the modern information society with the computer as a universal calculation machine. It is tempting to conclude that all this is inevitable: that any kind of sociocultural progress necessarily goes along with an increased use of formalized symbols, letters and numbers, and algorithms processing them. Such a narrative, however, becomes questionable, as soon as one looks beyond the horizon of industrial civilisations. In other places, society and culture have evolved differently. Thinking of them as less developed negates all their own achievements, just because they do not fit into the right pattern. What these cultures have accomplished in the past centuries and millennia, is insufficiently appreciated. This

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applies to indigenous cultures, under which the Nubian culture can be subsumed, but also arts and craft cultures which are still sustained in many areas of Western Europe and other industrialized regions. To some extent, the current digitisation of business and society also raises doubts about the inevitability of increased abstraction, as it turns the attention away from complex logical symbols and expressions and rediscovers the value of images, sounds and holistic displays of application contexts. This leads back to spontaneous, intuitive experience of the world, which many theorists of the nineteenth and twentieth century would probably have considered a backlash into earlier stages of human development. Where real-life development takes another turn, it is also necessary to review the concepts and methods used to study and control it. Accustomed views require extensive revision. The concept of knowledge provides an important example. It nowadays expands into areas which would not have been related to knowledge before, as they include tacit content which can neither be put into formal-symbolic expressions nor shared and reflected (Goffin & Koners, 2011; Oguz & Sengün, 2011). The treatment of such forms of understanding gains increasing attention in a variety of different contexts, such as interactions in communities, built around practices that evade static hierarchies. This phenomenon creates new opportunities for cultures that do not conform to classic western organisational schemes to follow their own tradition and still progress – differently than industry has done so far and true to their own past. New forms of innovation break down the impassable boundaries of formal education in academic and industrial research and development (Fritzsche, 2016), up to a degree where the notion of technical devices itself becomes performative in a given social context (Fritzsche, 2017). All this involves a mixture of sound, image, text and other forms of expression as tools for making the world accessible, create environments to live in, define social roles and perceptions of the self, whether through digital programming, articulation of the body, imaging, simulation or visual construction, working with the past and the future in the present (Ascott, 2000). Open Labs have already been widely recognized as a means to expand innovation activities to new social and cultural settings and involve new interest groups (Gershenfeld, 2008; De Arias et al. 2014; McPhee et al. 2012; Cohen et al. 2016). So far, however, little has been said about their potential to surpass established operative structures and norms of interaction and tap into the richness of other cultures to find new directions for innovation. As Fritzsche (2018) has shown, innovation spaces in open labs can be approached as third spaces on the background of social theories that are compatible with post-colonial thinking (Bhabha, 2004; Rutherford 1990). Expanding this line of thought, the following pages show on the example of a Nubian community in Aswan in the South of Egypt, how this additional contribution of an Open Lab to innovation can be realised and how it can create value.

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2 Background: A Short Sketch of the Nubian Culture Allan has shown Nubians and their land as the “objects” of development, whether at the hands of the Egyptian state or in the eyes of international development organizations. It should go without saying, however, that Nubians and their land are not onedimensional abstractions (Allen 2016). Accordingly there is no simple answer to the question “who are the Nubians.” Historically speaking, the term “Nubian” denotes those people who settled south of the Nile’s first cataract, from Aswan to Dongola, in what is now Egypt and Sudan. “Nubia” which denotes a dynamic and constantly shifting space of land below Aswan, long predates territorial notions of Egypt and Sudan. It was on these lands that the ancient Kingdom of Kush reigned intermittently from before the third millennium B.C. until 350 A.D. Many discussions of contemporary Egyptians begin with references to Egypt’s Pharaonic history. Similarly, many people, including those with Nubian background, inextricably link the notion of “Nubians” and “Nubia” to an ancient past, which includes both national mythologies and actual history. One of the most pervasive assertions in Nubia is that Egypt’s Nubians had not changed or evolved since Pharaonic times they were supposed to represent a distant ancestor to modern-day Egyptians, frozen and unchanging since ancient times (see Allen, 2016). Nubians have watched development projects transform their land for over a hundred years. When president Gamal Abdel Nasser began plans to construct the Aswan High Dam in the 1960s, he relocated around 50,000 Egyptian Nubians from their historic homes in the name of ‘development’. Nasser was the first person to refer to this group as the “Nubians” when discussing the relocation process with Egyptian media (Allen 2016). Before this moment, no one had ever used the term “Nubians” to describe this group of people. While in the past the Egyptian government had largely dealt with the Nubians on an individual basis, they were now treated as a large and monolithic whole, as a problem of resettlement (Allen 2016) that needed to be solved. They became Egyptian citizens at an important juncture in Egypt’s history, when Nasser defined this sovereignty through modernity, technological prowess, and expertise. In this discourse Nubia is regarded as Egypt’s African ancestor – an ancestor that links Ancient Egypt to the rest of the North African cultures. The strongest tie appears to be the Nubian pastoral nomadic lifestyle, the same pastoral background commonly shared by most of the ancient Saharan and modern sub-Saharan societies. Thus, not only did Nubia have a prominent role in the origin of Ancient Egypt, it is considered a key area for the origin of the entire African pastoral tradition (Gatto, 2009) with an emphasis on matrilineal lineage as the core of the society where family heritage is divided and distributed according to the woman’s lineage. The Nubians of Upper Egypt distinguish among themselves in a number of ways. There are well over fifty different Nubian villages, each with their own history. There are two different languages that Nubians speak, Kenzi and Fadicca. A Kenzi speaker does not not understand Fadicca, and vice-versa.

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Furthermore, a large number of Nubians lives outside of Egypt, for example in the Gulf, Europe, and the United States. There are also Nubians who settled in cities across Egypt, such as Cairo and Alexandria. In the younger generation of Nubians, there are some that have never visited the “Old Nubia,” which refers here to the land between the Nile’s first cataract and the Egyptian border with Sudan, a rough approximation of the region where Nubians in Egypt used to live. Some Nubians may not even self-describe as Nubian, but rather as Egyptian, Egyptian-Nubian, African, Islamist, or Socialist, or any number of labels of self-identification.

3 How is it Related to Egypt and the Western World? Because of the compulsory relocations that Nubians faced during the early twentieth century due to the construction of dams near Aswan, very few Nubian villages remain in their original locations. The original villages that do still exist are located near Aswan city and West Aswan. As a peripheral population existed largely outside Egyptian government administration before the high dam project relocation, the image of Nubians as a traditional people bound by their limited geographic resources and unchanged since antiquity was a dominant narrative during the time of the relocation (Allen, 2016). Additionally, international intervention (in the context of the high dam) in Nubia did not consider the cultural preservation. The example shows the large discrepancy between an international development organization and the group of people upon which they implemented their projects. Both national and international actors like the Egyptian government and the Ford Foundation told Nubians that they are underdeveloped, and Nubians continue to fight that label to this day. Nubians themselves, however, recount this history differently: When Nubians came to the new villages, most of them were educated. They established schools for example prep schools and primary schools. They came with their educated teachers, and their knowledge education level was much higher than the villagers they found there. The non-Nubian villagers in this area had to study in their schools. They did not have any educational institutions Therefore they (the Nubians) did not only teach in their schools, but also in the schools of non-Nubians (Allen, 2016). The literature available on Nubians today is vast and multidisciplinary in scope. Archaeologists and Egyptologists have long been interested in studying the historical artefacts located in the former Nubian lands. From the nineteenth century onwards, numerous western institutions sent archaeological missions to excavate and document monuments in Egypt’s south. This trend intensified after Nasser’s announcement to build the High Dam, when the United Nations Educational, Scientific, and Cultural Organization (UNESCO) inaugurated the International Campaign to Save the Monuments of Nubia [1960–80] in order to excavate and record the historical

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artefacts south of Aswan before the flood. The International Conference on Nubians Studies, which has begun in 1966, gathers every four years to present archaeological findings from Nubia (from both Egypt and Sudan). Considering Nubians not as a discrete population, but rather as an integral component of the Egyptian’s state identity, the Open Lab research will have to take into consideration the significance of Nubians as a minority, peripheral, traditional “other” against which mainstream Egypt has formed its identity from the Nasser period until today. ‘Development’ of Nubians and their land was an integral component in the construction of Egypt as a modern state (see Allen, 2016).

4 An Open Lab in the Nubian culture New models of co-creating communication (and learning) in an Open Lab constitute a fundamental contribution to the global strategies of self-learning services. As mentioned elsewhere in this book, they can create new ways of image based knowledge transfer, opening potential that so far has been disregarded. Besides questions of possibilities and boundaries of self-learning, visual strategies of digital communication provoke questions of visual recognition and general consciousness procedures, including aesthetic categories of likes and dislikes and their awareness. The example discussed here expands extant work on Open Labs in new directions. It gives insight into open lab structures as critical means of observing digital images as idea generation tools for knowledge transfer between English and Arabic speaking knowledge communities in an African context. Regarding the complexity of media communication, open labs are confined to link thinking ‘virtual’ and thinking ‘actual’ (Deleuze, 2002). Agha has identified the Nubian concept of ‘memory’ as a nostalgic strategy, which allows contemporary Nubians to move between the two spaces and to move features from one to another, especially the noted reincarnations of elements and spatial constitutions from their imaginaries to their material space – similar to virtual online spaces (Agha, 2019). Deleuze built his conception of the virtual in reference to Proust’s understanding of memory as ‘real but not actual, ideal, but not abstract’. This can be read as an offer to understanding the relation between virtual spaces and the material world and to investigate the behaviour of the players in online, virtual worlds (Castronova 2008); as Castronova observes, these virtual spaces are a ‘porous membrane’ and he claimes that there are two-way movements of the player between the material and the virtual space, which allows the players to easily step in and out. Likewise Deleuze’s concept of the virtual has the aspects of ‘nowhere’ “which can be interacted with’ and the aspect of potentiality, i.e. a generative nature (Deleuze, 2002), Deleuze identified the virtual as a continuous multiplicity, inseparable from the movement of its actualization (Deleuze, 2002).

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Constantly moving between past and present, virtual and actual can be regarded as a highly adaptive way of dealing with change of creative perception in an ecology of innovation. Lotto has shown the connection of deviation and adaptability (Lotto, 2017). According to Lotto innovation does not arise from knowing or data collection as information is per se agnostic. It is rather understanding that enables to transcend contexts with the crucial question of asking ‘why’ things are this way and not another. Asking ‘why’ collapses contexts – a new one can emerge. Asking good questions is a learnable skill and anything creative is initiated by this kind of philosophical questioning. ‘Why asking’ past patterns of perception enable innovative perception – as a complex system the brain can progress to a ‘self criticality’ – therefore knowing what is a good question and the craft of finding it is beyond any technology, it is unpredictable, non-linear, its components are interconnected and they interact. Lotto argues, that nearly all of our perceptions, conceptions, and behaviours are in one way or another linked to uncertainty. The science of perception gives us the permission to become an observer of your own perceptions – doing so gives you the need to thoughtfully deviate repeatedly in order to ask questions that might change our world. Nearly all of our perceptions, conceptions, and behaviours are in one way or another linked to uncertainty (Lotto, 2017). An open lab will work well when people feel taken care of and safe to experiment. Trust is fundamental to leading others into the darkness of uncertainty, since trust enables fear to be ‘actionable’ as courage rather than actionable as anger. The bedrock of trust is faith ‘that all be OK’ within uncertainty. Therefore leading by example creates a space that is trusted – and without trust, there is no play. Following Lotto I argue, that in order to create a successful ecology of innovation we must look into our own biology and tap into our very own neural nature that balances efficiency and creativity. The two qualities must exist in dynamic equilibrium – and what is more, the system must develop. The process of development is the process of adding dimensionality, called complexification. As development is the process of innovation incarnate: the process is very intuitive, starting simple (few dimensions), then refine (lose dimensions) through trial and error and repeat. Thus an Open Lab in Nubia will contribute to contemporary research on virtual spaces benefiting from understanding the relationship between the material space surrounding Nubians and the space of their disembodied territory: the nostalgic space can be conceptualized as a territory disembodied in its materiality and virtual in its dimension – a territory produced and maintained through emotional labor, in this case, nostalgia. In fact the territory has interesting spatial characteristics. It contains a shared vision as well as a fluctuating layout, and their belonging is not contingent on their geographic proximity to the territory’. Besides it allows its occupants to live simultaneously in multiple spaces; it allows them to move between and acknowledge both the imagined and the material as lived spaces. Through this acknowledgement, the virtual territory is able to inflict change on the material world (Agha, 2019). Nostalgia, in this case, is a mode of territoriality, as it fuels the making of a territory. Moreover, the

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territory becomes a space of production rather than a space of preservation, as it is morphed by the space of the now rather than the space it claims to preserve, making it a tool for space making (Agha, 2019). A Nubian Lab will offer the opportunity to structure the constant shift of virtual and actual in the practical use of communication tools aiming at a broad understanding of communication strategies in Nubian communities. According to the Ministry of Foreign Affairs Egypt seeks to restore its economic and cultural role in Africa. Due to rapid demographic development the local market demands exceed the available supply. Excellent economic possibilities can be recognized besides oil and gas in the sectors of tourism and the set-up of an efficient service sector. A Nubian Open Lab allows participants to learn about and apply a human-centred service approach to self-learning innovation and technology development through a variety of formats supplied by the participants: Design Thinking workshops allow participants to explore ethnographic research methods in multidisciplinary teams and develop solutions to challenges they encounter in service innovation and society together with anticipated recipients of the technologies. Technology workshops provide an opportunity to discuss which skills are needed to start and operate projects and ventures. This iterative approach will enable us to test a variety of methodologies and approaches to service innovation with a diverse group of stakeholders and develop locally adapted best practices for research and education institutions to apply their work to markets. The Nubian Open Lab designs and examines both a program and tools that a) enable the creation of a self-learning community and grows connections between the different actors, b) mediate the exchange of knowledge through formal and informal interactions and c) supply the necessary equipment to create self-learning innovations. The community will include relevant stakeholders from the innovation ecosystem, including students from different fields, senior researchers, entrepreneurs and business representatives. The intended outcome will be a sustainable process model for open labs as physical spaces. Furthermore, it will contribute to Aswan University’s planned ERASMUS project on virtual museum and site services. Researching digitalizing strategies in self-learning services support IT-applications for self-learning as social digitalization. It will provide sustainable knowledge for steering mechanisms of self-learning projects and programs (e.g. neighbourhood networks, IT-applications for self-learning, digital information services, etc.). The Nubian Open Lab we will be able to get deeper insights into potentials of self-learning strategies, socio-technical innovation and business model development in socio-economic challenged regions and thus contribute to the scientific canon on this topic. It provides us with first hand data that can be used to reflect, refine and adjust to similar contexts. The Nubian Open Lab activities are guided by overarching focus points, representing a critical view on self-learning, social digitalization, emerging technologies and service innovation: knowledge transfer – understanding points of view – collective problem solving- transfer of outside/foreign structures into something that can become alive in Nubian culture – Is it possible to

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thus build real solutions? What could be reached with additional technical equipment (3D-printer, etc.)? – Which cultural forms of expression appear in a Nubian Lab, that otherwise would get lost? – How can mobiles, computer, etc. be integrated? The scientific goal of a Nubian Open Lab is to gain improved understanding of self-learning and innovation strategies and spaces in challenging socio-economic situations and add to the currently scarce literature about this phenomenon and contribute to an understanding of how to move between and link the virtual and the actual. The role of labs in innovation ecosystems, their agency in the development of innovation in the education industry has not been thoroughly researched yet (Friederici, 2017; Jiminez, 2016). A Nubian Open Lab will contribute to the development of a framework for impact measurement of innovation learning, which does not exist yet (De Beer et al., 2017). Furthermore a Nubian Lab will examine which factors are hindering and fostering innovation and self-learning in the Nubian culture in particular. One specific goal is to develop recommendations on how to leverage these factors in a way that contributes to a positive socio-technological and economic growth.

5 Potentials of an Open Lab The Faculty of Engineering and Technology at Aswan University has signalized interest in a Nubian Open Lab as it aims to meet technical needs of the industrial enterprises and service interests in different disciplines. The faculty contributes to the enrichment of engineering sciences through authentic research, community service and environment. The faculty wishes to be a leader in the field of engineering education, scientific research, community service and the environment. Based on a systemic and collaborative approach to innovation, we therefore propose the creation of a Nubian Open Lab as a platform and physical space for self-learning that fosters a service community of various stakeholders from the emerging ecosystem of Aswan (Aswan Investors Association) Upper Egypt and East Africa. Technological Research is going to cover technical informatics for community needs, system design, hardware programming, software programming, database management, and data analysis, custom hardware design, high-performance computing and cloud computing and networking. To prepare a sound conceptual approach to such a lab, field research on forms of interaction in the Nubian community of Elephantine Island was conducted over many years. Participant observation in informal groups of mainly male meetings made it possible to gather various and complex insights into the networking style on Elephantine Island in Aswan, which is one of the few not relocated communities. The island consists of 2 villages and a prominent archaeological site managed by the German and Swiss archeological mission. Furthermore the island hosts a Moevenpick Resort with a conference center, which recently hosted the Arab African Youth Forum under the auspice of president Abdel Fattah el-Sisi.

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Since the high mass tourism cruise boat seasons in the 1980ies, many locals had worked in the tourism sector, specifically in boat services, guest houses, or as tour guides. In the years after the 2011 revolution, this sector broke down completely. Since 2017 new tourism groups have emerged: culturally interested individual tourists and tourist groups from China, due to the Egyptian-Chinese culture exchange agreements. They add new niches to the tourism market with considerable profit margins for the inhabitants of the region. To address these niches, support is required to meet infrastructural needs from housing to service delivery and communications. Design studies in the context of the open lab are therefore focused on linking the environmental design questions with infrastructure development and service innovation. Elephantine Island social life revolves around informal private-public meeting places, operated by key members of the local communities who do not have any official function, but take over the roles of boundary spanners and opinion leaders in their social network (see Figure 24.1). Coffee and tea are served; people constantly come and go, have conversations in a deep rooted culture of knowledge exchange, conversing, listening and as I have experienced, idea development. Such meeting places serve as communication hubs – they are open labs in an informal sense. A culture built on personal relation opens insights into a sophisticated tradition of networking, time management and problem solving, where innovation is slowly and constantly integrated and assimilated in the community. As a contextualized space, which remains culturally accessible to its visitors, it also draws on

Figure 24.1: Crafts shop on Elephantine Island functioning as an informal innovation space.

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tacit forms of knowledge, implications of images, sounds, and physical objects, and shared understanding based on common experience. Elephantine Island (the ancient city of Aswan) has been in the role of connecting the Mediterranean and the African markets since ancient times – the fact of 4000 years of continuous documented history in this place is not only quite unique in the world, but speaks its own language as well as Egypt’s newly growing economic and political interest is to ‘make’ Aswan the future gateway to Africa. As African economies continue to emerge and grow, there are some lessons to learn. To succeed, information is priority. Innovation without information is destined to fail (Ahinon and Havemann, 2018). In a world where data is the new currency of value, establishing new information highways to connect Africa to the digital world provides a real platform for growth for other nations on the continent. Many of the young Nubians on Elephantine are well equipped with smart phone technology. As we stride towards a new chapter in Africa’s digital growth, much of the continent’s future fortunes will be dependent on those economies that invest in the required infrastructure and provide the appropriate regulatory environment to adapt and flourish in the global digital economy. A Nubian Open Lab will have the elasticity of bridging informal with formal styles of communication, the virtual and the actual: Three dimensional space, 3D objects, 3D terrains, images, text, music and sound can be connected with reactive and/or interactive behavioural attributes. The relation between an actual and authored artificial physics becomes experiential to the participant within such environments through multiple sensual means.

References Agha, M. (2019). Nubia still exists. On the utility of nostalgic space. Humanities 2019, 8(1), 24. Available at: https://www.mdpi.com/2076–0787/8/1/24 Ahinon, J. S. and Havemann, J. (2018). Open Science in Africa – Challenges, Opportunities and Perspectives. EconPapers. Retrieved from https://econpapers.repec.org/paper/osfafrica/ m5kuc.htm on 24 July, 2019. Allen, S. (2016). Nubians and development: 1960–2014. Cairo: American University of Cairo Press. Ascott, R. (2000). Edge-Life: Technoetic structures and Moist Media, in: Ascott, Roy (Ed.) Art, Technology, Consciousness. Mind @ Large (pp. 2–6). Bristol: intellect. Bhabha, H. K. 2004. The Location of Culture. London and New York: Routledge. Castronova, E. (2008). Synthetic Worlds: The Business and Culture of Online Games. Chicago: University of Chicago Press. Cohen, B., Almirall, E., & Chesbrough, H. (2016). The city as a lab: Open innovation meets the collaborative economy. California Management Review, 59(1), 5–13. De Arias, A. R., S. D. Masi, D. Dorigo, F. A. Rojas, M. C. Vega, and M. Rolon (2014). Living Labs, spaces for open innovation and technology transfer. An alternative to the solution of social problems in Paraguay. Social Sciences, 3 (3): 74–79.

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De Beer, C., Secundo, G., Passiante, G., & Schutte, C. S. (2017). A mechanism for sharing best practices between university technology transfer offices. Knowledge Management Research & Practice, 15(4), 523–532. Deleuze, G. (2002). The Actual and the Virtual. In: Dialogues II. Ross Albert, E. (Eds), New York and Chichester: Columbia UP, 148–152. Friederici, A.D. (2017). Evolution of the neural language network. Psychonomic Bulletin & Review, 24(1), 41–47. Fritzsche, A. (2016). Open innovation and the core of the engineer’s domain. In Michelfelder, D. P., Newberry, B. & Zhu, Q., Philosophy and Engineering: Exploring boundaries, expanding connections (pp. 255–266). Cham: Springer. Fritzsche, A. (2017) “Dancing the device: a translational approach to technology.” In Pitt, J. & Shew, A. (Eds.) The Routledge Companion to Philosophy of Technology. New York: Routledge. Fritzsche, A. (2018). Corporate foresight in open laboratories: a translational approach. Technology Analysis & Strategic Management, 30, 646–657. Gatto, M.C. (2009). The Nubian Pastoral Culture as Link between Egypt and Africa: A View from the Archaeological Record, Archaeopress – British Archeological Reports, Oxford, England. Gershenfeld, N. (2008). Fab: the coming revolution on your desktop-from personal computers to personal fabrication. New York: Basic Books. Goffin K, Koners U (2011) Tacit Knowledge, Lessons Learnt, and New Product Development. Journal of Production and Innovation Management, 28:300–318. Hegel, GWF (1807/1967) Phenomenology of Mind, translated by J. B. Baillie. London: Harper & Row. Jimenez, T., Sanchez, B., McMahon, S.D. and Viola, J. (2016). A vision for the future of community Psychology Education and Training. American Journal of Community Psychology, 58(3–4). Kraemer, S. (1988). Symbolische Maschinen. Die Idee der Formalisierung in geschichtlichem Abriss. Darmstadt: Wissenschaftliche Buchgesellschaft. Lotto, Beau (2017), Deviate: The Science of Seing differently, New York: Hachette. Marx, K. (1887) Capital: A Critical Analysis of Capitalist Production. London: Swan Sonnenschein, Lowrey, & Co. McPhee, C., M. Westerlund, and S. Leminen (2012). Editorial: Living Labs, Technology Innovation Management Review, 2 (9): 3–5. Oguz F, Sengün A (2011) Mystery of the unknown: revisiting tacit knowledge in the organizational literature, Journal of Knowledge Management, 15(3):43–59. Piaget, J. (1950) The Psychology of Intelligence. London: Routledge and Kegan Paul. Rutherford, J. (1990). Identity: Community, Culture, Difference Identity. London: Lawrence and Wishart Limited. Schelling, FWJ (1800/1978) System of Transcendental Idealism, translated by P. Heath, Charlottesville: University Press of Virginia. Vygotsky, L. (1962) Thinking and Speech. Harward: MIT Press.

Max Jalowski

25 Facilitating Participatory Design in the Cyber-Physical Lab 1 Introduction For quite some time now, multi-channel approaches to customers have been discussed in the context of marketing (Neslin et al., 2006; Verhoef, Neslin and Vroomen, 2007). The idea behind it is that as digitalisation progresses it no longer makes sense to distinguish between online channels on the one hand and offline channels on the other. With smartphones as a constant companion and other technical devices that are ubiquitously present, offline and online communication can be combined with each other in a variety of ways so that they always complement each other optimally to create an optimal customer experience (Verhoef et al., 2009). From a completely different point of view, similar considerations are discussed in the context of engineering. They revolve around the concept of cyber-physical systems, in which physical structures are mapped in real time to digital representations, opening up a plethora of new approaches to their control and regulation (Lee, 2008a; Rajkumar et al. 2010). Cyber-physical environments are effectively also multi-channel models in which the difference between interaction with a tangible entity and its digital twin loses importance. In addition, the concept of cyber-physical systems also results in another innovation with regard to interaction, which is the subject of a lively discussion especially in the context of manufacturing (Kang et al., 2016; Oks, Fritzsche and Möslein, 2018): humans are no longer the only protagonists. Interconnected technical installations equipped with sensors and actuators and controlled by intelligent algorithms can also have an agency in the interaction. Wherever they are associated with a specific material entity, they are then described as smart products; otherwise they are addressed as smart services (Boukhris and Fritzsche, 2019).

2 Open Innovation and Technology Innovation management has long recognized the importance of digital media for the fertilization of idea generation and solution development. The possibilities for opening

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up innovation processes via online networks have been explored most prominently. This includes various interaction formats that are well known from the internet: – Crowdsourcing, with which large groups of contributors from around the world can be brought together to participate in an innovation project. In many cases this is done in the mode of innovation contests (Boudreau, Lacetera and Lakhani, 2011) with e.g. financial rewards for the winners (Adamczyk, Bullinger and Möslein, 2012), but it can also be translated into forms of microtasking, where many people are rewarded for specific small contributions, which only in sum unfold their full effect (Kittur, Chi and Suh, 2008). – Discussion forums and social networks in which people interested in a specific field of innovation meet to discuss a topic. New ideas for products and services or suggestions for improving existing solutions can be jointly developed, reviewed and refined (Bullinger et al., 2012). This can be driven by the members of the forums themselves or by companies whose products are being discussed. It is also possible to carry out such activities within an organisation (Bansemir, Neyer and Möslein, 2012) – Online marketplaces that bring together different groups that have diverse interests in innovation. In the most rudimentary case they are seekers and solvers: people who have a problem and people who make it disappear (Billington and Davidson 2012). In more complex cases, other roles can be distinguished, such as sponsors, beneficiaries, suppliers of individual solutions and integrators (Whelan et al., 2011), whereby the platform on which they meet becomes the basis for an entire innovation ecosystem (Gawer and Cusumano, 2014). – Configurators that allow users to choose online from a variety of different options while designing new solutions. In more complex cases, users can even engage in sophisticated constructions based on expert design toolkits and virtual studios online (Naik and Fritzsche, 2017). In most cases, a company in the background will invest in the development of such configurators or toolkits in order to benefit from the creativity of customers. Innovation laboratories are also driven by the idea of opening up innovation processes. In doing so, they apply technology in a variety of ways. Labs that focus very much on customer interaction, for example, use technical devices as boundary objects for communication about the subject matter at hand (Perez Mengual et al., 2018). As boundary objects, the devices are highly important to connect interest groups with contradictory views of innovation (Fritzsche, 2018). A different approach is apparent in the context of the Maker Movement (Hatch, 2014), whose name already suggests that it is driven by technologies for the fabrication of objects. Especially 3D printing plays a leading role in these labs. 3D printing allows processes that were previously reserved for large research and development centres to be carried out under comparatively simple conditions. Fab labs, maker spaces, tech

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shops and similar facilities give visitors the opportunity to work on such equipment in their leisure time (Troxler, 2016). Object designs created and tested in the labs can be shared online and then spread around the world. This allows the application of the abovementioned open innovation technologies online, which are used in communities by 3D enthusiasts in multiple forms. However, these online dynamics hardly feed anything back into the labs that play such an important role in the origins of design. Although online and offline channels are combined for innovation in this case, this cannot yet be understood as multi-channel innovation. It can only show that there is a potential for linking online and offline activities that open labs need to explore further. In addition, the devices used can hardly be understood as smart products. They have no agency in the innovation process, but are subordinated to human actors as instruments. As a consequence, only a fraction of the potential offered by modern technology for innovation in open laboratories has been used so far. Against this background, this chapter discusses various ways in which open laboratories can become real cyber-physical spaces where online and offline innovation activities complement each other variably and are actively supported by smart technology.

3 Goals of the Cyber-Physical Lab From the perspective of design theory, open laboratories are places where different people contribute to the creation of a new artifact. Depending on the specific setting of the laboratory, these may be people with specific expertise who are particularly attracted to the lab, or a broader group of participants who approach the topic from a laypersons point of view. A lab at a trade fair or one where complicated machines or formal concepts are used will tend to attract people with a particular professional background. A lab in the city centre that uses simple equipment and a common vocabulary is more likely to attract a wider group of visitors. While in the first case one would speak of collaborative design (Sanders and Stappers, 2008), in the second of participatory design (Sanders, Brandt and Binder, 2010). Since experts and laypersons bring along different measures of efficiency in dealing with concepts, instruments and methods, the way in which they deal with the technical infrastructure of a laboratory naturally also differs (cf. Naik and Fritzsche, 2017). Cyber-physical approaches to innovation in open laboratories can therefore cover different levels of elaborateness in addressing their users, similar to any other communication situation (Bernstein, 1964). A common characteristic of all open laboratories, however, is that visitors do not work on innovation in the same way as they would in a project they pursue at their workplace. Visitors to the laboratory cannot be expected to work with full dedication to a fixed schedule, clear roles and clear goals. Instead, one

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must assume that motivation varies, that visitors are easily distracted, and that their patience to familiarize themselves with a topic is very limited. This has important consequences for the way technology needs to be discussed in the open laboratory. A factory using cyber-physical systems is a closed place. The operations to be performed in the course of a production process are contained in a controllable quantity. The workers involved are an exclusive subset of the total population. Open Labs target the exact opposite. They want to give everyone the opportunity to contribute and allow exploration, especially as no exact expectations can be formulated about the outcome. Drawing on from the concepts of Open Innovation and User Innovation, the following general objectives of the use of technology must therefore be in the foreground: on the one hand, facilitating the flow of knowledge between all actors and their appropriate coupling (Enkel, Gassmann and Chesbrough, 2009) and, on the other hand, democratising the decision-making processes so that the interests of all participants can be taken into account (von Hippel, 2009). Comparable to the discourse on enabling citizens in smart cities, the activation of learners in education and training, or the fostering of competent patient behaviour in medicine. It is always about participation in problem solving, which has already been investigated in many ways in design research. Based on this work, the possibilities for setting up cyber-physical innovation laboratories will now be characterized.

4 Success Factors of Participatory Design Following Lee’s (2008b) model, the work in the Open Laboratory moves in the field of tension between community participation and design participation. One thus works simultaneously with people and the design community. Collaboration engineering describes an approach to designing and providing collaborative working methods with practitioners (Kolfscholten et al., 2006). The development of social products summarizes the latest approaches to collaborative design, production and innovation (Forbes and Schaefer, 2017). Muller et al. (1993) gives a compilation of participatory design activities and categorises them according to their position in the development cycle and the participation context. Sanders (2013) propose a further developed framework that names and categorises tools and techniques of participatory design. They can be divided into three types of participation: [1] making tangible things, [2] acting, enacting, and playing, and [3] talking, telling, and explaning (Sanders, 2013). Sanders collects various existing methods, tools and techniques and categorizes them according to the respective types of participation. An important success factor in collaborative and participatory design is motivation. In research, strategies from different disciplines exist to improve motivation in

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the context of collaboration. Social technologies are a promising means to increase the participation of users (Hagen and Robertson, 2009). The motivation of users in such processes is a decisive factor. Through entertainment, learning new things and cooperation in a community, the motivation of participants can be successfully increased. The support of supervisors and tools can also have a positive influence here (Antikainen, Mäkipää and Ahonen, 2010). This topic is also being explored in the context of open innovation platforms. In more practical design phases, extrinsic motivation occurs. Intrinsic motivation often occurs in combination with extrinsic motivation (Battistella and Nonino, 2012). The spread of technologies also influences the motivational factors of participatory design. Particularly interesting for open labs are observations made by Jarvela and Jarvenoja (2011) with students in collaborative scenarios. From this, five main topics for facilitating participatory processes were derived: – personal priorities – work and communication – teamwork – collaboration – external constraints. Further analysis of the collected data shows six strategies to increase students’ interest in the given task: – task structuring – social reinforcing – efficacy management – interest enhancement – socially shared goal-oriented talk – handicapping of group function.

5 A Framework for Technical Support Although cyber-physical solutions for innovation in open laboratories have not yet been discussed, there are various research papers that deal with the question of how participatory design can be technically supported in specific applications. On the basis of this work, different design dimensions of cyber-physical labs can now be demonstrated. Existing work on the use of technology in collaborative settings is usually on a conceptual and strategic level. For example, the influence of ubiquitous computing is investigated (Brereton and Buur, 2008). Frequently, virtual collaborative environments are also the subject of the research (de Vreede et al., 2013). In general, the use of social technologies and their influence on collaborative design is

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also considered (Hagen and Robertson, 2009; Stibe and Oinas-Kukkonen, 2014). The use of technologies can also increase digital motivation (Algashami et al., 2017). First implementations of cyber-physical labs show the use of innovative technologies such as augmented/virtual reality or large interactive screens to support collaboration (Costa and Thomann, 2019). Perez Mengual et al. (2018) identified three categories of tools for integrating visitors in a living lab: [1] tools for passive integration, such as tracking or observation, [2] tools for reactive integration, such as voting mechanism, questionnaires or toolkits, and [3] tools for cocreation, such as open feedback and dialogue (Perez Mengual et al., 2018). So far, however, there has been no systematization of technologies that can be used in cyber-physical labs. This chapter is intended to contribute to the development of an overview and a framework. Building on prior considerations on the topics in Jalowski, Fritzsche, and Möslein (2019), the following technologies can be considered as the main design dimensions for a cyber-physical lab: [1] Augmented and Virtual Reality, [2] Artificial Intelligence, [3] Displays, Lights and Sound, [4] Games, [5] Internet of Things, [6] Message-based reminders, [7] Physical Tags, [8] Prototyping Toolkits, [9] Robotics, [10] Sensors and Analytics, [11] Smartphones and Tablets, and [12] Social Networks. Applications for these technologies are described in the following sections. Augmented and Virtual Reality Augmented reality applications are used to extend physical settings. In cyberphysical labs a combination of online and offline worlds can take place. Visitors can see further information. Thus, for example, offline locations can be extended by interactive and collaborative elements. Virtual reality applications put the user in virtual scenarios, for example to experience new environments, e.g. (Ganesh, Marshall, Rogers and O’Hara, 2014; Monahan, McArdle and Bertolotto, 2008). Artificial Intelligence With the help of artificial intelligence, large amounts of data can be analyzed and reacted to based on this. In cyber-physical labs, a large number of sensors are used to record the visit. Artificial intelligence applications can respond directly to behavior and provide information adapted to the situation, e.g. (Decker, Sycara and Williamson, 1997). Displays, Lights and Sound Displays, lights and sound play a predominantly informative role. In combination with other technologies, users can be provided with appropriate information. This can contribute to a targeted guidance of the users to influence their behaviour (de Vries, Galetzka and Gutteling, 2014).

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Games Games can play different roles in cyber-physical labs. Serious games can be used, for example, to increase user participation, reflect on opinions or enable learning, e.g. (Gram-Hansen and Ryberg, 2015). In general, games also serve to promote acting, enacting and playing. Internet of Things In the internet of things, various devices are smart and connected to each other via the internet. These devices are often equipped with sensors whose values are made accessible via a network. Examples are wearables such as fitness trackers or smartwatches or smart home devices. An exemplary application is e.g. (Li et al., 2011). Message-based Reminders Message-based reminders can be used to foster interaction or to remind people of certain events and activities, e.g. (Daskalova et al., 2014). Like displays, lights and sound, these can primarily serve to guide visitors and influence their behaviour. In combination with artificial intelligence, for example, they can also be used to involve passive participants. Physical Tags Physical tags can be RFID tags or QR codes, for example. They can be used to provide additional information in a location-dependent and context-based manner. This increases user activity and enables location-based participation, e.g. (Basten, Ham, Midden, Gamberini and Spagnolli, 2015). Prototyping Toolkits Some forms of prototyping toolkits are already being used in cyber-physical labs. Classic toolkits can be supplemented by technology components to enable technology-focused prototyping, e.g. (Boukhris, Fritzsche and Möslein, 2016). Robotics Robots in the context of cyber-physical labs are primarily understood as humanoid robots. These robots have often already integrated a large number of sensors and actuators and artificial intelligence in the form of speech and image recognition. Humanoid robots can thus act as assistants to users or guide them through processes, e.g. (Henkemans et al., 2017). Sensors and Analytics Various sensors are used to record user behaviour and increase participation. The data is evaluated by analyses in order to react accordingly to the user, e.g. (Aipperspach, Cohen and Canny, 2006). These analyses can be improved by artificial intelligence algorithms.

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Smartphones and Tablets Smartphones and similar devices are omnipresent, so they allow direct interaction with the user. In combination with QR codes or special apps, users’ input can be recorded directly and they can also be informed or guided. In addition, sensors in the devices can record additional data and provide information. Social Networks Social networks offer the opportunity to involve users who are not present in cyberphysical lab. They bridge the online/offline gap and also allow the virtualization of collaborative processes. Social technologies also promote participation (Hagen and Robertson, 2009).

5.1 Summary of the Technologies The three types of participation of Sanders (2013) already describe a multitude of methods, tools and techniques, which, however, have no direct relation to technologies. Nevertheless, these three types of participation can be supported by the technologies described above. Figure 25.1 shows an overview and exemplary tools of the three types of participation and general challenges in cyber-physical labs.

Talking, telling and explaining e.g. cards, paper spaces, voting dots

Making tangible things e.g. mock-ups with Lego, prototypes

Acting, enacting and playing e.g. game boards, role playing

General challenges e.g. personal priorities, work and communication, teamwork, collaboration, external constraints

Figure 25.1: Overview of different types of participation and general challenges in cyber-physical labs (cf. Sanders, 2013; Jarvela and Jarvenoja, 2011; Jalowski et al., 2019).

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The already described key factors for motivated users and the strategies for increasing interest can also be enriched by technologies. For example, smartphones, tablets, message-based reminders, robots and displays can support task structuring. Social reinforcement can be enhanced by social networks, physical tags and games. Sensors, analytics and message-based reminders facilitate efficacy management. Interest enhancement can be supported, for example, by augmented or virtual reality, prototyping toolkits or games. Socially shared goaloriented talk is particularly suitable for social technologies (smartphones, social networks, etc.). The handicapping of group function can be enhanced by artificial intelligence, sensors, analytics and message-based reminders. The described technologies and considerations can be summarized in a framework of different technologies for different application scenarios in cyber-physical labs. Table 25.1 summarizes the twelve technologies and their possible application domains. Table 25.1: Overview of technologies and application fields in cyber-physical labs. Technology

Making tangible things

Talking, telling and explaining

Acting, enacting and playing

Augmented and Virtual Reality

X

X

X

General challenges

Artificial Intelligence

X

X

Displays, Lights and Sound

X

X

Games

X

Internet of Things

X

X

Message-based reminders

X

X

Physical Tags

X

X

Prototyping Toolkits

X

Robotics

X

X

Sensors and Analytics

X

X

Smartphones and Tablets

X

X

Social Networks

X

X

X

X

6 Summary and Future Perspectives In this article we presented the concept of cyber-physical labs, especially the connection of online and offline channels. We have described twelve different technologies that can be used in cyber-physical labs. Some of the mentioned technologies

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are already widespread and can no longer be excluded from everyday life. Others are still in a visionary state and others only offer very special application possibilities. However, all these technologies have in common that they can be used to increase user participation. Figure 25.2 summarizes the vision of a cyber-physical lab be showing all of the described technologies and visualizes them in a conceptual setting of such a lab.

Social Networks Artificial Intelligence

Displays, Lights and Sound

Games

Augmented and Virtual Reality

Sensors and Analytics Message-based reminders Physical Tags

Internet of Things

Prototyping Toolkits

Smartphones and Tablets Robotics

Figure 25.2: Summary of the vision of a cyber-physical lab.

The added value of open labs to conventional innovation activities depends strongly on the willingness and ability of users to share their knowledge, experience and opinions. A cyber-physical lab when equipped with a variety of sensors to ensure that optimal conditions always prevail. Sensors and all other devices are connected to each other. The collected data is analyzed and evaluated with the help of artificial intelligence in order to react to the users in real time. State of the art technologies are also used to make new ideas, concepts and visions tangible for visitors. Examples include augmented and virtual reality, high-resolution and interactive displays, robots and prototyping tools. Gamification approaches are used to increase user activity and provide a playful component. Visitors are guided through the lab by assistants, who can take on different forms. For example, both the user’s smartphone and a humanoid robot can take on the role of assistant. These each work with reminders and guide through the process. Physical tags are used to promote location-based participation. Finally, social networks complete the combination of online and offline strategies.

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An important role is played by the operation of the lab by a third party, which remains independent of the firms involved in the innovation process. Only such a third party can gain the necessary confidence of the visitors of the laboratory that their personal data will not be misused for the wrong purposes. The operators of the laboratory must create the highest possible transparency so that all visitors are always given the opportunity to inspect the processing logic of the data. Other non-governmental institutions can also be involved to monitor the scenario. In the near future, gradations of this vision can be rapidly implemented with already established technologies, paving the way to a complete cyber-physical lab.

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List of Figures Figure 2.1 Figure 3.1

Figure 3.2

Figure 3.3

Figure 4.1 Figure 4.2 Figure 4.3 Figure 5.1 Figure 5.2 Figure 7.1 Figure 7.2 Figure 7.3 Figure 8.1 Figure 9.1 Figure 9.2 Figure 10.1 Figure 12.1 Figure 12.2 Figure 12.3 Figure 12.4 Figure 13.1 Figure 13.2 Figure 14.1 Figure 14.2 Figure 14.3 Figure 14.4 Figure 15.1 Figure 15.2 Figure 15.3 Figure 15.4 Figure 15.5

The construction and design of JOSEPHS® from an interior perspective (Quelle: Fraunhofer SCS/IIS) 13 Employment in the knowledge-intensive sectors in the City of Nuremberg in the years 2010 and 2017 (based on statistics from: Federal Employment Agency, 2018; Comission of Experts for Research and Innovation (EFI), 2010) 24 No. of employees working in information and communication technologies, a comparison by city Wirtschaftsstandort Nürnberg Positionsbestimmung 2018, 2019 25 City of Nuremberg theme island in JOSEPHS® (https://www.josephs-servicemanufaktur.de/besucher/vorherige-themenwelten/themenwelt-innovatio nen-spielend-entwickeln/) 31 Framework for digital transformation (Klötzer und Pflaum 2017) 37 Reference process for digital transformation (according to Pflaum und Gölzer 2018) 39 Smart service innovation ecosystem 44 Conceptualization of co-creation in open lab settings according to Ramaswamy and Ozcan (2018) 53 Fields of action in future retail with respect to elements of co-creation and open lab functionalities 57 82 Layout of JOSEPHS® The TEAA project allows visitors to test a new technology in the car 86 Mifitto’s 3D foot scanner for shops 88 Impression of a medieval market by Félix de Vigne (Wikimedia/ public domain) 94 105 Sequence of projects in JOSEPHS® (based on Fritzsche, 2015) Planning interactions in the open lab with a model 107 Impression of ZOLLHOF 114 Characteristics of engagement platforms 134 Structural holes in innovation ecosystems 135 Function of engagement platforms in innovation ecosystems 136 Complex structures of innovation ecosystems 137 The ICT/telecommunications industry and vertical industries represented in WIVE (Source: WIVE material) 145 Defined use cases in the WIVE project (Source: WIVE material) 147 Layers of smart service ecosystems 154 LESSIE as mediator in smart service ecosystems 158 Steps of the LESSIE approach 158 Steps of the LESSIE approach 160 Service productivity of an OI-Lab (based on Daiberl, 2020) 167 Conceptual structure of NSPIRET navigator (Daiberl, 2020) 171 Excerpt of an NSPIRET snapshot developed in the context of an OI-Lab (see also Daiberl, 2020) 173 Screenshot of sharing a failure mode using the NSPIRET shoutbox (see also Daiberl, 2020) 175 Screenshot of sharing an innovation mode using the NSPIRET shoutbox (see also Daiberl, 2020) 176

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Figure 15.6 Figure 15.7 Figure 19.1 Figure 19.2 Figure 19.3 Figure 19.4

Figure 19.5 Figure 23.1 Figure 24.1 Figure 25.1

Figure 25.2

List of Figures

Screenshot of an exemplary portfolio of failure modes depicted in the NSPIRET shoutbox (see also Daiberl, 2020) 177 Screenshot of an exemplary portfolio of innovation modes depicted in the NSPIRET shoutbox (see also Daiberl, 2020) 177 Presentation of ideas for constructive deliberative discourse 215 Explorative deliberative prototyping 216 Mapping and discussing innovation space 217 Customer functional requirements as conditional and dynamic trajectories. (a) Modelled as fixed and static; (b) Modelled as dynamic and flexible within ranges of tolerance (2D projection to be imagined as 3D) 220 Sustainability as value co-creation 223 Capturing value from DiY Labs 268 Crafts shop on Elephantine Island functioning as an informal innovation space 283 Overview of different types of participation and general challenges in cyber-physical labs (cf. Sanders, 2013; Jarvela and Jarvenoja, 2011; Jalowski et al., 2019) 294 Summary of the vision of a cyber-physical lab 296

List of Tables Table 3.1 Table 3.2 Table 4.1 Table 4.2 Table 4.3 Table 7.1 Table 14.1 Table 14.2 Table 20.1 Table 20.2 Table 20.3 Table 20.4 Table 20.5 Table 25.1

Tools used to promote innovation as part of Nuremberg’s economic policy 23 Overview of participation in the 19 theme islands at JOSEPHS® (2014–2019) (based on JOSEPHS, 2019) 29 Characteristics of product-oriented and data-driven enterprises (according to Pflaum and Klötzer 2019) 38 43 Service offerings at JOSEPHS® and their contribution Ecosystem partners and value contribution 44 Benefits of Co-creation projects in open laboratories 85 Potential benefits associated with smart service ecosystems 154 Examples for methods that can be applied for smart service engineering 157 OI Capability Archetypes with Closed innovation internal focus 230 OI Capability Archetypes with Open innovation external focus 232 OI Capability Archetypes with both internal and external innovation focus 233 OI Capability Archetypes 234 Strategies for OI capability archetypes 239 Overview of technologies and application fields in cyber-physical labs 295

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Contributors Jürgen Anke is professor of software engineering and information systems at HTW Dresden University of Applied Sciences. His research focusses on methods and models for the engineering of smart service systems, which enable data-based value creation based on smart products. He is also interested in higher education didactics and project-based teaching, especially in software engineering education. Wilhelm Barner-Rasmussen is professor of business administration at Åbo Akademi University School of Business and Economics in Turku, Finland. Knowledge sharing is among his longstanding research interests. He has published in Journal of International Business Studies, Journal of World Business, Management and Organization Review, and Journal of International Management among others. John Bessant Originally a chemical engineer, John Bessant is currently Professor of Innovation and Entrepreneurship at the University of Exeter with visiting appointments at the universities of Erlangen-Nuremburg, Germany and Stavanger, Norway. In 2003 he was elected a Fellow of the British Academy of Management and in 2016 a Fellow of the International Society for Professional Innovation Management (ISPIM). He has consulted widely and is the author of 30 books and many articles on the topic. See www.johnbessant.org for more details. Roderick J. Brodie is Professor in the Department of Marketing at the University of Auckland, New Zealand. His 110 journal plus articles have appeared in the leading international journals including Journal of Marketing, Journal of Marketing Research, International Journal of Research in Marketing, Management Science, and Journal of Service Research. He is an Associate Editor for the Journal of Service Research and has served on Editorial Boards of the Journal of Marketing, International Journal of Research in Marketing. He was the first President of ANZMAC and in 2004 and was made a Founding Fellow. In 2011 he became a Fellow of EMAC. Christofer F. Daiberl is the managing director of JOSEPHS® Prior to that, he was a research associate at the Chair of Information Systems – Innovation and Value Creation at the FriedrichAlexander-University of Erlangen-Nuremberg (FAU). He gained practical experience in various companies such as Audi AG or MunichRe AG. His research interests include (open) service innovation, service productivity and service network design. Frank Danzinger is Head of the Department for Innovation and Transformation at the FraunhoferInstitute for Integrated Circuits (IIS). In the context of the Bavarian-state funded project “Service Factory Nürnberg”, he is in charge of the development and the operations of the open innovation laboratory JOSEPHS®. Sebastian Engel As Head of Research and Pre-Incubation, Sebastian is the mediator and main link between ZOLLHOF and universities / research institutes. With his 10+ years experience in startup support and entrepreneurship education, he coaches founders as well as corporate employees in innovation and digitalization topics. He studied at FAU Erlangen-Nürnberg, where he also received his PhD in 2014.

https://doi.org/10.1515/9783110633665-028

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Contributors

Julia A. Fehrer is Lecturer in Digital Marketing at the University of Auckland Business School, New Zealand and Research Fellow in Marketing & Service Management at the University of Bayreuth, Germany. Her research focus includes actor engagement in networks, systemic business models, market shaping and systemic innovation. Her research has been published in leading journals such as the Journal of Service Research, Industrial Marketing Management and the Journal of Service Management. She has twelve years of professional experience with senior roles in marketing across countries in the insurance industry. Michael Fraas, born in Nuremberg in 1968, is Deputy Mayor for Economic Affairs of the City of Nuremberg. He is a doctor of law. He used to work in an international law firm and the German Federal Ministry for Economic Affairs. Albrecht Fritzsche is acting chair of technology and process management at Ulm University. He holds a doctoral degree in management from Hohenheim University, Stuttgart, and a doctoral degree in philosophy from TU Darmstadt. He has worked for a long time in the German manufacturing industry. He received his venia legendi from Friedrich-Alexander University Erlangen-Nuremberg for his research on innovation and digital strategies. For various years, he was involved in the JOSEPHS® project. Katharina Greve is a Research Associate at the Centre for Science, Technology and Innovation Policy (CSTI), University of Cambridge. She is currently working on a government funded project on demonstration environments for emerging technologies. Katharina completed her EPSRC-funded PhD in Engineering entitled “Facilitating Co-creation in Living Labs” at the University of Cambridge, where she was part of the Cambridge Service Alliance. Albert Heuberger is Executive Director of the Fraunhofer Institute for Integrated Circuits. Since 2011, he is holding the Chair of Information Technologies with Focus on Communication Electronics at the Erlangen University (“Friedrich-Alexander-Universität Erlangen-Nürnberg” – FAU). Jörg Härtwig studied computer science at the HTWK Leipzig and at the University of Leipzig. After he received his diploma, he worked at the Max Planck Institute for Human Cognitive and Brain Sciences. His research interest was then focused on process modelling and context-based knowledge management at the Institute for Computer Science at the University of Leipzig and there he received his doctorate. He is currently researching at the Institute for Digital Technologies (IFDT) where he also works on the topic of technology business management. Lisa Hübner is a member of the JOSEPHS lab team. She has graduated in International Cultural and Business Studies at the University of Passau. Before working in the open innovation lab, she worked as a sales manager at an exchange program agency where she organized long term stays abroad for students – uniting a passion for both sales and abroad experiences. Max Jalowski holds a Master’s degree in Computer Science (with a focus on IT security and distributed systems). He is a research associate at the Chair of Information Systems – Innovation and Value Creation at FAU Erlangen-Nürnberg. His research interests focus on the application of persuasive technologies in participatory design processes and business model development for AIbased applications.

Contributors

307

Julia M. Jonas is an expert in open, digital and service innovation. She graduated with a major in Service Management from Karlstad University Sweden, gained a few years of experience as a project manager in Open Innovation and then pursued her dissertation at the Institute for Information Systems of the University of Erlangen-Nuremberg. There, she worked as a lecturer for Service Design and Innovation Management, managed research projects for digital and interorganisational service systems and participated in the realisation of the open laboratory JOSEPHS®. Pramoth Kumar Joseph is a part-time Research Scholar at IIIT, Bangalore who is working under the supervision of Prof. Srinivasan (IIM-Bangalore) and Prof. Sridhar V (IIIT, Bangalore). Pramoth researches on topics related to Open innovation, Platform business models and Network effects as part of his ongoing Research. Pramoth is employed at Amadeus Labs India as Director Engineering in their Digital Solutions for Airlines Business unit. Valtteri Kaartemo is Post-Doctoral Researcher at Turku School of Economics, University of Turku, Finland. He is a coauthor of several books, book chapters, conference papers and peer-reviewed articles. His research interests include market shaping, service research, technology, innovation management, network dynamics, international entrepreneurship, business models, value cocreation, and various processes within and linking these phenomena. His research has been published, for instance, in Industrial Marketing Management and Journal of Service Management. Amy Kaminski is program executive for prizes and challenges at NASA Headquarters where she advises and strategizes regarding NASA’s use of these methodologies. She previously held positions in NASA’s Office of the Chief Scientist, the White House Office of Management and Budget, and the Federal Aviation Administration. She earned her Ph.D. in science and technology studies from Virginia Tech. Heike Karg was from 2013 to 2019 a project manager at the Fraunhofer-Institute for Integrated Circuits (IIS) and in this role responsible for the operation and development of the open innovation lab JOSEPHS® on site. She formed the interface between science and industry and thus all important stakeholders of the project. In 2019 she became a senior consultant and member of the management of a public relations agency specializing in citizen communication. Anne Krefting is a service designer and cultural scientist. She develops edu service projects for the African market in cooperation with Egyptian partners. She was curator of the Art&Technology series of Fraunhofer IIS in Erlangen. 2008–2012 she was professor for design theory at the German University in Cairo, 2013–2016 visiting professor for service and environmental design at South Valley University in Luxor and Arab Academy in Aswan. Sandeep Lakshmipathy is a part-time Research Scholar at BITS Pilani who is working under the supervision of Prof.Srinivasan (IIM-Bangalore) & Prof.Raghunathan R (BITS Pilani). Sandeep researches on topics related to Platform Business Models as part of his ongoing doctoral work. Employed at GE Healthcare as a Director (Software) leading their Edison AI Platform development work, Sandeep is involved in shaping the future of AI powered products within GE Healthcare. Jan Mehlich holds a PhD degree in chemistry and a master degree in applied ethics. He worked at the European Academy Bad Neuenahr-Ahrweiler on ethical assessment of emerging technologies

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Contributors

before pursuing an academic career with a postdoctoral Humboldt fellowship in Taiwan and subsequent affiliation as faculty member of the International School of Technology and Management at Fengchia University in Taichung, Taiwan. Fabian Memmert studies Industrial Engineering (M.Sc.) at the Friedrich Alexander University Erlangen-Nuremberg and conducted a seminar in collaboration with the open innovation laboratory JOSEPHS®. In his study, he investigated the implementation of the JOSEPHS concept into a telecommunication retail store. Kyrill Meyer studied Business Occupations at the W.C.S.C.C. in Smithville, Ohio (USA) and computer science at the University of Leipzig, where he also received his doctorate. His research interests include service engineering and management and smart services as well as IT-based knowledge-, cooperation- and innovation management and applied computer science. He is currently involved as researcher and network manager at the Institute for Digital Technologies (IFDT), a transregional research and transfer platform for universities and companies. Kathrin M. Möslein is Professor and Chair of Information Systems, esp. Innovation & Value Creation at the FAU School of Business, Economics and Society and Vice President of the FriedrichAlexander-Universität Erlangen-Nürnberg (FAU), Professor of Management at HHL Leipzig Graduate School of Management and Academic Director of HHL’s Center for Leading Innovation & Cooperation (CLIC). EIASM VP, EURAM Fellow and president-elect. Andy Neely is Pro-Vice-Chancellor for Enterprise and Business Relations at the University of Cambridge and former Head of the Institute for Manufacturing (IfM). He is a Fellow of Sidney Sussex College and Director of the Centre for Digital Built Britain and Founding Director of the Cambridge Service Alliance. He is widely recognised for his work on the servitization of manufacturing, as well as his work on performance measurement and management. Susanne Ollila is Professor in Organizational Behavior at the Department of Technology Management and Economics at Chalmers University of Technology, Sweden. She is the founder of the Managing In-between research group, focusing on understanding innovation created in the spaces in-between established organizations. Her research is focused on organizational behavior, management of innovation and knowledge sharing. Maximilian Perez Mengual is a research associate at IIS/SCS in Nuremberg, Germany. His formal education includes a B.Sc. in Psychology and a MBA. His research is oriented towards the application of human sensing technologies in innovation processes and the development of in-situ methodology for innovation spaces. Anna-Greta Nyström is senior lecturer at Åbo Akademi University School of Business and Economics, Finland, from which she holds a doctoral degree in International Marketing. Her current research interests include business networks and innovation, business opportunities, and market creation. She has published her research in, e.g., Industrial Marketing Management, Journal of Business Research, and Journal of Engineering and Technology Management. Michaela Pichlbauer is Managing Director of the Günther Rid Stiftung für den bayerischen Einzelhandel who gives support to small and medium sized enterprises in the retail sector. Her main topic is to enable change and innovation in our society.

Contributors

309

Alexander Pflaum is Head of the Center for Applied Research on Supply Chain Services SCS at the Fraunhofer Institute for Integrated Circuits IIS and holds the Chair for Supply Chain Management at the Faculty of Business and Economics, University of Bamberg. As an expert on how to use data to create added value, he enables businesses to emerge strengthened from their digital transformation. Hanan Prince is a socioeconomic researcher and African affairs consultant for the Egyptian Women Assembly Association for Development. Hanan is also CEO of Second Chance Center for Communication and Networking for African Business Development and a PhD candidate in the counseling psychology and education department (Cairo University). Steve Nash Rader currently serves as the Deputy Manager of NASA’s Center of Excellence for Collaborative Innovation which is working to infuse challenge and crowdsourcing innovation approaches at NASA and across the federal government. Steve has a Mechanical Engineering degree from Rice University and has worked at NASA’s Johnson Space Center for 30 years. Amit Rawal is a Doctoral Candidate at the Brunel Business School’s Globalization, Strategy, and Entrepreneurship division. He holds an MSc in Management from the Brunel Business School. His research interest spans across entrepreneurship, innovation, and failure in organizing. He is the founder of www.1takemotivation.com a motivational speaker service, and Consultant on digital innovation management. Maximilian Reiter graduated at the University of Bayreuth, Germany, with a major on strategic and international management. He wrote his master thesis on the ‘Commercialization of Radical Technological Innovation: The role of Network Shaping to form Ready Markets’. He was Research Assistant with the Department of Marketing at the University of Auckland, New Zealand and the University of Bayreuth, Germany and has work experience in strategy and innovation development in the German automobile industry, retail and medical technology. Angela Roth is a professor at the Institute of Information Systems at Friedrich-Alexander University Erlangen-Nuremberg. Since 2011 she also runs the Open Service Lab, which initiates and fosters joint projects on service innovation and service systems in the region of Nuremberg. Her research focus is on open innovation and service innovation. Additionally, she is looking at the phenomena of digital transformation. In this vein, she is leading a couple of joint projects with companies from different branches, other universities and Fraunhofer. David Sarpong is a Reader in Strategic Management at the Brunel Business School. His research interests revolve around strategic management, innovation management, organizational foresight, Heideggerian approach to ‘practice’, and micro-historia. His research has been published in journals such as Technovation, Technological Forecasting and Social Change, International Marketing Review, Work Employment and Society, Journal of Business Research, Scandinavian Journal of Management, European Management Journal, Technology Analysis and Strategic Management, Futures, and Foresight. Rebekka Schmidt, researcher in the Department for Innovation and Transformation at the Fraunhofer-Institute for Integrated Circuits (IIS), is jointly responsible for the open innovation lab

310

Contributors

JOSEPHS®. She is responsible for test design, data collection and moderation methods as well as data analysis and customer relations. R Srinivasan is Professor of Strategy at the Indian Institute of Management Bangalore (IIMB). His research and teaching interests include platform business models, digital transformation, and competitive strategy. Ingeborg Steinmetz holds a diploma in business administration from Saarland University with a focus on international trade, market research and business informatics. Since March 2014 she has been in charge of event planning and organization as well as the execution and evaluation of qualitative interviews and the moderation of various service design thinking and other co-creation formats at JOSEPHS®. Mitchell Tseng is the founding dean of the ISTM at Fengchia University, Taichung, Taiwan. After an outstanding career in industry (Xerox, Digital Equipment Corporation) and academia (University of Illinois, MIT, Hong Kong University of Science and Technology), he is a renowned expert on technology for mass customisation and personalisation, systems integration, design, and manufacturing automation. Stefan Wolpert is a research associate at Fraunhofer IIS/SCS in Nuremberg, Germany. He was educated at the OU London, the Cooperative State University of Heidenheim, the Tongji University of Shanghai and the Friedrich-Alexander University of Erlangen-Nuremberg. Stefan conducts research in the field of user integration with primary interests in technology based retail services. He is responsible for the subject area “Retail Science” at Fraunhofer SCS. Anna Yström is Associate Professor at the Department of Management and Engineering, Linköping University, Sweden. She is a founding member of the Managing In-between research group at Chalmers University of Technology, Sweden and her research focuses on management of different forms of inter-organizational collaboration and new ways of organizing innovative work.

Index 3D Printing 288

Experimentation 74, 196

Activism 249 Agora 75 Artificial Intelligence 35, 40, 247, 292 Ashby’s law 4 Attention economy 247

Fab Labs 109, 119, 265 Filter Bubble 248 Foresight 246 Fraunhofer Gesellschaft 104 Gestalt Theory 76

Boundary Crossing 195, 203 Boundary Spanning 197, 206, 233 Business Models 106 Capabilities – Archetypes 230 – Customer 269 – Dynamic 229 Citizen Laboratories 266 Co-creation 3, 51, 84, 132, 142, 222, 279 Communities 232 Contextualization 275 Crafts 76, 276 Creativity 75, 191 Critical Theory 248 Crowdsourcing 103, 247, 254, 288 Cyber-Physical Systems 287 Design – participatory 76, 290 – technology 294 Design Science Research 168 Design Thinking 76, 281 Digital Transformation 35, 142, 185, 287 Diversity 256 DiY 263 Ecosystem – Business 22 – Entrepreneurship 117 – Innovation 42, 136, 280 Ecosystems – Innovation 4 – Service 153 Effectiveness 255 Engagement 98, 130 Entrepreneurship 113, 193, 248 Evangelism 247

https://doi.org/10.1515/9783110633665-029

Hackathons 116, 267 Heterotopia 209, 249 Hubs 130, 265, 283 In-between Space 204 Incubators 113, 193 Informal Labs 283 Innovation – and Idealism 275 – Fuzzy Frontend 129 – Technology 96, 104, 199 Innovators – Types 99 JOSEPHS 5–62, 77–120, 138, 215, 235 Laboratory Studies 74 Laboratory work 101 Legitimacy 75 Living labs 3, 50, 82 Location 150, 153, 185, 196 Maker Movement 265, 288 Maker Spaces 109 Mindset 18 Mobile Communication 284 Multi-Channel 287 NASA 253 NSPIRET 170 Nubian Culture 277 Nuremberg 93 Open innovation 4, 7, 103, 141, 213, 256 – Capabilities 227 – Communities 258, 288

312

Index

– Platforms 3 – Productivity 167 – Tools 287 Open Laboratories 5, 73, 81, 263 Piloting 3 Platforms 125, 130 – digital 35, 104 – Crowdsourcing 253 – physical 47 Post-Colonial Theory 205, 276 Prosumer 2, 94 Retail 30, 54, 87, 102 Robots 221, 256, 293 Salon 191 Sensemaking 95, 206 Service Productivity 166 Service Systems 153 Smart Services 36, 38, 153, 156, 287 Space Travel 254

Stakeholder 98, 105, 134 Strategic Partnerships 270 Structural Holes 126 Technology Acceptance 85 Technology Assessment 75 Third Space 125, 205, 276 Tolerances 214 Tourism 99, 281 Tournament Lab 253 Trading Zones 2, 94 Translation 186, 208 Value Creation and Capture 268 Virtual Reality 292 Virtual Space 255, 279 Vocational Education 76 Wisdom of the Crowd 263 ZOLLHOF 114, 138