Universities, Entrepreneurial Ecosystems, and Sustainability 9783110670219, 9783110670165

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Universities, Entrepreneurial Ecosystems, and Sustainability

De Gruyter Studies in Knowledge Management and Entrepreneurial Ecosystems

Series Editor João J. Ferreira

Volume 3

Universities, Entrepreneurial Ecosystems, and Sustainability Edited by Cristina Fernandes, Marcela Ramírez-Pasillas, and João J. Ferreira

ISBN 978-3-11-067016-5 e-ISBN (PDF) 978-3-11-067021-9 e-ISBN (EPUB) 978-3-11-067028-8 ISSN 2698-4806 Library of Congress Control Number: 2021942781 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. © 2022 Walter de Gruyter GmbH, Berlin/Boston Cover image: Ajwad Creative/DigitalVision Vectors/Getty Images Plus Typesetting: Integra Software Services Pvt. Ltd. Printing and binding: CPI books GmbH, Leck www.degruyter.com

Contents Cristina Fernandes, Marcela Ramírez-Pasillas, and João J. Ferreira Universities, entrepreneurial ecosystems, and sustainability: An overview 1

Part I: Universities and entrepreneurial ecosystems Elisa Thomas and Bjørn Asheim Chapter 1 Entrepreneurial ecosystems, learning regions, and the role of universities 11 Ana Dias Daniel, Susana Oliveira, and Joaquim Borges Gouveia Chapter 2 The dark side of the university’s participation in innovation ecosystems Geraldina Silveyra, Lucía Rodríguez-Aceves, and Allan Villegas-Mateos Chapter 3 Enablers to fostering interactions during entrepreneurship events within university-based entrepreneurship ecosystems (U-BEEs) 43 Mariana Pita, Joana Costa, and António Carrizo Moreira Chapter 4 Does education ensure entrepreneurial initiative? Approaching an entrepreneurial ecosystems taxonomy 63 Daniel Feser and Till Proeger Chapter 5 The ambiguous role of best practice examples for knowledge spillovers: Evidence from universities and start-ups in the Berlin entrepreneurial ecosystem 87

Part II: U–I cooperation and sustainability Jeandri Robertson, Leyland Pitt, and Ian P. McCarthy Chapter 6 Building sustainable entrepreneurial ecosystems: A mediated model of university–industry collaboration, knowledge creation, and the entrepreneurial environment 107

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Alicia Trejo Vásquez, María Jesús Rodríguez-Gulías, Manuel González-López, and David Rodeiro-Pazos Chapter 7 University–industry collaboration to support sustainability: An analysis of the determining factors for European Union countries 133 João Paulo do Carmo and Adonai J. Lacruz Chapter 8 University business incubators as drivers of sustainability: The perspective of critical success factors 161 Pedro Mota Veiga and Sérgio J. Teixeira Chapter 9 Universities as change agents of SMEs’ sustainable innovation: A knowledge transfer view 183 Bart Henssen, Talia Stough, Elien Crois, and Luana Jassogne Chapter 10 University research centres as catalysers in entrepreneurial ecosystems: Fostering the transitions towards sustainable business models via the university–business interface 199 Ronnie Figueiredo, Raquel Reis Soares, Marcela Castro, and Pedro Mota Veiga Chapter 11 The Spinner Innovation: Factors for inclusion and advocating in sustainable ecosystems 215

Part III: Universities and entrepreneurial activities Thomas Lauvås and Ola Edvin Vie Chapter 12 Boundary spanners enabling knowledge integration for sustainable innovations in university–industry research centres 243 Orkun Yildiz and Serkan Sahin Chapter 13 An entrepreneurial ecosystem support model in the digital era: Crowdfunding 265

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Chux Gervase Iwu and Abdullah Promise Opute Chapter 14 Steering productive entrepreneurship activities in emerging markets: The role of the university 289 Brendan Dolan, Caroline McGregor, and James A. Cunningham Chapter 15 Medical device scientists’ influence on research impact within entrepreneurial ecosystems: A systematic literature review 311 Kseniya Sorokina, Paula Odete Fernandes, and Jeyhun Mammadov Chapter 16 Students’ perceptions of university social responsibility: A cross-cultural comparison 333 Veselin Vukotic Chapter 17 Which pathways lead us to the university of the future? List of Figures

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List of Tables

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Contributors

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Cristina Fernandes, Marcela Ramírez-Pasillas, and João J. Ferreira

Universities, entrepreneurial ecosystems, and sustainability: An overview Sustainable development is the most important goal of our time (Howard-Grenville et al., 2017). The climate crisis, chemical pollution, misuse of land, health issues, peace, social justice, equality, and migration are among the greatest challenges to global prosperity and the conservation of the earth system. These challenges affect the necessary preconditions for maintaining the biosphere and our ecosystems. In this context, the United Nations adopted the 2030 Agenda for Sustainable Development in 2015 (United Nations, 2015). This agenda includes 17 Sustainable Development Goals (SDGs), which calls for engagement, action, and collaborations to tackle challenges faced by humanity and nature. Thus, responding to these challenges requires relevant research and practice (George, Howard-Grenville, Joshi & Tihanyi, 2016; Howard-Grenville et al., 2017), including advancing how universities and entrepreneurial ecosystems support new sustainable enterprises and social/sustainable innovations in creating processes, products, services, and collaborations (Volkmann, Fichter, Klofsten & Audretsch, 2019). Such a focus requires understanding the complexity of connections, flows, and collaborations among processes promoting sustainable development (Kim, Bansal & Haugh, 2019). Thereby, the growing body of literature on universities and the ecosystems demonstrates that new enterprises encapsulating and diffusing knowledge and employing spillovers from universities, other R&D institutions, and other NGOs are important. Hence, literature linking universities and ecosystems to sustainability is rapidly emerging but remains with opportunities to explore (i.e., Cohen et al., 2006; Pankov, Velamuri & Scheckberg, 2019; Volkmann et al., 2019). Entrepreneurial ecosystems have a set of interdependent facets (i.e., key actors, resources, and structures) that foster entrepreneurship within and between any geographic area. Thus far, research has identified these facets’ role, i.e., including financiers, clients, infrastructures, support organizations, regulatory frameworks, cultures and individual attitudes (Acs, Stam, Audretsch & O’Connor, 2017; Brown & Mason, 2017; Ghio, Guerini & Rossi-Lamastra, 2019; Spigel, 2017). However, understanding the role of universities and entrepreneurial ecosystems engaging in sustainability is limited. Emerging research identifies the process by which ecosystems become sustainable and how ecosystems’ support programmes lead to sustainable regional development (Volkman et al., 2019), the contextual aspects promoting an entrepreneurialecosystem’s change towards sustainability (Pankov et al., 2019), and entrepreneurial processes in sustainable ecosystems. As sustainability is a relevant lens for studying entrepreneurial ecosystems and universities, investigating their role in supporting

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sustainable products, services, and enterprises will allow us to obtain more insights into how these organizations generate social change and promote systemic transformations. Yet, such an approach is anchored in the interactions, collaboration, and networks among enterprises, public research institutions, and nonprofit organizations like the university, supporting the emergence of new sustainable enterprises from within entrepreneurial ecosystems. Sustainable enterprises blend social, environmental, and financial goals and approaches to solve sustainability challenges (Muñoz & Dimov, 2015). To this purpose, this book examines how universities and entrepreneurial ecosystems interact better to grasp universities’ connections and their surrounding environments, generating and promoting sustainability. The book contains both concepts, i.e., ecosystems and entrepreneurial universities, and examines their interactions and contributions towards organizations and regions’ sustainability. It also seeks to understand how universities operate as an institutional context crucial to entrepreneurial ecosystems and drive social/sustainable innovation.

1 Overview of the chapters The book is organized into three parts and includes seventeen chapters. The first part, “Universities and Entrepreneurial Ecosystems,” includes five chapters. These chapters set the conceptual ground for understanding key elements of entrepreneurial ecosystems in interaction and/or collaboration with universities. They relate to learning, innovation, entrepreneurship, and regions. They also gather a variety of geographical contexts, including Portugal, Brazil, Mexico, and Germany. Chapter 1, “Entrepreneurial ecosystems, learning regions, and the role of universities,” by Elisa Thomas and Bjørn Asheim, explores universities’ role in creating learning regions as development coalitions. The authors looked into a case study of three universities in the Southern Brazil city of Porto Alegre that, by allying, decided to create a learning region with the ultimate goal of developing the entrepreneurial ecosystem to contribute to making new and existing businesses sustainable. Chapter 2, “The dark side of the university’s participation in innovation ecosystems,” by Ana Dias Daniel, Susana Oliveira, and Joaquim Borges Gouveia, addresses a qualitative approach focusing on firms’ perspectives that are enrolled in industrial clusters. The authors show that universities play four main roles within an innovation ecosystem: problem-solver, knowledge producer, network promoter, and human resources training. Chapter 3, “Enablers to fostering interactions during entrepreneurship events within university-based entrepreneurship ecosystems (U-BEEs),” by Geraldina Silveyra, Lucía Rodríguez-Aceves, and Allan Villegas-Mateos, explores the enablers (knowledge self-efficacy, reciprocity, and relational capital) that foster interactions in

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university-based entrepreneurship ecosystems, aiming to identify the specific motivations and benefits of nascent and mature entrepreneurs during networking events. The authors reveal that nascent entrepreneurs’ motivation for attending such networking events is to expand their relational capital and acquire specialized knowledge for their venture. Chapter 4, “Does education ensure entrepreneurial initiative? Approaching an entrepreneurial ecosystems taxonomy,” by Mariana Pita, Joana Costa, and António Carrizo Moreira, has a twofold foundation: 1) it relies on a taxonomic approach to entrepreneurial ecosystems, 2) connects the multi-layer characteristics to the entrepreneurial initiative. The authors argue that the effect of education contrarily to previous is not always positive and, depending on context conditions, it acts as a constraint to entrepreneurship or as an enhancer. Chapter 5, “The ambiguous role of best practice examples for knowledge spillovers: Evidence from universities and start-ups in the Berlin entrepreneurial ecosystem,” by Daniel Feser and Till Proeger, analyses the role of knowledge spillovers in the diffusion of sustainable innovations in regional helix ecosystems. Specifically, focus on the link between the diffusion of artificial intelligence technologies and sustainable development, based upon a sample of artificial intelligence experts in the entrepreneurial ecosystem in Berlin drawn from universities and start-ups. The second part, “U-I Cooperation and Sustainability,” is constituted by five chapters. These chapters rely on sustainability to change our understanding of entrepreneurial ecosystems, incubators, research centres, and university–industry collaboration. While the chapters explore aspects or factors that facilitate and intensify sustainability processes and related outcomes, they also bring about the challenges and limitations of working with and becoming sustainable. The chapters comprise the European Union countries, Portugal as well as Brazil and Norway. Chapter 6, “Building sustainable entrepreneurial ecosystems: A mediated model of university–industry collaboration, knowledge creation, and the entrepreneurial environment,” by Jeandri Robertson, Leyland Pitt, and Ian P. McCarthy, contributes to the literature regarding university–industry collaborations and knowledge creation by exploring how it acts as building blocks for sustainable entrepreneurial ecosystems. The study conceptualizes the relationship between university–industry collaborations and the entrepreneurial environment within sustainable entrepreneurial ecosystems. These relationships are comparatively analysed across diverse economic markets in different ecosystems. Chapter 7, “University–industry collaboration to support sustainability: An analysis of the determining factors for European Union countries,” by Alicia Trejo Vásquez, María Jesús Rodríguez-Gulías, Manuel González López, and David RodeiroPazos analyses panel data to explain which macro factors influence the different levels of university–industry collaboration. Authors try to explain why the university–industry collaboration in certain countries is more intense than in others and which factors determine this difference.

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Chapter 8, “University business incubators as drivers of sustainability: The perspective of critical success factors,” by João Paulo do Carmo and Adonai J. Lacruz, identifies the critical success factors of the enterprise incubation network programme, of the Federal Institute of Espírito Santo, in Brazil. In general, the process by which support programmes for entrepreneurial ecosystems promote sustainability in developing countries. Chapter 9, “Universities as change agents of SMEs sustainable innovation: A knowledge transfer view,” by Pedro Mota Veiga and Sérgio J. Teixeira, assesses the characteristics that promote knowledge transfer innovation activities, as well as the impact of knowledge transfer on innovative capacity. Chapter 10, “University research centers as catalyzers in entrepreneurial ecosystems: Fostering the transitions towards sustainable business models via the university–business interface,” by Bart Henssen, Talia Stough, Elien Crois, and Luana Jassogne, explores how a research centre for sustainable entrepreneurship’s unique positioning at the interface of theoretical advances in sustainable business models and real-world implementation of sustainable business models help foster sustainable transitions in business and yields critical insights for future innovations of these models. Chapter 11, “The Spinner Innovation: factors for inclusion and advocating in sustainable ecosystems,” by Ronnie Figueiredo, Raquel Reis Soares, Marcela Castro, and Pedro Mota Veiga, attempts to investigate what are the factors for inclusion and advocating in sustainable ecosystems. With resource a systematic literature review, the authors identified different thematic groups to classify sustainable ecosystems. Lasty, the third part designed by “Universities and Entrepreneurial Activities,” comprises the remaining six chapters. These chapters examine how universities stimulate entrepreneurship considering the role of researchers, infrastructure, institutional environment, social responsibility, and technology in shaping the university of the future. Chapter 12, “Boundary spanners enabling knowledge integration for sustainable innovations in university–industry research centres,” by Thomas Lauvås and Ola Edvin Vie examine knowledge integration in organizational dynamics supporting sustainable innovation. They conduct a longitudinal study of research centres in Norway and conclude that middle managers play a central role in university–industry research centres facilitating knowledge integration for sustainable innovation. Chapter 13, “An entrepreneurial ecosystem support model in the digital era: Crowdfunding,” by Orkun Yildiz and Serkan Sahin, attempts to explore how some autonomous structures have been developed to serve under the corporate identities of universities to enable the academic, student, or external participating entrepreneurs to transform their scientific knowledge into a successful commercial product. The authors propose a new financial business model for the enterprises’ success that will take place in technoparks based on blockchain technology. Chapter 14, “Steering productive entrepreneurship activities in emerging markets: The role of the university,” by Chux Gervase Iwu and Abdullah Promise Opute

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demonstrates how the university as a veritable locale for improved entrepreneurial gains ties into a meaningful realization of productive entrepreneurship activities in emerging markets. The authors offer a support institution viewpoint that focuses mainly on universities’ role as providers of essential entrepreneurship education and the facilitating role of government and private sector stakeholders in impacting university activity towards active and productive entrepreneurship. Chapter 15, “Medical device scientists’ influence on research impact within entrepreneurial ecosystems: A systematic literature review,” by Brendan Dolan, Caroline McGregor, and James A. Cunningham, examines how a principal investigator can influence or affect impact (beyond scientific) from medical device research. The authors argue that the university, and the actors within their ecosystems, plays a pivotal role in ensuring research is relevant and impactful for society and the environment. Chapter 16, “Students’ perceptions of university social responsibility: A crosscultural comparison,” by Kseniya Sorokina, Paula Odete Fernandes, and Jeyhun Mammadov, aims to understand the perception of corporate social responsibility from the point of view of the international students that attended the Polytechnic Institute of Bragança (Portugal), how they react to the questions on the given topic, do they find the concept vital or not. The authors conclude that higher education institutions are an important factor for students to develop their social and personal responsibility. Finally, Chapter 17, “Which pathways lead us to the university of the future?,” by Veselin Vukotic, examines the idea of university’s crisis. This chapter gives one of the possible visions of developing the future university through the case study of the University of Donja Gorica (Montenegro). The author argues that the university’s bureaucracy is growing, and in such an environment, it is difficult to develop an entrepreneurial ecosystems and teach students critical thinking and employable skills. The chapter presents ideas for a future university anchored in a life perspective.

2 Final remarks This introductory chapter presents theoretical background on universities, entrepreneurial ecosystems, and sustainability considering the individual, organizational, collaborations, and regional features explored in this book’s seventeen chapters. The book responds to calls for research to advance concepts and understanding in the entrepreneurial ecosystem literature with a sustainability lens (Volkmann et al., 2019). Thereby, the book is one of the first to examine, in a broader sense, how universities and entrepreneurial ecosystems promote sustainability. It denotes a vital contribution in that direction as it offers a vision for understanding the main magnitudes of universities and entrepreneurial ecosystems interlinked with sustainability

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to respond to grand challenges. Our central message is that responding to grand challenges is anchored in interactions and connections among entrepreneurial ecosystems, university–industry, and nature. Interactions and connections require a multidisciplinary approach to reconnect with nature. They are vital for transforming organizations, individuals’ perceptions and fostering new sustainable enterprises. Thereby, our book provides hints and insights on the role of universities, ecosystems, research centres, partnerships, and individuals actively committed to promoting sustainability. It shows that advocating sustainability via universities and entrepreneurial ecosystems has a processual and systemic nature that is central to building sustainability awareness, knowledge, and collective capabilities. The book intends to inspire research agendas that look closer into the influence of sustainability in shaping university, collaborations, and ecosystems. Such an agenda recognizes the importance of conserving nature and invites us to examine how universities and entrepreneurial ecosystems strive to reconnect with nature. In line with Kim et al. (2019), our book invites us to shape our present sustainably. We hope that the topic attracts more attention and provides academics and policymakers with new insights to contribute to this field of knowledge in the future.

References Acs, Z. J., Stam, E., Audretsch, D. B. & O’Connor, A. (2017). The lineages of the entrepreneurial ecosystem approach. Small Business Economics, 49(1), 1–10. Brown, R. & Mason, C. (2017). Looking inside the spiky bits: A critical review and conceptualization of entrepreneurial ecosystems. Small Business Economics, 49(1), 11–30. Cohen, B. (2006). Sustainable valley entrepreneurial ecosystems. Business Strategy and Environment, 15(1), 1–14. George, G., Howard-Grenville, J., Joshi, A. & Tihanyi, L. (2016). Understanding and tackling societal grand challenges through management research. Academy of Management Journal, 59, 1880–1895. Ghio, N., Guerini, M. & Rossi-Lamastra, C. (2019). The creation of high-tech ventures in entrepreneurial ecosystems: Exploring the interactions among university knowledge, cooperative banks, and individual attitudes. Small Business Economics, 52(2), 523–543. Howard-Grenville, J., Davis, J., Dyllick, T., Joshi, A., Miller, C., Thau, S. & Tsui, A. S. (2017). Sustainable development for a better world: Contributions of leadership, management and organizations. Academy of Management Discoveries, 3, 107–110. Kim, A., Bansal, P. & Haugh, H. (2019). No Time like the present: How a present time perspective can foster sustainable development. Academy of Management Journal, 62, 607–634. Muñoz, P. & Dimov, D. (2015). The call of the whole in understanding the development of sustainable ventures. Journal of Business Venturing, 30(4), 632–654. Pankov, S., Velamuri, V. K. & Scheckberg, D. (2019). Towards sustainable entrepreneurial ecosystems: Examining the effect of contextual factors on sustainable entrepreneurial activities in the sharing economy. Small Business Economics. doi:s11187-019-00255-5 Spigel, B. (2017). The relational organization of entrepreneurial ecosystems. Entrepreneurship Theory and Practice, 41(1), 49–72.

Universities, entrepreneurial ecosystems, and sustainability: An overview

United Nations (2015). Transforming our world: The 2030 agenda for sustainable development. Resolution adopted by the General Assembly on 25 September 2015 New York: Author. Accessed February 18, 2021 https://www.un.org/ga/search/view_doc.asp?symbol=A/RES/ 70/1&Lang=E Volkmann, C., Fichter, K., Klofsten, M. & Audretsch, D. B. (2019). Sustainable entrepreneurial ecosystems: An emerging field of research. In Small business economics (pp. 1–9). Springer.

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Part I: Universities and entrepreneurial ecosystems

Elisa Thomas and Bjørn Asheim

Chapter 1 Entrepreneurial ecosystems, learning regions, and the role of universities 1.1 Introduction This chapter aims at discussing the role of universities in establishing entrepreneurial ecosystems, especially in terms of how learning regions, understood as development coalitions, can be used in the formation process. An entrepreneurial ecosystem, by generating value and distributing it among the members of the ecosystem, can create sustainable economic, technological, and societal impacts (Audretsch, Cunningham, Kuratko, Lehmann & Menter, 2019). To develop an entrepreneurial ecosystem, the concept of “learning region” contributes by pointing to the establishment of a strategy for long-term, bottom-up, and broad partnership-based development coalitions (Asheim, 2012). A learning region emphasizes cooperation and collective learning in regional networks resulting in the promotion of entrepreneurship and innovation, and, as a consequence, the creation of sustainable businesses (Asheim, 2012). Universities as knowledge generating institutions have the potential of going beyond traditional support for entrepreneurship, focused mainly on the creation of academic spin-offs or licensing activities, towards promoting regional cultural change and network building activities (Pugh, Soetanto, Jack & Hamilton, 2019). That broadens the understanding of university engagement in a developmental role, which is based on the medium to long-term alignment of university capabilities with the needs of local actors (Marques, Morgan, Healy & Vallance, 2019). However, little is known of universities as the main drivers of the development of entrepreneurial ecosystems, especially in the context of emerging economies. This chapter will, therefore, fill this lacuna by analysing the role of universities in creating entrepreneurial ecosystems in emerging economies by exploiting learning regions as development coalitions. To answer this, we look into a case study of three universities in Southern Brazil that, by forming an alliance, decided to act towards the creation of an entrepreneurial ecosystem to contribute to promoting sustainable new and existing businesses. Specifically, we explore how the alliance of universities worked to establish a bottom-up, horizontally based cooperation between different actors, including the activities performed by universities’ leaders to mobilize regional stakeholders in the development of the ecosystem.

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1.2 Universities, entrepreneurial ecosystems, and learning regions: Theoretical perspectives There are studies about universities and entrepreneurial ecosystems which look at universities mostly as institutions with a supporting role as knowledge and human resources generators. A bibliometric review by Malecki (2018) shows that universities are the most frequently identified actor in entrepreneurial ecosystems after entrepreneurs but, as a matter of fact, the most important function of a university is still to provide highly skilled and specialized talents. Ierapetritis (2019), for instance, analyses the role of Greek universities in fostering entrepreneurial ecosystems, emphasizing their actions with the academic community as business incubation and education, guidance and counselling on entrepreneurship and innovation. Pugh et al. (2019) show that the university of Lancaster, in the UK, has taken a role of facilitating learning activities not only at the individual entrepreneurial level but also at the wider regional level, helping the development of the entrepreneurial ecosystem. Shaw and Allison (1999) go a little further and include the university’s contribution to regional governance by its involvement in local regional and strategic planning processes. Their focus in Australia is on universities’ contribution to regional development, by strategically positioning the region as a learning region in the knowledge economy. Some universities “act not only as educators but also as institutional entrepreneurs, proactively networking, shaping regional strategies and attempting to change local routines as well as national policies” (Raagmaa & Keerberg, 2017, p. 270). The ecosystem concept has become increasingly popular the last years, but often it has been used without any clear ideas of what it actually means. It is also often used interchangeable with regional innovation systems (RIS), and although there are similarities between these two concepts, they are different in many aspects. A RIS is constituted by two sub-systems, an exploration sub-system of knowledge generating and diffusing organizations such as universities, research organizations and technology transfer agencies, and an exploitation sub-systems of firms, often located in (regional) clusters, surrounded by an organizational and institutional support structure. A variant of the RIS concept is the triple helix construct (Etzkowitz & Leydesdorff, 1997), where the public sector adds to universities and industries on the same level as a key actor of the regional set-up, while the public sector in the RIS context is part of the organizational and institutional support structure. Both frameworks are most often a result of top-down planning initiatives as part of a public regional innovation policy, even if there are examples of organic developments of RIS, especially in regions with a long industrial tradition. Consequently, most examples of well-functioning RIS can be found in well-developed countries and regions with good governance and strong institutions, where trust and social capital provide the glue for a tradition of close public-private collaboration.

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Ecosystems on the other hand are most often developed organically driven by innovative entrepreneurs and venture capitalists, as most typically seen in Silicon Valley and US science parks such as the Research Triangle Park in North Carolina. In addition to these two actors, universities, often with an explicit vision of becoming entrepreneurial universities with a strong focus on the commercialisation of research through IP rights is a third active organization of the ecosystem. In contrast, the role of universities in a RIS is broader than just commercialization of research, and contains also university–industry cooperation in general, as well as more specifically a mode 2 oriented research cooperation between universities and industries which often involves industrial doctorates and adjunct professors paid by industry (Trippl, Sinozic & Lawton Smith, 2015). Industry more generally is also part of an ecosystem, but more as springboards for new entrepreneurs and customers of entrepreneurs’ products and services, and the public sector has a more indirect role, as funders of universities and specific support agencies for entrepreneurs, research, and commercialization activities. An ecosystem is not normally a planned, top-down event, but organically developed in a bottom-up fashion. Cooke and Leydesdorff (2006) summarizes these differences talking about two forms of RIS, an Institutionalized RIS (IRIS), as we know it from European countries with a coordinated market economy (Hall and Soskice, 2001) as we described above, and an entrepreneurial RIS (ERIS), which we will call an entrepreneurial ecosystem. Conceptually, this chapter draws on the entrepreneurial ecosystem concept from Spigel (2017): “combinations of social, political, economic, and cultural elements within a region that support the development and growth of innovative start‐ups and encourage nascent entrepreneurs” (Spigel, 2017, p. 50), complemented by Stam (2015) who emphasizes entrepreneurship not only as the output of the system, but entrepreneurs as important players in creating a sustainable ecosystem. The relations among a set of interdependent actors and factors coordinated in a systemic way to enable productive entrepreneurship (Stam, 2015) can, given internal and external favourable conditions, lead to sustainable regional development (Volkmann, Fichter, Klofsten & Audretsch, 2019). Actions by connected previously independent actors targeted at regional development can be initiated and fostered by place leaders, who organize collective development efforts to benefit both the actors’ individual objectives and the objectives of the region (Beer et al. 2019; Beer & Clower, 2014; Grillitsch & Sotarauta, 2020). Place leaders have shown “an important position in regional development when policy makers and local governments fail to provide resources and support for innovation and entrepreneurship, which is a common reality in developing countries” (Thomas, Faccin & Asheim, 2021). In Colombia, Porras-Paez and Schmutzler (2019) found that the Chamber of Commerce at Atlantico region acted as the articulating actor for the development of the entrepreneurial ecosystem, because it was perceived as a locally embedded trustworthy institution, independent from the government.

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In the process for place leadership to mobilize the broad range of stakeholders, including NGOs and the civil society in addition to the private and public sectors, which were necessary to successfully build the alliance in Porto Alegre, the concept of the learning region as a development coalition can be helpful (Asheim, 2012). It represents a horizontal bottom-up co-operation between different actors in a local or regional context, based on a socially broad mobilization and participation of people (Ennals & Gustavsen, 1999). Our case study shows a region that aims at becoming a hub for innovation and entrepreneurship, but it lacks the previous step of actors’ participation in networks. The universities in the alliance are working towards building localized, interactive learning, and cooperation. “Learning regions should be looked upon as a policy framework or model for formulations of long-term, partnership-based development strategies for initiating learning-based processes of innovation, change and improvement” (Asheim, 2001, p. 75). In the formation of learning regions, the role social capital and trust plays in promoting formal and informal interfirm networks and the process of interactive learning must be in focus, because trust can only be earned through repeated transactions (Asheim, 2001; Morgan, 1997). The development of new routines requires time, resources and, a collective vision of regional renewal (Morgan, 1997), which was exactly what the three universities managed to achieve by taking on the role of place leadership. Factors that influence the development of entrepreneurial ecosystems are placespecific, and the geographic area shapes the nature of entrepreneurial activities (Audretsch and Belitski, 2017). The next session, therefore, presents the regional setting where the case Alliance for Innovation takes place.

1.3 Porto Alegre and the metropolitan region: Background for the alliance for innovation The city of Porto Alegre is the capital of Brazil’s southernmost state, Rio Grande do Sul, and has a population of 1.4 million people. Its metropolitan region covers an area of 6,900 square kilometres and hosts around 3 million inhabitants. The area houses a petrochemical cluster, manufacturing sites and innovation facilities of large international companies, such as General Motors, Dell Computers, Hewlett Packard, SAP, and Stihl. Although unemployment in the metropolitan region was reduced in 2019 compared to previous years, it still reaches around 10% (IBGE, 2020). Rio Grande do Sul has a high level of development in Brazil, and the comparatively high level of education and strong academic environment are of particular importance in the region (The Swedish Agency for Growth Policy Analysis, 2016, April). That said, Porto Alegre has a strong tradition in the formation of human resources, although recently it has been difficult to retain talents (Zen, Gazzaro, Faccin & Gonçalves, 2018).

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Fischer, Queiroz, and Vonortas (2018) explain that, in Brazil, knowledge-intensive entrepreneurship is concentrated in and around a few urban areas, with the presence of research-oriented universities functioning as a vector, however, urban disorders as theft and lack of adequate infrastructural conditions hamper the generation and attraction of knowledge-intensive new ventures. In a wrong manner, such overarching conditions are usually left out of discussions related to initiatives aiming at increasing entrepreneurial activity in Brazil (Fischer et al., 2018). Another fact worth emphasizing about Porto Alegre is its strong tradition of entrepreneurship, which derives from its relatively late colonization consisting of European smallholders. “This group of wage earners, who came to Brazil on their own initiative in the hope of a new future, were not only skilled within their respective field, but were also constituted in a considerably more market-oriented context” (The Swedish Agency for Growth Policy Analysis, 2016, April). As a matter of fact, it has been a long-term goal in Porto Alegre to develop an ecosystem that retains talents, supports new knowledge-based firms and fosters the renewal of big established firms. A programme called Porto Alegre Tecnopole (from 1994 to 2007) had the aim of transforming the metropolitan region into a knowledge-based economy. It was run by the city hall with the participation of universities, industry, and workers associations. Applying the triple helix concept (Etzkowitz & Leydesdorff, 1997), the initiative focused on creating the necessary conditions and support for the development of high technology firms through establishing technology parks, incubators, TTOs, and so on. The programme generated positive results, especially in developing science and technology parks (STPs) in the metropolitan region. As explained by Bencke, Dorion, Prodanov and Olea (2019), the development of STPs in the state of Rio Grande do Sul had a direct influence of political, business, and university leaderships, and happened as an endogenous development arising from the existing resources and opportunities of the territory and network. However, there were barriers for the programme’s sustainability, specifically the fact that it was run by the city government (Dienstmann, 2019), which led to the programme’s closure when there was a shift in mandates. Another barrier to Porto Alegre Tecnopole’s sustainable success was the fact that the private venture capital firm was not “closely linked to the formally planned elements of this cluster, preferring to work independently” (Tiffin & Bortagaray, 2008). The programme therefore had a strong reliance in public funding. There was also an attempt in Porto Alegre towards building civic participatory budgeting for the distribution of municipal funds. However, after 20 years of operations, independently of the public administrations’ idealism, enthusiasm, and branding (Junge, 2018), the participatory budget did not manage to develop an ecosystem that was positive to economic growth, being centred on basic improvements, as paved streets, sewer investments, and school constructions, for example (Abers, 1998).

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The case study we explore in the present chapter attempts to overcome some of the challenges previously faced by programmes towards the development of innovation ecosystems, as the short-termism of government mandates, civic participation led by governments, and the exclusion of issues such as public safety and infrastructure, which influences ecosystems that aim at retaining talents and benefitting the growth of new knowledge-based firms.

1.4 Method This research took a qualitative approach, using a case study strategy, to analyse the role of universities in creating learning regions as development coalitions. Interviews were the main source of data collection, complemented by secondary data. In 2018 and 2019, we have interviewed 41 people from universities, the City Hall, industry associations, firms and people who represent organizations from the civil society. The interviews varied from 30 min to 1 h and were audio-recorded to allow further consultation. Researchers followed a semi-structured protocol with open questions. To ensure interviewees’ anonymity, each one of them was assigned a random number, which we used in the description of the case. Secondary data was gathered from documents and from the media to complement information given by respondents. The initiative called Alliance for Innovation, with the objective of fostering high impact actions towards advancing the entrepreneurial ecosystem in the region, is an articulation between three universities in the city of Porto Alegre: – UFRGS (Universidade Federal do Rio Grande do Sul): publicly funded university, founded in 1934. Around 14,000 people are involved in research and technology development including bachelor and graduate students, laboratory technicians, professors, and visitors (UFGRS, 2020). Its technology park established in 2012 focuses on entrepreneurship training and start-up incubation. – PUCRS (Pontifical Catholic University of Rio Grande do Sul): private non-forprofit university, founded by Marist Brothers (religious congregation) in 1948. It has more than 4,000 faculty and staff members, and over 170,000 alumni. Its technology park established in 2002, TecnoPUC, has 170 companies generating more than 8,000 jobs. The university accounts for 43 granted domestic patents, 36 international granted patents, and 34 registered software. – UNISINOS (Universidade do Vale do Rio dos Sinos): private non-for-profit university, founded by the Jesuit Network (religious congregation) in 1969. Its technology park established in 1999, Tecnosinos, has 75 companies generating 6,000 jobs. Tecnosinos has 120 intellectual property registers. It has around 900 faculty members, and over 90,000 alumni.

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1.5 The alliance for innovation After the crisis of 2015–2016, where federal initiatives and support were non-existent, Brazilian regions were forced to find their own solutions to their problems (The Swedish Agency for Growth Policy Analysis, 2016, April). The metropolitan region of Porto Alegre needed to boost its economy, because, as some of the interviewees explained: The state’s economy is very dependent on agriculture so, if it doesn’t rain, we are bad (I1); The government doesn’t have money to invest. The industrial manufacturing is not doing well. Our economy is based on old things, on things that are going to stay in the past, so if we don’t innovate, we are done, we are dead (I2); One of our weaknesses is regarding raising funds for innovation and start-ups (I3).

In 2017, with a new mandate in Porto Alegre’s city hall, a new economic development public policy was created, especially aiming to promote innovation and entrepreneurship. The city’s Innovation Council was led by the rector of PUCRS, who was also the president of the Latin American Division of the International Association of Science and Technology Parks and Areas of Innovation (IASP). This shows the proximity of universities to the regional government regarding innovation. In Brazil, universities are crucial for the success of technological initiatives because science and technology infrastructure are mostly localized at universities (Lahorgue & Cunha, 2004). Through his role at IASP, he had contact with Josep M. Piqué, a Spanish consultant on developing ecosystems for innovation and entrepreneurship (IE), who was invited to deliver a seminar for the Innovation Council of Porto Alegre. Key regional stakeholders were invited for the seminar, where they started discussing the future of the city. Leaders who were involved in the previous Porto Alegre Tecnopole Program and new stakeholders continue to work for the development of the IE ecosystem “by assuming new roles in the institutions or by assuming functions in different institutional spheres” (Bencke et al., 2019). A problem to the development of this project was the lack of trust from the business community in the public administration. As some of the interviewees comment: Municipalities, not only in Porto Alegre but in Brazil, are more concerned with politics. And companies don’t want to take part in one side and another. Public bodies at this moment don’t have (I am going to use a strong word) credibility from the business community (I4); Government people have different ideologies, and ideologists criticize each other. Another problem is that they are thinking only for 4 years because they are in government for just this period. No one thinks beyond their time (I5).

For reasons such as these, the three main universities in the region offered to jointly coordinate a project to mobilize the business community to improve and boost an innovation and entrepreneurship ecosystem. The universities’ long tradition of triple-helix partnerships for innovation and of positive impacts on society rendered

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them a trust-worthy place in the region. Formalized in 2018, the Alliance for Innovation has been the driving force of the IE ecosystem development in Porto Alegre. Important activities in different realms were necessary to start the project, such as financing, communicating with the community and, most importantly, mobilizing stakeholders and having them committed to participate. Therefore, the first joint effort by the three universities was to engage companies, industry associations, the civic society, public authorities, and other institutions to make them part of the project, which was not easy. The lack of trust and collaboration culture among regional actors was pointed out by several interviewees: A weakness here is people’s mindset. Some people just don’t want to help; they only think on their own firms (I6); People are always fighting with each other. We have two football teams. People are really fanatic about their teams. If you are from one, I don’t like you. Some people get a little radical. We have the same with politics (I8); First, we don’t have the give back concept. Most of the people that made money in the past don’t live here anymore. They move to Sao Paulo or abroad. And most of the companies move to Sao Paulo when they get bigger. So, they can’t give back to the community. The second weakness of the region, for some cultural reason, people just want to work by themselves, they don’t want to cooperate (I2); We are very polarized in this state. As a result from the Alliance, in the short term, I expect we move towards a common centre (I1).

For around 6 months, vice-chancellors from the 3 universities met more than 80 entrepreneurs, businessmen, and industry associations in the region to present the Alliance and motivate the stakeholders to participate. One of the initial big achievements, that allowed the economic sustainability of the project, was to get the support of three banks, Sicredi, Agibank, and Badesul, to provide funds to hire the Spanish consultant, who became the mentor for the project’s progress. In addition, the biggest local media company got on board. By that, the main newspaper started writing about the need for innovation and entrepreneurship as a solution to overcome the region’s socio-economic challenges. The discussion in the newspaper helped to mobilize the broader community. The wishes of the local businessmen and entrepreneurs were broadcasted, stressing that they wanted a region where social, environmental, and economic concerns were met. The strategy for raising awareness in the region and engaging more actors worked well. People started to be willing to work together to guarantee sustainability in the economy and social environment in the long term. This could be seen on an open event in which more than 600 people showed up at Unisinos University on a Wednesday morning in July 2018. Representatives from the government, the media, start-up incubators, technology parks, researchers, businessmen, industry associations, and other organizations that were interested in developing the IE ecosystem in the city

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were invited to “build the future of Porto Alegre together.” The mayor of the city attended the event with around 200 employees from the city hall to show his support for the project. The speakers presented examples of functioning IE ecosystems abroad and in other places in Brazil, where the society collectively developed the regions to be positive to sustainable businesses and to be better in terms of quality of life for its citizens. The event’s attention from the media raised the motivation and engagement from many actors. From that moment on, the Alliance for Innovation formalized a Board of Directors formed by 75 institutions (6 universities, 5 other educational institutions, 1 start-up incubator, 5 start-ups, 15 large firms, 33 business associations, 1 non-governmental institution, and public administration agencies). This coalition established an executive group and officialized a partnership with the city hall (entitled Pacto Alegre agreement) to realize practical projects addressing the regional socio-economic needs. The initial result from the direct involvement of universities in the development of the IE ecosystem is the engagement and formal commitment of the banks, the City Hall and other institutions who signed the agreement to be part of the Board of Directors. Participants’ expectations reveal that indeed people trusted the universities to drive the creation of a learning region based on collaboration and trust among the actors: In the short term I think we can get a very spread mobilization from people that were previously working alone and separately, as everyone is now getting together, and the subject is getting stronger. Also, we can bring some investments as well because of the changes in this environment (I3); The initial result will be the common view for the future. Through this common view in the long-term we can make changes regarding living conditions and the mobility, and then we can build this entrepreneurship ecosystem that could change the scenario of the city (I7); For the very long term, I hope for a cultural change, helping each other, making partnerships, working together, this is the most important contribution that the project can bring. This can be a game-changer for us (I2).

1.6 Legacy: Power delegation to the community With the officialization of the executive group, formed by professors, PhD students, and members of City Hall, the universities began transferring decision-making and the operation of projects to the network of stakeholders (Thomas, Faccin & Asheim, 2021). This network is self-organized, and its executive group functions as a facilitator of activities. The initial initiatives from the network were to run a series of thematic workshops, where 135 participants from civil society, universities, government, and firms

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gathered to discuss the current conditions of the city and to plan new actions. The result was a definition of six grand challenges of the local IE ecosystem: City Identity; Modernization of Public Administration; Education and Talents; Business Environment; Urban Transformation; and Quality of Life. The participants created 24 initial projects to address the challenges. By targeting these six areas, the coalition aims at creating a better city for citizens and an ecosystem where entrepreneurship and innovation will flourish. The current step of the ecosystem development in Porto Alegre has been the operation of these hands-on projects within the Pacto Alegre agreement. The projects are being run simultaneously by different groups, and some participants are involved in more than one project. Some projects have a long development period, for example, the integration of health data, that will unite information about a patient from different public hospitals and clinics, and the improvement of tap water, which currently follows all health and safety regulations, but could have a better taste by using less chlorine. Four professors from UFRGS are collaborating with the municipal water department on an 18-month pilot test for a new treatment method (Vargas, 2019). Another example is a crowdfunding portal, initiated by Badesul, the regional development bank, focused on early-stage startup using the equity crowdfunding strategy. After one year of Pacto Alegre execution, results can be seen from the initial projects that were doable in a short-term period, for example, training public employees on start-up culture and innovation ecosystems, within the grand challenge “Modernization of Public Administration”; targeting the challenge of “Education and Talents,” all the students from public schools have watched a movie on entrepreneurship and innovation; within the grand challenge “Business Environment,” the mayor of Porto Alegre launched an Innovation Fund, aiming at fostering the implementation of disruptive projects and accelerating start-ups that contribute to improve the quality of public services (Pacto Alegre, 2019). In addition, new projects have been established by the network of stakeholders, focusing on other needs that were identified. Through transferring the decision and execution regarding hands-on projects to the network, the three founding universities of the Alliance for Innovation managed to delegate power and share the decision-making with the broad network of stakeholders involved with the goal of developing the ecosystem towards innovation and entrepreneurship. Figure 1.1 illustrates the case of the Alliance for Innovation. The bottom-up development of the entrepreneurial ecosystem had as the point of departure the regional weaknesses felt by the community, and the awareness of the need for a broad movement. The three universities took on a place-leadership position for the creation of the development coalition which, through self-organized hands-on projects, is now running the operations.

Chapter 1 Entrepreneurial ecosystems, learning regions, and the role of universities

Regional needs for a sustainable entrepreneurial ecosystem

Collaborative hands-on projects to develop the learning region

(Bottom-up) ecosystem development

Movement awareness

Universities take on the leadership position

Power delegation

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Stakeholders mobilization and engagement

Agreement with the City Hall (Pacto Alegre)

Practice of place leadership and the creation of the development coalition

Figure 1.1: Framework of the bottom-up development of an entrepreneurial ecosystem by universities as place leaders.

1.7 Discussion and conclusions By analysing the role of universities in entrepreneurial ecosystems using a case study from Southern Brazil, our research shows that the Alliance was responsible for a combination of approaches that could imply a bigger role in emerging economies for the institutions developing the ecosystems applying a bottom-up learning region approach. An emerging economy, such as Brazil, where good governance, solid institutions and a long tradition of collaboration are not clearly present, does not offer the best conditions for planning and building an RIS in a top-down way. On the other hand, there is neither the presence of dynamic and knowledge-based entrepreneurs and venture capitalists as found in the USA, nor the well-founded, private research university as Stanford and MIT are typical examples of. Therefore, ecosystems being more bottom-up and a result of an organic formation fit better with the possibilities found in a Brazilian context. Thus, in this chapter we argued that a modified ecosystem concept is what works best to describe what took place in Porto Alegre around the alliance initiated by the three largest universities in the region. The modifications of the ecosystem concept concern the three most important actors of the systems, the entrepreneurs, venture capitalists, and universities. In addition to what is already highlighted above concerning entrepreneurs and venture capitalists, it is necessary to enlarge the idea of who can be an entrepreneur from the innovative entrepreneur in the business sector, as is the common view of the ecosystem concept, to also include institutional entrepreneurs and place leadership, which is termed the “trinity of change agency” (Grillitsch & Sotarauta, 2020). The broader conceptualization of what should be understood as entrepreneurial activities has also implications for the role of these universities, which goes far beyond

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the functions of an entrepreneurial university to take on the comprehensive roles of an engaged or civic university (Goddard, 2016; Trippl et al., 2015). As we see in the case of the Alliance, the universities’ vice-chancellors demonstrated institutional entrepreneurship by engaging in this task, which goes beyond the normal focus of universities on teaching and research, and also beyond the normal types of third mission activities. It is noticeable that this has been carried out by three universities which normally are competitors, as is also the fact that they together have acted as place leaders in addressing regional economic and social needs (Thomas et al., 2021). In a climate of lack of trust and collaboration in both the private and public sectors, the place leadership role could be taken on by the universities as they were the only organizations that had sufficient trust and legitimation from the general public, due to respect earned by their teaching and research activities. Important here is also the fact that two of the three universities are catholic institutions, which shows the established position of the catholic church in Brazilian higher-education by adding a stronger social dimension to universities’ regional engagement and impact (Thomas & Pugh, 2020). Finally, the use of learning regions as a strategy to build an innovative ecosystem is also a novel aspect in emerging economies, with potentially important policy implications for bottom-up development initiatives. Therefore, the main finding from our study is the need for strong place leadership and the creation of learning regions in a bottom-up effort to promote the development of an entrepreneurial ecosystem in emerging economies.

References Abers, R. (1998). From clientelism to cooperation: Local government, participatory policy, and civic organizing in Porto Alegre, Brazil. Politics & Society, 26(4), 511–537. Asheim, B. (2012). The changing role of learning regions in the globalizing knowledge economy: A theoretical re-examination. Regional Studies, 46, 993–1004. Asheim, B. T. (2001). Learning regions as development coalitions: Partnership as governance in European workfare states?. Concepts and Transformation, 6(1), 73–101. Audretsch, D. B., & Belitski, M. (2017). Entrepreneurial ecosystems in cities: establishing the framework conditions. The Journal of Technology Transfer, 42(5), 1030–1051. Audretsch, D. B., Cunningham, J. A., Kuratko, D. F., Lehmann, E. E. & Menter, M. (2019). Entrepreneurial ecosystems: Economic, technological, and societal impacts. The Journal of Technology Transfer, 44(2), 313–325. Beer, Andrew; Ayres, Sarah; Clower, Terry; Faller, Fabian; Sancino, Alessandro; Sotarauta, Markku (2019) Place leadership and regional economic development: a framework for cross-regional analysis, Regional Studies, 53:2, 171–182. Beer, Andrew; Clower, Terry (2014) Mobilizing leadership in cities and regions. Regional Studies, Regional Science, 1:1, 5–20, DOI: 10.1080/21681376.2013.869428. Bencke, F. F., Dorion, E. C. H., Prodanov, C. C. & Olea, P. M. (2019). Community leadership and the Triple Helix model as determinants of the constitution of science parks. Benchmarking: An International Journal, 27(1), 21–40.

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Cooke, P. & Leydesdorff, L. (2006). Regional development in the knowledge-based economy: The construction of advantage. Journal of Technology Transfer, 31(1), 5–15. Dienstmann, J. (2019, Feb. 21). Pacto busca impulsionar inovação em Porto Alegre. Jornal do Comercio. Retrieved from: https://www.jornaldocomercio.com/_conteudo/especiais/cenario_ digital_2019/2019/02/669564-pacto-busca-impulsionar-inovacao-em-porto-alegre.html Ennals, J. R. & Gustavsen, B. (1999). Work organization and Europe as a development coalition (Vol. 7). Amsterdam: John Benjamins Publishing. Etzkowitz, H., & Leydesdorff, L. (1997). Universities and the Global Knowledge Economy: A Triple Helix of University-Industry-Government Relations. London: Pinter. Fischer, B. B., Queiroz, S. & Vonortas, N. S. (2018). On the location of knowledge-intensive entrepreneurship in developing countries: Lessons from São Paulo, Brazil. Entrepreneurship & Regional Development, 30, 612–638. Goddard, J., Hazelkorn, E., Kempton, L. & Vallance, P. (eds.). (2016). The civic university: The policy and leadership challenges. Cheltenham: Edward Elgar Publishing. Grillitsch, M.; Sotarauta, M. (2020). Trinity of change agency, regional development paths and opportunity spaces. Progress in Human Geography, 44(4), 704–723. Hall, Peter A.; Soskice, David (2001). Varieties of Capitalism: The Institutional Foundations of Comparative Advantage. Oxford: Oxford University Press. Ierapetritis, D. G. (2019). Discussing the role of universities in fostering regional entrepreneurial ecosystems. Economies, 7(4), 119. doi:10.3390/economies7040119 Instituto Brasileiro de Geografia e Estatística – IBGE (2020). Pesquisa Nacional por Amostra de Domicílios Contínua (Pnad Contínua). Retrieved from: https://www.ibge.gov.br/estatisticas/ sociais/trabalho/9173-pesquisa-nacional-por-amostra-de-domicilios-continua-trimestral. html?edicao=27704&t=destaques. Accessed August 15, 2020. Junge, B. (2018). Cynical citizenship: Gender, regionalism, and political subjectivity in porto alegre. Brazil. Albuquerque: University of New Mexico Press. Lahorgue, M. A. & Cunha, N. D. (2004). Introduction of innovations in the industrial structure of a developing region: The case of the Porto Alegre Technopole ‘Homebrokers’ project. International Journal of Technology Management & Sustainable Development, 2(3), 191–204. Malecki, E. J. (2018). Entrepreneurship and entrepreneurial ecosystems. Geography Compass, 12(3). doi:10.1111/gec3.12359 Marques, P., Morgan, K., Healy, A. & Vallance, P. (2019). Spaces of novelty: Can universities play a catalytic role in less developed regions?. Science & public policy, 46(5), 763–771. Morgan, K. (1997). The learning region: Institutions, innovation and regional renewal. Regional Studies, 31, 491–503. Pacto Alegre, (2019, Dec. 03). Pacto Alegre mostra as primeiras entregas para transformar a Capital. Retrieved from: https://pactoalegre.poa.br/index.php/noticias/pacto-alegre-mostraprimeiras-entregas-para-transformar-capital. Accessed May 15, 2020. Porras-Paez, A. & Schmutzler, J. (2019). Orchestrating an Entrepreneurial Ecosystem in an emerging country: The lead actor’s role from a social capital perspective. Local Economy, 34(8), 767–786. Pugh, R., Soetanto, D., Jack, S. L., & Hamilton, E. (2021). Developing local entrepreneurial ecosystems through integrated learning initiatives: the Lancaster case. Small Business Economics, 56(2), 833–847. Raagmaa, G. & Keerberg, A. (2017). Regional higher education institutions in regional leadership and development. Regional Studies, 51(2), 260–272. doi:10.1080/00343404.2016.1215600 Shaw, J. K. & Allison, J. (1999). The Intersection of the Learning Region and Local and Regional Economic Development: Analysing the Role of Higher Education. Regional Studies, 33(9), 896–902. doi:10.1080/00343409950075533

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Spigel, B. (2017). The relational organization of entrepreneurial ecosystems. Entrepreneurship Theory and Practice, 41, 49–72. Stam, E. (2015). Entrepreneurial ecosystems and regional policy: A sympathetic critique. European Planning Studies, 23, 1759–1769. The Swedish Agency for Growth Policy Analysis (2016, April). Innovative global cities – how places attract knowledge-intensive industries. Report. Retrieved from: https://www.tillvaxtanalys.se/ download/18.62dd45451715a00666f1b5a6/1586366156539/pm_2016_08_Innovative% 20global%20cities.pdf. Acessed November 1, 2020. Thomas, Elisa; Faccin, Kadigia; Asheim, Bjørn Terje (2021). Universities as orchestrators of the development of regional innovation ecosystems in emerging economies. Growth and Change, 2(2), pages 770–789. DOI: https://doi.org/10.1111/grow.12442 Thomas, E. & Pugh, R. (2020). From ‘entrepreneurial’ to ‘engaged’ universities: Social innovation for regional development in the Global South. Regional Studies, 54(12), 1631–1643. https:// doi.org/10.1080/00343404.2020.1749586 Tiffin, S. & Bortagaray, I. (2008). Fostering innovation: Technological innovation in urban clusters. In Haar, J. & Price, J. (Eds.), Can Latin America compete? Confronting the challenges of globalization (pp. 121–141). New York: Palgrave Macmillan. Chapter 6. Trippl, M., Sinozic, T. & Lawton Smith, H. (2015). The role of universities in regional development: Conceptual models and policy institutions in the UK, Sweden and Austria. European Planning Studies, 23(9), 1722–1740. UFGRS (2020). Pesquisa e Inovacao. Retrieved from: http://www.ufrgs.br/ufrgs/pesquisa-einovacao/apresentacao. Accessed November 1, 2020. Vargas, B. (2019, Dec. 03). Dados de saúde integrados, novo tratamento de água e financiamento de startups: O balanço do Pacto Alegre. ClicRBS. https://gauchazh.clicrbs.com.br/portoalegre/noticia/2019/12/dados-de-saude-integrados-novo-tratamento-de-agua-efinanciamento-de-startups-o-balanco-do-pacto-alegre-ck3qignqv02be01ll5b766e3w.html. Accessed July 15, 2020. Volkmann, C., Fichter, K., Klofsten, M. & Audretsch, D. B. (2019). Sustainable entrepreneurial ecosystems: An emerging field of research. Small Business Economics. doi:https://doi.org/ 10.1007/s11187-019-00253-7 Zen, Aurora; dos Santos, Diego; Faccin, Kadigia; Franke, Leonardo. (2019). Pacto Alegre: Mapeamento do ecossistema de inovação, percepções e desafios. Report. Retrieved from: https://pactoalegre.poa.br/sites/default/files/2019-03/MAPEAMENTO%20DO%20ECOSSIS TEMA%20DE%20INOVA%C3%87%C3%83O%20-%20percep%C3%A7%C3%B5es%20e%20de safios.pdf

Ana Dias Daniel, Susana Oliveira, and Joaquim Borges Gouveia

Chapter 2 The dark side of the university’s participation in innovation ecosystems 2.1 Introduction The concept of innovation ecosystem is inspired by the literature on ecological systems, where a set of living organisms promote collaborative behaviours to thrive in a specific community. In the case of an innovation ecosystem, it is the firm that has the pivotal role in the ecosystem, in order to succeed in an ever-increasing globalized world, where markets are becoming more and more competitive. According to Jacobides, Cennamo and Gawer (2018), an innovation ecosystem is a group of organizations that make use of their unique or supermodular complementarities to foster customers’ value creation. The interaction among ecosystem’s actors catalyses creativity, trigger inventions, and accelerate innovation development across different scientific and technological disciplines (Carayannis & Campbell, 2009). Thus, this structure benefits both the firm and the end-customer. On the one hand, there is the firm that through the contact with external environment improves its dynamic capabilities (Teece, 2007), innovation processes (Rohrbeck, Hölzle & Gemünden, 2009), and knowledge stock (Carayannis et al., 2018). As mentioned by Distanont and Khongmalai (2018), innovation is the cornerstone of firms’ competitive advantage and, more generally, of economic development (Etzkowitz, Webster, Gebhardt & Terra, 2000). Nevertheless, due to the increasing complexity of emerging market demands, firms do not possess all of the needed knowledge and resources to create innovations able to fulfil those demands (Foss, 1996; Grant, 1996). Therefore, the development of networks of collaboration among firms, and between firms and other public organizations, such as universities and other public actors, is of paramount importance for firms to fill in their knowledge and resources gaps. On the other hand, this collaborative network of organizations creates value for the customer through the development of new products and services, as well as new value propositions that no single actor in the system would be able to create alone (Adner, 2016). Despite the existence of different types of innovation ecosystems (Nambisan & Baron, 2013; Zahra & Nambisan, 2012), it is possible to identify common features among them, such as being composed by a large group of organizations, that are inter-connected and interdependent, and that are organized in such a way that might experience a lifecycle that follows a co-evolution process (Gomes, Facin, Salerno & Ikenami, 2018; Zhang & Liang, 2011). Among an innovation ecosystem, there are several key actors, such as local firms, customers, suppliers, as well as public actors and academia (Ferasso, Wunsch Takahashi & Prado Gimenez, 2018), https://doi.org/10.1515/9783110670219-003

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which perform different roles. According to Iansiti and Levien (2002) firms can play three main roles within an ecosystem: dominator, keystone, and niche firm. Those scholars explore the different roles within the context of the computing industry. In turn, Lu, Rong, You, and Shi (2014) identify three bundles of stakeholders in the ecosystem: dominator, participant, and opportunist when studying the Chinese emerging electric vehicle industry. However, in other studies, universities, and public research organizations are mostly involved in knowledge production (Romano, Passiante, Vecchio, & Secundo, 2014), and in supporting knowledge recombination and mutation that may foster the development of technological platforms (Attour & Lazaric, 2018). Moreover, universities promote the development of an entrepreneurial mindset, through the implementation of a set of activities related to entrepreneurship education (Daniel, 2016), and the promotion of start-ups and spin-off companies (Maas & Jones, 2017). Moreover, different studies have highlighted the relationships between industry, university, and government (Romero, Ferreira, and Fernandes, 2020). Thus, as mentioned by Romero et al. (2020) an entrepreneurial university “is able to take several roles in society and in the innovation (eco) system” (p. 901). Nevertheless, the different studies so far failed to understand what is the downside of the university’s new roles within the context of an innovation ecosystem. This study aims to fill this gap through a qualitative approach focused on the perspective of firms enrolled in several industrial clusters. More specifically, the study involved the collection of 25 interviews between businessmen from different companies related to different sectors of the economy, and business contexts. This study contributes to a better understanding of the role of higher education institutions in an ecosystem, thus allowing the design of strategies and approaches that can improve their impact on the development of the ecosystem and on improving the well-being of communities. The remainder of this paper is organized as follows. Firstly, in Section 2.2, the relevant literature for the topic is reviewed. Section 2.3 outlines the methodology of the research. In Section 2.4, our findings are presented and their interpretation is discussed. Finally, in Section 2.5, some conclusions are presented which highlight some key limitations in our analysis and directions for further research are suggested.

2.2 Theoretical background 2.2.1 Firm’s innovation processes and the emergence of innovation ecosystems Innovation is acknowledged by theorists as the driver of firms’ performance and growth in contemporary market economies (Sam & Sijde, 2014). It enables firms to gain sustainable competitive advantage (Di Cintio, Ghosh & Grassi, 2017) through capitalizing on opportunities offered by technology and changing marketplaces, structures, and

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dynamics (Anning-Dorson, 2018). Nevertheless, firms’ innovation processes, regardless of their type, are considered highly risky activities due to the uncertainty related to its outcomes, such as earning expectations, scheduling, feasibility, and market penetration (Baregheh, Rowley & Sambrook, 2009). Additionally, there are several challenges, obstacles, and difficulties that can hamper the innovation process, which are usually addressed as innovation barriers (Santiago, De Fuentes, Dutrénit & Gras, 2017). The role of inter-organizational networks in the promotion of innovation has been widely addressed in the literature, since collaborating firms can exchange sensitive information, share costs and benefits, and can concentrate investment on specific assets (Dekker, 2003; Mirow, Hoelzle & Gemuenden, 2008), and thus overcome many of the innovation barriers. In the literature there are an extensive set of terms to name different types of inter-organizational networks, such as innovation system, business ecosystem, innovation ecosystem and entrepreneurial ecosystem, just to name a few. Despite the existence of several definitions, an innovation ecosystem is an organic and dynamic interrelationships that a given organization has with various external organizations that favours fundraising and facilitates business growth (Ferasso et al., 2018), through hasten innovation across scientific and technological disciplines, as well as from public and private sectors (Carayannis & Campbell, 2009). According to Russo-Spena, Tregua, and Bifulco (2017) several factors have contributed for the emergence of innovation ecosystems. On the one hand, firms’ innovation processes have become more open to external sources of ideas and knowledge giving raise of the open innovation processes. Open innovation may be defined as “the use of purposive inflows and outflows of knowledge to accelerate internal innovation and expand the markets for external use of innovation” (Reichert, 2019, p. 20), which means that in the open innovation model companies combine internal and external pathways to market. On the other hand, innovation approaches are becoming broader to include technological, social, environmental, and economic perspectives, in order to meet current pressing challenges that can only be addressed by multiple actors Russo-Spena et al. (2017). Although the concept of ecosystem has gain considerable interest among the research community, there are still many questions related to its emergence process (i.e. how an ecosystem is formed) (Chesbrough, 2012). According to Attour and Lazaric (2018), the birth of a business ecosystem is the result of the evolution process of a technological platform that was initially developed by a local public actor. Also, Motoyama and Knowlton (2017) highlight the relevance of local industrial symbiosis experiments (e.g. any kind of experiment in the form of a project, research, task, mission, network formation action) that can later on add up and evolve to the ecosystem level. In this case, the articulation of expectations and visions, social network building and learning activities are crucial for the gradual evolution from the local industrial symbiosis experiments level to the regional industrial ecosystem niche level. Despite these bottom-up approaches, there are other examples of a more top-down, policy driven strategies aiming the creation of

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Ana Dias Daniel, Susana Oliveira, and Joaquim Borges Gouveia

an innovation ecosystem (Susur, Hidalgo & Chiaroni, 2019), but several authors have stressed several weaknesses in this approach (Grobbelaar, 2018).

2.2.2 The changing role of the university During the last decades, universities’ mission has shifted to cope with current economic and social challenges, resulting in a stronger mandate for the exploitation of technological and scientific knowledge, and the promotion of economic development (Isenbeck, 2010). As a result, on top of their research and education activities, many universities also promote the transfer of knowledge and technologies to the business sector, as well as the promotion of entrepreneurship and the creation of new companies (Czarnitzki, Rammer & Toole, 2014; Mascarenhas, Marques, Galvão & Santos, 2017). The contribution of universities to regional innovation is usually attributed to the training of a skilled workforce, research commercialization, and spin-off companies’ creation. More specifically, universities foster human capital development through teaching and training activities that enable the flow of talented graduates to the labour market, which then contributes to leverage the innovation potential of the regional economy (Guerrero & Urbano, 2012). Moreover, universities cooperate with local firms through providing knowledge and resources along their innovation processes, enabling that knowledge and technologies produced within universities are transferred to firms through collaborative research projects, problem-solving services, consultancy, testing services, patents, and aesthetic design (Lawton-Smith, 2007; Saeed, Yousafzai, Yani-De-Soriano & Muffatto, 2015). In this case, universities have the crucial role of acting as knowledge intermediates through reducing the knowledge distance among the different partners, and, since partners share a common knowledge base, they are more willing to cooperate (Cohen, Nelson & Walsh, 2002). Also, the flow of knowledge between ecosystem partners and other external actors can be facilitated by the university that, in this case, acts as a knowledge gatekeeper (Ardito, Ferraris, Messeni Petruzzelli, Bresciani & Del Giudice, 2019). Due to their research and education activity, universities are in the right position to better evaluate the available knowledge, and assess which knowledge can be better exploit (Cohen et al., 2002). Universities and public research organizations have a critical role not just in knowledge production, but also in supporting knowledge recombination and its mutation, which favours the establishment of business ecosystems, especially in their early phases (Attour & Lazaric, 2018). Knowledge recombination refers to the process that occurs when “research effort is applied, new ideas arise out of existing ideas in some kind of cumulative interactive process” (Schaeffer, Fischer & Queiroz, 2018, p. 332). Ecosystem’s public actors and universities act as a tenant anchor through diminishing the role of traditional gatekeeper and being fully involved in the

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transfer function (Attour & Lazaric, 2018). The tenant anchor is critical for building partnerships and generating knowledge among the partnership (Weitzman, 1998). Finally, the development of new companies has been widely promoted by universities through actively encourage students and graduates to pursue an entrepreneurial path (Czarnitzki et al., 2014). Thus, universities have been promoting entrepreneurship education programmes that focused not just on new company creation, but also on teaching students’ entrepreneurial skills and attitudes (Clarysse, Wright, Bruneel & Mahajan, 2014). Academic spin-offs, in turn, contribute to local economies through buying goods or services and attracting other companies that use the same technology through clustering (Almeida, Daniel & Figueiredo, 2019). A summary of the roles university play in an innovation can be found in Table 2.1. According to Tejero, Pau, and Leon (2019) universities also have a role to play in attracting research institutes and companies to be located in their proximity that foresees to benefit from informal knowledge sharing, as well as face-to-face contact with academics involved in the research. As a consequence, the university has been assigned with a double role in regional innovation ecosystem: as a central regional actor due to its role of knowledge production, and a cultural actor that facilitate regional interactions (Russo-Spena et al., 2017). According to Russo-Spena et al. (2017), this new centrality of university is related to the way the “businesses and governments see the university and its members as ideally suited to ‘connect the dots’ because they are impartial, driven by curiosity and long-term perspectives, rather than by commercial interests and short-term roles” (p. 7). Nevertheless, despite the benefits that may arise from the university’s new roles, several authors have highlighted several disadvantages. One the one hand, Jonkers, Tijssen, Karvounaraki and Goenaga (2018) mentioned that this new focus on entrepreneurship turns the university away from conducting basic research. In the same line, Nedeva (2007) mentioned that academics are being pressured to conduct academic research and in seeking for its commercialization which contradicts the practices of the scientific community. On the other hand, Markman, Siegel and Wright (2008) argue that university’s management is shifting away from a traditional organic, bottom-up approach towards a more interventionist top-down push approach to address the third academic mission. Therefore, university independency is being under pressure, due to more commercial-oriented and business-like objectives (Philpott, Dooley, O’Reilly & Lupton, 2011).

2.3 Methods Despite the existence of several studies describing the new role of the university in ecosystems (Krimsky, Ennis & Weissman, 1991; Motoyama & Knowlton, 2017; RussoSpena et al., 2017), those fail to assess the main limitations of such behaviour. Therefore, the research method selected for this study was a case study approach (Schiuma

●

Cohen et al. ()

●

●

Roberts, Murray, and Kim ()

Russo-Spena et al. ()

Dondofema and Grobbelaar ()

●

Civera, Meoli, and Vismara ()

Chen and Lin ()

Hass ()

●

●

Meissner and Shmatko ()

Susur et al. ()

●

Shane ()

●

●

●

●

Weitzman ()

●

●

Knowledge and Knowledge Knowledge Knowledge technology producer filter intermediares gatekeepers

References

Table 2.1: Roles the university plays in innovation ecosystems.

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Knowledge evaluators

●

●

●

Tenant anchor

●

●

●

●

New firms promoter

●

●

●

●

●

Orchestrator Human resources trainer

30 Ana Dias Daniel, Susana Oliveira, and Joaquim Borges Gouveia

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& Carlucci, 2018), and it focused on the following research question: what is the downside of university’s new roles within an innovation ecosystem? This approach is considered appropriate when the extant research fails to explain the variations in the phenomenon that require clarification (Yin, 2003). Information was collected using several sources, such as firms, that were pioneers in the development of several industrial clusters in Portugal, and intermediary entities through interviewing participant-informants, such as firm’s owners, CEOs, or entrepreneurs. The interview protocol was initially designed based on the literature analysis performed. The interviews checklist was constructed based on previous studies, namely the studies of (Eisenhardt, 1989) on Italian Small and Medium Enterprises, and the study of (Massa & Testa, 2008) on SMEs’ internal research capacities to exploit external scientific and technical knowledge and to use networks of innovators. Four pilot interviews with entrepreneurs and former public innovation agents were used to validate the interview protocol. The outcomes of the pilot interviews allowed us to iteratively adjust the interview protocol, resulting in the final semistructured interview protocol. This protocol encompasses an initial part aiming at understanding the history of the firm. Then, in the second part interviewees were invited to reflect about innovation and the firm’s innovation processes, as well as their perception on how universities contribute to that process and the innovation ecosystem. Finally, the last part aimed at understanding the downside of the university’s new roles in the innovation ecosystem. We conducted 25 interviews in 21 firms (Table 2.2), which represent 71.3% of small and medium enterprises, from the following economic sectors: metal mechanics industry, technology (ICT), electronic and electric equipment, food, furniture, forest, and energy, and participated in one or more of the 20 Portuguese industrial clusters acknowledged by the Portuguese Government in 2017. All the interviews were recorded and later transcribed (verbatim transcription). For the interpretation of data, it was used all transcripts of interviews, but also the information exchanged before and after recording. All data were analysed using NVivo 12 software (QSR Internacional), which allowed a detailed analysis of specific topics, once all the information was encoded, and provided a systematic process in data analysis and research, increasing validity and reliability of the study.

2.3.1 The Portuguese cluster strategy The Portuguese clustering policy was initiated in 2008, through the publication of the “Framework for Collective Efficiency Strategies.” The aim was to foster the development of the Portuguese economy through acknowledging value chain networks and ecosystems in economic sectors relevant to the Portuguese economy. Each cluster encompasses a partnership of firms, industry associations, and other relevant support institutions, universities and research organizations, sharing a common strategic vision

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Table 2.2: Roles the university plays in innovation ecosystems. Size

Firm (code)

Economic sector

Large

Firm  Firm  Firm  Firm  Firm  Firm 

Forest Furniture Metalworking Food industry Energy Energy

Medium

Firm  Firm  Firm  Firm  Firm  Firm  Firm  Firm  Firm 

Metalworking Technology – ICT Electronic and electric equipment Metalworking Food industry Food industry Technology – ICT Metalworking Furniture

Small

Firm  Firm  Firm  Firm  Firm  Firm 

Electronic and electric equipment Food industry Technology – ICT Technology – ICT Technology – ICT Electronic and electric equipment

and aiming at promoting innovation, knowledge transfer and firms’ internationalization. Prior to application, each cluster had to be legally constituted as a non-profit association. In 2009, a group of 19 Portuguese Clusters were then recognized, representing several value chains relevant to the national economy. Later on, a second wave of clusters’ recognition was set in 2015 for the 2017–2023 period. In 2017, 20 newly recognized clusters add up to the Portugal Clusters Partnership and continue promoting activities and projects with impact in its value chains, contributing to the internationalization and competitiveness of Portuguese firms Thus, Portugal Clusters network is a partnership aimed to gather the Portuguese clusters to explore synergies and joint areas of common interest, enhance innovation and competitiveness of different value chains, encourage cross-fertilization of common strategic interest activities, sharing of experiences and knowledge between clusters, develop collaborative activities and contribute to the national and European economic development effort (Bougrain & Haudeville, 2002).

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2.4 Results and discussion 2.4.1 University’s role in the innovation ecosystem Universities are considered the engine of innovation, through the production of novelty and access to privileged technical and scientific information, as well as technology trends. In this case, universities were considered as being at the forefront of scientific innovations and fundamental research, which should be used to support companies in the resolution of problems related to innovation and technical issues. As stated by firm 1, “. . . they are the engine of development and the support of companies in the research phase, besides being elements that create the conditions for the development of scientific innovations.” Therefore, one of the university’s roles identified is of a problem-solver through assisting firms in solving their technical problems. In this case, support should be provided through the identification of new processes, training employees, and providing access to research and lab facilities. As mentioned by firm 3, “. . . we have collaborations with universities, not only at product level, but also at equipment level, as well. For example, in the development of an electronic component. It has helped us to solve some problems, so we are permanently looking for some support from universities. It also includes, very often, the enrolment of students that come from the university in different stages of their curricular training.” Knowledge producer is another role mentioned by the interviewees. In a society where the knowledge production is made in such a grand level and such high velocity, and since the level of knowledge specialization that is needed is very high, it is impossible for firms to have within their boundaries the set of competencies that would allow them to have responses and solutions to all of their problems. Firms see the university as a knowledge resource with whom they can complement their knowledge gaps. As mentioned by firm 13, “. . . they are the knowledge centers. I think that they need some guidance, especially when we are talking about research in applied fields. In the case of fundamental research, that is extremely necessary, it can only be done in the university, and only in the university, and later on it can be used by the rest of the scientific and business community.” Thus, firms acquire knowledge from various external sources, in particular from universities and research institutes. But, in the case of firms of small size and with lower R&D budgets, their reliance on academia is even bigger when compared to other knowledge sources (Meissner & Shmatko, 2017). In this case, Hass (2015) found that, while the training of human capital contributes to innovation outputs, its impacts is of minor relevance as compared to higher education institutions that achieve excellence in research. From the data gathered, firms in our sample also highlighted the production of new knowledge through the prosecution of fundamental research which is not the focus of firms but, later on, it could be used by the business community.

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Additionally, universities act as a network promoter or orchestrator since those organizations are avid for industrial partners to develop specific applied research projects. This type of projects is mostly supported through national or regional programmes aiming at fostering the development of specific economic sectors or regions. This role is highlighted by firm 7 that mentioned: “they [universities] like to come to the company to present a project to be developed together, or to bring some trainees to do some work.” Moreover, university’s researchers are becoming key players in the promotion of firms’ networks, as argued by firm 19 “I have seen good cases . . . where they [researchers] go out and talk to companies, and are always trying to connect companies. Sometimes it is really confusing because it is difficult to identify who comes from a company and who comes from academia.” In this case, researchers are searching for partners with complementary skills and competences, and that may be interested in the commercialization of the project’s outputs. The ability of higher education institutions to leverage business networks has been neglected in innovation ecosystems literature. Several studies have acknowledged the existence of an orchestrator in the ecosystem (“Portugal Cluster,” n.d.; Susur et al., 2019), which is the main actor responsible for the design and management of the innovation ecosystem (Dhanaraj & Parkhe, 2006). According to Ferraris and Grieco’s (2015) systematic review, most of the studies identify industrial firms as ecosystem orchestrator, which may raise issues related to the management of ecosystem internal competition. In other words, since ecosystem’s “actors compete with each other to capture more value from the ecosystem, the orchestrator not only has to manage potential tensions but must also discourage any form of competition in the innovation ecosystem” (Ferraris & Grieco, 2015). In turn, as pointed out by Russo-Spena et al. (2017) “university’s new centrality is inextricably intertwined with its role of orchestrating multi-actor innovation networks” (p. 7), since those institutions are not driven by commercial interests and short-term goals. Another university role is the training of human resources for the job market, to supply the labour force with specialized technicians. As mentioned by firm 4, “It is very important, not only in academic terms, but also in business terms, that students understand what a company is, since students will be our next collaborators.” Human resources are a mean of knowledge spillover that has the ability to disseminate the knowledge that is within the university’s boundaries to the market. According to spillover theory authors, knowledge is a public good that is perceived differently across the economic agents and is economically useful (Yaghmaie & Vanhaverbeke, 2019). Yet, there is a general tendency of higher education institutions to adapt to a changing educational landscape through the promotion of educational reforms that emphasis on problem-based learning in cooperation with external (Acs, Braunerhjelm, Audretsch & Carlsson, 2008; Russo-Spena et al., 2017), and the data gathered through the interviews show exactly that. Thus, in the case of our sample, it was observed that the role of universities is not only limited to equipping the future

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workforce with theoretical knowledge, but also provides the scenario where students experience the real business context, which allows them to develop a wide range of soft skills and business skills.

2.4.2 The downside of the university’s new roles in the innovation ecosystem Regardless of the important role of universities, respondents have also identified several problems when universities become more and more engaged in an innovation ecosystem. In this case, access and use of knowledge were considered a major issue by the interviewees. As stated by firm 18: “I have the idea that the knowledge that is generated [in universities] stays inside, either because they [the researchers] do not know how to turn it into practical things, or because they do not communicate with the companies when they actually have that knowledge.” On the one hand, interviewees have highlighted the academics’ lack market knowledge which prevents the conversion of knowledge and technologies that are available in the universities into marketable solutions. In the perspective of firms, universities lack market orientation since usually are not involved in activities aiming at understanding the current and future consumers’ needs, which is precisely what the firms are more concerned about. As mentioned by firm 12, “the greatest difficulty for these technologies to succeed in the market has to do precisely with its right positioning in the market. The perception of what is the market segment, and what are the potential consumers, as well as what is the business model that I can develop with this technology is crucial. And that’s where the link between universities, university’s technology and companies can bring many benefit.” On the other hand, the level of knowledge codification and its abstract character turns it harder for more traditional SMEs to perceive the most appropriate use of knowledge. This is related with firms’ absorptive capacity which, in the case of Portuguese SMEs, is considered limited due to the low level of employees’ educational background (Ashmarina & Nikulina, 2017). The cultural gap, that is mentioned in other studies, is also identified by the group of firms we have interviewed. Technological firms, that often had their birth in universities, did to mention this barrier. Additionally, the use of knowledge by firms is sometimes restrained due to the negotiation process with universities. The negotiation of intellectual property rights is one of the challenges highlighted regarding the exploitation of the results of innovation projects. Discussions about intellectual property rights may impair the development of projects or even cease it, according to our respondents. Firm 14 states that “an important issue for us is the negotiation of intellectual property. Normally these issues do not progress as we expect. Therefore, we have always chosen to have universities collaborating with us differently, particularly through service contracts. Thus, in a more externalized way. Let’s say that it is not so common to

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develop a project in partnership with universities, because these questions usually arise, and we have some reluctance in negotiating it with universities.” In this case, the research produced by universities is sometimes very distant from a market application, and further research and investment are needed to develop an invention that worthwhile being protected through a patent, or other protection mean. In the same line, there is an overestimation of the value of the university’s intellectual property, since the university’s staff lack the knowledge about how the market will valorize the invention. Similar conclusions were obtained by Costa, Fernández-Jardon Fernández, and Figueroa Dorrego (2014). Universities’ financial constraints due to the shortage of government funds have turned universities into active market players, presenting themselves as competitors of the firms that they might partner with. As mentioned by firm 19: “. . . because universities have themselves become quite a bit of a business. Nowadays, universities have to generate funds for themselves. Therefore, they have already realized that they can only survive if they transform the knowledge they generate into products.” Through the provision of services or technology transfer, the university has been able to assure revenue streams that enable them to meet the governmental funding shortage. Moreover, the development of academic start-ups and spin-offs with privilege access to universities’ know-how and facilities is pointed out by firms as a form of unfair competition. The fuzzy role of universities in some business areas turns firms suspicious when releasing information, or even when the relationship with universities is initiated. In this case, universities have more difficulties in building trustful relationships. As firm 19 argues: “there are university professors who belong to spin-off companies which are, therefore, competitors of existing companies. And one needs to be careful, because sometimes we’re not talking to a professor, we’re talking to an entrepreneur, a businessman, a competitor.” As a consequence, the ability of universities to foster the creation of networks is diminished. Table 2.3 presents the main positive and negative impacts that universities have on the ecosystem.

2.5 Conclusion This study explores the different roles played by universities within an entrepreneurial ecosystem to understand what is the downside of those behaviours. Universities were seen as the engine of innovation since those are at the forefront of scientific innovation and detain privileged information about technical and scientific aspects, as well as technology trends. Those organizations act as problem-solvers, where firms can go to resolve problems, identify new processes, train their employees, and have access to resources and competencies. The production of knowledge and being a stock of accessible knowledge is another role identified, as well as the education of

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Table 2.3: Summary of the main positive and negative impacts that universities have on the ecosystem. University’s role in the innovation ecosystem

Impact on the ecosystem Positive

Negative

Problem-solver

Development of new solutions to company’s problems; Allow access to research resources and infrastructure

Unfair competition in the provision of services; Management of intellectual property rights

Knowledge producer

Promotion of innovation; Training of human resources

High level of knowledge codification; Lack of market orientation

Network orchestrator

Promotion of new relationships Difficulties in building trustful among ecosystem’s actors relationships due to competitive behaviours

qualified human resources that firms can latter on employ. Moreover, the ability to build multi-actor innovation networks was also pointed out as a relevant role. This ability fosters the access of universities and many of its partners to the global knowledge that is crucial for innovation (Russo-Spena et al., 2017). These results support the idea that academic institutions have a crucial role in innovation ecosystems. The main drawbacks of universities’ new roles within innovation ecosystems are related to the access and level of knowledge codification, negotiation of intellectual property, competition with companies, and difficulties in building trustful relationships. One the one hand, despite being acknowledged as a knowledge producer, academic institutions are not being able to transfer the knowledge produced to the economic fabric due to researchers’ lack of market knowledge that prevents the identification of suitable applications for the knowledge produced. On the other hand, firms lack the absorptive capacity needed to interpret and use the knowledge available. Moreover, the intellectual property management practices, the lack of funding to support the development of projects in collaboration, and the lack of market orientation of the research made in the university have limited the capacity of universities to transfer knowledge and technology to firms. Another obstacle is related to an undefined role that universities play in the market, which sometimes poses as competition to firms. This fact contributes to the growth of a suspicious environment between firms and universities, which have an impact on the university’s ability to build trustful relationships which underlie the creation and the development of innovation networks. This result is relevant and relatively unexplored in the literature. Therefore, many further research avenues can be pursued based on the present research. For instance, the development of studies that includes the perception of the university or other stakeholders would be invaluable. Moreover, it would be relevant to understand how the identified drawbacks are affecting the

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development of innovation ecosystems, and its different configurations. Nevertheless, this study contributed to better understand the relationship between firms and universities, to the role that each of the actors assumes in an innovation ecosystem, and to the perception firms have on the university’s role. As in any research, this study presents several limitations, such as the limited number of respondents, and the risk of respondents’ social desirability bias due to self-reported answers. Thus, further studies are needed to confirm the results in a broader population and in other countries. Acknowledgements: We are grateful to all the interviewees who participated in this study and shared their deep personal experience in the topic of this study. Funding: This study was supported by Research Program “CeNTER – Community-led Territorial Innovation” (CENTRO-01-0145-FEDER-000002) funded by Programa Operacional Regional do Centro (CENTRO 2020), PT2020.”. Also, this work is funded by national funds through FCT – Fundação para a Ciência e a Tecnologia, I.P., under the Scientific Employment Stimulus - Institutional Call - reference CEECINST/00026/2018. Conflicts of interest: The authors declare no conflict of interest. The funders had no role in the design of the study, as well as in the collection, analysis, or interpretation of data, writing of the manuscript or in the decision to publish the results.

References Acs, Z. J., Braunerhjelm, P., Audretsch, D. B. & Carlsson, B. (2008). The knowledge spillover theory of entrepreneurship. Small Business Economics, 15–30. doi:https://doi.org/10.1007/s11187008-9157-3 Adner, R. (2016). Ecosystem as structure: An actionable construct for strategy. Journal of Management, 43(1), 39–58. doi:https://doi.org/10.1177/0149206316678451 Almeida, J., Daniel, A. D. & Figueiredo, C. (2019). The future of management education: The role of entrepreneurship education and junior enterprises. International Journal of Management Education. doi:https://doi.org/10.1016/j.ijme.2019.100318 Anning-Dorson, T. (2018). Innovation and competitive advantage creation: The role of organisational leadership in service firms from emerging markets. International Marketing Review, 35(4), 580–600. doi:https://doi.org/10.1108/IMR-11-2015-0262 Ardito, L., Ferraris, A., Messeni Petruzzelli, A., Bresciani, S. & Del Giudice, M. (2019). The role of universities in the knowledge management of smart city projects. Technological Forecasting and Social Change, 142, 312–321. doi:https://doi.org/10.1016/j.techfore.2018.07.030 Ashmarina, S. & Nikulina, E. (2017). Assessment of global trends impact on development of higher education system. Problems and Perspectives in Management, 15(3), 365–376. doi:https:// doi.org/10.21511/ppm.15(3-2).2017.06 Attour, A. & Lazaric, N. (2018). From knowledge to business ecosystems: Emergence of an entrepreneurial activity during knowledge replication. Small Business Economics, 1–13. doi:https://doi.org/10.1007/s11187-018-0035-3

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Geraldina Silveyra, Lucía Rodríguez-Aceves, and Allan Villegas-Mateos

Chapter 3 Enablers to fostering interactions during entrepreneurship events within universitybased entrepreneurship ecosystems (U-BEEs) 3.1 Introduction In the literature regarding entrepreneurial ecosystems, there are a considerable number of studies that focus on the university context itself (Di Gregoria & Shane, 2003; Siegel & Wright, 2015), including the role of the university as a catalyst for the initial formation and expansion of ecosystems (Neck, Meyer, Cohen & Corbett, 2004). The university-based entrepreneurship ecosystem (U-BEE) concept emerged to define the complex system of multi-level collaborative links between major actors (or stakeholders; e.g. university, business, local government, and students) with several related elements that influence knowledge transfer and commercialisation by industry and universities (Belitski & Heron, 2017; Bischoff, Volkmann & Audretsch, 2018). A more comprehensive and thorough perspective provided by Green, Rice and Fetters (2010, p. 2) suggests that a U-BEE involves activities like: entrepreneurship course offerings, the development of innovative pedagogies and teaching materials, student-led conferences, alumni entrepreneurs as faculty and speakers, on-campus new business creation and development, educational extension of entrepreneurship education into areas like family businesses, social entrepreneurship and corporate innovation, funded entrepreneurship and multidisciplinary research, and outreach initiatives that build a metaecosystem connecting entrepreneurs and support organisations (i.e. nascent and mature businesses, non-governmental organisations, other universities, government agencies and entrepreneurship communities).

Literature on U-BEEs addresses its key components (Rideout & Gray, 2013), assessment methods to develop strategies for successful U-BEEs (Meyer, Lee, Kelley & Collier, 2020), in depth analysis of key elements of U-BEEs, such as university-based venture development organizations (VDOs) acting as incubators and accelerators, technology parks, technology transfer offices, and venture funds (Hsieh & Kelley, 2019; Siegel & Wright, 2015; Yang, Kher & Lyons, 2018). In addition, U-BEEs have been studied in resource-constrained contexts (Bedő, Erdős & Pittaway, 2020), expansion into under-represented communities (O’Brien, Cooney & Belker, 2019), their role in facing the Industrial Revolution 4.0 (Novita, Ritonga & Jalaludin, 2020), as well as how they facilitate knowledge spill-overs among a plurality of stakeholders (Secundo, Mele, Del Vecchio & Degennaro, 2020). https://doi.org/10.1515/9783110670219-004

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Scholars have also been engaged in studying the importance of a U-BEE, which is grounded in how individuals, students, entrepreneurs, faculty, firms, and institutions interact and are interconnected to create value (Link & Sarala, 2019). Others have mentioned that universities are expected to promote entrepreneurial thinking and engagement through various activities and initiatives that extend outside of the university itself to develop, nurture, and support networking with relevant internal and external stakeholders (Wilson et al., 2009). As a result, a large quantum of resources of different organizations, and universities in particular, are devoted to networking events to foster entrepreneurial activity among both nascent and mature entrepreneurs. Nevertheless, the means by which such interactions can be successfully promoted and leveraged has been given limited attention, especially in Mexico. Therefore, there is a need to understand enablers of interactions in networking events organized by universities, which we believe are a key element of U-BEEs. The concept of U-BEE presupposes the promotion of interconnectedness with all internal and external stakeholders (Fauzi, Tan, Thurasamy & Ojo, 2019). Entrepreneurship is inherently dependent on such networking activity. Immersion in networking that takes place at interactive events opens opportunities to access a key resource: knowledge. In the process of this acquisition and transmission of knowledge, entrepreneurs are leveraged through social interactions (Estay, Durrieu & Akhter, 2013) and various forms of participation and engagement (Wasko & Faraj, 2005). However, a question arises: What are the perceived benefits that drive an entrepreneur to share his or her knowledge in an entrepreneurship networking event in order to create value for others? A possible answer is found in research on the motivators of human behaviour, as is the case of the reciprocity and knowledge self-efficacy (Vallerand, 1997). Another approach is the need to increase relational capital aiming to acquire knowledge (Granovetter, 1992; Scarmozzino, Corvello & Grimaldi, 2017). In this study we analysed three enablers that foster interactions within U-BEEs: knowledge self-efficacy, reciprocity, and relational capital, aiming to identify specific actions that inspire and promote entrepreneurship in such arrangements. Specifically, we analysed why such enablers lead both mature and nascent entrepreneurs to attend entrepreneurship networking events in order to acquire benefits for themselves and others. The main contribution of this chapter is its deductive analysis of how U-BEEs support entrepreneurship by facilitating the interconnectedness of their actors, through the three enablers introduced above, distinguishing between nascent and mature entrepreneurs as they have different needs and motivators, and act accordingly. Furthermore, we propose entrepreneurship networking events as a key element of U-BEEs that has not yet been analysed in previous literature (Green, Rice & Fetters, 2010; Meyer et al., 2020; Siegel & Wright, 2015). The chapter describes U-BEEs, subsequently presenting the enablers that motivate the participation of both nascent and mature entrepreneurs in networking events, drawing on two hypotheses related to the benefits obtained. To test the hypotheses, a

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sample of entrepreneurs attending an entrepreneurship event organized and hosted by a private university in Mexico was studied through the application of a survey. To analyse the data a Mann–Whitney U test was applied to compare both groups of participants, mature entrepreneurs (established start-ups), and nascent entrepreneurs (developing projects), resulting in the clear emergence of differences between the groups. Finally, some recommendations to successfully promote networking events in U-BEEs are shared.

3.2 University-based entrepreneurship ecosystems (U-BEEs) University-based entrepreneurship has existed since the 1980s, although its early stages focused more on the technology transfer process through patents and licensing (Siegel & Wright, 2015). In 2001 Etzkowitz suggested that entrepreneurship had become the third mandate of universities. As a result, over the years, universities developed new approaches to teaching and encouraging entrepreneurship among students, faculty and staff (Rodríguez‒Aceves, Mojarro‒Durán & Muñiz‒Ávila, 2019). In 2010, Fetter et al. coined the term U-BEE, conceptualized as a multi-stakeholder environment in which entrepreneurs are centred on a field of university-related resources surrounded by supporting or contributing stakeholders that ultimately results in outputs and outcomes. The entrepreneurs themselves may include faculty, staff, and students located within a campus, with connections bridging them to the broader community (Sherwood, 2018). We understand U-BEEs as a set of engaged actors related to the university (students, staff, faculty, community, alumni, among others), who intentionally participate in activities (teaching, mentoring, supporting, funding, protecting knowledge, networking, etc.), and rely on the university’s internal organizations and associations with external actors (incubators, accelerators, technology transfer offices, governmental offices, etc.) to nurture individuals to reach their full potential in positively transforming their communities and external industries through entrepreneurial initiatives. We believe this approach is mostly needed in emerging economies, as in the case of Mexico as well as Latin America in general, where social inequality is substantial and an overall lack of confidence in institutions generally exists (OECD et al., 2019), which leads us to propose a new concept: University-based sustainable entrepreneurship ecosystem (U-BSEE). Due to its critical contribution to entrepreneurial expansion, U-BEEs have received a lot of attention from a variety of contemporary scholars. Literature on U-BEEs addresses its key components (Perkmann et al., 2013; Rideout & Gray, 2013), assessment methods for developing strategies for successful U-BEEs (Meyer et al., 2020), and in-depth analyses of the key elements of U-BEEs, like university-based

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VDOs (incubators and accelerators) (Hsieh & Kelley, 2019; Yang et al., 2018). In addition, U-BEEs have been studied in constrained context (Bedő et al., 2020), as well as in developed countries (Lahikainen, Kolhinen, Ruskovaara & Pihkala, 2019). Finally, researchers have also explored the expansion of U-BEEs into under-represented communities (O’Brien et al., 2019), their role in facing the Industrial Revolution 4.0 (Novita et al., 2020) and how they facilitate knowledge spill-overs among a plurality of stakeholders (Secundo et al., 2020). Scholars suggest that U-BEEs’ main components are entrepreneurship courses, engagement with alumni entrepreneurs, incubators, prototyping services, funding support, and technology transfer services (Perkmann et al., 2013; Rideout & Gray, 2013), but we also suspected that entrepreneurship events in which entrepreneurs do networking have equal importance. Entrepreneurship events aim to serve as a platform in which U-BEE actors can safely; formally or informally, interact and exchange useful knowledge for their own benefit as individuals and, consequently, for both nascent and mature ventures. In fact, entrepreneurship ecosystems emphasize the interaction between individuals and their institutional contexts, which results in entrepreneurial action that is based on the attitudes, ability, and aspirations of individuals (Ács et al., 2014). Moreover, the concept of U-BEEs proposes interconnectedness with all stakeholders, organizations, institutions, and entrepreneurial processes by the dynamic university system which coexist to connect, mediate, and govern the performance of the entrepreneurial environment (Fauzi et al., 2019). Due to the fundamental importance of actors’ interconnectedness to the success of U-BEEs, it is relevant to identify the enablers of interaction in such arrangements.

3.3 Enablers for interaction in U-BEEs The encouragement of networking for entrepreneurs has increasingly become a goal of U-BEEs in Mexico. “To network” and “networking” denote the action by which an entrepreneur develops and maintains contacts for trading and business development purposes (Chell & Baines, 2000). In this respect, entrepreneurial networking generally refers to the activities that entrepreneurs engage in when creating and shaping network ties and therefore includes the formation of such ties and relationship maintenance behaviours (Vissa, 2012). Entrepreneurial networking research commonly involves Granovetter’s (1985) conceptualization of strong and weak ties. People linked by strong ties trust one another, whereas weak ties may be of short duration and low frequency. The strength of weak ties is that they enable entrepreneurs to reach actively and purposefully outside of their social and professional circle in order to gain access to information, advice, and assistance from a large and diverse number of contacts.

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Research in entrepreneurship argues that weak tie networking is a fundamental element of entrepreneurial behaviour (Aldrich & Zimmer, 1986). When entrepreneurs are pursuing an opportunity, access to resources such as contacts, knowledge, funds, and talent, among many others, is required. Research shows that what entrepreneurs do to shape their personal networks matters for the creation and discovery of opportunities, the mobilization of resources and the formation of interorganizational partnerships (Engel, Kaandorp & Elfring, 2017). Entrepreneurship is thus inherently a networking activity. Even though the value of networking for young ventures has been recognized, there is evidence that entrepreneurs do not practice it as often as assumed (Chell & Baines, 2000). The reasons could be the lack of time, established individualism, anti-participation culture (Gray, 2001), and also the lack of spaces fully dedicated to such purpose. The latter is related to the necessity of creating opportunities for interaction that facilitate spatial proximity and the presence of social settings that enhance opportunities for people from diverse social and professional circles to meet and interact (Stam, 2010). According to Oliva and Kotabe (2019), participation in entrepreneurship events in which social interaction is possible is one of the most relevant practices adopted when aiming to acquire knowledge. Alvarez and Busenitz (2001) suggest that entrepreneurs’ social interactions expose them to a broader range of people and situations that sharpen their abilities to acquire and apply knowledge to new ventures in useful ways. In this respect, entrepreneurship events that create value for entrepreneurs are those that serve as networking platforms facilitating “informal encounters” (Ingram & Morris, 2007) that allow entrepreneurs to identify and exploit business opportunities and to take into account the balance of resource exploitation. In other words, searching not only for the economic, but also for the social and environmental impact. Networking immersion occurs at these interactive events, opening access to a key resource: knowledge. During the process of acquisition and transmission of such knowledge, entrepreneurs leverage social interaction (Estay et al., 2013) and various forms of participation and engagement (Wasko & Faraj, 2005). However, a question arises: What are the perceived benefits that drive an entrepreneur to share their knowledge in an entrepreneurship event? During social exchange, while acting as motivators of human behaviour, the benefits can be extrinsic, as in the case of the reciprocity; or intrinsic, such as knowledge self-efficacy (Vallerand, 1997). Self-efficacy relates to an individual’s perception regarding what they can do with the skills they possess (Bandura, 1996). When people share expertise that is useful to others, they gain confidence in terms of what they can do, and this brings the benefit of increased self-efficacy (Constant, Kiesler & Sproull, 1994). Knowledge self-efficacy is typically manifested in the form of individuals believing that their knowledge can help to solve job-related problems, improve work efficiency or make a difference to their organization (Kollock, 1999). This belief can serve as a self-

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motivational force for entrepreneurs to attend entrepreneurship events to contribute knowledge to other participants (Bock & Kim, 2002). Knowledge sharing is also enhanced by a strong sense of reciprocity: favours given and received and a strong sense of fairness (Chang & Chuang, 2011). The logic is that if the invested efforts in knowledge sharing can be reciprocated, people will be motivated to contribute more. Knowledge contributors expect future help from others in lieu of their contributions (Kollock, 1999). In fact, previous research shows that people who share knowledge in communities believe in reciprocity (Wasko & Faraj, 2005). Nevertheless, it is necessary to have experience in the field in order to share knowledge that is valuable to others. The process of entrepreneurial learning is based on knowledge acquisition that makes use of experience as the main source of learning material, which commonly occurs when a venture is in a mature stage (Scarmozzino et al., 2017). Therefore, we proposed: H1. Reciprocity and knowledge self-efficacy are social exchange benefits that experienced entrepreneurs expect to obtain when sharing knowledge in networking events.

From another perspective, the literature identifies three dimensions of intellectual capital: human capital, structural capital, and relational capital (Bontis, 2002; Kaufmann & Schneider, 2004; Marr & Roos, 2005; Sullivan, 1999; Viedma & Marti, 2001). Relational capital is based on the understanding that businesses cannot be considered isolated systems, but rather are strongly dependent on the relations they build up in their environment. This type of capital considers the potential value generated by such relations, not only with customers, suppliers, or shareholders, but with all groups of interest, both internal and external (Bontis, 1996; Ordóñez De Pablos, 2003; Roos, Bainbridge & Jacobsen, 2001; Stewart, 1998). Relational capital is considered an intangible asset that is commonly used to acquire the necessary resources a new venture needs to survive and grow. At the beginning, most ventures do not possess all the required knowledge; consequently, they must rely on links outside organizations and individuals to acquire knowledge (Wasko & Faraj, 2005). Liao and Welsh (2003) argued that the growth potential of a new venture depends not just on its resource endowment, but also on its social ties with other players in its founding environment. Previous studies have found that early venture contacts tend to be very few, and come naturally in the form of friends, family members, and work-related ties (Renzulli, Aldrich & Moody, 2000). Nevertheless, at some point nascent entrepreneurs find that required resources cannot be provided by pre-existing contacts (Vissa, 2012). Consequently, they search for new, complementary network contacts that can be found in networking events (Semrau & Werner, 2014; Vissa, 2011) to increase their levels of relational capital and gain access to the knowledge needed (Tsai & Ghoshal, 1998). Such acquired knowledge encourages nascent entrepreneurs to implement new ideas,

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to adopt new ways of doing things, to undertake new methods of operation and to invest in new processes and technology (Scarmozzino et al., 2017). Therefore, we proposed: H2. Nascent entrepreneurs participate in entrepreneurship events to increase their relational capital and to acquire knowledge.

To test the proposed hypotheses regarding mature and nascent entrepreneurs’ motivations for attending U-BEE networking events, the researchers chose an event called INC Crowded, hosted by a university and its entrepreneurship ecosystem in Mexico for study. In the next section, the methodology is explained.

3.4 Methodology 3.4.1 Study context and data collection INC Crowded is an entrepreneurship event organized and hosted by a private university in Mexico located in Guadalajara, Jalisco, for the purpose of enhancing innovative, tech-based, scalable and sustainable entrepreneurial initiatives. In order to contextualize the role of the university in promoting entrepreneurship in the region, it is noteworthy to mention that it is the only Latin American university included in “The Princeton Review: Top Schools for Entrepreneurship Ranking 2021,” published by The Princeton Review and Entrepreneur Magazine. The institution occupied the fifth position in 2021 and featured in the top ten list with universities like Babson College.1 The entrepreneurship event gathers key local players to support high-performance start-ups. A specific component of the event is the “Start-up Street” (see Figure 3.1), where entrepreneurs present their initiatives and carry out networking with potential partners. In 2019, 50 start-ups between launch (nascent) or growth (mature) stages participated at the event at INC Crowded. From these, we collected a sample of n = 30 responses to our distributed evaluation survey. It is noteworthy that the selected start-up projects could win an entry to other big entrepreneurial competitions. The evaluation survey was submitted after the entrepreneurship event ended and it was designed to measure three different constructs: knowledge self-efficacy, reciprocity, and relational capital (Kankanhalli, Tan & Wei, 2005; Li, Wang, Huang & Bai, 2013; Liao & Welsh, 2003; Scarmozzino et al., 2017). The participants were divided into two groups according to their stage of entrepreneurial development, project = 0 (nascent), and

1 https://www.princetonreview.com/press/top-entrepreneurial-press-release

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start-up = 1 (mature). This data provided first-hand experience regarding the INC Crowded event and allowed comparisons between both groups.

Figure 3.1: Start-up street at INC Crowded 2019. Source: Own elaboration

In the networking event, various internal organizations from the university participated as coordinators, such as the business incubator, the social incubator, the accelerator, the maker space, the “innovaction gym” and different academic programmes. Diverse actors from the U-BEE such as faculty, students, and alumni participated by either playing the role of entrepreneurs or support staff (mentors, investors, etc.). The event also reached out to partners from the industry for the purpose of mentoring, providing feedback, and connecting to other actors involved in the local entrepreneurship ecosystem. Such industry partners are associations interested in fostering entrepreneurship (i.e. Redincuba, Clúster Médico de Jalisco), government branches (i.e. Secretaría de Cultura de Jalisco, Secretaría de Innovación Ciencia y Tecnología (SICyT)), accelerators (i.e. BlueBox, Reto Zapopan, Teamlabs), successful start-ups (i.e. Concé Marketing, D&D Labs, Ópera Arte, Sorbos de Emprendimiento), other universities (i.e. EGADE Business School, IE:EGL, UNIVA, Universidad de Guadalajara CU Tonalá), investors (i.e. Zulu Ventures), and support services (i.e. VILA Abogados). The event included keynote speakers and workshops in subjects such as Human Centred Value Creation, Bio Entrepreneurship, Artificial Intelligence, Drones, Innovation and Technological Development and Social Innovation. Most of the start-ups and projects that participated in INC Crowded had a sustainable perspective; that is, not only do they aim for profit, but they also address social or environmental challenges.

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3.4.2 Sample characteristics In total, the pool of data had n = 30 participants from Start-up Street in 2019, out of which 13 were projects (nascent) and 17 were start-ups (mature). The sample comprised participants that live in and are developing their entrepreneurship activities in the state of Jalisco in Mexico or its surroundings. A description of the total sample is provided in Table 3.1. The sample shows that 56.7% were participants between 18 and 24 years of age, most of them (73.3%) with a university degree. Regarding the project and start-up characteristics, 33.3% expressed having been working on it between 6 and 12 months and almost half (43.3%) were in a stage of business creation. When differentiating between the groups, 47.1% of the start-up’s group were in the business growth stage. In terms of size, 46.7% of the projects and start-ups had three to five employees at the time when the survey was conducted. In terms of founder’s funding, the authors noticed a gap between projects and start-ups. While the majority of projects’ investment range was between $1 and $500 USD, for startups, the majority of investment range was between $2,501 and $7,500 USD. In both groups, more than half of the participants had not received any external investment. Finally, before proceeding to the analysis of the constructs, Pearson’s chisquared tests were conducted to evaluate the similarities of the samples, revealing that they were not significantly different. In the following section, the authors explain how the constructs were measured. Table 3.1: Sample composition (N = 30). Sample characteristics

Age ranges in years

Educational attainment

Total % of total –

 .%

–

 .%

Start-up seniority

 .%

Start- % of up total  .%



.%



.%



.%



.%

–



Vocational professional

 .%

 .%

 .%

 .%

University/college

.%

Project % of total

 .%

 .%

MA, Ph.D.

 .%



.%

 .%

Not started yet Less than  months

 .%  .%

 .%  .%

 .%  .%

– months

 .%

 .%

 .%

– months

 .%



.%

 .%

More than  months

 .%



.%

 .%

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Geraldina Silveyra et al.

Table 3.1 (continued) Sample characteristics

Start-up stage

Total % of total Ideation of the business Business creation

Number of formal employees

Range of founder’s investment

Range of external investments received

Project % of total

Start- % of up total

 .%

 .%



.%

 .%

 .%

 .%

Supply chain development

 .%



.%

 .%

Business growth

 .%



.%

 .%

Does not have employees yet

 .%



 or  employees

 .%

 to  employees

 .%

 to  employees None

 .% .% .%

 .%

 .%

 .%

 .%



 .%

 .%

 .%



 .%

 .%

 .%

$ to $, USD

 .%

 .%

 .%

$, to $, USD

 .%

 .%

 .%

$, to $, USD



.%



.%

 .%

More than $, USD



.%



.%

 .%

 .%

 .%

$ to $ USD

None

 .%

$ to $ USD



$ to $, USD



.%

.%

.%



.%

 .%

 .%



.%

$, to $, USD



.%



.%



.%

$, to $, USD

 .%



.%

 .%

More than $, USD

 .%



.%

 .%

Source: Elaborated by the authors

.%



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3.4.3 Measures The evaluation survey was divided into sections that evaluated the three main constructs: (1) relational capital, (2) reciprocity, and (3) knowledge self-efficacy. These three constructs were measured using multi-item scales that contained three questions each (see Table 3.2 for items and sources). The questions were answered on a sevenpoint Likert scale (where “completely disagree” = 1 and “completely agree” = 7). Table 3.2: Variables, items, and sources. Variables

Items

Relational capital

Understanding that a support network is a group of people Scarmozzino, Corvello, (i.e. family, friends, mentors, colleagues, partners, etc.) Grimaldi () who accompany an entrepreneur in the process of starting or developing a project/business. – Your support network has made it easier for you to create your business – Your support network is a good forum to discuss new business ideas – Your support network provides you with access to resources (i.e. funds, mentors, business groups, etc.)

Reciprocity

– – –

Knowledge self-efficacy

– –

–

When I share my knowledge at networking events, I think I will get an answer for every answer I give When I share my knowledge at networking events, I expect someone to answer me when I need it. When I share my knowledge at networking events, I hope to gain knowledge when I need it

Sources

Kankanhalli et al. ()

I am confident in my ability to bring knowledge that others consider valuable at networking events. I have the necessary expertise to provide valuable knowledge for other entrepreneurs in networking events At networking events, many people can provide knowledge that is more valuable than what I can offer

Source: Elaborated by the authors

The authors measured the internal consistency of each construct using Cronbach’s alpha, as it aids in indicating how well a set of questions or items from a survey measures a single unidimensional latent construct (Cronbach, 1951). To do so, the items of each of the three constructs were selected by sets to apply the Cronbach’s alpha test. The results of these analyses are presented in Table 3.3. Nunnally (1978) recommended a level of 0.7 or above for the alpha coefficient, and in this case, it is possible to observe that the resulting alpha coefficients were above the recommended threshold, except for knowledge self-efficacy (0.599). For this, the authors followed a

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generally accepted rule that an alpha coefficient of 0.6–0.7 indicates an acceptable level of reliability (Ursachi, Horodnic & Zait, 2015), providing evidence of acceptable reliability and consistency. Consequently, with these Cronbach’s alpha results, it is possible to apply procedures like principal component analysis (PCA) or factor analysis as variable reduction procedures to recalculate the set of questions of each construct into new summarized variables for further analysis, as described in the next section. Table 3.3: Scale reliability. Scales

Number of Items

Cronbach’s Alpha

Relational capital



.

Reciprocity



.

Knowledge self-efficacy



.

Source: Elaborated by the authors

3.4.4 Method After conducting the reliability analyses, the authors selected PCA to recalculate the sets of items that measure each construct into new summarized variables. The PCA is well known as a powerful multivariate tool that is useful to analyse complex data and reduce dimensionality by using a linear combination of optimally weighted observed variables (Dunteman, 1994; Hotelling, 1933; Lagona & Padovano, 2007; Stevens, 1992). The method provided three new summarized variables for the constructs that contain most of the variations within the data (Jolliffe, 2002) that can be used to compare the differences between projects and start-ups. Also, Kaiser‒Meyer‒Olin (KMO) and Bartlett tests were conducted in a prior internal consistency validation process of the survey. The KMO statistics were above the 0.5 acceptable, indicating that the PCA was viable with our sample (Dziuban & Shirkey, 1974). In addition, a high level of significance (p-value = 0.001) from the Bartlett tests (below 0.5) was obtained (Tobias & Carlson, 1969). Since it was viable, the PCA was conducted, and the authors chose the best test for mean comparison, which directly depended on the data distribution. Therefore, normality tests were conducted (Kolmogorov–Smirnov & Shapiro–Wilk), and the results of these tests revealed that the new variables were not normally distributed in any of the groups. Thus, the Mann–Whitney U non-parametric test for means comparison was selected as the most appropriate method to compare between both samples and groups, as it has been reported as considerably more efficient and robust than the t-test when sample distributions are not normal (Conover, 1998, p. 16). The Mann–Whitney U test was applied to

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compare both groups of participants, start-ups, and projects. The results are explained in the following section.

3.4.5 Results The results from the analysis with the Mann–Whitney U test comparing the sample of project and start-up participants are reported in Table 3.4. In total, there was only one significant difference in the perceptions that was more favourable for project participants regarding the relational capital (z = − 1.785, p = 0.074). Therefore, only hypothesis 2 is supported. This result is aligned with the research of Semrau and Werner (2014) and Vissa (2011), which asserts that entrepreneurs in an early stage seek to expand their networks beyond existing contacts in order to acquire knowledge they still do not possess, and one way to do so is by attending networking events such as INC Crowded. Table 3.4: Mann–Whitney U test results. Scales

Group

Relational capital

Project



.

. .

Start-up



.

.

Reciprocity

Project



.

. .

Start-up



.

.

Project



.

. .

Start-up



.

.

Knowledge self-efficacy

Valid Standard cases deviation

Mean ranges Mann–Whitney U Z

p-value

–. .

–. .

–. .

Notes: * p ˂ 0.1; Source: Elaborated by the authors.

Conversely, the rest of the results were not statistically significant, although it is important to highlight that, in general terms, the other two variables: (1) Reciprocity and (2) Knowledge self-efficacy were perceived more favourably by start-up participants. Regarding reciprocity, Wasko and Faraj (2005) established that members of a community have a strong sense of their membership. It is important to mention that start-up entrepreneurs who attended INC Crowded mostly belonged to the start-up community in Guadalajara, an entrepreneurship ecosystem that is strongly characterized by the strength of their entrepreneurial communities, as represented by their willingness to help one another (Rodríguez‒Aceves et al., 2015). Regarding knowledge self-efficacy, the results show that, unlike early-stage entrepreneurs, those who have more experience, and whose entrepreneurial initiatives have developed further, have higher confidence in providing and sharing useful information to other entrepreneurs.

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Based on the work of Ba and colleagues (1996) and Kollock (1999), knowledge selfefficacy is manifested by the entrepreneurs’ belief that their knowledge can make a difference, in this context, to other entrepreneurs. Thus, it is a self-motivational force for more mature entrepreneurs attending entrepreneurship events to contribute knowledge to other participants (Bock & Kim, 2002) and the results of this study support these previous findings.

3.5 Discussion In this study, we focused on the perceived benefits (knowledge self-efficacy, reciprocity, and relational capital) that foster interactions within U-BEEs, aiming to identify specific actions that will inspire and promote entrepreneurship in such arrangements. We specifically analysed why such motivators lead mature and nascent entrepreneurs to attend entrepreneurship networking events. Our findings demonstrate that the motivators and benefits that nascent and mature entrepreneurs perceive when attending a networking event are different. Nascent entrepreneurs attend networking events looking to expand their relational capital in order to acquire specialised knowledge for their venture, mainly because they reach a point at which they require resources that cannot be provided by pre-existing contacts (Vissa, 2011). In this specific case, the U-BEE serves as a meeting point for actors, such as entrepreneurs, mentors, and investors with diverse backgrounds, who, exchange valuable knowledge with each other through interaction. For mature entrepreneurs, our findings show that they attend entrepreneurship events in order to gain confidence in terms of what they know and can do, which brings them the benefit of increased self-efficacy (Constant et al., 1994), causing them to be motivated to contribute their knowledge to other participants (Bock & Kim, 2002). In other words, mature entrepreneurs become aware of the value they provide to the U-BEE, not only as a receiver, but also as a provider of knowledge gained through their experience. Knowledge self-efficacy is complemented with reciprocity, for which the logic is that if the invested efforts of knowledge-sharing can be reciprocated, mature entrepreneurs will be motivated to contribute more (Chang & Chuang, 2011; Safdar et al., 2020). Although there are differences between nascent and mature entrepreneurs, they both benefit from the interconnectedness that the U-BEE provides through networking events. Our findings extend our knowledge regarding the impact that entrepreneurship networking events have on entrepreneurs. From prior research, we know that events are a source of relational capital for nascent entrepreneurs (Oliva & Kotabe, 2019; Hormiga, Batista‒Canino & Sánchez‒Medina, 2011; Ordóñez De Pablos, 2003), however the motivations for attending a networking event differ between nascent and mature entrepreneurs. Another important contribution is the fact that universities provide safe spaces for entrepreneurs to interact with others (entrepreneurs, mentors, role

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models, etc.) and share their knowledge, in order to build trusting and reciprocal relationships, developing the base for valuable knowledge sharing among the actors of an ecosystem. The findings of this research have threefold practical implications for those in charge of designing and articulating a U-BEE. The first concerns the importance of knowledge self-efficacy and reciprocity on the promotion of knowledge-sharing for entrepreneurs at all stages. Investing in resources that allow entrepreneurs to assess and be made aware of what they know and what they can share, as well as what they can obtain from their interaction within the entrepreneurship ecosystems, could encourage participation and therefore add greater motivation and value for them. Second, promoting networking events is a fundamental element to accomplish U-BEE’s purpose, which is to promote the interconnectedness between the different stakeholders in order to detonate a social change through entrepreneurial initiatives. Third is that successful entrepreneurship events require effective coordination and logistics. We identified three key moments of a networking event from the perspective of organisers. First, the creation and dissemination of an attractive call, which depends on the confirmed participants in the event. Usually, entrepreneurs are looking to connect with multidisciplinary, diverse and credible stakeholders. It is also important to have a clear definition of the vocational focus of the entrepreneurship event, to make it easier for entrepreneurs to decide which event is appropriate for them to attend. A second important moment is that it is essential to generate a good experience for all stakeholders during the event. This could be translated in terms of the establishment of a safe and proactive environment for stakeholders to connect with each other, clear communication and the inclusion of details which lead to exceeding expectations. Finally, an important moment which is sometimes overlooked is what follows when the event is over. A key activity is to follow up and monitor the value that is generated through the interactions that took place during the entrepreneurship event. Although this research has proposed important contributions, it is not without limitations; one being the size of the sample. To further advance our knowledge of UBEEs, a possibility is to study its specialization or vocation. To do so, we propose it is essential to provide specific and purposeful training to mentors, mature entrepreneurs, professors, students, alumni, technology transfer office coordinators, and investors who are part of the U-BEE and commonly attend entrepreneurship events. If such training focuses on broadening the U-BEE’s actors’ vision to promote the creation of ventures with the potential of reducing the social inequalities that prevail in Latin American contexts, while allowing entrepreneurs to reach their full potential, that could bring to life the university-based sustainable entrepreneurship ecosystem (UBSEE). We invite scholars to discuss and contribute further examinations to the concept of UBSEEs, which we proposed as a necessary approach for the advancement of emerging economies that enables individuals to reach their full potential by positively transforming their communities through their entrepreneurial initiatives.

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Chapter 4 Does education ensure entrepreneurial initiative? Approaching an entrepreneurial ecosystems taxonomy 4.1 Introduction The term entrepreneurial ecosystems (EEs) is one of the most discussed among entrepreneurship research arenas as a result of the recognition of EEs as promoters of economic development and growth. The EE is a concept that embraces multidimensionality and ambidexterity as their conception is influenced by the regional and national environmental conditions and resources. As a result, differences among EEs have been addressed using quantitative and qualitative methodologies (Mack & Mayer, 2016). EEs are viewed as interrelated forces that stimulate innovation, economic growth, and regeneration (Malecki, 2018) allowing the development of more adequate environments to enhance entrepreneurship. While developed economies have progressively embedded entrepreneurship in academia, industry, and governance levels to stimulate vibrant entrepreneurial atmospheres (Stam, 2015), in less-favoured regions – that is, with fragile economies, limited resources, and less access to education – the growth of EEs could be undermined. Therefore, while entrepreneurship is accepted as beneficial, each country tends to adopt and adapt specific strategies to their idiosyncrasies, in an evolutionary movement (Mack & Mayer, 2016). Other studies denote that entrepreneurship is not just a matter of individual characteristics, but other aspects such as social, economic, or cultural aspects could encourage or hinder entrepreneurial initiative (Barreneche García, 2014). Accepting the importance of exogenous factors towards entrepreneurial initiative, the stream of research devoted to EEs gained special attention in the last years. While first studies attempted to establish the framework elements of EEs, others focused on their performance or context conditions, as the phenomenon turned inherently geographically bounded. Along with entrepreneurship, education is also a tool of paramount importance to thrive in complex and challenging environments, allowing individuals to be better equipped with knowledge and skills to act entrepreneurially. As a result, countries are interconnecting their entrepreneurship and educational strategies to strengthen their potential. But, despite evidence suggesting that education impacts positively on individuals’ desire to create new ventures, some studies point otherwise, suggesting the need of additional research about the role of education. https://doi.org/10.1515/9783110670219-005

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To the best of our knowledge, despite the increasing awareness about the topic, the concept of entrepreneurial ecosystems still lacks clarity regarding boundaries, interactions, applicability, and focus (Adner, 2016), which explicitly denotes the gap about the multidimensionality of the phenomenon. Education is an important dimension for entrepreneurship and plays a significant role in developing entrepreneurial thinking. Therefore, this chapter seeks to address the gap in the literature about the role of education on entrepreneurial initiative, through the lens of entrepreneurial ecosystems. The chapter has the following structure. The next part reviews the entrepreneurial ecosystem concept and entrepreneurial initiative, and education. The third section presents the methodology adopted and next, the results and discussion of the findings are exposed. At last, the conclusion offers the most relevant findings and limitations of the study.

4.2 Theoretical background 4.2.1 Entrepreneurial ecosystems Entrepreneurship is acknowledged to play a significant role fostering economic growth, employment, and innovation (Acs, Stam, Audretsch & O’Connor, 2017; Audretsch, Cunningham, Kuratko, Lehmann & Menter, 2019), but only after the 1990s become a buzzword. As a result, the entrepreneurship research field was flooded with different perspectives around EEs. In the last decade, the topic gained special attention on its multidimensionality, suggesting that endogenous and exogenous factors are important contributors to foster entrepreneurial initiative (Mason & Brown, 2014). As a result, several studies about EEs (Acs et al., 2017; Isenberg, 2011; Mack & Mayer, 2016; Stam, 2015) attempt to understand their multiple configurations and how they nurture entrepreneurship. The EE concept holds multifaceted connections between numerous individual and organizational stakeholders that make up the ecosystem, and it is expressed throughout the formation and operation of new ventures (Autio & Levie, 2017). Although the relationship between entrepreneurial initiative and the personal characteristics has been explored widely with some authors arguing that entrepreneurs differ from other individuals, justifying the usage of entrepreneurial intention as one of the most relevant predictors of entrepreneurship (Laffranchini, Kim & Posthuma, 2018), other authors argue that the EE framework underlines the importance of the context in enhancing entrepreneurial initiative (Stam, 2015). EEs are geographically delimited, with several factors contributing to the recognition of entrepreneurial opportunities and influencing the entrepreneurs’ desire to enterprise (Audretsch & Belitski, 2017), according to the environment where they

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are embedded in (Acs et al., 2017; Stam, 2015). The ultimate outcome of this entrepreneurial dynamism depends on the individual’s characteristics, her/his perception about the viability of the opportunity and characteristics of the entrepreneurial ecosystem (Hechavarria and Ingram, 2019). The development of EEs aims to push entrepreneurs to enterprise but the myriad of interactions between stakeholders diminishes the effectiveness of the system. Using both theoretical and empirical approaches, the literature has focused on covering the tacit elements of EEs, such as: government policies, institutions or culture (Isenberg, 2011); widening the EE definition and its conceptualization (Acs et al., 2017; Spigel, 2017); exploring its scope (Roundy & Fayard, 2019); or its evolutionary dynamics (Mack & Mayer, 2016). The work of Isenberg (2011), a seminal contribution to the topic, places entrepreneurial as a strategy that allows the stimulus of economic prosperity by encompassing a variety of idiosyncratic factors: policy (leadership and government); finance (financial capital); culture (success stories and societal norms); supports non-government institutions, support professions and infrastructures); human capital (labour and educational institutions); and markets (networks and early customers). For the author, each entrepreneurial ecosystem is a unique configuration that results from the stimulus of the virtuous circles among all elements. As such, to avoid the “fallacy of market failure inevitability,” the focus should be placed on hotbeds and self-sustaining entrepreneurial ecosystems. Definitely, the phenomenon inherently roots a geographical perspective but to Isenberg (2011) the formulation of policies to enhance entrepreneurial ecosystem should rely not only on national but also on regional contexts (Roundy & Fayard, 2019). As the positive effect of EE on the establishment of early-stage ventures are accepted, the mechanism that allows EE to influence the entrepreneurs to act and sustain entrepreneurship are not yet uncovered. Time, effort and resources are needed to build distinctive business environments – as Silicon Valley, Austin or Boston – and policy makers should acknowledge that disconnected approaches to turn entrepreneurial ecosystems more impactful in a short-term, may lead to harmful consequences. There are evidences on the literature about entrepreneurial ecosystems within regions (Stephens, Butler, Garg & Gibson, 2019) or countries (Brooks, Vorley & Gherhes, 2019) offering different perspectives about the bounded geography of entrepreneurial ecosystems. For example, to acknowledge differences between the performance of EEs within regions, the European Commission was pushed to develop the Regional Entrepreneurship and Development Index (REDI) emphasizing the importance those who starts new businesses play (Acs et al., 2018). With a broader scope, the Global Entrepreneurship Monitor (GEM) measures the differences of entrepreneurial activity among countries, to uncover the factors that determine or influence the national levels of entrepreneurial activity (Reynolds et al., 2005). Some research focuses on the profiles of EEs analysing on their components. For instance, according to Terjesen, Bosma and Stam (2016), the regulative framework of

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EEs encompasses legal, political, and economic pillars with the purpose to create more stable contexts to support entrepreneurs. However, for the author, entrepreneurship impact can be limited if the study of the phenomenon relies on generalizations ignoring its different types. To bring together the set of interdependent actors and factors that enable entrepreneurship, Stam (2015) distinguishes the framework conditions (formal institutions, culture, physical infrastructure, demand) and the systemic conditions (networks, leadership, finance, talent, knowledge, and support services) as the EE elements. This model emphasizes the importance of entrepreneurship quality withdrawing a more traditional perspective grounded on entrepreneurship indicators maximization, using entrepreneurial activity and aggregating value creation of EEs as dynamic dimensions. Following prior contributions, Mack and Mayer (2016) attempt to understand the evolutionary dynamics of EEs since few studies are devoted to document the interdependencies between components and how they evolve over time. For the authors (Isenberg, 2011 Mason & Brown, 2014b) tend to be prescriptive as they focus on the elements of EEs neglecting the influence of the context and its evolution. The development of EEs comprises – birth, growth, sustainment, and decline – phases where different elements – policy, finance, culture, support, human capital, markets – interact and acquire a critical role according to each EE stage of development. The analysis of its progress is crucial to design grassroot strategies allowing a blend approach as suggested by Mason and Brown (2014b) to propel the uniqueness of each EE. Entrepreneurs are considered to be the heart of the EEs despite the traditional focus on the externalities of business creation (Stam, 2015). EEs determine individuals perception about support conditions to enterprise and since their decisions are usually local context-based, the influence of government policies, programs, financing, education, R&D transfer, infrastructures, culture, and market openness can be geographically bounded (Reynolds et al., 2005). Indeed, empirical data shows significant differences between regions across countries in terms of entrepreneurial activity and entrepreneurial ecosystem conditions (Bosma & Kelley, 2019). The studies suggest that more favourable environments could provide better conditions to support entrepreneurship and, therefore, stimulate more entrepreneurial initiative contrasting to fragile ecosystems, where the perception of individuals about the surroundings is adverse. As shown in the literature, more vibrant EEs possess higher concentrations of entrepreneurship (Carayannis, Provance & Grigoroudis, 2016). Knowledge about EEs has grown, and different conceptions and perspectives were provided as; however, the challenges around EEs still require additional research particularly around the interdependency among the factors (Mack & Mayer, 2016). For Spigel (2017), the “umbrella” approach that involves EE leave behind interdependencies and relations between factors uncovered. Recent research denotes spatial preferences with studies attempting to recognize local and specific characteristics to explain entrepreneurial ecosystems

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(De Brito & Leitão, 2020). Despite some fine-grained studies exploring entrepreneurship determinants using environmental thresholds, it is not clear yet how EEs explain the differences of entrepreneurial initiative among geographic diversity, suggesting the need for additional studies.

4.2.2 Entrepreneurial initiative and education Isenberg (2011) advocates that “entrepreneurship is no panacea for society’s ills” but can certainly help to foster societies and spread entrepreneurial mindset at large, enhancing innovation, growth and building sustainable economic development, especially when interconnected with other disciplines. Entrepreneurship and education are seen as two extraordinary fields that can fuel innovation, employment and economic growth, where the development of human capital can leverage societies and businesses of the future (WEFORUM, 2009). For the World Economic Forum, education needs to be one of the priorities of governments, along with entrepreneurship, since it has the power to develop entrepreneurial thinking and prepare human capital for the future. Combined with an environment that empowers entrepreneurs to strive and encourage individuals to tackle business opportunities, education can enhance entrepreneurship. This brings to light the relevance of education systems since it allows to equip people to act entrepreneurially on their environment (WEFORUM, 2009). Due to its recognition as a key as an important precursor for business creation, education is commonly investigated within the entrepreneurship landscape research (Reynolds et al., 2005). Governments and education institutions are implementing strategies to embed entrepreneurship in the educational curriculum expecting to improve students’ motivation, knowledge, and skills that are essential for launching a successful venture (Lee et al., 2014). Also, formal and informal entrepreneurial activities within educational contexts are being conducted, along with the implementation of tools and methodologies oriented to encourage creativity and innovation. Besides the purpose of training students to acquire skills and managerial abilities, additionally it is expected that entrepreneurship underpins the development of a positive attitude entrepreneurial action (Fayolle & Gailly, 2015) with educational institutions as game-changers. Despite several contributions about the role of education and entrepreneurship education towards more entrepreneurial action, the research landscape points mix evidence about its effects. For instance, Oosterbeek, Van Praag, and Ijsselstein (2010) assessed the impact of an entrepreneurship education program on students’ entrepreneurship skills and motivation to enterprise and the results pointed a negative effect on the intention to become entrepreneur. In a different perspective, Fayolle and Gailly (2015) discovered that the impact of education is more beneficial on entrepreneurial intention when prior entrepreneurial exposure is inexistent.

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Bae, Qian, Miao, and Fiet (2014) bring additional contributions, confirming a positive relation between entrepreneurship education and entrepreneurial intention. But more relevant is that business education, comparatively, seems to have a lower impact on entrepreneurial initiative. Furthermore, the study recognizes the positive role of culture and social context, and a negative effect of gender, underlining that are effects that vary across national cultures.

4.2.3 Other determinants of entrepreneurial initiative The entrepreneurship landscape acknowledges other factors that stimulates entrepreneurial initiative, such as sociodemographic, environmental, and economic variables (age, gender, marital status, parent’s occupation, household income, culture, opportunities’ recognition, fear of failure, social background, previous employment, education, entrepreneurial skills and ability, financial support, ethnicity, and religion) (Liñán et al., 2011; Bae et al., 2014). Gender is one of the various factors that has been studied in entrepreneurship studies, since traditionally self-employment is an activity more prevalent among male. Despite several studies point that females tend to enterprise less due to their lower propensity to take risks, recent studies observed a raise of female entrepreneurship (Bosma & Kelley, 2019). According to Heilman (2001), gender stereotypes can have significant impact on career intentions revoking women involvement in business creation. Additionally, the obstacles encountered by women entrepreneurs are superior (Hossain, Zaman & Nuseibeh, 2009). Elam and Terjesen (2010) explored gendered entrepreneurship among 11 countries using the GEM database and concluded that are gender differences towards institutional factors and that entrepreneurs respond differently to the environment based on gender. Traditionally societies often observe inequalities along business realities with men assuming prevailing positions, but the raise of female entrepreneurship is producing effects on labour force balance. The social norms and culture are also a powerful influence on gender gap, but there is a silent inversion on employment landscape pushing women to the lead. Aligned with Social Cognitive Theory, there are evidences highlighting the influence of role modelling on entrepreneurial initiative (Newman, Obschonka, Schwarz, Cohen & Nielsen, 2019). The exposure of would-be entrepreneurs to more knowledgeable and entrepreneurial individuals can determine entrepreneurial desire (Van Trang, Do & Luong, 2019). The family background significantly influences the student’s entrepreneurial intention and self-employed parents affect the entrepreneurial interest as well as the career choice of their children (Egerová, Eger & Mičík, 2017). The family context tends to act as a positive influence to create awareness of entrepreneurship

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and boost entrepreneurial initiative. The exposure to entrepreneurial role models serves to enhance the desire for an entrepreneurial career, acting as a driver for entrepreneurial initiative (Fellnhofer and Puumalainen, 2017). Failure and entrepreneurship are interrelated concepts as the creation of new ventures involves risk (Shane & Venkataraman, 2010). Very often entrepreneurs face the lack of resources and undertake new businesses under constant vulnerability conditions (Markman & Baron, 2003). Individuals with high risk aversion and high fear of failure are less involved in entrepreneurial activity. The GEM work also acknowledges fear of failure as one of the barriers to entrepreneurship, preventing individuals to pursue entrepreneurial journeys (Levie & Autio, 2007). In fact, Van Trang et al. (2019) found that individuals with higher levels of fear of failure are less engaged within entrepreneurial activities, since the risk and failure occurrence is higher. Nevertheless, the authors discovered that individuals with personal entrepreneurial connection and gaining entrepreneurship knowledge, skill, and experience have reduced the fear of business failure. Gender also influences entrepreneurial initiative as women tend to have higher levels of fear of failure and less ability to identify opportunities (Minniti & Nardone, 2007). The multi-level construct offered by GEM (Acs, Amorós & Bosma, 2009; Bosma et al., 2020; Reynolds et al., 2005) acknowledges the contemporaneity of the relation entrepreneurship-education by providing analysis based on countries factors – entrepreneurial ecosystems (Ács, Szerb, Lafuente & Lloyd, 2010) – and individual factors – such gender (Kelley et al., 2017), age (Schott, Rogoff, Herrington & Kew, 2017), social orientation (Bosma, Schhtt, Terjesen & Kew, 2018), or education (Coduras Martínez, Levie, Kelley, Schøtt & Saemundsson, 2010).

4.2.4 Research hypotheses Summarizing, the reviewed topics – entrepreneurial ecosystem, education, and entrepreneurial initiative – share a deep engagement and uncovers the importance of designing ecosystems and public policies to stimulate individuals to follow an entrepreneurial career option. Distinctive EEs appear to impact individuals differently concerning entrepreneurial initiative. Considering, in one hand, that the entrepreneurial ecosystem is an exogenous condition which is not controlled by the individual and, in the other hand, that education is widely recognized as a driver to achieve better employment conditions, this chapter aims to explore the moderation role of education between entrepreneurial ecosystems and entrepreneurial initiative. The arguments suggest three hypotheses that are explored in the empirical study, as follows:

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Hypothesis 1: The more favourable entrepreneurial ecosystem the higher the entrepreneurial initiative Hypothesis 2: Education increases entrepreneurial initiative Hypothesis 3: Education has an asymmetric effect on individuals accordingly to the entrepreneurial ecosystems

By drawing on the entrepreneurial ecosystem taxonomy, the effect of education is assessed to explore how entrepreneurial ecosystems policies can be harnessed to enhance the entrepreneurial initiative among individuals from different contexts. The theoretical framework of this study is founded on the EEs literature and Entrepreneurial Initiative (see Figure 4.1). The literature is still in the examination of a multidimensional analytical context opening the opportunity to explore causes and effects regarding entrepreneurial initiative.

Education H2

H3

H1

EE

EI

Figure 4.1: Hypothesized model for the effect of education.

4.3 Research methodology 4.3.1 Dataset The Global Entrepreneurship Monitor provides information and standardized metrics related to several aspects of entrepreneurship, namely regarding EI and EES (Kelley et al., 2017). To understand the moderation role of education on entrepreneurial initiative, it was used a sample of 45 countries retrieved from GEM 2016 dataset, comprising data from Adult Population Survey (APS) and National Expert Survey (NES). To allow a comprehensive perspective about both dimensions – individuals and entrepreneurial ecosystems – among countries, a panel analysis was conducted to ensure information from both survey in a given context. At individual level, the sample gathered 147,689 observations.

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4.3.2 Data collection : Entrepreneurial ecosystem taxonomy The entrepreneurial ecosystems taxonomy (EET) allows to evaluate countries dynamics concerning the entrepreneurship ecosystem quality (EEQ) and entrepreneurial initiative (EI). For obtaining EI per country and year, statistical procedures were implemented after data harmonization. Afterwards, using similar techniques it was determined EEs average, per variable, per year and per country. Considering that all EEs dimensions contribute to the quality of EE, it was conceptualized a new indicator, here in, EEQ (Pita & Costa, 2020). Using taxonomic quadrants for cross section purposes, the relation between EEQ and EA was examined allowing a sub-sampling segmentation based on the groups placed on each quadrant.

4.3.3 Data analysis This study uses a logistic regression (using SPSS) since the independent variables are dichotomous or categorical and the dependent variable is dichotomous. This logistic regression method is adopted from Van Trang et al. (2019) that aimed to estimate the role of entrepreneurship determinants on entrepreneurial process. Due to the categorization of EEs, new independent variables were introduced regarding taxonomic position of each EE and coded using a binary principle.

4.3.4 Variables It is important to note that most of the variables included in this study (see Table 4.1) were directly obtained from GEM (2016): dependent variable – entrepreneurial initiative (binary); independent variables – entrepreneurial ecosystem (multinomial), education (multinomial); control variables – age (multinomial); gender (binary); social context (binary); skills perception (binary) and fear of failure (binary). Table 4.1: Variable’s description. Variable

Description

Source

Entrepreneurial initiative

Individuals currently engaged in starting a business: (no = ; yes = )

GEM APS 

Gender

Male = ; female = 

GEM APS 

Age

Age of respondents in  levels

GEM APS 

Education

Education level:  (primary),  (secondary),  (tertiary)

GEM APS 

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Table 4.1 (continued) Variable

Description

Source

Social context

Individuals who know someone which is already entrepreneur (no = ; yes = )

GEM APS 

Skills perception

Individuals mentions having the skills to start a business (no = ; yes = )

GEM APS 

Fear of failure

Individuals mentions being afraid to start a business (no = ; yes = )

GEM APS 

EEQDieHard

Countries with low EEQ and low EI

GEM APS and NES 

EEQGoGetter

Countries with low EEQ and high EI

GEM APS and NES 

EEQSugarCoated

Countries with high EEQ and low EI

GEM APS and NES 

EEQFrontRunner

Countries with high EEQ and high EI

GEM APS and NES 

Source: Elaborated by the authors.

The variable EI indicates if individuals pursued entrepreneurship or not. Gender allows to capture the number of individuals according to the gender. Age captures de number or individuals distributed in seven categories. Education calculates individuals’ level of education according to three levels. The variable skills perception determines if individuals are perceived as having the skills to start a business or not. Fear of failure analyses how many individuals fear to enterprise or not. As for the other four variables – EEQDieHard1, EEQGoGetter2, EEQSugarCoated3, and EEQFront4Runner – they allow to capture country positioning following the EET (Pita & Costa, 2020).

4.3.5 Descriptive statistics The selected variables and their corresponding summary statistics are presented in Table 4.2. The dataset included 147,689 observations comprised in 45 countries. The application of EET allowed to cluster the countries into four taxonomic positions – Die-Hard, Go-Getter, Sugar-Coated and Front-Runners – according to the EEQ of each country (Pita, Costa & Moreira, 2018; see Table 4.3). Data procedures follow GEM protocol to ensure reliability of the study. In summary, countries are distributed along four taxonomic positions, with Sugar-Coated being the most represented position, with more than 35% of the overall sample (see Table 4.4). In the opposite, Go-Getter group is the smaller with only 17.8% of the total number of countries. Following this case, Front-Runners group has a similar proportion while Die-Hard group is represented by 28.89%.

73

Chapter 4 Does education ensure entrepreneurial initiative?

Table 4.2: Summary statistics. Obs

Mean

Std. dev.

Min

Max

Entrepreneurial initiative

,

.

.





Gender

,

.

.





Age

,

.

.





Education

,

.

.





Social context

,

.

.





Skills perception

,

.

.





Fear of failure

,

.

.





EEQDieHard

,

.

.





EEQGoGetter

,

.

.





EEQSugarCoated

,

.

.





EEQFrontRunner

,

.

.





Valid N (listwise)

,

Source: Elaborated by the authors.

Table 4.3: Countries positioning according to entrepreneurial ecosystem taxonomy. Die-Hard

Go-Getter

Sugar-Coated

Front-Runners

(low EI, low EEQ)

(high EI, low EEQ)

(low EI, high EEQ)

(high EI, high EEQ)

Country Observations Country Observations Country Observations Country Observations 

,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,



,

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Mariana Pita, Joana Costa, and António Carrizo Moreira

Table 4.3 (continued) Die-Hard

Go-Getter

Sugar-Coated

Front-Runners

(low EI, low EEQ)

(high EI, low EEQ)

(low EI, high EEQ)

(high EI, high EEQ)

Country Observations Country Observations Country Observations Country Observations 

,



,



,



,



,



,



,

–

,

–

,

–

,

–

,

Source: Elaborated by the authors.

Table 4.4: Proportion of countries positioning according to entrepreneurial ecosystem taxonomy. Die-Hard

Go-Getter

Sugar-Coated

Front-Runners

(low EI, low EEQ)

(high EI, low EEQ)

(low EI, high EEQ)

(high EI, high EEQ)

Country Observations Country Observations Country Observations Country Observations .%

.% .%

.% .%

.% .%

.%

Source: Elaborated by the authors.

Before analysing how education affects the impact of entrepreneurial ecosystems on entrepreneurial initiative, a detailed attention should be placed on the distribution of countries according to their taxonomic position. It is important to note that only eight countries are in Front-Runners (EI, EE) the remains are lagging behind partially or totally. The correlation matrix shows that gender and age are negatively correlated with entrepreneurial initiative (see Table 4.5). Education and social context are positively correlated with entrepreneurial initiative. Age and gender are positively correlated; however, they are negatively correlated with social context. Education is negatively correlated with age and social context. Regarding the entrepreneurial ecosystem taxonomic positions, EEQ1 is negatively correlated with EI and social context, while the correlation with age is positive. EEQ2 exhibits a positive correlation with EI and social context. The contrary effect is verified with age and education. For EEQ3, the results point to a negative correlation with EI, while a positive one with age, education and social context. Lastly, EEQ4 evidences a positive correlation with EI and social context, and a negative connection with gender.

−.**

.

.

.

−.*

.**

−.**

.**

−.**

.**

S. context

EEQ

EEQ

EEQ

EEQ

Note: ***p < 0.01; **p < 0.05; *p < 0.1. Source: Elaborated by the authors.

−.

.*

.**

−.**

Age

Education



−.**



Gender

Gender

EI

EI

Table 4.5: Correlation matrix.

−.

.**

−.**

.**

−.**

−.**



Age

.**

.**

−.**

.

.**



Education

.**

.*

.**

−.**



S. context

−.**

−.**

−.**



EEQ

−.**

−.**



EEQ

−.**



EEQ



EEQ

Chapter 4 Does education ensure entrepreneurial initiative?

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4.4 Results and discussion The study aims to acknowledge if education affect individuals’ entrepreneurial initiative according to the entrepreneurial ecosystems. Prior theoretical background allows to sustain that education fosters entrepreneurial initiative and, simultaneously, entrepreneurial ecosystems are also recognized as relevant to enhance individuals to act entrepreneurially. Both contributions legitimize a novel approach to assess how education impacts individuals within different entrepreneurial ecosystems. For study purposes and considering the research hypotheses, six models were used based on a cohort analysis departing from the Entrepreneurial Ecosystem Taxonomy. Using the dependent and independent variables as the basis for research objectives, Model 1 includes additionally the three Taxonomic Positions – Go-Getter, Sugar-Coated and Front-Runner – to assess the impact of changing position. Model 2, Model 3, Model 4, and Model 5 represent the EE cohorts (Die-Hard, GoGetter, Sugar-Coated and Front-Runner, respectively) and take into account the quality of different ecosystems. Model 6 includes four variables – Edu X DieHard, Edu X GoGetter, Edu X SugarCoated, Edu X FrontRunner – representing the combined effect of education in the different EE cohorts.

4.4.1 Empirical results Table 4.6 contains the results of the regressions of the Models 1, 2, 3, 4, 5, and 6. The first hypothesis, stating that Entrepreneurial Initiative is enhanced by a more favourable environment, is supported. The literature points entrepreneurial ecosystems as an enhancer and suggest that environments with proper conditions can support the flourishment of more enterprises. The results evidence that moving from different entrepreneurial ecosystems affects differently the entrepreneurial initiative. Departing from a Die-Hard context, herein the more fragile due to a lower quality, the change to the Go-Getter environment is positively significant, meaning that individual’s enterprise more in this setting. The movement from Go-Getter to Sugar-Coated context continues to be positively significant although the probability to enterprise decreases. Lastly, the change from Sugar-Coated to a Front-Runner environment raises the probability to enterprise, being again positively significant. The results allow to infer that more entrepreneurial ecosystems hinder entrepreneurial initiative, which emphasizes the importance of the favourable environment foster entrepreneurship. Models 2–5 support partially the second hypothesis regarding the effect of education on entrepreneurial initiative, contrary to expected. Model 2 reports the effect of education in the Die-Hard context and shows that is not statistically significant.

Chapter 4 Does education ensure entrepreneurial initiative?

77

This can suggest that education do not play a relevant role in environments with poorest conditions. Model 3 presents an unexpected result since education appears as significant for entrepreneurial initiative but as a deterring factor. In other words, individuals with higher levels of education have less probability to follow an entrepreneurial journey, when engaged on a Go-Getter environment. Model 4 presents a contrary result and education emerges as positively significant to entrepreneurial initiative. The Sugar-Coated is characterized as a favourable environment and education is an enhancer to enterprise. Lastly, Model 5 similarly to Model 3 shows education negative effect. Despite presenting a lower significance, education appears as inhibitor of entrepreneurial initiative. This means that is this specific favourable environment, individuals with higher levels of educations pursue less entrepreneurial careers. This can suggest that job market is larger and offers employment alternatives withdrawing individuals desire to enterprise. To predict the probability of the combined effect of education in different entrepreneurial ecosystems, it was run Model 6. The moderator role of education assessed in this last model allows to understand its impact differences according to the context. The results support the hypothesis three and evidence that education combined affects differently entrepreneurial initiative conditioned to entrepreneurial ecosystem. Therefore, in a more adverse environment such as the Die-hard, education impacts negatively on the entrepreneurial initiative. The combined effect of education on Go-Getter and Front-Runner is positively significant towards entrepreneurial initiative, while the impact is negative for Sugar-Coated. These results are surprising since Sugar-Coated ecosystem exhibit a higher quality while, in contrary, Go-Getter environment is frailer concerning entrepreneurship support. Such findings could suggest that more educated individuals in more favourable environments have access to better job opportunities and, in result, avoid to enterprise. Concerning the control variables – gender, age, skills perception, social context, and fear of failure – that were added to the model, and looking to the four models (2, 3, 4, and 5) that captures the different quality environments its evident that, in general, entrepreneurial initiative is affected but differently. Gender impacts negatively in entrepreneurial initiative in all different contexts, showing that women tend to enterprise less when compared to men. Age presents a similar result, indicating that independently of the entrepreneurial ecosystem, older individuals are less prone to follow an entrepreneurial journey. As for Social Context, the impact of knowing entrepreneurs has a positive effect on entrepreneurial initiative evidencing the importance of role-modelling. The perception of having the skills to pursue entrepreneurship is also significant towards entrepreneurial initiative, suggesting that individuals need to have confident on their knowledge to enterprise. Fear to fail also appears as significant but with a negative effect on entrepreneurial initiative in all entrepreneurial ecosystems.

EEQ Sugar-Coated

EEQ Go-Getter

Fear of failure

Skills perception

Social context

Education

Age

Gender

Variables

−.** (.)

(.) . (.) .*** (.) .*** (.) −.*** (.) –

(.)

−.***

(.)

.***

(.)

.***

(.)

−.***

(.)

.***

(.)

.***

–

(.)

−.***

−.***

(.)

.***

(.)

(.)

–

–

(.)

.***

(.)

−.***

(.)

−.***

(.)

−.***

−.***

−.***

Model 

Model 

Model 

Table 4.6: Econometric estimations. Coefficients

–

–

(.)

−.***

(.)

.***

(.)

.***

(.)

.***

(.)

−.***

(.)

−.***

Model 

–

–

(.)

−.***

(.)

.***

(.)

.***

(.)

−.*

(.)

−.***

(.)

−.***

Model 

–

–

(.)

−.***

(.)

.***

(.)

.***

−

(.)

−.***

(.)

−.***

Model 

78 Mariana Pita, Joana Costa, and António Carrizo Moreira

Robust standard errors in parentheses Note: ***p < 0.01; **p < 0.05; *p < 0.1. Source: Elaborated by the authors.

Observations

,

(.)

(.)

–

–

EduXFrontRunner

−.***

–

–

EduXSugarCoated

−.***

–

–

EduXGoGetter

Constant

–

–

–

(.)

.***

EduXDieHard

EEQ Front-Runner

,

(.)

−.***

–

–

–

–

–

,

(.)

−.***

–

–

–

–

–

,

(.)

(.) ,

−.***

(.)

.***

(.)

−.***

(.)

.***

(.)

−.***

–

−.***

–

–

–

–

–

Chapter 4 Does education ensure entrepreneurial initiative?

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Mariana Pita, Joana Costa, and António Carrizo Moreira

4.5 Conclusion Prior studies demonstrate the effects of several determinants – education (Fayolle & Gailly, 2015), gender (Heilman, 2001), age (Schott et al., 2017), social context (Newman et al., 2019), and fear of failure (Van Trang et al., 2019) – on entrepreneurial initiative. However, these studies do not differentiate the environments where EI occurs and do not fully understand the interconnected effect of variables regarding the context. The literature establishes a positive link between entrepreneurial dynamism and education, emphasizing that more educated individuals possess knowledge and tools to pursue new businesses (Barreneche García, 2014). In this respect, it can be said that education becomes a central tenet for all countries as a way to tackle inequalities in the job market and provides individuals better professional conditions. These results suggest that the encouragement of entrepreneurial initiative depends on multiple factors – endogenous and exogenous – emphasizing the need to design policies according to entrepreneurial ecosystems heterogeneity. Despite the recognition of entrepreneurship as a global driver for economic growth and development, particular attention should be paid to the effect of different drivers on different contexts. This study highlights that education drives differentiated effects of entrepreneurial ecosystems on entrepreneurial initiative. This finding provides a new perspective about the role of education, depending on the characteristics of the environment. To date, few researchers have argued that education is not beneficial towards entrepreneurship, but the results acknowledge that education effects are mixed. Considering an entrepreneurial ecosystem with lower quality as Die-Hard, education is not significant, which is interesting as education being recognized as a powerful leverage to ignite entrepreneurship. Considering the Go-Getter context, education appears as significative deterring factor, suggesting that more educated people tend to follow career options more stable. This could be connected to the fact that EE presents a lower quality, raising the perception of risk and increasing the fear of failure. Therefore, individuals could tend to pursue employee opportunities. In more favourable environments, as Sugar-Coated, education appears as positively significant. The perception of better conditions to enterprise induces more educated individuals to enterprise, but simultaneously, the fear of failure is very expressive. This could denote a culture of prejudice for those who don’t succeed, particularly those who are better equipped with skills and knowledge. Lastly, for Front-Runners, education, similarly to Go-Getter, appears to have a negative effect. Considering that this group is embedded in an Entrepreneurial Ecosystem with high quality, the results could suggest that more educated individuals recognize more accurately the environment competitiveness along with market risks. Also, being a more developed EE, the employment opportunities are higher, in number and quality, weakening the desire to pursue entrepreneurial endeavours.

Chapter 4 Does education ensure entrepreneurial initiative?

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The study calls into question the role of education and as the results demonstrate a negative relationship with entrepreneurial initiative in some entrepreneurial ecosystems. More interesting was to denote that education is not significative among fragile ecosystems, which points the need to develop long term entrepreneurship policies on building more favourable contexts. Following prior contributions that acknowledges time and resources to build EE, policymakers should consider long-run strategies and actions turning entrepreneurship as a sustainable activity. Entrepreneurship public policies should be designed to stimulate more educated people to enterprise, and simultaneously respecting those who fail. The study shoes that education has a higher effect within more favourable environments, when compared with context that are more unstable, but for Sugar-Coated, the entrepreneurial initiative stills below the average. This shows that despite EE clearly possess conditions to support entrepreneurs to strive, individuals tend to not be choosing entrepreneurship. In sum, the findings evidence the mixed effects of education, with more educated individuals having less entrepreneurial initiative, although embedded in more favourable environments. Also, entrepreneurial ecosystems seem to be effective only to a portion of the population, revealing a biased orientation, since its effect is not linear. This could suggest that EEs are elitist frameworks, more oriented to “good students” as those more knowledge and prepared to draw EE potential, while other less educated struggle to access or benefit equally from the environment conditions. This raises the suspicion about possible “facade” entrepreneurship, with so-called entrepreneurs absorbing resources from EE, while “true” entrepreneurs obtain a limited support. To conclude with a final remark on entrepreneurial ecosystem differences, it should be noted that the taxonomic approach used emphasize the heterogeneity of entrepreneurial environment in the assessed countries, suggesting the need to capture similarities and differences between groups and acknowledge influences to boost entrepreneurial initiative in the long run.

4.5.1 Implications and limitations Entrepreneurship is a dynamic field of action and research, therefore, public policy should be conducted and adapted according to the different environments and stages of EE. In the context of the study, several recommendations are provided to turn EE active sources of EI, using education and other determinants to ignite entrepreneurial spirit more broadly. Often, policy debate is focused on whether EE have or don’t physical conditions or if education provide or not conditions to students, and usually its assessment relies on direct measures. However, the findings bring to light the need to enrich the analysis based on the intersection of several factors. More, the multiplier effect of education shows that the different ecosystems limit or enhance entrepreneurial

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initiative. Hence, there is an argument to push public policy to act differently according to their environmental conditions, with varied instruments, dropping out an “one-size fits all approach.” Lastly, this chapter observed several factors theorized that influence entrepreneurship, with a special focus on the role of education, using a Global Entrepreneurship Monitor database, since offers a reliable and solid framework. However, although using a reliable database, the findings should be consolidated by further studies, including other variables. The findings should be reexamined, extending the study to different types of education and also acknowledging the role of education institutions, crossing individual attributes with other theories.

References Acs, Z., Amorós, J. E. & Bosma, N. (2009). From entrepreneurship to economic development: Celebrating ten years of global entrepreneurship monitor. . . . Frontiers of Entrepreneurship Research . . ., 29(16), 1–15.http://digitalknowledge.babson.edu/fer/vol29/iss16/1/ Acs, Z. J., Stam, E., Audretsch, D. B. & O’Connor, A. (2017). The lineages of the entrepreneurial ecosystem approach. Small Business Economics, 49(1). doi:https://doi.org/10.1007/s11187-017-9864-8 Ács, Z. J., Szerb, L., Lafuente, E., & Lloyd, A. (2018). The global entrepreneurship and development index. In Z. J. Ács, L. Szerb, E. Lafuente, & A. Lloyd (Eds.), Global Entrepreneurship and Development Index 2018 (Springer B, Issue January). Springer. https://doi.org/10.4337/ 9780857935540. Adner, R. (2016). Ecosystem as structure: An actionable construct for strategy. Journal of Management, 43(1), 39–58. doi:https://doi.org/10.1177/0149206316678451 Audretsch, D. B. & Belitski, M. (2017). Entrepreneurial ecosystems in cities: Establishing the framework conditions. Journal of Technology Transfer, 42(5), 1030–1051. doi:https://doi.org/ 10.1007/s10961-016-9473-8 Audretsch, D. B., Cunningham, J. A., Kuratko, D. F., Lehmann, E. E. & Menter, M. (2019). Entrepreneurial ecosystems: Economic, technological, and societal impacts. Journal of Technology Transfer, 44(2), 313–325. doi:https://doi.org/10.1007/s10961-018-9690-4 Autio, E., & Levie, J. (2017). Management of Entrepreneurial Ecosystems, In & T. K. G. Ahmetoglu, T. Chamorro-Premuzic, B. Klinger (Ed.), The Wiley Handbook of Entrepreneurship (John Wiley, Issue July, pp. 423–449). John Wiley & Sons.https://doi.org/10.1002/9781118970812.ch19. Bae, T. J., Qian, S., Miao, C. & Fiet, J. O. (2014). The relationship between entrepreneurship education and entrepreneurial intentions: A meta-analytic review. Entrepreneurship Theory and Practice, 38(2), 217–254. doi:https://doi.org/10.1111/etap.12095 Barreneche García, A. (2014). Analyzing the determinants of entrepreneurship in European cities. Small Business Economics, 42(1), 77–98. doi:https://doi.org/10.1007/s11187-012-9462-8 Bosma, N., Hill, S., Ionescu-Somers, A., Kelley, D., Levie, J. & Tarnawa, A. (2020). GEM – Global entrepreneurship monitor. Bosma, N. & Kelley, D. (2019). Global Entrepreneurship Monitor: 2018/2019 Global Report. Bosma, N., Schhtt, T., Terjesen, S. A. & Kew, P. (2018). Global Entrepreneurship Monitor 2015 to 2016: Special Topic Report on Social Entrepreneurship. SSRN Electronic Journal, May. https://doi.org/10.2139/ssrn.2786949

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Brooks, C., Vorley, T. & Gherhes, C. (2019). Entrepreneurial ecosystems in Poland: Panacea, paper tiger or Pandora’s box?. Journal of Entrepreneurship and Public Policy, 8(3), 319–338. doi: https://doi.org/10.1108/JEPP-04-2019-0036 Carayannis, E. G., Provance, M. & Grigoroudis, E. (2016). Entrepreneurship ecosystems: An agentbased simulation approach. Journal of Technology Transfer, 41(3), 631–653. doi:https://doi. org/10.1007/s10961-016-9466-7 Coduras Martínez, A., Levie, J., Kelley, D. J., Schøtt, T. & Saemundsson, R. J. (2010). Global Entrepreneurship Monitor Special Report: A Global Perspective on Entrepreneurship Education and Training. De Brito, S. & Leitão, J. (2020). Mapping and defining entrepreneurial ecosystems: A systematic literature review. Knowledge Management Research and Practice, 00(00), 1–22. doi:https:// doi.org/10.1080/14778238.2020.1751571 Egerová, D., Eger, L. & Mičík, M. (2017). Does entrepreneurship education matter? Business students ’ perspectives. Tertiary Education and Management, 3883(0). doi:https://doi.org/ 10.1080/13583883.2017.1299205 Elam, A. & Terjesen, S. (2010). Gendered institutions and cross-national patterns of business creation for men and women. European Journal of Development Research, 22(3), 331–348. doi:https://doi.org/10.1057/ejdr.2010.19 Fayolle, A. & Gailly, B. (2015). The Impact of Entrepreneurship Education on Entrepreneurial Attitudes and Intention: Hysteresis and Persistence. Journal of Small Business Management, 53(1), 75–93. doi:https://doi.org/10.1111/jsbm.12065 Fellnhofer, K. & Puumalainen, K. (2017). Can role models boost entrepreneurial attitudes?. International Journal of Entrepreneurship Innovation Management, 21(3), 274–290. doi:https://doi.org/10.1504/IJEIM.2017.083476 Hechavarría, D. M. & Ingram, A. E. (2019). Entrepreneurial ecosystem conditions and gendered national-level entrepreneurial activity: A 14-year panel study of GEM. Small Business Economics, 53(2), 431–458. doi:https://doi.org/10.1007/s11187-018-9994-7 Heilman, M. E. (2001). Description and Prescription: How Gender Stereotypes Prevent Women’s Ascent Up the Organizational Ladder. Journal of Social Issues, 57(4), 657–674. Hossain, A., Zaman, K. N. & Nuseibeh, R. (2009). Factors influencing women business development in the developing countries. International Journal of Organizational Analysis 17(3), 202–224. Isenberg, D. J. (2011). The Entrepreneurship Ecosystem Strategy as a New Paradigm for Economic Policy: Principles for Cultivating Entrepreneurships. The Babson Entrepreneurship Ecosystem Project, 1(781), 1–13. http://www.wheda.com/uploadedFiles/Website/About_Wheda/Babso nEntrepreneurshipEcosystemProject.pdf Kelley, D. J., Baumer, B. S., Brush, C., Greene, P. G., Mahdavi, M., Majbouri, M., . . . Heavlow, R. (2017). Global Entrepreneurship Monitor: Women ’ s Entrepreneurship 2016 / 2017 Report. Laffranchini, G., Kim, S. H. & Posthuma, R. A. (2018). A metacultural approach to predicting self-employment across the globe. International Business Review, 27(2), 481–500. doi: https://doi.org/10.1016/j.ibusrev.2017.10.001 Lee, S., Florida, R., & Acs, Z. (2014). Creativity and entrepreneurship: A regional analysis of new firm formation. Regional Studies, 38(8), 879–891. doi:https://doi.org/10.1080/ 0034340042000280910 Levie, J. & Autio, E. (2007). Entrepreneurial framework conditions and national-level entrepreneurial activity: Seven-year panel study. Third Global Entrepreneurship Research Conference, 1–39. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.462.6312&rep=rep1&type=pdf% 0Ahttp://www.gemconsortium.org/assets/uploads/ 1326045129Entrepreneurial_Framework_Conditions.pdf

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Liñán, F., Santos, F. J., & Fernández, J. (2011). The influence of perceptions on potential entrepreneurs. International Entrepreneurship and Management Journal, 7(3), 373–390. doi: https://doi.org/10.1007/s11365-011-0199-7 Mack, E. & Mayer, H. (2016). The evolutionary dynamics of entrepreneurial ecosystems. Urban Studies, 53(10), 2118–2133. doi:https://doi.org/10.1177/0042098015586547 Malecki, E. J. (2018). Entrepreneurship and entrepreneurial ecosystems, Geography Compass, 12 (3), 1–21. doi:https://doi.org/10.1111/gec3.12359 Markman, G. D. & Baron, R. A. (2003). Person-entrepreneurship fit: Why some people are more successful as entrepreneurs than others. Human Resource Management Review, 13(2), 281–301. doi:https://doi.org/10.1016/S1053-4822(03)00018-4 Mason, C. & Brown, R. (2014). Entrepreneurial Ecosystems and Growth Oriented Entrepreneurship, (Issue January). Mason, C. & Brown, R. (2014b). Entrepreneurial ecosystems and growth oriented entrepreneurship. Oecd, 1–38. doi:https://doi.org/10.1007/s13398-014-0173-7.2 Minniti, M. & Nardone, C. (2007). Being in Someone Else’s Shoes : The Role of Gender in Nascent Entrepreneurship. Small Business Economics, 28, 223–238. doi:https://doi.org/10.1007/ s11187-006-9017-y Newman, A., Obschonka, M., Schwarz, S., Cohen, M., & Nielsen, I. (2019). Entrepreneurial self-effi cacy : A systematic review of the literature on its theoretical foundations, measurement, antecedents, and outcomes, and an agenda for future research. Journal of Vocational Behavior, 110(May 2018), 403–419. https://doi.org/10.1016/j.jvb.2018.05.012 Oosterbeek, H., Van Praag, M. & Ijsselstein, A. (2010). The impact of entrepreneurship education on entrepreneurship skills and motivation. European Economic Review, 54(3), 442–454. doi: https://doi.org/10.1016/j.euroecorev.2009.08.002 Pita, M. & Costa, J. (2020). Entrepreneurial Ecosystem Quality. Proceedings 56th International Scientific Conference on Economic and Social Development. Aveiro, Portugal. Pita, M., Costa, J. & Moreira, A. C. (2018). Measuring the Effect of Entrepreneurial Ecosystem Quality on Entrepreneurial Activity : The Mediation Role of Education. Encuentro Iberico. Proceedings Encuentro Ibérico de Investigación Y Educación en Emprendimiento GEM-CEE. Santiago de Compostela, Spain. Reynolds, P., Bosma, N., Autio, E., Hunt, S., De Bono, N., Servais, I., . . . Chin, N. (2005). Global entrepreneurship monitor: Data collection design and implementation 1998–2003. Small Business Economics, 24(3), 205–231. doi:https://doi.org/10.1007/s11187-005-1980-1 Roundy, P. T. & Fayard, D. (2019). Dynamic capabilities and entrepreneurial ecosystems: The Micro-foundations of regional entrepreneurship. Journal of Entrepreneurship, 28(1), 94–120. doi:https://doi.org/10.1177/0971355718810296 Scott, S., & Venkataraman, S. (2000). The promise of entrepreneurship as a field of research, The Academy of Management Review, 25(1), 217–226. doi:https://pdfs.semanticscholar.org/e777/ 71389077a13c680c124a005da85fbb5b3742.pdf. Access date: 1 March 2021 Shane, S. & Venkataraman, S. (2010). The promise of entrepreneurship as a field of research. The Academy of Management Review, 25(1), 217–226. Academy of Management. 25 (1),217. Source: The Academy of Management Review https://pdfs.semanticscholar.org/e777/ 71389077a13c680c124a005da85fbb5b3742.pdf Spigel, B. (2017). The relational organization of entrepreneurial ecosystems. Entrepreneurship: Theory and Practice, 41(1), 49–72. doi:https://doi.org/10.1111/etap.12167 Stam, E. (2015). Entrepreneurial ecosystems and regional policy: A sympathetic critique. European Planning Studies, 23(9), 1759–1769. doi:https://doi.org/10.1080/09654313.2015.1061484 Stephens, B., Butler, J. S., Garg, R. & Gibson, D. V. (2019). Austin, Boston, Silicon Valley, and New York: Case studies in the location choices of entrepreneurs in maintaining the

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Technopolis. Technological Forecasting and Social Change, 146, 267–280. doi:https://doi. org/10.1016/j.techfore.2019.05.030 Terjesen, S., Bosma, N. & Stam, E. (2016). Advancing Public Policy for High-Growth, Female, and Social Entrepreneurs. Public Administration Review, 76(2), 230–239. doi:https://doi.org/ 10.1111/puar.12472 Van Trang, T., Do, Q. H. & Luong, M. H. (2019). Entrepreneurial human capital, role models, and fear of failure and start-up perception of feasibility among adults in Vietnam. International Journal of Engineering Business Management, 11, 184797901987326. doi:https://doi.org/10.1177/ 1847979019873269 WEFORUM. (2009). Educating the Next Wave of Entrepreneurs. Executive Summary. A Report of the Global Education Initiative. April. www.weforum.org

Daniel Feser and Till Proeger

Chapter 5 The ambiguous role of best practice examples for knowledge spillovers: Evidence from universities and start-ups in the Berlin entrepreneurial ecosystem 5.1 Introduction The sustainable development goals (SDGs) as agreed by the United Nations reflect the growing global relevance of sustainability. Innovation scholars have reacted to this, demanding to adjust existing policy frameworks, for example, in the form of regulatory experimentation (see, e.g. Bauknecht et al., 2020). The suggested innovation policy 3.0 (Schot & Steinmueller, 2018) integrates the need for transformative change and the necessity of innovation to contribute to sustainability. At the European level, the Green New Deal operationalizes the role of innovation policy in sustainable development to “increase significantly the large-scale deployment and demonstration of new technologies … building new innovative value chains” (EC, 2019, p. 18). Digital technologies like artificial intelligence (AI) have been identified as potentially crucial drivers to solve multidimensional problems of sustainability. As a central concept, entrepreneurial ecosystems are used to understand how digital technologies scale up and contribute to regional reconfiguration processes (Autio, Kenney, Mustar, Siegel & Wright, 2014; Sussan & Acs, 2017). Specifically, new firms and growing firms are expected to support regional competitiveness. For regional innovation systems, entrepreneurial ecosystems are key to understanding the dynamics of regional knowledge production, exchange, and recombination (Bischoff & Volkmann, 2018; Volkmann, Fichter, Klofsten & Audretsch, 2019). Building on Arrow (1962), the “knowledge paradox” reflects the notion that investments in the knowledge economy do not automatically lead to a higher level of competitiveness, despite the public goods characteristics of knowledge (Audretsch & Keilbach, 2008); rather, the competitiveness depends on the interaction of regional actors in helix systems (Carayannis & Rakhmatullin, 2014). The literature on regional multi-helix ecosystems describes the interaction between administration, businesses, societal groups, and research interacting as explanatory factors determining the knowledge economy of these ecosystems (Leydesdorff, 2012). Empirical evidence presents insights from sustainable entrepreneurial ecosystems, while their contribution to sustainable innovation remains underexplored and the literature is not yet saturated (Volkmann et al., 2019). Particularly the role of knowledge spillover as knowledge

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“left uncommercialized as a result of the uncertainty inherent in knowledge, serve as a source of knowledge generating entrepreneurial opportunities” (Audretsch & Keilbach, 2007, p. 1246) requires further research regarding its impact on sustainable development. Our chapter therefore contributes to this research gap by discussing the role of knowledge spillover in sustainability-oriented entrepreneurial ecosystems. In the knowledge economy, several tools exist to boost sustainability with new services, products, or business models. Nonetheless, how to achieve sustainable innovation to replace harmful materials, reduce the use of products, and improve their re-use remains unclear and requires a revision of the existing concepts. Translating the theoretical potential of technologies into regional impact could potentially accelerate the diffusion of sustainable innovation. We will focus on the role of mal- or best practice examples as knowledge spillover, which have been used as instruments concerning how regional settings need to be designed and how socio-technical trajectories will be influenced. Our case study concentrates on knowledge spillovers with relation to AI in the entrepreneurial ecosystem in Berlin, where a critical mass of start-up companies, investors, and university-related research institutions have the potential to develop an ecosystem with a role model character for European AI diffusion, contributing to sustainable development. Mal- and best practices play a central role as knowledge spillover learning from international and domestic examples of how to best implement an innovation framework that supports entrepreneurial engagement. Multidimensionality adds extra complexity to practical examples when these include sustainable development. In our case studies, we can show that best practices in AI concerning the university–entrepreneurial ecosystem interaction are often described as extreme cases. Indeed, we find international and regional examples for this phenomenon. Insights from behavioural research can be used as an explanation, illustrating that the anchoring bias may promote these results. Our concluding policy implications concentrate on the missing link between practical examples and knowledge that might actually be useful. The remainder of this study is structured as follows. We review the relevant literature for our approach in section two, followed by the methodological section in section three, which features a description of our sample. In section four, the results on the role of best practices are analysed and connected to behavioural insights, before section five finally concludes.

5.2 Literature review The interplay between knowledge production and knowledge exploitation in regional innovation systems has attracted the interest of many scholars (Benneworth, Pinheiro & Karlsen, 2017; Fritsch & Slavtchev, 2011; Frykfors & Jönsson, 2010). Knowledge

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exchange in helix structures between companies, research institutions, and administration has triggered a long research agenda (Frykfors & Jönsson, 2010; López-Rubio, Roig-Tierno & Mas-Tur, 2020). Particularly the commercialization around universities has been discussed (Acosta, Coronado & Flores, 2011; Benneworth et al., 2017; Karnani, 2013) to upgrade regional knowledge configurations. Traditionally technology transfer offices have been analysed coordinating knowledge transfer activities (Huyghe, Knockaert, Wright & Piva, 2014; Karnani, 2013). Codified knowledge often played a substantial role in the form of patenting and licensing. Structural support for entrepreneurship has become more important in recent years, supporting the growing relevance of start-up companies with the support of universities (Kolympiris & Klein, 2017). While the importance of entrepreneurial ecosystems has been formulated and impact mechanisms analysed (Bischoff & Volkmann, 2018; Volkmann et al., 2019), little is still known about how alignment to normative goals – like the SDGs – can be supported by knowledge spillover in entrepreneurial ecosystems. Sustainability transition scholars have extensively analysed the role of innovation and the institutional conditions for technological pathways promoting sustainable development (Geels, 2002; Markard, Raven & Truffer, 2012). Research has focused on the diffusion processes from niche innovation to systemic changes (Geels, 2010). Recently, spatial processes have been discovered for new research (Binz, Coenen, Murphy & Truffer, 2020; Coenen, Benneworth & Truffer, 2012). The local configuration in network and the embeddedness in regional institutional frameworks can be important in explaining differences in the contribution to sustainable development (Coenen et al., 2012). Proximity plays a major role in fostering innovation processes (Gugerell & Penker, 2020). The role of knowledge spillover for sustainable development remains underexplored to understand socio-technical transitions. The knowledge spillover theory of entrepreneurship is the theoretical outline for the commercialization of new knowledge by entrepreneurs and the integration of innovation in companies promoting competitiveness (Ács, 2009; Audretsch & Keilbach, 2007). A large strand of literature has used different means to explain knowledge spillover in channels like R&D cooperation (Fritsch & Franke, 2004), digitization (Proeger & Runst, 2019), and social norms (Guerrero & Urbano, 2014). Especially between universities and entrepreneurial ecosystems, knowledge spillovers can serve as a channel to improve the regional innovativeness and accelerate the knowledge exchange (Acosta et al., 2011; Acosta Seró, Coronado Guerrero & Flores, 2011; Benneworth et al., 2017). Knowledge spillover are expected to play a crucial role to enable and foster sustainable innovation in entrepreneurial ecosystems and serve as mediating factor for university–industry partnership (Ferreira & Carayannis, 2019; Kaklauskas et al., 2018). In this chapter, we will concentrate on the role of knowledge spillover for sustainable innovation in the regional innovation system. While best practices for regional innovation have been critically discussed (Tödtling & Trippl, 2005), the role

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of role model implementation in other regions or technologies for knowledge circulation in ecosystems and their contribution to sustainable innovation remains unclear.

5.3 Data and methodology This chapter builds upon established case study methodology and aims to formulate theoretical insights (Eisenhardt, 1989; Eisenhardt & Graebner, 2007). Reflexive methods and interaction with participants are particularly useful to answer “how” research questions (Yin, 2003). Research on innovation systems and entrepreneurial ecosystems has applied a broad range of qualitative and mixed-method approaches (Bank, Fichter & Klofsten, 2017; Howells, 2006; Tiemann, Fichter & Geier, 2018). The exploratory stage of research on knowledge spillover in sustainability entrepreneurial ecosystems and knowledge spillover call for inductive methods since structured data sources are missing, clear definitions have not yet been established and heterogeneous perspectives of actors in the field can be better covered. We use semi-structured expert interviews to derive inductive insights and formulate theoretical implications. Our aim is to formulate hypotheses about behavioural insights into how knowledge spillovers in entrepreneurial ecosystems contribute to sustainability-related goals like the SDGs. The AI ecosystem of Berlin was chosen due to three reasons: 1) Historically, activities of various societal groups in the Berlin region1 have promoted an innovation framework to support sustainable development. Since 1993, Berlin has established official stakeholder participation processes to integrate ideas of regional sustainable development into the regional policy framework (Berlin House of Representatives, 2006). The official declaration of the Berlin City Council to contribute to the SDGs was the manifestation of regional sustainable development activities. The innovation strategy of the Berlin region consequently prioritizes sustainable innovation and green technologies leading to regional inclusive growth (City Council of Berlin and State of Brandenburg, 2019). 2) The Berlin region has developed a favourable innovation system for technology-focused start-ups and digital entrepreneurship. Embedded in a comparatively innovative SME sector, a specialized AI entrepreneurial ecosystem has developed (Feser, 2018). Furthermore, venture capital investment companies have identified Berlin as an advantageous location to invest in fast-growing AI start-ups in Europe (EC, 2018). Moreover, Berlin has attracted attention for sustainability-directed entrepreneurial activities (Bank et al., 2017). 3) The research institutions and universities in the Berlin region follow an internationally recognized research agenda on AI applications. Between 2007 and 2017,

1 The Berlin region includes the German capital of Berlin and the federal state of Brandenburg.

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universities and research organizations received 117 million Euro from the German government to support this specific research goal. This often entailed collaborative research projects and common product development with start-ups, which aimed for close-to-market solutions (Feser, 2018). Berlin’s research organizations pursue strategies to establish innovation and creativity labs, supporting students and researchers to establish high-tech companies and disseminate their knowledge in the entrepreneurial ecosystem. The interviews are part of a project about the development of the digital ecosystem with a focus on the state of AI in the Berlin region. The Berlin City Council aspired to support AI-related activities to support the entrepreneurial ecosystem, improving the innovativeness of the Berlin region. The projects included desk research, data analysis, stakeholder workshops, and semi-structured expert interviews.2 The interviews selected for the analysis in this chapter were conducted between March and July 2018 in person, online and via phone and they lasted between 21 and 88 min (see Table 5.1).3 The interviews were conducted based on a semi-structured questionnaire with mostly open-end questions relating to the interviewees’ expertise. The guideline was structured in three parts. In the first part, the interviewees explained their involvement in AI development, describing their role and position in the Berlin ecosystem. In the second part, the interviewees were asked to elaborate on the systemic perspective. In particular, impediments and challenges for the development of the ecosystem were discussed in this part of the interview. In the final part, the future development of the Berlin ecosystem and policy implications for further development were discussed. In order to avoid biases during the interviews, sustainability topics were only discussed in relation to the systemic perspective. Building on theoretical sampling (Glaser & Strauss, 2008), our selection strategy aimed to collect insights from the perspectives of the relevant actors in the Berlin AI entrepreneurial ecosystem. To gain saturation of the sample, we contacted stakeholder from the business ecosystem to receive a balanced view from the different helices (university, administration, established businesses, and start-up businesses). Access to the field was achieved with the help of local policy-makers and regional innovation experts. Furthermore, we used the snowball technique to increase our sample of experts via recommendations of interviewed experts. For this chapter, the interviews were selected that specifically built upon university–entrepreneurial ecosystem interaction. This article systematically gathered information from the recorded and transcribed interviews and publicly available sources. The results are derived from the qualitative content analysis (Mayring, 2000). Following this approach, we reduced

2 A business report on the state of AI in the Berlin region summarizing the region was published as an outcome of the project by one of the authors (Feser, 2018). 3 Interview #3 and #4 were conducted in one interview session.

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material in an iterative procedure to identify the relevant content concerning our research question and built inductive categories to formulate theoretical insights. For validation, one of the authors presented and discussed preliminary results at three stakeholder workshops with participants from the Berlin ecosystem including stakeholders from administration, research, and entrepreneurs. Finally, the results were connected to findings from the literature with a focus on relevant behavioural studies. Table 5.1: Overview of interviews. No.

Role

Focus

Time

#

Science

Basic research

 min

#

Administration

IT service

 h  min

#

Science

Applied research

 h  min

#

Science

Applied research

 h  min

#

Business

SME/start-up

 min

#

Business

IT consulting/customer

 min

#

Business

Large enterprise

 min

#

Business

Customer

 min

#

Investor

AI/digital technologies

 min

#

Intermediary

Co-working space/networking

 min

#

Science

Applied research

 min

#

Science

Applied research

 min

#

Business

SME/start-up

 min

#

Science

Basic research

 min

#

Science

Large research

 min

#

Science

Basic research

 min

#

Intermediary

Open technology

 min

#

Business

Large enterprise

 min

#

Science

Applied research

 min

In the subsequent results part, we outline the regulatory and institutional conditions for the entrepreneurial ecosystem contributing towards sustainability, followed by insights into best practice from the interviews, consequently triangulated by insights from behavioural research.

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5.4 Results AI-related entrepreneurial activities in Berlin have noticeably increased in recent years and have led to an increase of sustainable innovation in the region. However, the entrepreneurial ecosystem remains in its infancy. In particular, entrepreneurs and researchers are developing high-tech business models. The innovation framework and institutional norms are currently being discussed between actors from research, administration, and businesses. Knowledge about AI significantly varies between the stakeholders. While all of the stakeholder groups see the potential for Berlin to establish itself as a European cluster for AI, knowledge spillovers are key to construct a favourable innovation framework that fosters sustainable development in Berlin. Especially the combination of financial resources, scientific knowledge, and entrepreneurs is needed to create a growing ecosystem and consequently contribute to the city’s innovativeness. Sustainable development plays a major role in diffusion and dealing with sustainability will be decisive for the acceptance and adoption of AI-driven solutions.

5.4.1 Challenges of collaborations promoting knowledge spillovers for sustainable innovation Knowledge spillovers are important to foster AI diffusion processes. The interviewees emphasized the challenges promoting collaborations for sustainable innovation related to translating scientific and applied knowledge of AI for customers and policy-makers to make informed decisions about the technology use and regulatory framework. Several reasons were offered during the interviews. First, the definition of AI has dramatically developed around applications that can perform tasks that normally require human intelligence. Recent changes in the definition can lead back to progresses of the current state of the art in software development, computing power, and data availability. Second, interdisciplinary research collaboration in fields like computer science, physics, engineering, and linguistics adds complexity to developing services, products, and business models. Third, the complexity of AI increases the time span to acquire sufficient knowledge to develop advanced AI solutions: some interviewees explained that more than ten years of intensive education and training is necessary to gain senior status in R&D departments. Fourth, AI can potentially be classified as general purpose technology (GPT), which places AI in a row with the combustion engines and electricity (Jovanovic & Rousseau, 2005; Trajtenberg, 2019). AI offers a countless number of applications in various sectors like health and automotive and business intelligence, which makes it complicated for policy-makers and customers to differentiate between the purposeful and beneficial use of AI and non-useful applications. Overcoming the complexity, examples of the practical use of AI play a dominant role for knowledge dissemination.

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The fragmented network structure in the Berlin region has influenced the knowledge spillover in the Berlin AI ecosystem. The actors in sub-systems like the scientific system and start-up ecosystem have developed ties within the network structures and they often exchange knowledge on an in-group basis. Often, the within-group interaction can be explained by the actors’ focus on developing AI applications in technological niches. This favours specialized networks in sub-fields like autonomous driving, natural language processing and image recognition. An overall oversight has been perceived as difficult when attempting to promote AI knowledge spillover cooperatively. In particular, outside of the inner network, actors do not know activities from other fields and they wonder whether entrepreneurial activities take place. In order to build a common innovation policy framework, publicly recognized mal- and best practices play an important role in the dissemination of knowledge, particularly in communication with policy-makers.

5.4.2 Multidimensionality of sustainable innovation Sustainable development as defined in the Brundtland Report (UN, 1987) influences the AI diffusion in Berlin, considering the next generation’s needs. Consequently, this requires focusing not only on promoting commercialization and short-term profit maximization but also societal, ecological, and economic aspects of sustainable innovation. While sustainable development was not directly addressed by the interviewer, sustainability-related topics influence the development of the Berlin innovation system. It was acknowledged that a multidimensional understanding of technology embeddedness in the regional sustainable development is key to successfully develop the ecosystem in Berlin, which can compete on a global level. AI can potentially support a reduction of energy usage by designing optimized processes that are more energy-efficient or optimize renewable energy sources. The examples include mostly positive practices on how the energy sector can become more sustainable in the future, with AI addressing clean and affordable energy (SDG 7) and sustainable industrial processes (SDG 11). Moreover, negative experiences with other digital technologies like the high energy use of Bitcoin are used as an example to warn that this needs to be taken into account when supporting innovative technologies. A negative impact on sustainable development can potentially influence the applicability of AI and consequently the diffusion of the sustainable innovation. Furthermore, the acceptance of AI among regional helix actors has been described as central for sustainable cities (SDG 11) and responsible production and consumption (SDG 12). The acceptance depends not only on business actors like consulting companies, IT services, and potential business customers, but also on broader perspectives from various societal groups and the involvement of the general public. This broader approach addressing not only core market actors is mostly

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driven by the fear that relevant actors such as potential customer companies or policy-makers base their choices on the general acceptance of AI. In particular, when technology use is discussed in public, the interviewees expressed concerns that misuse can lead to a slowdown in the adoption of AI solutions. The GPT characteristics enable the use of AI for a broad range of use cases. The flexibility of AI applications helps to promote applications in sectors that contribute to the SDGs. One example developed in the Berlin region was a research project using an AI system that supports blind and visually impaired persons to read with text recognition, contributing to inclusive and equitable quality education (SDG 4). For publicly promoted research, these applications support sustainable development while also actively promoting technology development, such as in this example of text recognition. The best practices on sustainable development in our sample cover a broad range, reflecting on the multidimensionality of sustainable development. The interviewees describe that a variety of interactions with different stakeholders in the regional helix will be necessary to promote the AI diffusion as sustainable innovation. Nevertheless, the multidimensionality of sustainable development increases the complexity of knowledge dissemination and the circulation of knowledge spillover.

5.4.3 Positive and negative practices and knowledge spillovers for sustainable innovation In order to illustrate the advantages and challenges of AI implementation, interviewees often referred to extreme examples. Mal- and best practice examples have been described in three different forms. First, the interviewees presented examples from national and international AI ecosystems that have already developed successful innovation policy frameworks. The practical examples are used to compare the status in the Berlin region and derive recommendations to overcome impediments. Second, experts presented AI-related activities in Berlin as mal- or best practices to innovate integrating AI-driven products and services and set up successful business models. Third, policy implications have been informed by insights from other policies in other regions or related technology diffusion, like the implementation of blockchain technology. Policy-makers also use best and mal-practices as direct knowledge transfer in entrepreneurial ecosystems, as discussed in the management literature (Oliva & Kotabe, 2019). Nevertheless, regarding the early stage of the AI ecosystem in Berlin, we focus on the systemic perspective and its undirected influence on the diffusion of AI. This includes positive examples of how problems with large data sets can be solved. Examples from medicine and pharmaceutical AI applications like image recognition for cancer recognition illustrate the positive influence at the societal health level (SDG 3). Moreover, mal-practices illustrating limitations of AI were used to describe the prerequisite for successful technology diffusion and show challenges. In three selected cases,

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we show insights from two international and one regional case to highlight how knowledge spillovers are shaped by mal- and best practices and influence the role of AI as a means to achieve sustainable innovation.

5.4.3.1 Best practice in Silicon Valley The most commonly used practical examples often stem from Silicon Valley. Thirteen out of 19 interviewees highlighted different aspects concerning AI development in Silicon Valley. The interviewed experts cover technology development in the USA. The emerging digital technologies in connection with global multinational enterprises have influenced the AI community’s perspective on the development of digital entrepreneurial ecosystems. Implicitly and explicitly, Silicon Valley is seen as the “gold standard” for a digital entrepreneurial ecosystem that would be desirable to translate elements of the innovation framework into the Berlin ecosystem. First, the collaboration of universities and other research organizations with companies has led to an accelerated innovation circle for the diffusion of AI-driven services and products. Researchers from Silicon Valley-based universities and companies have published in major peer-reviewed journals and contributed to large AI conferences. Second, the potential for entrepreneurial activities has been evaluated as exceptional by the experts, with a large number of highly specialized companies working on AI. Moreover, the famous digital enterprises Google, Apple, and Facebook stemming from Silicon Valley contribute to regional innovativeness. Especially these companies have grown in a short time span, successfully commercializing digital solutions and investing large sums in research on AI. Third, the opportunities for venture finance are more favourably evaluated in Silicon Valley compared with Berlin. The experts acknowledge that there are more public and private funding opportunities. Furthermore, venture capital investments in Silicon Valley are considerably higher, as explained by a more open culture for funding risky and disruptive projects that exist in Silicon Valley. One explanation of the popularity of Silicon Valley as a role model for the Berlin entrepreneurial ecosystem is the coverage and role model function of Berlin as the “European Silicon Valley” (Neate, 2014) in the news. This is reflected by the hope of adapting elements of Silicon Valley as policy best practice to commercialize business models in interaction between research organizations and entrepreneurs in Berlin. An idea exists concerning how the optimal conditions of AI entrepreneurial ecosystems should be configured to support regional sustainable development. Nevertheless, the learning from the US example remains limited and despite the frequent reference it offers little information on how to accelerate the growth of the entrepreneurial ecosystem and enable sustainable innovation. Berlin-based expert #11 summarized sceptically that “if you compare [Berlin] with Silicon Valley, what happens here is a fraction of what happens there.” The experts expect only limited transferability of knowledge for the Berlin ecosystem since it is very different and

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therefore only valid to assume the ability to adapt for clearly defined elements like the introduction of organizational innovation by setting up innovation labs like those that exist in Silicon Valley. Clear instructions for businesses and policy implications for regional innovation processes to learn from the best practice can hardly be derived. Knowledge spillovers are not expected to have a major impact to benefit sustainable development.

5.4.3.2 Learning from the Chinese AI strategy While the USA has dominated the discussion of AI diffusion to date, the Chinese efforts to develop AI entrepreneurial ecosystems have attracted the attention of AI experts. In 2017, China published the New Generation Artificial Intelligence Development Plan, which set the goal of China being the global AI leader by 2030, supported by a collective research program and underlining funding of AI entrepreneurial activities (O’Meara, 2019). Furthermore, examples of large enterprises like Baidu and Alibaba have raised awareness as examples of successfully fast-growing digital entrepreneurship. This ambitious approach and the dynamic growth in China have influenced the AI discussion in Berlin. The interviewees presented China’s AI strategy as an influential example for the development of the Berlin entrepreneurial ecosystem. Chinese policymaking has included R&D subsidies for AI enterprises and support for the development of an AI entrepreneurial ecosystem, like in Beijing. Especially the role of the state and collective action driven by policy strategies have impressed the experts, since it seems distinct in comparison with Germany and the USA. Some of the interviewees have worked in China or cooperated with Chinese entrepreneurs to develop AI solutions. Nonetheless, the examples used to describe Chinese practices are influenced by the language barrier, which – in comparison with the US examples – often requires third-party translation. Therefore, secondary sources are often used to describe the experience implementing AI-focused innovation in China. China is often used as a negative example, which is expressed by a different innovation culture as well as a different innovation policy framework. To date, for the AI experts the development of AI in China is more puzzling than that of entrepreneurial ecosystems in the USA. American brands are still more famous than Chinese ones. Moreover, economic and societal aspects of AI development seem more distant compared with the USA. In some cases, experts evaluate conditions in China as unfavourable and examples of how not to do it. One example mentioned is the use of personal data and privacy rights for AI. Learning from these cases can hardly be derived. Interviewee #19 concluded “It [the learning] is missing or inadequate for anything to happen at all,” highlighting the lack of knowledge spillover from both positive and negative international practices in AI for the Berlin region.

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5.4.3.3 German regional projects for technology acceptance In our third example, the AI experts discussed regional projects for influencing technological acceptance including actors from societal groups. These projects have been set up in recent years to foster the introduction of digital technologies (see, e.g. Proeger & Runst, 2019) and its diffusion via entrepreneurial activities (Aly & Galal-Edeen, 2020), which has been perceived as difficult and slower in Germany in an international comparison. In order to foster the diffusion of AI, the interviewed experts stressed the relevance of acceptance by society and specifically by policy-makers. Practical examples of projects showing the benefits of digital technologies for sustainable development were central during the interviews. These projects entailed public funding for formulating suggestions for a sustainable innovation framework that fits the German innovation system and its innovation culture. Mostly collaborative communication platforms and dialogue events targeting different societal actors and key players have been organized, aiming to improve the acceptance and foster user interaction, contributing to the prospective technology development. The public awareness of AI among large parts of society who might not have detailed but rather rudimentary knowledge led to a focused discussion on the acceptance of AI in services and products. Driven by science fiction and philosophical discourses, public arguments about digital ethics as well as human–machine interaction are central. Scenarios with strong AI applications capable of undertaking complex tasks by themselves or singularity where AI is more capable than human intelligence often dominate the discussion. At present, these scenarios are not yet technically feasible and according to the interviewees they will not be realistic in the foreseeable future. Nonetheless, it is relevant to understand and take into account the risk of lacking acceptance. Although they agreed about the general approach of setting up acceptance projects, the experts criticized the existing approaches and expected only little impact from these projects on sustainable innovation. Specifically, the lack of evidence of positive impact, the complexity of the communicated topics and the sceptical perspectives among the public concerning AI regarding topics like automatization and labour rationalization form the basis for the experts’ arguments that these practices offer only limited insights for prospective projects.

5.5 Discussion Our results can be linked to the existing literature which emphasizes the role of uncertainty for the knowledge transfer between universities and entrepreneurial ecosystems (Ferreira & Carayannis, 2019; Kolympiris & Klein, 2017). Furthermore, we demonstrate that the issue of knowledge spillovers via best and mal practices can

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contribute to a better understanding of the “black box” of knowledge transfer (Ferreira & Carayannis, 2019, p. 353). Particularly for international knowledge transfer of sustainable innovation, the context of entrepreneurial ecosystems and the role of research organization need to be considered (Tiemann et al., 2018). Building upon these fields of research: how can our results be interpreted? We suggest that empirical evidence from behavioural research can help to place the results in perspective and provide suggestions on how to improve knowledge spillovers, apart from simplistic and unhelpful mal- and best practice examples. In our view, the extreme reliance on examples by institutions and persons trying to foster knowledge spillovers can be explained by the anchoring bias, which can be seen as a subconscious strategy for making decisions in situations with very little information or time to analyse a given information set. In these situations, humans rely on specific pieces of information available at the time, which might even be completely unrelated to the task at hand. Following the seminal study by Tversky and Kahneman (1974), anchoring has become one of the most thoroughly studied behavioural bias, whose “robust and pervasive influence” (Furnham & Boo, 2011, p. 39) has been emphasized by psychologists and economists alike. The basic example of its substantial effect on human decision-making comprises experimental subjects being asked to answer a simple numerical assessment question, which can hardly be answered correctly by participants. At the same time, a wheel of fortune is used to generate a random number. On average, the subjects’ assessments are consistently and significantly biased towards this – obviously – irrelevant value. This has been shown for numerous experimental situations and a large set of real-world domains of decision-making as diverse as economic forecasts, art auctions, real estate purchases or sports betting (see Meub & Proeger, 2015 for further examples from the literature). Furthermore, it has been shown that anchoring is stronger in situations of social decision-making, meaning that anchors are not generated randomly but rather by social processes. These “social anchors” provide additional legitimacy to the anchor and thus reinforce its biasing effects (Meub & Proeger, 2015). While anchoring can similarly be seen as a quasi-rational strategy for decisionmaking in situations of absolute ignorance and with no other information available, it can be seen as a detrimental bias in situations in which potentially useful sources of information are available. In these situations, the mere reliance on simplistic pieces of information or simple narratives to provide guidance in domains of technological and economic development is likely to prompt policy-makers to divert resources and fail to foster knowledge spillovers due to their reliance on false mental models, which serve as anchors. There are broad discussions on how to reduce individuals’ reliance on the anchoring bias. While the most general answer is that the anchoring bias is a basic feature of human decision-making that cannot be eliminated from decision-making, there are proven factors reducing its effects. Overall, all measures that provide better knowledge, reduce uncertainty, and lead to a stronger reliance on experts in the

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respective fields tend to reduce the effects of anchoring. Therefore, in order to reduce anchoring in the field of knowledge spillovers and AI policy, new ways of connecting policy-makers with knowledge producers and entrepreneurs are required to reduce the reliance on overly positive or negative examples that bear little relation to the actual realities of the broad technological developments associated with AI, particularly in the field of sustainability transition. Our main policy implication is therefore to increase the role of factual knowledge and shared experience between the relevant actors in the field to reduce their reliance on anchoring.

5.6 Conclusion This qualitative case study formulates insights from Berlin, which has also been previously documented as a prominent European example for the development of a successful entrepreneurial ecosystem. We conducted a case study on AI in the regional innovation system in Berlin with a focus on the sustainable entrepreneurial ecosystem, including research institutions, universities and large enterprises and start-ups. This article contributes to the discussion on knowledge spillover and its role in fostering sustainable development in entrepreneurial ecosystems, concentrating on knowledge spillover between various actors. Our results show that best and mal-examples play an important role for knowledge spillovers. Closed network structures favour practical examples as easy ways to discuss and foster spillovers. In the case of AI, the interviewees evaluated experience that they have gained themselves as being most valuable to communicate. This experience knowledge has a higher value than mal- or best practice examples without practical impact. The collaboration between research and business can be a useful way to create common learning experiences and broader the spectrum of technological solutions. We expect that new links in networks can help to foster knowledge spillover with more valid examples being specified for the existing challenges during the actual technology adoption. Factual knowledge in the case of international examples can be a helpful link to reduce uncertainty and adequately address the multidimensional characteristics of practical examples concerning sustainable development. Setting up applied research projects can be useful to provide a basis for factual knowledge that can be useful and applicable for regional solutions. The focus on overly positive or negative examples potentially based upon the anchoring bias could thus be potentially reduced. In particular, for technologies with GPT characteristics, examples are often rare and not easily transferable to the regional conditions. Careful investigation focusing on the transfer links and preparation might result in knowledge spillover with a better applicability for users and stakeholders. We expect that learning from

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international cases can improve the conditions for interaction between the different actors, ultimately resulting in cooperation and foster knowledge exchange. The regional technology communication strategy plays an important role for the knowledge flow in the entrepreneurial ecosystem and it is a link between the entrepreneurial ecosystem and sustainable development. An improved coordination of existing dialogue platforms with clear communication strategies can help to accelerate the use of existing communication channels. In the future, this can help to communicate practical examples that add extra value for the recipients’ knowledge and complement existing mal- and best practices. Our chapter has certain limitations that should be acknowledged. For instance, while the large number of AI-related experts and organizations can help to understand diffusion processes, more examples from peripheral regions could help to understand the role of sustainability-related topics in knowledge-based economies. Large-scale data sets and technology-specific case studies from other regions – including with a focus on different digital technologies – can help to gain additional theoretical insights. Furthermore, while the role of knowledge spillover is acknowledged as a central driver for innovation and regional competitiveness, further insights from quantitative and qualitative studies can help to better understand undirected knowledge flows for sustainable development. The increased relevance of knowledge-intensive ecosystems and the accelerated entrepreneurial dynamics are promising for innovation scholars analysing sustainability transitions. Acknowledgements: Financial support for conducting the interviews from the Projekt Zukunft funded by the Berlin Senate Department for Economics, Energy and Public Enterprises and the Federal Ministry of Education and Research with funding code 16IFI110 is gratefully acknowledged.

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Part II: U–I cooperation and sustainability

Jeandri Robertson, Leyland Pitt, and Ian P. McCarthy

Chapter 6 Building sustainable entrepreneurial ecosystems: A mediated model of university–industry collaboration, knowledge creation, and the entrepreneurial environment 6.1 Introduction University–industry collaborations (UICs) are regarded as a valuable source of knowledge creation (Cunningham & Link, 2015; Robertson, McCarthy & Pitt, 2019). Interest in UICs stems from the belief that these collaborative efforts may lead to innovative outcomes (Ambos, Mäkelä, Birkinshaw & d’Este, 2008; Ankrah, Burgess, Grimshaw & Shaw, 2013; Bruneel, d’Este & Salter, 2010) and play a key role in modern entrepreneurial activities (Theodoraki, Messeghem & Rice, 2018). As educators of the future workforce and an important vector for technology transfer (Arvanitis, Kubli & Woerter, 2008), universities facilitate economic, societal, and technological impact by providing entrepreneurial actors and industry with access to their knowledge, research, and technologies (Audretsch, Cunningham, Kuratko, Lehmann & Menter, 2019). In the face of intensified environmental pressures, such as resource scarcity and climate change, society is also increasingly looking to universities and their industry collaborators to assume the additional role of securing the economic and environmental sustainability of future generations (Etzkowitz & Leydesdorff, 2000; Nave & Franco, 2019; Wagner, Schaltegger, Hansen & Fichter, 2019). University-related sustainability efforts promote interaction with external stakeholders (e.g. firm or industry investment in innovation) and internal contributions (e.g. knowledge transfer and spillover based on research and expertise) to contribute to an ever-expanding ecosystem of actors addressing economic and environmental issues. In the entrepreneurial context, sustainability encompasses entrepreneurial activities that embrace the economic, ecological, and social dimensions of sustainability (Belz & Binder, 2017; Bischoff, 2019). Extant research has explored the role of knowledge creation on entrepreneurial firm performance (Li, Huang & Tsai, 2009; Steyaert & Katz, 2004; Zahra, 2015), yet, little is known about the relationship between UICs and knowledge creation for ecologically sustainable entrepreneurial environments, with increased calls for a more nuanced understanding of the connections (Carlsson, Acs, Audretsch & Braunerhjelm, 2009; Demirel, Li, Rentocchini & Tamvada, 2019; Muñoz & Cohen, 2018; Theodoraki et al., 2018).

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As a metaphorical vehicle to explore this notion of sustainable entrepreneurial activity through geographically bound institutional networks, the concept of sustainable entrepreneurial ecosystems has received intensified interest among researchers and policymakers in recent years (Bischoff, 2019; Cohen, 2006; Pankov, Velamuri & Schneckenberg, 2019; Volkmann, Fichter, Klofsten & Audretsch, 2019). Sustainable entrepreneurial ecosystems focus on addressing societal and ecological challenges, while growing and creating economically profitable ventures (Volkmann et al., 2019). Bischoff (2019, p. 2) defines sustainable entrepreneurial ecosystems as “the interconnected set of entrepreneurial stakeholders in a regional entrepreneurial environment that directly focus on fostering engagement in sustainable entrepreneurship in order to contribute to the transition to a more sustainable regional environment.” Societal transitions towards sustainability are considered to be fundamentally knowledge-driven (Wagner et al., 2019), implying a heightened role for UICs in sustainable regional development (Sedlacek, 2013). As key actors in this ecosystem, UICs are thus central in addressing societal and ecological challenges, while growing and creating economically profitable ventures in their respective regions. The literature assessing the role of UICs in a sustainable entrepreneurial context is limited, therefore this chapter seeks to contribute to the literature by specifically focusing on sustainable entrepreneurial ecosystems, using the knowledge creation theory as a theoretical lens (Nonaka, 1994; Nonaka & Takeuchi, 1995; Nonaka, Von Krogh & Voelpel, 2006). Two objectives guide this enquiry. First, we conceptualize the relationship between UICs and the entrepreneurial environment within sustainable entrepreneurial ecosystems, employing knowledge creation as a mediating factor to hypothesize the relationships. Second, the impact of these constructs on sustainable entrepreneurial ecosystems is quantitatively assessed by comparatively analysing the conceptualized relationships across diverse economic markets, and as such, across different ecosystems. The rest of the chapter is structured as follows. It commences with a review of the literature on the sustainable entrepreneurial ecosystems concept so as to track the conceptual development of the field. The role of UICs in sustainable entrepreneurial ecosystems is then discussed to show its impact on the continued growth of the field. We then provide a conceptualization of the relationship between these constructs by proposing hypotheses, using the theory of knowledge creation as the underpinning theoretical framework. Secondary data is collected and analysed to comparatively assess these relationships across different market ecosystems. The results are discussed, implications are presented, and concluding remarks are presented, including noted limitations and areas for future research.

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6.2 Literature review 6.2.1 The sustainable entrepreneurial ecosystem concept: A review of the literature Sustainable entrepreneurial ecosystems are a concept for understanding how to foster sustainable entrepreneurship (Bischoff, 2019). Sustainable entrepreneurship explores what is to be sustained (e.g. nature, ecosystems, and communities), as well as what is to be developed (e.g. individuals, the economy, and society; Shepherd & Patzelt, 2011). For its part, an entrepreneurial ecosystem approaches new value creation through a regional development lens (Acs, Stam, Audretsch & O’Connor, 2017), with geographic dimensions being of particular importance when examining the determinants of entrepreneurial activity in sustainable entrepreneurial ecosystems (Bischoff, 2019). The ecosystem mostly comprises a diverse and interconnected set of actors that interact in a non-hierarchical manner to co-create mutually beneficial outcomes in the form of “value.” In other works, an ecosystem fosters sustainable regional economic growth and addresses environmentally sustainable challenges, from a sustainable entrepreneurial ecosystem perspective (Pankov et al., 2019). The entrepreneurial ecosystem concept highlights that everything in the system is interconnected (Spigel, 2017), and that “the actions of any given actor directly or indirectly influence other actors in the system” (O’Shea, Farny & Hakala, 2019, p. 3). This implies a systemic perspective, as no single actor should be considered in isolation. Entrepreneurial ecosystem actors can represent innovative organizations such as research institutes, science and technology parks, universities, and industry partners (see McCarthy, Silvestre, Von Nordenflycht & Breznitz, 2018). Linked to these innovative organizations are entrepreneurial organizations (e.g. start-ups, venture capitalists, and public sector agencies), and innovative and entrepreneurial processes (e.g. new business model platforms). Cohen (2006, pp. 2–3) describes the entrepreneurial ecosystem as “. . . a diverse set of interdependent actors within a geographic region that influence the formation and eventual trajectory of the entire group of actors and potentially the economy as a whole [which] evolve through a set of interdependent components which interact to generate new venture creation over time.” Another premise of the entrepreneurial ecosystem concept is that entrepreneurship thrives when multiple stakeholders synergistically collaborate (Van De Ven, 1993). Multi-stakeholder collaboration furthermore necessitates formal and informal information exchanges to jointly enable the coordination of activities among all involved (Simatupang, Schwab & Lantu, 2015). In turn, this implies that these stakeholders will create local conditions that foster entrepreneurial activities, which simultaneously address geographic societal challenges as well as foster economic development (Belz & Binder, 2017; Pankov et al., 2019). The diversity and collective ability of each member in the ecosystem to learn, adapt, and innovate together, are key determinants of

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its long-term success (Autio & Thomas, 2020; Robertson, Pitt & Ferreira, 2020; Roundy, Bradshaw & Brockman, 2018). Assessing entrepreneurship more holistically, as part of an entrepreneurial ecosystem, has additionally led to a deeper understanding of the regional traits, institutions, knowledge-based assets, and context-related environmental factors that facilitate or hinder sustainable entrepreneurship as a whole (Belz & Binder, 2017). These ecosystems encompass clusters including universities, firms, and government that collaboratively create structure for the entrepreneur to exploit potential opportunities (Robertson et al., 2019). While entrepreneurial ecosystems focus on the creation and growth of new businesses in a particular geographical area to heighten the competitiveness of the region or grow its economic outlook (Isenberg, 2010; Roundy, Brockman & Bradshaw, 2017), sustainable entrepreneurial ecosystems operate under different competitive conditions (Pankov et al., 2019). As their strategic focus is on pursuing a sustainable business rather than being profit-oriented (Cohen, 2006), contextual factors have a disparate effect on actors in sustainable entrepreneurial ecosystems. Research asserts that sustainable entrepreneurs face higher information asymmetries, more regulatory complexity, and increased difficulties in achieving market breakthroughs (Cohen & Winn, 2007; Dean & McMullen, 2007; Schaltegger & Wagner, 2011). Sustainable entrepreneurs pursue venturing opportunities with due consideration of the contextual geographic conditions, whilst still accounting for the economic, social, and ecological objectives of the region within which the opportunity exists (Klofsten & Lundmark, 2016). Sustainable entrepreneurial ecosystems prioritize both sustainability and profitability as core elements of its business model (Bischoff, 2019; Volkmann et al., 2019). The objective is to not only move individual organizations toward a more sustainable business model, but economic and societal sectors as a whole. As entrepreneurial activity is increasingly adopting a sustainability focus in response to dire environmental challenges (Lüdeke-Freund, 2020), the notion of sustainable entrepreneurial ecosystems has gained prominence among scholars and entrepreneurs who emphasize that there are forces beyond the boundaries of any single organization that can contribute to the overall welfare of society as a whole (Stam & Spigel, 2016). Despite the increase in sustainable entrepreneurial activities, Bischoff (2019) asserts that the sustainable entrepreneurial ecosystems domain is still an understudied area, which holds much promise for further exploration. Table 6.1 provides a concise overview of the current lay of the land in terms of the literature, including the purpose of each study and recommendations for future research. An assessment of Table 6.1 highlights four particular areas for future research. First, a number of authors underscore the importance and impact of contextual factors on the emergence and growth of sustainable entrepreneurial ecosystems (Cohen, 2006; Pankov et al., 2019). Second, an explicit need for comparative research across different ecosystems and regions are articulated (Bischoff, 2019; Nave & Franco, 2019). Third, to

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Table 6.1: Previous research on sustainable entrepreneurial ecosystems. Author/s

Purpose of study

Recommendations for future research

Grigore and Dragan ()

An assessment of the impact of political entrepreneurs on the evolution and sustainability of entrepreneurial ecosystems.

A replication of the study across different regional, national and entrepreneurial ecosystem contexts from a sustainable entrepreneurial ecosystem perspective.

Bischoff ()

An assessment of the importance of regional entrepreneurial culture and stakeholder support and collaboration to create sustainable entrepreneurial ecosystems.

Use the developed questionnaireas a tool to measure and compare sustainable entrepreneurial ecosystems in other regions, countries and contexts.

Nave and Franco () A study of university-firm cooperation Future case studies would offer a from a sustainable entrepreneurship comparison to identify perspective. commonalities and differences across different geographicalregions, countries, cultures andsocial and economic contexts. O’Shea et al. ()

To provide a framework to examine how entrepreneurial opportunities co-evolve within a sustainable entrepreneurship ecosystem.

An examination to determine which social capital elements among ecosystem actors would be crucial for the emergence of a sustainable entrepreneurial ecosystem.

Pankov et al. ()

An investigation of the interrelation between contextual factors and sustainable entrepreneurial activities within the sharing economy, by means of conducting in-depth interviews withfounders and senior managers of sharing ventures.

Future research could measure the relation between specific contextual factors and the growth of sustainable entrepreneurial ecosystems, e.g. interregional comparisons to explore the particularities of different regions.

Sunny and Shu ()

An exploration of the relationship between institutional constellations, i.e. regulatory policies, innovation climates, and social norms, that affect sustainable venture creation in geographic clusters.

An examination of the extent to which location and access to resources provide legitimacy from different actors and stakeholders, thereby becoming more of a recognized category.

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Table 6.1 (continued) Author/s

Purpose of study

Recommendations for future research

Volkmann et al. ()

Assess how ecosystems can specifically promote sustainable entrepreneurship and contribute to the sustainable development goals as set by the United Nations.

Advance our understanding by using different research methods, in particular quantitative studies to compare ecosystems from different regions.

Wagner et al. ()

An analysis of how university-related support programmes for entrepreneurship contribute to the sustainable economic and socioecological development of a region.

Longitudinal studies to assess impact and long-term effects.

Bischoff and Volkmann ()

A systematic literature review of the fields of sustainable entrepreneurship, stakeholder management and entrepreneurial ecosystems.

Comparative case studies of different regional sustainable entrepreneurial ecosystems are required to examine potential influencing factors which could explain possible differences.

Muñoz and Cohen ()

Systematic literature review of research on sustainable entrepreneurship.

More research is needed to examine the territorial embeddedness of sustainable entrepreneurs and their ventures, as well as more advances in empirical research to better understand the sustainable entrepreneurial phenomenon.

Neumeyer and Santos ()

This study assesses the differences in social connectivity between sustainable and conventional business ventures in an entrepreneurial ecosystem.

Future research should examine how success is defined in sustainable entrepreneurial ecosystems, and further probe measurement metrics for sustainable entrepreneurial ecosystems.

Theodoraki et al. () An exploratory research study drawing on face-to-face interviews with key members of sustainable university-based entrepreneurial ecosystems through the lens of social capital theory.

Future research should conduct comparative studies across different regions, taking into consideration the specific characteristics and entrepreneurial culture of each region.

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Table 6.1 (continued) Author/s

Purpose of study

Recommendations for future research

Banks et al. ()

This study explores the relationship between incubator programs and the creation of sustainable ventures.

More research is required to assess how sustainable incubators ensure an inflow of tenants, how they organize their activities and whether the incubator environment affects tenant recruitment.

Simatupang et al. ()

An investigation into the creation and development process of sustainable entrepreneurial ecosystems to support innovation and new business creation.

More comparative case studies focusing not only on the actions of multiple firms within the same ecosystem, but also comparative studies across and between entire ecosystems.

Cohen ()

An exploration of how components of the formal and informal network, physical infrastructure and culture within a community, contribute to a sustainable entrepreneurial ecosystem.

Research regarding the interdependency of components in a sustainable entrepreneurial ecosystem, to assess the impact of component weaknesses on the entire system.

complement the existing conceptual investigations, more quantitative empirical studies are called for (Muñoz & Cohen, 2018; Volkmann et al., 2019). Finally, the impact and role of collaborative and inter-institutional actors on the growth and success of sustainable entrepreneurial ecosystems, seems to provide fertile ground for future exploration (Bank et al., 2017; Neumeyer & Santos, 2018; Sunny & Shu, 2019). Addressing all four of these research gaps is beyond the scope of this chapter. The study does, however, set out to comparatively assess the contextual impact of collaborative inter-institutional actors, like UICs, on sustainable entrepreneurial ecosystems across different market economies and regions. The next section discusses the pertinence of UICs as institutional drivers of sustainable entrepreneurial ecosystems.

6.2.2 UICs and sustainable entrepreneurial ecosystems A renewed push towards regional development strategies that encourage entrepreneurial (Brown & Mason, 2017; Isenberg, 2010; Malecki, 2018; Roundy & Fayard, 2019) and innovative outcomes (Bramwell, Hepburn & Wolfe, 2012; Fernandes, Ferreira, Veiga & Peris-Ortiz, 2019; Grillitsch, Asheim & Trippl, 2018; Walrave, Talmar, Podoynitsyna, Romme & Verbong, 2018), has paved the way for a heightened emphasis on UICs and

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triple helix initiatives (Etzkowitz, 2003; Etzkowitz & Leydesdorff, 2000; Guerrero & Urbano, 2017). These initiatives are seen as powerful tools to establish stronger links between the private and public sectors and facilitate sustainable economic growth (Cunningham & Link, 2015). Extant research on UICs has identified motives and challenges (Siegel, Waldman & Link, 2003; Yusuf, 2008), examined institutional barriers (Bruneel et al., 2010) and different modes of governance (Bodas Freitas, Geuna & Rossi, 2013), focused on existing collaboration heuristics between institutional collaborators (Bjerregaard, 2010; Bloedon & Stokes, 1994; Davenport, Grimes & Davies, 1999), and have suggested alternative ways in which to approach the collaborations (Rajalo & Vadi, 2017). In recent years the role of UICs as mechanisms to drive change through sustainable development goals, have also received attention (Mauri-Castello, Alonso-Gonzalez & Peris-Ortiz, 2019; Volkmann et al., 2019; Wagner et al., 2019). The rationale is that entrepreneurial and innovative outcomes in UICs may lead to commercial success and regional competitive advantage, while also promoting sustainable practices (Nave & Franco, 2019). In an entrepreneurial ecosystem, actors are most successful when they have the needed access to human, financial, and professional resources, and can operate in an institutional environment where norms and policies encourage and safeguard entrepreneurs (Simatupang et al., 2015). For organizations seeking specialized research expertise, universities have established themselves as interesting partners (Al-Tabbaa & Ankrah, 2016; Ambos et al., 2008; Schofield, 2013), as universities may have the technological ability to develop and address the environmental challenges of an industry partner or collaborator. Collaborative university–industry relationships that promote and implement sustainable entrepreneurial practices mostly revolve around shared research projects and objectives (Nave & Franco, 2019). Through the knowledge created in the form of R&D, the firm finds a solution to its environmental concern, while the university can patent the technology for subsequent commercialization. The participation of both entities is essential to reach the desired end goal, as the firm can test the technology during its development with a view to see if it addresses and satisfies its sustainability practices, while the university practically applies its knowledge to respond to a specific need in the business sector (Nave & Franco, 2019). As such, the UIC serves public interest through research and education (Bruneel et al., 2010), whilst also exploiting the value of the knowledge base (Plewa et al., 2013). It is helpful to employ both an innovation systems and entrepreneurial ecosystems approach in examining and explaining the role of UIC and its impact on the sustainable development of a region (Wagner et al., 2019). Both concepts share a focus on the external business environment and the forces beyond one’s organizational boundaries, but within those of a region, that may contribute to the overall competitiveness and impact of a firm (Stam & Spigel, 2016). The role that knowledge plays in the two respective concepts is the most nuanced conceptual difference. Innovation systems emphasize the importance of university to industry knowledge

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spillovers, while entrepreneurial ecosystems stress the crucial role of entrepreneurial knowledge in the innovation process (Stam & Spigel, 2016; Wagner et al., 2019). In an entrepreneurial ecosystem, university knowledge spillover via an innovation system function improves the entrepreneurial ecosystem for sustainable entrepreneurial efforts at different intervention points (Wagner et al., 2019). Different UICs operate under specific regional conditions, which enable and determine alternative pathways and roles based on contextual conditions. Through these idiosyncratic knowledge spillover modes, sustainable entrepreneurial ecosystems are positively affected by UICs (Wagner et al., 2019).

6.2.3 Building sustainable entrepreneurial ecosystems through knowledge creation in UICs Knowledge creation encompasses the “ability to develop new and useful ideas and solutions” (Andreeva & Kianto, 2011, p. 1010) relating to organizational activities, new products or services, technological processes and managerial procedures (Nonaka & Von Krogh, 2009). Nonaka and Takeuchi (1995) describe the complex knowledge creation process as a spiral that flows between tacit and explicit knowledge, involving interaction between multiple actors in an ecosystem (Faccin, Balestrin, Volkmer Martins & Bitencourt, 2019). By optimizing different types of tacit or explicit knowledge at differing knowledge levels (Tzokas & Saren, 2004), knowledge creation competencies promote new thinking and capabilities within networked environments (Nonaka et al., 2006), as well as in ecosystems (Clarysse, Wright, Bruneel & Mahajan, 2014). Regarded as a key construct of knowledge-based dynamic capabilities with close ties to innovative performance outcomes (Faccin et al., 2019), knowledge creation has been linked to the competitive advantage of firms (Gupta, Malhotra, Czinkota & Foroudi, 2016). Theoretically, the knowledge creation cycle (Nonaka, 1994) posits that knowledge is transferred in four steps: first, by creating shared experiences (socialization), after which the knowledge is second externalized (externalization), then third recombined (combination), and finally, internalized (internalization) (De Wit-de Vries, Dolfsma, Van Der Windt & Gerkema, 2019). Research shows that all four stages affect knowledge transfer within collaborative academic engagements (Johnson & Johnston, 2004). Nonaka et al. (2006) propose that by interacting and sharing tacit and explicit knowledge, individuals enhance their own capacity to define a problem or situation, and enable themselves to apply their own knowledge to act and specifically solve a problem. In the context of UICs all members contribute new knowledge to collaboratively find solutions. Zahra (2015, p. 729) states that knowledge created in entrepreneurial environments in particular, are heterogeneous “reflecting the content and complexity of the knowledge itself,” while Li et al. (2009) propose that knowledge creation is a key mechanism

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through which to develop an entrepreneurial orientation to accomplish favourable performance. Little is however known about the impact of knowledge creation, as an outcome of UICs, in a sustainable entrepreneurial ecosystem context. As a key objective of this study, the relationship between UICs, knowledge creation and the entrepreneurial environment within sustainable entrepreneurial ecosystems is conceptualized next.

6.2.3.1 UICs and the entrepreneurial environment in sustainable entrepreneurial ecosystems Extant research indicates that university–industry research collaborations consisting of well-developed regional clusters of cooperation, lead to better innovation outcomes and more successful entrepreneurial endeavours (Nave & Franco, 2019). Furthermore, Wagner et al. (2019) propose that UICs that approach sustainable entrepreneurial development through an entrepreneurial ecosystem perspective, positively impact the sustainability of a region. In light of the above, the following is hypothesized: H1a: UICs are positively related to sustainable entrepreneurial ecosystems. H1b: The relationship between UIC and sustainable entrepreneurial ecosystems is mediated by the entrepreneurial environment.

6.2.3.2 Knowledge creation through UICs and the entrepreneurial environment in sustainable entrepreneurial ecosystems Knowledge created in UICs is inherently cooperative and collaborative, with the intent of creating and exploiting a shared knowledge base (Clarysse et al., 2014) to address a common challenge (Zahra, 2015). Research indicates that knowledge creation fosters entrepreneurial activity, particularly in university–industry R&D initiatives (Cunningham & Link, 2015). In sustainable entrepreneurial ecosystems, a favourable entrepreneurial environment is however posited to facilitate the successful transfer of knowledge, based on the four-step knowledge creation cycle as proposed by Nonaka (1994). As such, we propose the following: H2a: Knowledge creation mediates the relationship between UICs and the entrepreneurial environment. H2b: The entrepreneurial environment mediates the relationship between knowledge creation and sustainable entrepreneurial ecosystems.

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6.2.3.3 The entrepreneurial environment and sustainable entrepreneurial ecosystems Research by Cohen (2006) and Pankov et al. (2019) indicates that contextually relevant components of the formal and informal network, physical infrastructure, and culture within a community contribute to a sustainable entrepreneurial ecosystem, therefore, we also hypothesize: H3: The entrepreneurial environment is positively related to sustainable entrepreneurial ecosystems.

These hypotheses are represented in Figure 6.1.

Knowledge Creation H2b Entrepreneurial Environment

H2a

H3

Sustainable Entrepreneurial Ecosystem

H1b UniversityIndustry Collaborations

H1a

Figure 6.1: Research model and hypotheses.

It would be prudent to acknowledge the heterogeneity of knowledge, as well as consider the variety of contextually idiosyncratic organizational capabilities and formats in which knowledge is created across different UICs. Some UICs may be more active and prolific in their knowledge creation abilities, reflecting their location, composition, and history, which would consequently have an impact on their entrepreneurial activities and the environment in which they exist (Theodoraki et al., 2018; Zahra, 2015). We posit that this would also be the case across different ecosystems in different contexts. Consequently, the relationships can be tested across different sustainable entrepreneurial ecosystems, as represented by developed, transition, and developing market economies. Therefore, to address the second objective of this study, we also seek to comparatively assess the impact of the various constructs on the sustainable entrepreneurial ecosystems of market economies at different stages of economic development.

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6.3 Methodology The research hypotheses, model, and articulated research question are tested using a descriptive research design, analysing quantitative secondary data. Secondary data entails freely available, previously collected data that a researcher collected for another purpose (Kothari, 2004). Secondary data carries the benefit of requiring less resources to obtain the data (Malhotra & Dash, 2016). A possible limitation to employing secondary data is however its usefulness to address a particular research problem which the data was not originally collected for or intended to address. An evaluation of the reliability of the data is thus necessary (Malhotra, 2010).

6.3.1 Data and sample The publicly available Global Innovation Index (GII) 2020 dataset is used as the data source for this research. The GII is a collaborative research effort between Cornell University, INSEAD and the World Intellectual Property Organization. The GII 2020 offers a near complete examination of countries across the globe, representing a significant proportion of the global population. The dataset spans 131 countries, representing 93.5% of the world’s population and accounting for 97.4% of the world’s GDP (Dutta et al., 2020). The GII also employs a broad operationalization of innovation, allowing for the data to reflect improvements made to outcomes (Dutta et al., 2020). To evaluate the usefulness of the secondary data, the reliability of the data, in particular the recency of the data, the method of collection and the possible presence of bias was examined (Kothari, 2004). The GII 2020 dataset is the most recent global innovation dataset with a record of dependable publication. The dataset is furthermore also subjected to a number of consistency and reliability assessments that include an examination of conceptual consistency, data checks, statistical coherence and a qualitative review (Dutta et al., 2020). As such, “the published GII 2020 ranks are reliable and, for most economies, the simulated 90% confidence intervals are narrow enough for meaningful inferences to be drawn” (Dutta et al., 2020, p. 379).

6.3.2 Measures The GII provides innovation input and output sub-indices. The innovation input sub-index comprises areas within national economies that enable innovation activities in a country. The innovation output sub-index encompasses information about outputs as a result of innovative activities within economies. Given the focus of the research model on UICs and knowledge creation as building blocks for sustainable entrepreneurial ecosystems, the components of the GII dataset relating to these

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constructs specifically were used as data. Operationally, knowledge creation is a result of both inventive and innovative activities, which includes patent applications filed at national or regional level, patent cooperation treaty applications, as well as scientific and technical journal publications. UICs refer to the extent to which institutional collaborations for the purpose of research and development take place, as well as the state of regional cluster developments with the view of jointly developing specialized services and solutions in a particular field. The entrepreneurial environment reflects elements that refer to the ease of starting a business as well as the ease of resolving insolvency in a particular country, while sustainable entrepreneurial ecosystems encompasses elements that relate to the GDP per unit of energy use, the country’s environmental performance based on the 2020 Environmental Performance Index, as well as the number of ISO 14001 certificates issued. All data for these constructs consist of composite variables that range from 0 to 100.

6.3.3 Data analysis The data was analysed using a variance-based structural equation modelling (SEM) technique, employing a partial least squares (PLS) approach (Ringle, Wende & Becker, 2015). SmartPLS was selected as the most appropriate software for this analysis as it does not require a large sample size (Hair, Risher, Sarstedt & Ringle, 2019), is especially suitable in offering flexible interaction between theory and data when working with secondary data (Hair et al., 2019), and allows the research focus to move from strictly confirmatory to predictive and causal-predictive modelling (Hair et al., 2019). PLS data analysis requires the assessment of the measurement and structural models (Chin, 1998; Hair, Ringle & Sarstedt, 2011). First, the psychometric properties of the measurement model were analysed and tested for common method bias, followed by the evaluation of the structural model. To test relationships by economic stage of development, the dataset was split according to the 2019 United Nations published country classification of economic development (United Nations Department of Economic and Social Affairs, 2019). Appendix 6.1 provides an overview of the different countries, grouped as either a developed, transition, or developing economy. The results are reported next.

6.4 Results Descriptive statistics for UIC, knowledge creation, the entrepreneurial environment, and sustainable entrepreneurial ecosystems appear in Table 6.2. All indicator items should load significantly on their latent construct to confirm the convergent validity of the measurement model (Anderson & Gerbing, 1988). The secondary data from

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the GII 2020 dataset only provides composite variables for analysis. This is often expected from industry reports, where measures are not necessarily created and refined over time for confirmatory analyses (Sarstedt & Mooi, 2019). The results section uses SmartPLS to test the hypotheses and research model of this study.

6.4.1 Assessment of the measurement model The GII 2020 report confirms reliable aggregates and provides Cronbach’s alpha values that are well above the 0.7 threshold for all aggregate sub-index scores (Dutta et al., 2020). The use of the dataset does not allow an opportunity to revise or refine the measurement model to achieve fit, and as such the variance inflation factor (VIF) was assessed to evaluate collinearity (Hair et al., 2019) and test for common method bias (Kock, 2015). Ideally, the VIF values should be close to 3.3 and less or equal to 5 (Becker, Ringle, Sarstedt & Völckner, 2015). Results confirmed that all VIF values were below the 3.3 threshold and are in other words well within the accepted range. Next, the discriminant validity of the measurement model was assessed by examining the heterotrait-monotrait (HTMT) matrix of correlations (Henseler, Ringle & Sarstedt, 2015). As a guideline, Gold, Malhotra and Segars (2001) propose that HTMT values of 1 indicate a lack of discriminant validity, while values χ

Appropriate estimator

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***p < 0.001.

management of intellectual property could generate disagreements in the CUE between economic interests that stop publication and the interest of publishing as soon as possible (Bruneel et al., 2010; Hall, Link, & Scott, 2003; Kneller et al., 2014), and also licensing terms and overreaching restrictive IP protection (Mason & Brown, 2014). The results on the initial hypothesis H3 show that through the percentage of high-tech employment in the countries (EMPTECS), there is a weak significance, but it could be considered that (to a certain extent) there is a relationship between the percentage of high-tech employment and UIC. Therefore, it could be said that the more technology and knowledge intensive the sectoral structure of the country is, the higher the degree of UIC will be. This finding could be supported by what is stated in the literature. Empirical studies have found that entrepreneurial ecosystems generate economic density, therefore specialized employment is required (Kenney & Patton, 2009; Stam & Spigel, 2016), especially in high technology (Engel, 2015; Molas-Gallart & Castro-Martínez, 2007; Molas-Gallart et al., 2002), and in turn specialized talent serves as a pole of attraction and provides absorption capacity to a company (Abreu et al., 2008; Lewandowska, 2015). Regarding H4, all of our models show an important and high significance. Therefore, there is an important and robust impact on the complementarity with both modes of cooperation: companies cooperating with the government and companies cooperating with private entities. This finding is in line with empirical studies that show the benefits of open cooperation, cooperation networks, a culture of cooperation and an entrepreneurial culture. The advantages of complementarity are recognized (Belderbos et al., 2004, 2004; Tether, 2002) – they generate knowledge absorption capacity and break the paradigm of open innovation (Cohen & Levinthal, 1990; Perkmann

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et al., 2011). Therefore, the opening of various actors in the ecosystem can be favourable and generates an entrepreneurial culture (Spigel, 2017). Finally, the time dummies that we used show that there is a significant difference between countries and periods in the 2012 period, which could be due to the impact of the economic crisis that triggered more fragile economies (OECD, 2012).

7.5 Conclusions and recommendations This chapter aims to explain which macro factors influence UIC and how this relationship might be key for obtaining a sustainable economic development. We have analysed the case of the EU countries, using data from the Community Innovation Survey and we have applied a panel data econometric analysis. Our findings show that there is a significant relationship between the resources for innovation and university–industry cooperation. Sufficient evidence was found to establish a significant relationship between the intensity of R&D expenditure (regardless of the sector such expenditure comes from) and UIC, as expected. Those countries that invest more in resources for R&D have higher levels of university and industry cooperation. In order of relevance, the sectors where UIC shows more intensity are governments, tertiary education, and businesses. This relationship is beneficial from the point of view that it links the capacities of the industry and the results of research, providing inputs and capital to innovative activity to generate competitive and sustainable advantage to the entrepreneurial ecosystem. We found that the quality of the university system is not significant when explaining university–industry cooperation. In this respect, we can conclude that the level of quality of the university system, using the most cited scientific publications as a proxy, has no relation to the level of cooperation in these countries. This could be due to the diverse interests of the industry and the university at the time of publication, as well as the time that it takes for the publications to become effective. It is also necessary to strengthen the factor of maturity, experience, and trust on entrepreneurial ecosystems. There is a significant relationship between the sector structure of the country and university–industry cooperation, as expected. A weak but sufficient relationship can be established to conclude that those countries whose high technology sector is strong and competitive (with intensive knowledge, a high percentage of employment in high technology and services) have a greater degree of university–industry cooperation. This finding confirms the importance of UIC in the creation of density and social capital as an aspect that impact entrepreneurial ecosystem, including the talent specialized in quality and quantity of employment in high-tech companies, the presence of research functions, and the development of high-tech industries.

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Finally, there is a strong relationship between the structure of the innovation system and university–company collaboration. All of the proposed models showed a strong relationship when innovative companies cooperate with consultants, commercial laboratories or private R&D institutes, and even more significantly when they cooperate with governmental or public research institutions. Therefore, given the nature of cooperation and the different types of innovation, we can conclude that those countries whose structure of their innovation system is efficient and diverse to respond to the requirements of innovative companies can offer greater possibilities to promote a culture of cooperation and complementarity of their actors. This culture of cooperation allows actors to be facilitators of continuous learning and innovation processes, which opens a range of ways and mechanisms of cooperation and creation of interrelationships and networks that help to achieve sustainable development.

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Chapter 8 University business incubators as drivers of sustainability: The perspective of critical success factors 8.1 Introduction In the last two decades, universities have been asked to contribute directly to local, regional and national economic development, by taking on a series of activities of the so-called third mission, such as business incubation, creation of university spin-offs, and entrepreneurship courses (Hassan, forthcoming). University-based business incubators can play a key role as the place where academic, business, and public forces integrate. In this chapter, we investigate how a university business incubator, operating as a network, acts as an important link in the entrepreneurial ecosystem of a higher education institution, to promote regional changes towards sustainability. The study of the critical success factors of university enterprise incubators is a plural intellectual movement that proposes multiple interpretations. Research in developing countries provides perspectives that can be useful to the entire field, as they contextualize practices usually identified with the logic of the functioning of the modern-colonial world system. These practices can be destabilized, in their hegemonic forms of meaning, to assume other characteristics that favour their understanding among the cracks of the hegemonic narratives on the entrepreneurship phenomenon across the world (Ramirez-Pasillas, Brundin & Markowska, 2017). Thus, objectively, we sought to identify the critical success factors of the “Enterprise Incubation Network” at the Espírito Santo Federal Institute (Ifes), in Brazil. Every science and technology institution in the country, such as Ifes, needs a technological innovation center (TIC) to support and manage its innovation policy, in order to protect the research carried out internally, besides leading all the technology transfer process between academia and the market (Act no. 10,973/2004). In addition to the typical tasks of a TIC, such as intellectual property management and technology transfer, the operation of an innovation centre also covers the administration of innovation habitats, such as a business incubator. These activities follow the model of spatial propellers (triple, quadruple, and quintuple), central to the entrepreneurial ecosystem, since academic incubators play an important role in promoting sustainability.

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Etzkowitz, Mello, and Almeida (2005) highlight the relevance of a university incubator for the Triple Helix, especially for what they call an entrepreneurial university. A university business incubator is a special type of business incubator. Our research focused on the general definition adopted by Barbero, Casillas, Ramos, and Guitar (2012): the university business incubator is a university-based institution that supports young people to start their businesses, by providing tangible and intangible services. From the Triple Helix perspective (Etzkowitz, 2008), it is an entity that links institutions outside the classroom, motivating innovative projects to seek partnerships in order to act as part of the Triple Helix, for the development of the region where it operates. For this author, teaching institutions qualify students, and incubators have the mission of training entrepreneurs to develop innovation in their enterprises. From the moment that a mechanism is created to manage and disseminate technological innovation through actors who dialogue with each other, innovation networks are an efficient and effective adaptable model of organization for the production of information and knowledge. Thus, they favour learning (learning by interaction) among social actors, fostering the emergence of innovations and providing more synergistic interactions (Kuppers & Pyka, 2002). Despite contributions from previous studies (Pankov, Velamuri & Schneckenberg, forthcoming; Volkmann, Fichter, Klofsten, &Audretsch, forthcoming), the understanding of how support programmes for entrepreneurial ecosystems promote sustainability is limited, especially in developing countries. Thus, we assume that (1) university business incubators are tools that accelerate entrepreneurship (Hassan, forthcoming) and lead to sustainability (Etzkowitz, 2008); (2) critical success factors have numerous vectors that affect them (actors, resources, and structure); and (3) they are key elements for managers’ decision-making. Therefore, this study sought to highlight the critical success factors of the Ifes’s programme “Enterprise Incubation Network” in Brazil. To achieve this goal, we analysed several documents (management reports and agreements) and conducted nine interviews with managers and former managers of Ifes’s Incubation Centers, which made up a case study on Ifes. Our findings, from which emerged 16 subcategories related to 3 analytical categories (organizational architecture, Triple Helix, and entrepreneurs), which, in turn, are connected by a central category (entrepreneurial university), provide a reference on the critical success factors of business incubator programmes with a network intervention model, aimed at ventures related to the sustainability of local productive arrangements in developing countries. Additionally, it adds to the theory on the dynamics of business incubators as a link in the entrepreneurial ecosystem of a higher education institution, for encouraging regional transformations towards sustainability.

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8.2 Theoretical background An organization’s innovative process consists of activities in collaborative networks, where the actors are companies, universities, and individuals. In this process, innovation becomes multidirectional, creating an informational flow that enhances the development of research, innovation and competitiveness (Hewitt-Dunas, 2011). The dissemination of the model proposed by Etzkowitz and Leydesdorff (1995) – the Triple Helix – increased the relevance of the actors mentioned above for the development of entrepreneurship and innovation. This model characterized the relationship university-company-government, and brought the academy into organizations, improving basic research and extending university investigations to address firms’ problems. The university–company–government tripod became strategic for solving the existing problems of society, which has become increasingly complex and globalized. Among these interactions, business incubators are crucial elements, being essential to bridge the gap between innovation and commercialization of new technologies (Etzkowitz, 2005), in addition to communicating with the government for innovation development. The main reason for investing public resources in business incubators is that the incubation programme strengthens new ventures, contributing to their success in the market, by creating jobs and income. These enterprises have the potential to transfer new technologies to society through the entrepreneurial university, seeking the country’s development (Tavoletti, 2013). Within incubators, innovation networks are interconnections of interaction processes among heterogeneous social actors (companies, universities, and government agencies), producing innovations on a global scale (Pyka, 2000). Innovation networks allow interactions among the actors by establishing information exchange, undertaking actions to promote innovation, especially in the public sector (Corsani, 2003). The main goal of a business incubator is to change the incubated ideas into successful ventures. For this process to happen, several factors are necessary, both internal and external, such as a strong management structure and articulation. Therefore, it is crucial to know the critical success factors, in order to understand and align all incubator’s processes and information, making the manager confident for making decisions. Critical success factors are the assumptions that affect an organization directly. Given these premises, it is possible to infer which factors are necessary to ensure its successful operation, making its procedures safer and the company more competitive in the market (Morioka & Carvalho, 2014). For Padrão (2011), they are the tools of managers and their team, which lead the company to reach its level of excellence. Hence, these factors are fundamental elements, within the company’s strategic or tactical executive plan, which make it achieve a satisfactory performance in all areas. Critical success factors must fill gaps that are essential for the incubator to achieve gains in its processes. They must be identified, so that managers may lead the

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organization to success (Caralli, 2004). We did an extensive research on this topic, in order to find out authors who addressed this concept for business incubators. According to Ortigara, Grapeggia, Juliatto, Lezana, and Bastos (2011), at first an incubator needs to detect the necessary infrastructure and have a marketing plan, in order to reach a good performance in its processes. After gaining recognition, it must worry about other critical success factors, such as personnel qualification and process management (strategic planning, marketing, and performance indicators). Finally, it is essential that incubator managers work with partner entities to ensure incubator’s sustainability. Anholon and Silva (2015) identified many critical success factors for incubators. For them, an incubator is a self-sustaining organism, with the capacity to generate financial resources. In addition, a determining factor for its success is the systemic monitoring of the incubated enterprises. Finally, an incubator needs to develop partnerships that are essential for the maintainability of its processes. According to Sun, Ni, and Leung (2007), critical success factors are linked to economic and social factors, such as the government’s commitment to incubation programmes; development of new products and processes; partnerships with teaching institutions; and technical and financial support for incubated firms. Lee and Osteryoung (2004) identified several critical success factors. Among them, we mention the strategic plan, coworking spaces, networking, business partnerships, research and development for new businesses, technical consulting, technology transfer processes, financial support, training, network of incubated companies, government support, and endorsement from the local community. Gozali, Masrom, Zagloel, and Haron (2016) carried out another important study on critical success factors. They interviewed 10 specialists in business incubators, and found out that incubator’s management, consulting to incubated firms, a network of partners, infrastructure, and financial support are the main critical success factors for incubators. Buys and Mbewana (2007) found more than 30 critical success factors in the literature. The main ones were infrastructure, business plan, economic sustainability, efficient management, partner network, government support, easy credit, and more rigid selection criteria. Gillotti and Ziegelbauer (2006) also found different critical success factors. An incubator, in order to operate effectively, needs to develop a strategic plan, visible to everyone. The incubator manager is the main critical factor, according to the authors. Support for incubated ventures should be steady. Coworking spaces are relevant, as well as the whole incubator’s infrastructure. Financing sources are important for the development process of the incubated enterprises. Finally, awareness and prospection of projects for incubation must be rigorous. Maletz and Siedenberg (2007) carried out a study with incubator managers to identify factors for businesses’ success. By interviewing the managers, some elements emerged, such as planning, team qualification, entrepreneurial culture, infrastructure,

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business plan, access to investments and financing, financial support, selection process for new ventures, incubator marketing, coworking spaces, partner network, support from the local community, and institutional link between the incubator and research centres. Finally, we present a summary of incubators’ critical success factors, identified in the literature review (see Table 8.1). Table 8.1: Incubators’ critical success factors. Critical success factors

Sources

Infrastructure and shared spaces

Ortigara et al. (), Lee and Osteryoung (), Buys and Mbewana (), Gillotti and Ziegelbauer (), Maletz and Siedenberg (), and Gozali et al. ()

Marketing strategies

Ortigara et al., () and Maletz and Siedenberg ()

Process management

Ortigara et al. (), Lee and Osteryoung (), Buys and Mbewana (), Gillotti and Ziegelbauer (), Maletz and Siedenberg (), and Gozali et al. ()

Collaborators’ qualification

Ortigara et al. (), Buys and Mbewana (), and Maletz and Siedenberg ()

Technology transfer

Lee and Osteryoung ()

Participative management

Ortigara et al. (), Lee and Osteryoung (), and Buys and Mbewana ()

Government support

Sun et al. (), Lee and Osteryoung (), and Buys and Mbewana ()

Creation of new technological products

Sun et al. () and Lee and Osteryoung ()

Financial support and funding

Sun et al. (), Lee and Osteryoung (), Buys and Mbewana (), Gillotti and Ziegelbauer (), Maletz and Siedenberg (), and Gozali et al. ()

Strategic planning

Ortigara et al. (), Lee and Osteryoung (), Buys and Mbewana (), Gillotti and Ziegelbauer (), and Maletz and Siedenberg ()

Relationship networks (institutional, incubated entrepreneurs, business)

Ortigara et al. (), Sun et al. (), Lee and Osteryoung (), Buys and Mbewana (), Anholon and Silva (), Maletz and Siedenberg (), and Gozali et al. ()

Consulting (accounting, financial, legal, and market)

Lee and Osteryoung (), Gillotti and Ziegelbauer (), Maletz and Siedenberg (), and Gozali et al. ()

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Table 8.1 (continued) Critical success factors

Sources

Incubator manager

Ortigara et al. () and Gillotti and Ziegelbauer ()

Monitoring incubated firms

Anholon and Silva ()

Self-sustainability

Anholon and Silva () and Buys and Mbewana ()

Entrepreneurial culture

Maletz and Siedenberg ()

Support to local community

Lee and Osteryoung () and Maletz and Siedenberg ()

Technical support and business training for incubated entrepreneurs

Sun et al. () and Lee and Osteryoung ()

Source: Elaborated by the authors.

Despite the undeniable contribution of previous studies in identifying critical success factors in incubators (see Table 8.1), the understanding of the process by which support programmes for entrepreneurial ecosystems promote sustainability is limited, especially in developing countries. Hence, the present study aims to highlight the critical success factors linked to the Ifes’s programme Enterprise Incubation Network in Brazil.

8.3 Method To achieve our goal, we carried out a case study at Ifes. To do that, we analysed a large set of documents (newsletters, selection notices, promotion notices, agreements, and management reports) and conducted nine semi-structured interviews with eight managers and one former management of Ifes’s eight incubating centres. We chose this qualitative approach for its main attributes that fit our research: a phenomenon examined in its context; data collected from multiple sources; examination of one or a few elements; no use of controls or manipulation; focus on a contemporary event; and results that strongly depend on the researcher’s capacity of integration (Yin, 2019). We chose Ifes’s Incubator as an object of investigation mainly due to its network management model, recognized as a pioneer in the Federal Network of Professional and Technological Education in Brazil; and because of its institutional context, with campuses and incubation centres located in different administrative micro-regions of the State, which provides proximity to different local productive arrangements. In addition, Ifes is regularly present in the ranking of resident patent applicants,

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prepared by the National Institute of Industrial Property (INPI), among the largest applicants of patents and computer programs in the country (INPI, 2020). The Ifes’s Enterprise Incubation Network, as detailed in the next section, involves the Ifes’s Innovation Agency (Agifes), the body that manages the network; the Incubator Centers located at Ifes’s campuses; and the incubated firms. From the study by Carmo and Rangel (2020), the analytical category “Manager” emerged. These authors used the Descending Hierarchical Classification technique of the research corpus, and this category was a strongly influential factor for the success of Ifes’s Enterprise Incubation Network. Therefore, we decided to interview managers and former managers of Ifes’s incubation centers. In addition, since there are incubation centres at different stages of implementation (awareness, planning, creation, etc.), and because it is not possible to rule out different critical factors at each stage, we chose to focus the study on incubation centres that were at the last stage of implementation (that is, public notice). This option contributed to the homogeneity of the units of analysis. Thus, we included eight incubation centres out of 11, which are institutionalized and operational within the Ifes’s Enterprise Incubation Network. Before conducting the interviews, in order to understand the structure and organization of the network, we carried out a documentary analysis of newsletters, notices, agreements, meeting minutes, and management reports, as shown in Table 8.2. Table 8.2: Research subjects. Incubation center

Interview

Documents

Interviewee

ID

Mode

Ifes Colatina

Manager

E

In person

Ifes Serra

Manager

E

In person

Ifes Serra

Former manager

E

In person

Ifes Cachoeiro

Manager

E

In person

Ifes Venda Nova

Manager

E

In person

Ifes São Mateus

Manager

E

In person

Ifes Vitória

Manager

E

By telephone

Ifes Linhares

Manager

E

By telephone

Ifes Vila Velha

Manager

E

By telephone

– Newsletters – Selection notices – Promotion notices – Diverse agreements – Management reports

Source: Elaborated by the authors.

We prepared a semi-structured protocol for conducting the interviews, following Spradley’s (1979) recommendations, which consisted of different kinds of questions –

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introductory, descriptive, structural, contrasting, and closing questions. We pre-tested the interview script with Agifes’s incubator manager, which indicated the need for improvement. We adopted active categorization for data analysis (Grodal, Anteby & Holm, forthcoming), together with the encoding script proposed by Corbin and Strauss (1990): open, axial, and selective encoding. In open encoding, we made a preliminary categorization through a label. Then we compared it with other labels in order to identify similarities and differences that would imply the gathering of labels or the creation of new labels. Next, still in open encoding, we organized groups of labels to facilitate hierarchy and the formation of categories. To this end, we created groups of labels linked to a common theme that allowed showing the underlying concept of each label. These procedures formed the baseline for building categories and subcategories that, in short, gathered the labels that had been grouped. In axial coding, we established relationships between the categories and subcategories, which formed the foundation for the study findings, in which we sought a small number of high-level concepts. Finally, in selective encoding, we unified the categories into a central category, by relating the categories created previously to a single central idea. This represents the central phenomenon of the study, that is, the critical success factors of the Enterprise Incubation Network.

8.3.1 Study context : Ifes incubators in Brazil In 2021, Brazil had 38 Federal Institutes within the Federal Network for Scientific and Technological Professional Education (RFEPCT). Ifes is located in Brazil in the southeast region. The Agifes, which is Ifes TIC, was created in 2012, to meet the demands of Ifes’s campuses and of the production sector, based on the federal innovation law (Act no. 10973/2004) and on the Espírito Santo state law (Complementary Act no. 642/2012). Ifes’s incubator has national recognition for its network cooperation work to develop entrepreneurial skills, in addition to innovative activities that involve Ifes’s academic community and the external community. The Enterprise Incubation Network is a programme of activities that provides ways to fulfil Ifes’s mission in society. Developed and managed by Agifes, it articulates, integrates and supports the activities of Ifes’s incubator centres. All activities, processes, and projects developed by the network are closely linked to the network. The Enterprise Incubation Network provides full support to the incubator centres, such as management, institutionalization, qualification for incubated managers and entrepreneurs, institutional partnerships external to Ifes, and process standardization and systematization. The systemic management of the network programme plans and

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carries out these activities, promoting the integration and information management of the teams located at different campuses. The integration of the Enterprise Incubation Network and the creation of its incubator centres led to the development of systematic processes, critical for the management of incubator procedures. In this sense, the programme has the following stages that are developed and shared within the network: – Awareness and prospecting: these activities show to the internal and external audience how the incubator centre works. There are also opportunities to raise funds and support innovative projects. Among the activities presented, innovation challenges and the hackathon stand out. Any individual who has an innovation idea or project in the early stage of development can participate in the challenges and workshops offered by the incubator. Through pitch techniques, ideas are assessed and awarded. Finally, the Enterprise Incubation Network carries out awareness and prospecting events through partnerships, by means of Technical Cooperation Agreements, with Sebrae (a private non-profit entity created by the Brazilian federal government to support small businesses), the Accelerator StartYouUp, and local governments. – Pre-incubation: this consists of the training stage for entrepreneurs who are interested in developing a business plan for their idea, in order to mature their project. At the end of this stage, the entrepreneur can create prototypes or even a Minimum Viable Product (MVP). Ifes’s employees provide this qualification, through an Initial and Continuing Training course (FIC). The target audience consists of people who need to improve their knowledge and management tools for innovation and entrepreneurship. There are two options for this stage: (1) preincubation – entrepreneurial skills, which offers a FIC course for entrepreneurs, divided in classroom and remote activities; and (2) pre-incubation – residence, where the selected entrepreneur will take the FIC course and, additionally, will attend the innovation environment of the incubator centre. He will have access to a coworking room, to develop the prototype or MVP, make new contacts, access opportunities, and experience the incubation process. – Incubation: it is the main process within the Enterprise Incubation Network. It has several stages of systematization, among which the initial selection of the enterprise and the assessment process by a group of people with skills in entrepreneurship and innovation. After project incubation, it goes through several processes that will help the venture to develop, generate indicators for the incubator, besides the technical qualification of all members of the project team. Finally, the incubator provides other services such as contract management among partners, support for fundraising, and strategies for market access. The legal process of incubation between the venture and Ifes occurs through a contract, in which the institute agrees to offer several services, such as training, consulting, participation in events, use of physical spaces, such as coworking and laboratories, guidance from Ifes’s professors and technicians, and

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access to investors, among others. At this stage, institutional activities promote the inseparable relationship between actions in the field of education (qualification), research and development (R&D) and extension (society/market), seeking to create innovations for the society through human and socioeconomic development (regional). – Post-incubation: comprises the partnership actions between the graduated company and the incubator centre. This partnership takes place through Technical Cooperation Agreements, with invitations for lectures at events organized by the incubator, such as those for raising awareness within the community. This is also an important stage, as it creates a rapprochement relationship with the entrepreneur, where he/she continues to support the incubation processes. Another positive element of this relationship is that the entrepreneur is linked to a centenary institution (in 2020, Ifes completed 111 years old), which fosters research and extension actions that encourage innovation. Ifes’s incubator keeps a close relationship with the firm, as both can share great opportunities and add value to each other, at this stage. The Enterprise Incubation Network works in line with the values described in Ifes’s Institutional Development Plan, and in Strategic Planning actions. This support offered by the network to incubator centres represents the “innovation habitat,” expressing the dynamics of the relationships in the recognized indissociability (teaching, research, and extension), with high educational potential. These systematic actions are essential for the success of all processes in the Enterprise Incubation Network. All enterprises are fundamental pieces where they build entrepreneurial skills and creative processes, for serving students, employees, and the community. Table 8.3 shows the incubator centres that participated in the research: Table 8.3: Ifes’s incubation centres. Campi

Date of Location (State of establishment Espírito Santo – Brazil)

Focus

Serra

 Metropolitan region

Technological base

Vitória

 Metropolitan region

Technological base, social and creative economy

Vila Velha

 Metropolitan region

Technological base (biotechnology and chemistry), social and creative economy

Venda Nova do Imigrante

 Southwest region

Agribusiness, agritourism, and to civil society associations, as well as to geographical indications

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Table 8.3 (continued) Campi

Date of Location (State of establishment Espírito Santo – Brazil)

Focus

Cachoeiro do Itapemirim

 Center-South region.

Technological base, social and creative economy ventures, with a focus on promoting innovations in the marble and granite sector, as well as supporting geographical indications

São Mateus

 Northeast region

Technological base, social and creative economy enterprises

Colatina

 Center-West region

Mixed-based ventures

Linhares

 Rio Doce region

Technological base, social and environmental initiatives

Source: Elaborated by the authors.

In 2020, the Enterprise Incubation Network finished a training course in New Business Modeling, had thirteen projects at the incubation stage, had graduated five other firms, and had an open incubation notice for eight incubator centres to receive new enterprises. Finally, it is a sine qua non condition to plan integrated actions and systematize the processes of the incubation centres of Ifes’s network, so that the Incubator can promote a set of support actions necessary for the development of startups and spin-offs, in order to meet the different needs of Brazil. Furthermore, the insertion of Ifes’ campuses in all regions and in local productive arrangements and local innovation systems in the state of Espírito Santo makes this Enterprise Incubation Network more solid, capillary, and relevant to the country’s development.

8.4 Results and discussion We carried out the codification process to identify how Ifes’s university business incubator operates as an important link in its entrepreneurial ecosystem, to foster regional changes towards sustainability (more objectively, its critical success factors). From this process, three analytical categories emerged – organizational architecture, Triple Helix, and entrepreneurs. At the beginning, we identified labels that were organized in order to facilitate hierarchy, and linked to 16 subcategories (that is, groups of labels linked to a common topic), for the formation of categories. Finally, we grouped the subcategories into categories, from which stemmed the central category “entrepreneurial university,” as shown in Table 8.4.

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Table 8.4: Synthesis of the categorization results. Central category

Categories

Subcategories

Entrepreneurial university

Organizational architecture

Infrastructure Participative management Lessons learned Human capital Self-sustainability

Triple Helix

Relationship network Partners Promotion Government Regional development Companies

Entrepreneurs

Entrepreneurial intention Innovation Entrepreneurial culture Local community Team

The subcategories that emerged from the codification process relate to other studies’ findings. For example, Gozali et al. (2016) identified infrastructure, participative management, relationship network, and financial support (promotion) as critical factors for business incubators. Ortigara et al. (2011) pointed out infrastructure, participative management and team as critical factors. Anholon and Silva (2015) detected self-sustainability and the network of relationships. In its turn, Sun and Leung (2007) mentioned the relationship network, promotion, government support and innovation as critical factors. Finally, Lee and Osteryoung (2004) identified infrastructure, relationship network, promotion, and support to the local community as critical factors for business incubators. It is interesting to notice that incubation centres’ managers did not focus only on the intrinsic aspects of each centre. They also paid attention to the profile of the incubated enterprises, the organizational architecture of the incubation network, and the relationships of this network with governments and companies. Hence, we observe the inseparable character of the results from the interviewees’ narrative. The success of an incubator is a multifactorial element. The sum of several factors contributes to the incubator reaching its objectives, which go beyond the incubation of successful ventures. (Verbal information – E9) In my conception, among the success factors of an innovation environment are the existing interactions between these environments [innovation ecosystem], which generate an exchange of experience, improve services and allow efforts to be more focused on more relevant and structuring actions. (Verbal information – E8)

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Since the first interviews, it was clear that, to a certain extent, the critical factors suggested were seen as temporary, often associated with the implementation stages, which strengthened the research option of choosing the centres that were in the last stage of deployment. . . . the manager initially felt very isolated and had no one to discuss the proposal of Colatina Incubator Center. Today the reality is different, there is a network within Ifes that wants to support us, that already has an experience with incubation centers; thus, we exchange messages, phone calls and experience with each other. (Verbal information – E1) At the beginning, the big problem was to approve all processes. Currently, they are already replicated. . .. (Verbal information – E2)

By relating subcategories that emerged from the study with previous research, we could unify subcategories that represented, in essence, the same phenomenon. For example, from “Management” and “Teamwork” emerged “Participative management,” related to the Organizational architecture category. On the other hand, we divided some subcategories. That was the case of the Entrepreneurial profile, from which stemmed the subcategories “Entrepreneurial culture” and “Entrepreneurial intention,” associated with the Entrepreneurs’ category.

8.4.1 Organizational architecture The Organizational Architecture category deals with the method of organization for the development of the incubation network functions, especially the relationship with incubator centres, incubated firms, partners, and other stakeholders. Agifes’s mediating role in knowledge transfer for the incubator centres stands out in this context. We are very dependent on what Agifes tells us to do, the paths that we have to take; hence, we are very connected to network tasks, we consult other campuses to find out what we need to do, because we are starting now in some areas . . . Our knowledge base ends up being the Agifes’s support. Everything we need regarding innovation consulting, we direct to Agifes. (Verbal information – E4)

8.4.2 Triple Helix On the other hand, the Triple Helix category refers to the educational institution’s bilateral and trilateral partnerships with companies and government, to support economic and social development (Etzkowitz & Leydesdorff, 1997). In the Triple Helix model, each of the three propellers can assume the role of the other two: universities can assume the role of companies by creating university spin-offs as a way to exploit their own knowledge economically; companies can

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provide financing for public universities by hiring research, thus replacing the government. In its turn, the government can act as a venture capitalist, replacing the industry by financing startups (Romero, Ferreira & Fernandes, forthcoming). This hybrid relationship is evident in the innovation ecosystem where Ifes’s Enterprise Incubation Network operates. In addition to our link with Ifes [federal autarchy], we have received financial support from the state government, through a Fapes [state authority] public notice, and from Vitoria’s local government. This last support comes from the assignment term for use of the space of Fábrica de Ideias [Ideas’ Factory], hub of Espírito Santo innovation ecosystem, managed by Ifes and Vitória City Hall. Since we are connected to Agifes, we understand that strategic actions must be shared, according to a network model. (Verbal information – E7)

8.4.3 Entrepreneurs The Entrepreneurs category, on the other hand, refers to the “production” of graduates with skills required by employers and, at the same time, suitable to become entrepreneurs of new businesses, therefore, employers. An entrepreneur is able to identify the best business opportunities, by creating several new organizational structures, actually exploring the opportunity. Entrepreneurship takes place in environments where there are qualified entrepreneurs, and entrepreneurial teams able to get resources for developing new technologies, new products, and new services (Borges, Bernasconi & Filion, 2013). We included, in the category Organizational architecture, the organizational culture focused on building entrepreneurial skills and innovation. Furthermore, the exchange of experience in environments such as incubators is essential to develop an entrepreneurial culture. In these environments, entrepreneurs already have enough experience to create exclusive and innovative products, making their ventures successful. They want to start a business, quit their jobs in order to have their own business and a basis to support their company, which has already started (or will begin). Hence, developing this entrepreneurial capacity and attitude, training these people, is essential. You have to get off paper, apply what they want to learn, take a practical course, make it happen, otherwise the project will not emerge . . . the incubator for Ifes is an important and strategic element. It is a way of bringing students together to study the incubated companies, to write articles, but not only academic, but to gather society and companies, because they have money to buy ideas. (Verbal information – E1) Since it is an incubator center inside a campus [Ifes’s] with technological courses, it will be necessary to involve professors and researchers from the campus, who develop technological products, in order to unify teaching and research with the entrepreneurial culture. (Verbal information – E9)

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8.4.4 Entrepreneurial university The central category “Entrepreneurial University” connects these three analytical categories: Organizational architecture, Triple Helix, and Entrepreneurs. The concept of entrepreneurial university refers, in short, to the educational institution that has as part of its mission the economic and social development through entrepreneurial initiatives (patenting, incubators, technology parks, spin-offs), incorporating these dimensions in its teaching, research and extension activities (Etzkowitz, 1998). The incubation centers are inside one of the Ifes’s campuses . . . an institution oriented towards innovation and regional development . . .. Agifes’s role favors the involvement of professors and researchers in the development of projects that connect teaching, extension, and research actions with the entrepreneurial culture and local productive arrangements. (Verbal information – E3)

In this sense, the definition of entrepreneurial university is strongly aligned, with both the concept of the Triple Helix (Etzkowitz, 1983) and entrepreneurship (Van Looy et al., 2011). Organizational architecture, in its turn, supports the development of the entrepreneurial university’s functions. For Lacruz (2016), the interface organizations that emerge from Triple Helix models, as Agifes in this specific case, are governance drivers for the sustainability of the complex structure of established interorganizational partnerships. Lastly, the network management model for Ifes’s incubators, where incubator centres have autonomy, allows the alignment of centres’ core business with the local productive arrangements of different regions. This contributes to fixing talents and fostering regional changes in a sustainable way. Therefore, universities can improve their role in society through an effective and well-integrated incubation system, so that university business incubators may contribute to promoting these changes towards sustainability.

8.4.5 Critical success factors Based on the results presented, we can do some analyses regarding the critical success factors mentioned in the literature and those that emerged from our study. However, we must be cautious with comparisons between analytical categories present in different studies, as they strongly relate to the research question of each study; to the theory that supports the intended analytical inference; and to the empirical context of the research. With regard to the organizational architecture, we considered as critical success factors: infrastructure, participative management, lessons learned, human capital, and self-sustainability. Ortigara et al. (2011), Lee and Osteryoung (2004), Buys and Mbewana (2007), Gillotti and Ziegelbauer (2006), Maletz and Siedenberg (2007),

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and Gozali et al. (2016) also emphasize that an incubator needs to take into account its facilities and shared spaces in order to achieve great results. After founding an incubator, it is necessary to create a management board in its organizational structure, with several representatives. In addition, it needs to establish links with regional actors, so that they can be part of the management board and contribute to the development of the incubator’s actions and to strengthening the incubated firms. For this reason, participative management emerged as a critical factor. Other authors like Ortigara et al. (2011), Lee and Osteryoung (2004), and Buys and Mbewana (2007) also mentioned it as a critical success factor. Self-sustainability is the capacity to contribute effectively to sustainable development. Castro and Souza (2012) observe that for an incubator to become self-sustainable it needs to mature its processes during, at least, 10 years. In addition, few TICs are able to achieve it in Brazil. TICs, together with incubators, have sought, within their organizational architecture, to systematize their processes, in order to manage their patents and carry out technology transfer. In other words, TICs need to get more experience and capacity to attract companies for licensing their innovations. This hinders TICs’ process of self-sustainability and, consequently, of business incubators. Anholon and Silva (2015) and Buys and Mbewana (2007) also considered self-sustainability as a critical success factor. The following subcategories relate to the triple helix category: relationship networks, partners, promotion, government, regional development, and companies, identified as critical success factors. Relationship networks are actors who seek to strengthen entrepreneurship, attract educational institutions, companies, trade associations, and new customers, with the aim of spreading the entrepreneurial culture. It is necessary for a project to grow, since its creation, become stronger, and generate more and more revenue. With value added through partners, the incubator also achieves greater visibility. Ortigara et al. (2011), Sun et al. (2007), Lee and Osteryoung (2004), Buys and Mbewana (2007), Anholon and Silva (2015), Maletz and Siedenberg (2007), and Gozali et al. (2016) believe that a network of partnerships is essential for a business to survive. Government support through funding is one of the main drivers for incubators’ growth in the country. Sebrae, Finep, and CNPq are among the agencies that most invest resources in business incubators and NITs. Sun et al. (2007), Lee and Osteryoung (2004), Buys and Mbewana (2007), Gillotti and Ziegelbauer (2006), Maletz and Siedenberg (2007), and Gozali et al. (2016) also showed that the lack of support to the incubators of public institutions’ NITs is a risk factor for their proper functioning. In the category of entrepreneurs, entrepreneurial intent, innovation, entrepreneurial culture, the local community, and the team emerged as critical success factors. Entrepreneurial culture at the incubator occurs through the formation of qualified entrepreneurs, who start to notice opportunities for doing great business. Entrepreneurship education should be promoted within an incubator, in order to spread

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entrepreneurial culture and innovative ideas in incubated firms. Maletz and Siedenberg (2007) also considered this subcategory as a critical factor. Support to the local community takes place from the moment the incubator gains credibility in the region where it operates. It is important that community representatives be part of the incubator’s management board. Therefore, the local economy develops together with new research that will benefit both the community and incubated firms. Support to the local community has also emerged as a critical success factor in the studies by Lee and Osteryoung (2004) and Maletz and Siedenberg (2007). Finally, we did not identify, in the literature review, the dimension “lessons learned” (related to the category Organizational architecture) as a critical success factor. This subcategory, in the innovation ecosystem where Ifes’s Enterprise Incubation Network operates, plays a fundamental role in Agifes’s action, as a mediator for knowledge transfer between incubation centres and incubated firms.

8.5 Conclusion In this chapter, we provide an analysis of the critical success factors in university incubators and, at the same time, question the role of support programmes for entrepreneurial ecosystems in fostering sustainability in developing countries. Objectively, we investigated, through a case study, the Ifes’s Enterprise Incubation Network programme in Brazil. This chapter outlines the critical success factors of a university business incubator, and shows our contribution to research on university business incubators as a support tool for entrepreneurial ecosystems. The case study showed the relevance of some management aspects of the innovation policy of academic institutions, especially the Ifes, for its maturity as an entrepreneurial university, under the Triple Helix perspective. The analytical categories that emerged from the study provide a reference on the critical success factors of business incubator programmes with a network intervention model, which is useful for understanding the dynamics of the business incubator as a link in the entrepreneurial ecosystem of a higher education institution in developing countries. The proposed analytical framework brings important advances to the area. First, it indicates the intertwining of factors intrinsic to the incubation centre, the profile of the incubated firm, and the role of Agifes as a mediator in the network of relationships that is established among the incubation centres of different Ifes’s campuses, and in their relationship with companies and the government. Second, it shows the temporal aspect of the critical success factors, which relate largely to the implementation stages of the incubation centres. This suggests that critical factors occur in a development continuum. In this scenario, Agifes, as a relationship

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mediator, catalyst, and driver of lessons learned, allows that ex ante reflections, in the process of developing nascent centres, to minimize difficulties faced by other centres that had to identify ex-cursum solutions. In other words, the ex-post evaluation of results driven by Agifes contributes to a virtuous cycle of learning in the incubation network. Finally, the network management model with autonomy for the incubation centres strengthens the alignment of the centres’ core business to the local productive arrangements of the regions where Ifes’s campuses are located. This aspect is essential for organic regional changes. Our analysis identified three contextual dimensions as critical factors. These interrelated dimensions contribute to improve the activities of incubator centres by transferring knowledge among the network’s vertices; through the articulation of partnership ties with the other spatial propellers of the Triple Helix model, especially local productive arrangements; and through the training culture of entrepreneurial skills. The implications brought about by the analytical framework that emerged when investigating the critical success factors of a business incubator network, provide a reference for decision makers to support even more business incubator programmes with a network intervention model, oriented towards ventures linked to the sustainability of local productive arrangements. In this structure, Agifes, as an “innovation habitat,” integrates efforts directly through a public enterprise incubator linked to an educational institution. Ifes’s incubator, through the institution’s capillarity, operates as an Enterprise Incubation Network, receiving all the support for its management processes from Agifes. The way the network is structured, present in all regions and local productive arrangements of Espírito Santo state, makes the centres even more relevant for regional development. Our study helps theorizing the dynamics of business incubators as an important link in the entrepreneurial ecosystem of a higher education institution, for the promotion of regional changes towards sustainability, favouring the debate on this topic. Future research can investigate the critical success factors of incubator networks in different environments and with distinct organizational arrangements. In the study, we provide evidence of the important interplay that exists among the analytical categories that insert the critical success factors of an Enterprise Incubation Network programme in the entrepreneurial ecosystem of a higher education institution; and the central role of the entrepreneurial university in connecting these dimensions. More than a simple observation, this finding highlights the importance of including the three analytical categories that we have mapped in upcoming productions, since theory can advance in the light of new practices, and vice versa. Finally, the university, by structuring the mechanisms for valuing the entrepreneurial culture and the creation of new businesses, guides its teaching, research. and extension approach towards economic growth. Therefore, it faces changes and growing demands by taking into account what society expects it to achieve (i.e. its “third mission”).

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Etzkowitz, H., Mello, J. M. C. & Almeida, M. (2005). Towards “meta-innovation” in Brazil: The evolution of the incubator and the emergence of a triple helix. Research Policy, 34(4), 411–424. Gillotti, T. & Ziegelbauer, R. (2006). Seven components of a successful business incubator. University of Wisconsin Extension. Let’s Talk Business, 119. Retrieved from https://fyi.exten sion.wisc.edu/downtowneconomics/files/2012/08/Seven-Components-of-a-SuccessfulBusiness-Incubator.pdf. Accessed October 10, 2020. Gozali, L., Masrom, M., Zagloel, T. Y. M. & Haron, H. N. (2016). A framework of successful business incubators for indonesian public universities. International Journal of Technology, 7(6), 1086–1096. Grodal, S., Anteby, M. & Holm, A. L. forthcoming Achieving Rigor in Qualitative Analysis: The Role of Active Categorization in Theory Building. Academy of Management Review, Retrieved from https://journals.aom.org/doi/10.5465/amr.2018.0482. (access date October 10, 2020) Hassan, N. A. Retrieved from. University business incubators as a tool for accelerating entrepreneurship: Theoretical perspective. Review of Economics and Political Science, forthcoming. https://www.emerald.com/insight/content/doi/10.1108/REPS-10-2019-0142/ full/pdf. Accessed October 10, 2020. Hewitt-Dunas, N. (2011). The role of proximity in university-business cooperation for innovation. The Journal of Technology Transfer, 38(2), 1–23. Instituo Nacional da Propriedade Industrial. (2020). Ranking dos depositantes residentes. Retrieved 29 September 2020, from https://www.gov.br/inpi/pt-br/composicao/estatisticas/ estatisticas-preliminares-2013-a-partir-de-2013. Kuppers, G. & Pyka, A. (2002). The Self-organization of Innovation Networks: Introductory remarks in Innovation Networks: Theory and Practice. Cheltenham: Edward Elgar Publishing. Lacruz, A. J. (2016). Governance for partnership sustainability: An approach from the agency theory. In Peris-Ortiz, M., Ferreira, J. J., Farinha, L. & Fernandes, N. O. (Eds.), Multiple helix ecosystems for sustainable competitiveness (pp. 27–43). Cham, Switzerland: Springer. Lee, S. S. & Osteryoung, J. S. (2004). A comparison of critical success factors for effective operations of university business incubators in the United States and Korea. Journal of Small Business Management, 42(4), 418–426. Maletz, E. A. & Siedenberg, D. R. (2007). A Gestão dos Fatores Críticos de Sucesso nas Incubadoras de Empresas da Região do Ruhr – Alemanha. Rio de Janeiro, RJ, Brasil: Anais do EnANPAD. Morioka, S. & Carvalho, M. M. (2014). Análise de fatores críticos de sucesso de projetos: Um estudo de caso no setor varejista. Production, 24(1), 132–143. Ortigara, A. A., Grapeggia, M., Juliatto, D. L., Lezana, A. G. R. & Bastos, R. C. (2011). Análise por agrupamento de fatores de desempenho das incubadoras de empresas. Revista de Administração e Inovação, 8(1), 64–91. Padrão, L. C. (2011). Fatores críticos de sucesso no desenvolvimento de produtos de empresas de base tecnológicas (Tese de Doutorado). São Paulo, SP, Brasil: Fundação Getúlio Vargas. Retrieved from http://hdl.handle.net/10438/8163. Accessed October 10, 2020. Pankov, S., Velamuri, V. K. & Schneckenberg, D. Retrieved from. Towards sustainable entrepreneurial ecosystems: Examining the effect of contextual factors on sustainable entrepreneurial activities in the sharing economy. Small Business Economics, forthcoming. https://link.springer.com/arti cle/10.1007%2Fs11187-019-00255-5. Accessed October 10, 2020. Pyka, A. (2000). Informal networking and industrial life cycles. Technovation, 20(1), 25–35. Ramirez-Pasillas, M., Brundin, E. & Markowska, M. (2017). Contextualizing entrepreneurship in-between. In Pasillas, M. R., Brundin, E. & Markowska, M. (Ed.), Contextualizing Entrepreneurship in Emerging Economies and Developing Countries (pp. 1–17). Cheltenham, UK: Edward Elgar Publishing.

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Pedro Mota Veiga and Sérgio J. Teixeira

Chapter 9 Universities as change agents of SMEs’ sustainable innovation: A knowledge transfer view 9.1 Introduction Knowledge is generated with the aim of contributing and assisting in the development of populations, improving their development, and increasing their value, with special importance for academia and companies (Hermans & Castiaux, 2017; McGinnis, Harvey & Young, 2020; Penco, Ivaldi, Bruzzi & Musso, 2020; Sun, Yuen, Zhang & Zhang, 2020; Teixeira, Veiga & Fernandes, 2019). Knowledge sharing, knowledge transfer, and cooperation for knowledge are key concepts in the knowledge management literature (Chang, Liao, Wu & Wu, 2017; McGinnis et al., 2020; Rubin & Rubin, 2013; Saraf, Langdon & Gosain, 2007; Van Der Zee, Bertocchi & Vanneste, 2020). In this sense, knowledge sharing can be defined as a voluntary and conscious act between individuals or organizations that results in the joint ownership of knowledge among the source and the recipient, that is, the spirit of cooperation (Arvanitis, Kubli & Woerter, 2008; Cervantes, 2017; Palvalin, Vuori & Helander, 2018; Stejskal, Meričková & Prokop, 2016). The study of knowledge transfer and cooperation between SMEs and universities is a valuable contribution to global development as they are fundamental to the innovative capacity of organizations (Aboelmaged, 2018; Chu, 2009; Miwa & Bell, 2017; Olanrewaju, Hossain, Whiteside & Mercieca, 2020; Pikkemaat, Peters & Chan, 2018; Silva et al., 2020; Sum, 2004; Zeng, Xie & Tam, 2010). In this development, The production, absorption, acquisition, reproduction and transfer of knowledge are considered fundamental characteristics to obtain a competitive advantage in this global development (Teixeira et al., 2019). According to the European Union Statistics Office Eurostat (2020), SMEs are often referred to as the backbone of the European economy, integrating a potential source of employment and economic growth. SMEs are defined by the European Commission as having less than 250 people employed. They must also have an annual turnover of up to 50 million euros or a total balance sheet of not more than 43 million euros (recommendation of the Commission of 6 May 2003). These concepts are needed to assess which companies can benefit from EU funding programmes designed to promote SMEs, as well as in relation to certain policies, such as specific competition rules for SMEs. The European Commission’s policy towards SMEs focuses mainly on five priority areas covering: the promotion https://doi.org/10.1515/9783110670219-010

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of entrepreneurship and skills; improving SMEs access to markets; reduce bureaucracy; improving the growth potential of SMEs; and strengthening dialogue and consultation with SME stakeholders. As companies are increasingly competing, it is necessary to understand how they can exploit and reconfigure their knowledge and technology resources to influence competitive advantages (Chen, 2012; Čivre & Omerzel, 2015; D’Ippolito, 2014; Díaz-Chao, Sainz-González & Torrent-Sellens, 2016; Fuentelsaz, Gomez & Palomas, 2012; Goh & Kauffman, 2013; Jeffers, 2010; F. La Rosa, Caserio & Bernini, 2019; Lechner & Leyronas, 2012; Nas & Kalaycioglu, 2016; Park & Kim, 1997; Raposo, Rodrigues, Dinis, Do Paço & Ferreira, 2014). For decades, the literature has recognized technology resources as a valuable resource to achieve a competitive advantage, however, the results are contradictory (Karia, 2018). However, the effects of knowledge transfer and cooperation between SMEs and universities have been poorly studied (Fernandes & Ferreira, 2013; Heredia Pérez, Geldes, Kunc & Flores, 2018; Makkonen, Williams, Weidenfeld & Kaisto, 2018; Mothe & Nguyen-Thi, 2017; Polzin, Von Flotow & Klerkx, 2016; Wu et al., 2018). This chapter aims to fill that gap, as it explores the relationship between knowledge transfer and the spirit of cooperation between SMEs and universities. Thus, the objective of this research is to fill this gap in the literature, examining which factors can affect the processes and relations of knowledge transfer and cooperation between SMEs and universities, such as entrepreneurial profile and company profile and innovative capacity. Hence our research questions: What are the characteristics of entrepreneurs and companies that promote knowledge transfer and innovation activities? What is the impact of knowledge transfer on innovation and business performance activities? This chapter is structured as follows. The next section analyses and discusses the concepts of the entrepreneurial profile, knowledge transfer, cooperation between companies and universities, innovative capacity, and business performance. The third section describes the methodology and methods used in collecting and processing data. The fourth section presents the main results of the study. The final section reflects the main conclusions of the study, as well as the implications and points out some suggestions for future research.

9.2 Literature review 9.2.1 The knowledge transfer and cooperation between SMEs and universities In knowledge management, knowledge transfer seeks to organize, create, capture, or distribute knowledge, ensuring its availability for future users. It is considered

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more than a communication problem (Da Rosa, Da Silva & Souza Lobato, 2020; Segarra-Ciprés, Roca-Puig & Bou-Llusar, 2014), because if it were just that, a memo, an e email, or a meeting would carry out knowledge transfer, but it is not that simple. Knowledge transfer is more complex because knowledge resides in the members of the organization, tools, tasks, and their networks and too much knowledge in organizations is tacit or difficult to articulate (Amar & Juneja, 2008; Okoroafor, 2014; Savolainen, 2019; Wang & Jiang, 2018). Knowledge transfer refers to sharing or disseminating knowledge and providing tools for problem solving (Chang & Gurbaxani, 2012; Ferreira et al., 2016; Fongwa & Marais, 2016; Koman & Kundrikova, 2016; Santoro & Chakrabarti, 2002). Cooperation between companies and universities plays a particularly important role in the performance of companies, both financially and at the level of the organization. It is extremely important since it allows the exploration and verification of the innovative transfer of updated knowledge (Aristei, Vecchi & Venturini, 2016; Un, Cuervo-cazurra & Asakawa, 2010). In this sense, cooperation agreements have become important with regard to the costs of innovation, since they are a major barrier to innovation, but they are not risking of innovation.

9.2.2 Entrepreneur profile Entrepreneurship, particularly in the academic field, can be interpreted as a set of essential utilities for the development of innovative technologies and products and services capable of providing significant business growth, profitability and sustainable competitive advantage (Benito-Hernandez, Platero-Jaime & Rodriguez-Duarte, 2012; Krstić, Mirić & Rakić, 2017; Muraro, Eberle, Verruck, Lazzari, Milan, 2018; Obschonka & Stuetzer, 2017; Ranfagni & Runfola, 2018). In this sense, the entrepreneurial profile represents, a professional differential, a facilitating factor for business success, for the generation of new jobs and, finally, a path to greater competitiveness (Benito-Hernandez et al., 2012; Gonzalez-Benito, 2010; Otim, Dow, Grover & Wong, 2012). The study of the entrepreneur’s behaviour allows the understanding of human beings in their process of creating wealth and personal fulfilment, where entrepreneurship is a field related to the process of understanding and building human freedom (Costa, Superior, & Empresárias, 2016; Hussain & Ismail, 2015; Nicolau & Foris, 2018; Zheng & Qian, 2017). It is to be expected that studies on SMEs tend to align with approaches that discuss the entrepreneurial profile, behaviour, location of companies, and talents. According to He and Huang (2018), innovative activities and the largest market share are in urban areas.

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9.2.3 Company profile The profile of a company usually includes a history based on how the company started, as well as the company’s vision and values. In terms of variables related to the characteristics of a company, it can usually be whether it is more professional or technological, seniority (in years), number of employees, number of employees with higher education (in percentage, and business location, if more rural) or urban (Dou & Li, 2013; Fu, Mohnen & Zanello, 2017; Liu, Wei, Ke, Wei & Hua, 2016; Radu-Daniel, Daniel, Cristian, Irina & Daniela-Rodica, 2015; Strobl, 2013; Tang & Luo, 2016; Westhead, Wright & Ucbasaran, 2001). In addition, the profile of a company also concerns its type of business and aims to inform a specific target audience about its services or products (Benito-Hernandez et al., 2012; Hight, Gajjar & Okumus, 2018; Leonidou & Katsikeas, 2003; Lindberg, 2001; Serbanica, Constantin & Dragan, 2015; Westhead et al., 2001). Therefore, the profile of a company does not it must only tell its audience what it sells, but it must also tell consumers why it sells it.

9.2.4 Innovative capacity Since Marshall’s early work on innovative capacity found in industrial districts, innovation has been one of the main topics covered by economic geography in the past decades in the extensive research tradition. Many studies have dealt with the beneficial effects of proximity in improving the processes of interactive learning and innovation between economic agents due to relationships of trust between various actors, easy observation, immediate comparison, intensified face-to-face interaction, and short cognitive distance (Biswas, 2017; Ferreira, Fernandes, Alves & Raposo, 2015; Ferreira, Fernandes & Raposo, 2017; Furman, Porter & Stern, 2002; Galindo, Vaz, & Nijkamp 2011; Moodysson, Coenen & Asheim, 2008). It is noticed that innovative capacity has been understood as one of the constituent characteristics of dynamic capabilities, and consists of innovation and creation of processes, behaviours, markets, products, technologies, and strategies to face the evolution of competition. To face the complexity of the innovative environment, organizations have been looking for ways to make sustainability dynamic, integrated as strategies and business models, where sustainability must be part of the strategy as an innovative competitive factor of adaptation and resilience.

9.2.5 Conceptual model To address the relationship between the profile of the entrepreneur and the company with cooperation with higher education institutions, as well as the impact of these

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three characteristics on innovative capacity, we developed a conceptual model, summarized in Figure 9.1.

Innovative Capacity

Entrepreneur Profile

Company Profile

Cooperation with Universities Figure 9.1: Proposed conceptual model.

9.3 Methodology 9.3.1 Data and measures 9.3.1.1 Data In order to achieve the objectives of the study, a questionnaire was applied during the year 2018, through telephone interview, to 200 Portuguese SMEs, distributed in services (6.0%, 136) and industry (32.0%, 64), distributed throughout the Portuguese territory (Norte – 24%; Centro – 32.5%; Lisboa – 22%; Alentejo – 5%; Algarve – 9%; Madeira – 2.5%; Açores – 5%). It is noted that 14.5% of the surveyed companies reported the existence of the company’s cooperation with universities in order to transfer knowledge.

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9.3.1.2 Dependent variables Regarding the transfer of knowledge, the questionnaire applied contained a question regarding the existence of the company’s cooperation with universities with a view to transferring knowledge (no vs. yes). To assess the innovative capacity of the companies included in the study, the number of innovations in the product/service or process in the last two years was asked.

9.3.1.3 Predictor variables The variables referring to the profile of the entrepreneur were Age (in years), Education level (Basic or Secondary vs. Higher) and Gender (Male vs. Female). As for the variables alluding to the characterization of the company, the sector of activity (Manufacture vs. Services), Seniority of the company (in years), Number of employees in 2016, Proportion of employees with higher education (in percentage) and Location of companies (Rural vs. Urban; Lisbon vs. other regions). Table 9.1: Variables used in the analysis.

Businessman profile

Firms profile

Variable

Units

Age of the entrepreneur/manager (AGE)

Years

Entrepreneur/person with university education (UNI)

No; yes

Female entrepreneur/manager (FEM)

No; yes

Manufacture (MAN)

No; yes

Company seniority (ANT)

Years

Number of employees in  (LAB)

Number

The proportion of employees with higher education (HIGH)

Percent

Located in urban areas (URB)

No; yes

Located in the Lisbon region (LIS)

No; yes

Company cooperation with universities to transfer knowledge (KT)

No; yes

Innovative capacity

Number

Number of product/service or process innovations (INNOV)

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9.3.2 Data analysis To characterize the sample of SMEs, descriptive statistics (means and standard deviations) of the variables included in the study were determined, as well as the respective correlations. Regarding the modelling of the variables that influence the company’s cooperation with universities, binary regression based on logistical distribution was used. By linearizing the logistic function with the logit transformation of the dependent variable, the econometric model of logistic regression under analysis is obtained, with the three models being estimated:   c j = β + β AGEj + β UNIj + β MALEj + β URBj + β MANj + β ANTj + β LABj Logit KT 0 1 2 3 4 5 6 7 + β8 HIGHj + β9 LISj In the models carried out with a view to determining the impact of the characteristics of the entrepreneur and the company and of cooperation with universities on the innovative capacity (number of product/service innovations and processes), count models were used, based on the Poisson distribution. The Poisson regression is derived from the Poisson distribution through a reparametrization of the relationship between the µ mean and the x regressors, with the four models being estimated for each variable of innovative capacity: d j  = eβ0 + β1 AGEj + β2 UNIj + β3 MALEj E½INNOV

+ β4 URBj + β5 MAN + β6 ANTj + β7 LABj + β8 HIGHj + β9 LISj + β10 KTj

To estimate the various parameters of the models, the maximum likelihood method with robust standard errors was used to eliminate possible heteroscedasticity problems. In all regressions, the existence of variables with potential multicollinearity effects through the variance inflation factors (VIF) was analysed. The data obtained were treated with IBM SPSS software version 27.0 (IBM Corporation, New York, USA).

9.4 Results 9.4.1 Descriptive statistics 9.4.1.1 Sample characterization As for the socio-professional characteristics of the entrepreneur or person in charge of the firm (Table 9.2), on average they were 42.1 ± 8.1 years old (AGE), 85.4% had higher education (UNI) and 76.9% were male (MALE). In terms of the characterization of the companies, it is observed that on average, they had 4.7 ± 7.5 workers

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(LAB), 81.4% were in urban areas (URB), 34.4% were Technological KIBS (TECH), and 31.6% were located in the Centro Region, 24.6% in the North Region and 20.6% in the Lisbon Region. About Knowledge Transfer (KT), 15.8% mentioned the company’s cooperation with universities with a view to it, 47.2% did not mention the implementation of any innovation in 2016 and 1.2% implemented three innovations (INNOV). Table 9.2: Sample characterization. N Businessman profile

UNI

Non-higher education Higher education

MALE

Feminine Male

AGE (Mean ± DP) Firms profile

URB

MAN

Innovative capacity

INNOV



.%



.%



.%

. ± . .%

Urban



.%

Services



.%



.%

. ± .

Norte



.%

Centro



.%

Lisboa



.%

Alentejo



.%

Algarve



.%

Madeira



.%



.%

No



.%

Yes



.%





.%





.%





.%





.%

Açores KT

.%



LAB (Mean ± DP)

Company cooperation with universities



Rural

Manufacture

Region

%

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9.4.1.2 Econometric modelling Initially, the potential effects of multicollinearity were evaluated, assessed by the VIF, and it was found that all of them are less than 5 (the highest was 2.45), thus there is no multicollinearity between the different exogenous variables of the different econometric models. The results of the two estimated models are shown in Table 9.3. Regarding the factors that predict cooperation with universities, modelled through binary logistic regression, it is observed that the age of the entrepreneur (AGE) (OR = 0.89; CI95%: 0.82–0.97), the location of the company in urban areas (URB) (OR = 5.88; CI95%: 1.17–28.14) and the seniority of the company (OR = 1.12; CI95%: 1.02–1.22) statistically influences knowledge transfer. The older the entrepreneur, the less likely they are to transfer knowledge from universities to companies, companies located in urban regions are more likely to cooperate with universities, and older companies show a greater tendency to cooperate with universities to knowledge transfer. Regarding innovations, companies located in urban areas (URB) (OR = 2.67; CI95%: 1.58–4.51) present a significantly higher number of innovations, the older the company (ANT) was, the smaller it was the number of innovations (OR = 0.95; CI95%: 0.92–0.98). The transfer of knowledge from universities to the company (KT) significantly increases the number of innovations (OR = 2.67; CI95%: 2.17–3.29). Table 9.3: Regression models (odds ratio e CI95%). Model I DV – KT

Model II DV – INNOV

AGE

,* (.–.)

. (–.)

UNI

, (.–.)

. (.–.)

MALE

, (.–.)

. (.–.)

,* (.–.)

.* (.–.)

, (.–.)

. (.–.)

ANT

.* (.–.)

.* (.–.)

LAB

. (.–.)

. (.–.)

HIGH

. (.–.)

. (.–.)

LIS

. (.–.)

. (.–.)

URB TECHN

KT *p < 0.05; DV – dependent variable.

,* (.–.)

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9.5 Final considerations The purpose of this chapter was to address the impact on innovation of knowledge transfer and cooperation between universities and SMEs. There are exactly three principles that we seek to find in research questions, a good theory needs to be simple, sober, and realistic (Teixeira et al., 2019). In this sense, at this stage of our investigation, we will answer the research questions that we proposed at the beginning of the research. After our statistical analysis, we found that, regarding the characteristics that promote knowledge transfer and cooperation between SMEs and universities, we found that the older the entrepreneur, the less likely they are to transfer knowledge from universities to companies. On the contrary, older companies located in urban areas were more likely to cooperate with higher education institutions. SMEs located in an urban environment present higher innovative performance, and younger companies demonstrate greater innovative capacity (Benito-Hernandez et al., 2012; Hight et al., 2018; Lindberg, 2001). As several authors have argued, we also conclude that knowledge transfer and cooperation between SMEs and universities play a particularly important role in the innovative performance of organizations. Thus, knowledge transfer and cooperation between SMEs and universities is extremely important because it allows the exploration and verification of innovative knowledge transfer (Aristei et al., 2016; Un et al., 2010). In this sense, cooperation agreements have become important regarding the costs of innovation, since they are a major barrier to innovation, but they are not risking of innovation. Appropriateness strategies do not seem to be relevant for research and development cooperation with universities (Arvanitis, Kubli & Woerter, 2011). Bearing in mind that companies are inserted in a medium of great uncertainty and complexity, the ability to adapt to these contingencies is essential. The greater your adaptability, the greater your competitive advantage over your competitors. One way to achieve this is precisely through cooperation with universities. SMEs must understand that to be at the forefront of knowledge; they need to look for where it really is generated, that is, in the academies. Another way to differentiate yourself is through innovation. The study also provides relevant implications for different stakeholders in the development of university–business links for sustainable innovations. The reduction of bureaucratic barriers to interactions with external agents by universities, as well as the establishment of incentive schemes that reward involvement with sustainable innovation, are fundamental to success. This requires cultural change to promote the type of change institutional framework that realigns academic incentives for sustainable innovations, integrating them into specific policies and strategies across the academic system. Entrepreneurs and the different types of SMEs firms need to intensify their links with the academic environment to promote innovation and sustainable development. It is essential to build an innovation ecosystem oriented to sustainability, where entrepreneurs and managers integrate sustainability in the organizational purpose, instead of focusing solely on financial objectives, and where

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new ways of collaborating with universities are used to achieve these objectives. Decision makers in the field of innovation and higher education policies must support a renewed mission of universities by incorporating social, environmental, and economic innovations. Thus, the combination of efforts between policy makers, university managers, companies and civil society are fundamental for the co-creation of sustainable innovations. Acknowledgements: The authors wish to acknowledge the Portuguese Foundation for Science and Technology (grants and NECE-UIDB/04630/2020) and ISAL Research Center of the Higher Institute of Management and Languages of Madeira provided support for this study.

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Bart Henssen, Talia Stough, Elien Crois, and Luana Jassogne

Chapter 10 University research centres as catalysers in entrepreneurial ecosystems: Fostering the transitions towards sustainable business models via the university–business interface 10.1 Introduction There is a critical need to address the unsustainable basis of current economic activity. For example, the planetary boundaries of biodiversity, nitrogen, and climate have already been transgressed (Whiteman, Walker & Perego, 2013). The effect of business activity on social and ecological systems has been the centre of a rise in literature on corporate sustainability. New paradigms call for a critical reflection of economic activity in relation to social and ecological boundaries (Raworth, 2017). While pioneers of corporate sustainability tended to be small players in niche markets, nowadays massmarket incumbents are also adopting aspects of corporate sustainability. Developing and refining business models for sustainability are vital for the transformation towards corporate sustainability (Upward & Jones, 2015). As stated by Schaltegger (2016: 266), “paying attention to particular characteristics of business models and their coevolutionary interplay is needed and promising to better understand the possibilities and limitations of sustainability transformations of markets.” The transition towards sustainable business models is reflective of the need for sustainable transitions in our society, and the need to close the knowledge–action gap between real-world attempts at transitions and academic theorizations. In this context, universities play an important role in the entrepreneurial ecosystem as catalysts for knowledge transfers for promoting sustainable entrepreneurship (Volkmann et al., 2019; Wagner, Schaltegger, Hansen & Fichter, 2019). University research centres on sustainable entrepreneurship are uniquely positioned to serve as catalysts between knowledge generated during real-world experiences of corporate sustainability and the further theorization about these business model transitions (academic knowledge generation). University–business interfaces around a shared value (i.e. sustainable transitions) could contribute to increased coherence in an entrepreneurial ecosystem, which would help strengthen the system (Roundy, Brockman & Bradshaw, 2017). Being able to distil transmittable learning from real-world experience is necessary for fostering the transformation towards sustainability, “Knowledge, experience, and information-processing are the foremost resources determining how aims of actions are set; how situations, opportunities,

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and risks are assessed; and how constellations, cues, and patterns are interpreted” (Meusburger, Werlen & Suarsana, 2017: 2). Vice versa, universities’ research centres with strong connections to the real world, that is, government, industry, civil society and an understanding of the environmental boundaries, coupled with a regional focus (e.g. the regional Helix model; Farinha, Santos, Ferreira & Ranga, 2020), are perfectly positioned to act as a catalyst of change. In this chapter, we will describe how a research centre for sustainable entrepreneurship’s unique positioning at the interface of theoretical advances in sustainable business models and real-world implementation of sustainable business models help foster sustainable transitions in business and yields critical insights for future innovations of these models. We first offer the reader an introduction to the literature on sustainable business models. Then we highlight an illustrative case of a university–business collaboration to describe how such collaboration helps close the knowledge–action gap, and in turn provide insights to further refine sustainable business model theorization.

10.1.1 Sustainable business models In the context of our ever-turbulent world, “the only companies that will remain viable in their markets will be the ones that create substantial value with their products and services for the systems they inhabit. This added value needs to exceed the value that the companies draw from the systems around them” (Glauner, 2016: foreword). Longterm viability and organizational resilience require business model innovations that allow organizations to cope with the local and global sustainability challenges they are confronted with. As seen in the quote above (Glauner, 2016), the concept of value is also critical for businesses moving forward. Traditionally “value” in the business context referred only to financial gain, but Glauner (2016) invites us to see value outside of old conceptualizations – value for the system in which a business is part of. Traditionally, business models are frameworks used by firms to consider how their activities are contributing to the creation of value. According to Osterwalder and Pigneur (2010), a business model captures the rationale of how a business creates, delivers, captures, and exchanges value for the customer. In line with the new conceptualizations of value, a new avenue of research has emerged focused on innovating new business models that can help develop integrative and competitive solutions by either radically reducing negative and/or creating positive external effects for the natural environment and society (Schaltegger et al., 2016). These “sustainable business models” are models that help in, “describing, analysing, managing, and communicating: i) a company’s sustainable value proposition to its customers, and all other stakeholders, ii) how it creates and delivers this value, iii) and how it captures economic value while maintaining or regenerating natural, social, and economic capital beyond its organizational boundaries” (Schaltegger et al., 2016: 6). In line with Glauner

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(2016), Schaltegger et al. (2016) saw “value” as beyond economic gain for the firm, but value for the system in which a business is part of. In understanding which elements distinguish business models as “sustainable,” Geissdoerfer et al. (2018) proposed the following defining elements. First, a sustainable business model is a business model that is pro-active, as opposed to the reactive nature of traditional business models. Second, sustainable business models integrate multi-stakeholder management. Critical for sustainable business models, is the inclusion of both internal and external stakeholders. Third, sustainable business models create monetary and non-monetary value for a broad range of stakeholders, as opposed to traditional business models which emphasize the creation a monetary value for privileged stakeholders. Fourth, sustainable business models adopt a long-term perspective, as opposed to traditional business models which are often short-sighted in nature. Elements of the definition by Geissdoerfer (2018) can be found in Evans et al. (2017) who offer a holistic view of sustainable value integrating economic, environmental, and social value, with a reference to the classic 3P (people, planet, profit) model, a multiple stakeholder perspective emphasizing mutuality and mutual value creation, the need for a value network under new rules (i.e., a new purpose, design and governance), and suggest applying strategies of PSS (Product-Service Systems) for sustainable business model innovation. Schaltegger et al. (2016) offer a comprehensive overview of the origins, present research, and future avenues of business models for sustainability, and a definition which stays close to the original Osterwalder model (2010) of economic value creation, while also maintaining or regenerating natural, social, and economic capital beyond organizational boundaries. On an applied level, Yang, Evans, Vladimirova, and Rana (2016) suggest a framework using value uncaptured for sustainable business model innovation, introducing four forms of uncaptured value: value surplus, value absence, value missed, and value destroyed. Similarly, Bocken, Short, Rana, and Evans (2013) offer concrete tools for sustainable business modelling with a focus on opportunities for new value creation, value missed, and value destroyed when switching to a more sustainable business model. In this chapter, we build upon the typology of business sustainability, which was proposed by Dyllick and Muff (2016), as we found it resonates best among businesses in terms of identifying the current situation and the elements needed in order to shift to a next phase of business model innovation. Based on a systematic analysis of the literature, the authors offer three types of business sustainability: 1.0, 2.0, and 3.0. Where a firm or organization falls on this trajectory is based on the organization’s main concerns, value created, and organizational perspective. Business sustainability takes the current economic paradigm of “business as usual” to a refined version of shareholder value management, for example, introducing new rules and processes for compliance, energy and climate management, and sustainable purchasing. Business sustainability 2.0 means broadening the stakeholder perspective and pursuing a triple bottom line approach, while business sustainability

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3.0 constitutes a truly sustainable business. The authors state that, “A Business Sustainability 3.0 firm looks first at the external environment within which it operates and then asks itself what it can do to help overcome critical challenges that demand the resources and competencies it has at its disposal,” which they call an outside-in approach. A short overview of Dyllick and Muff (2016) typology is provided in Table 10.1. Dyliick and Muff (2016) describe that movement from business as usual towards business sustainability 3.0 requires 1) a shift in concerns taken into account by the firm, 2) a shift in how “value” is conceived by the firm, and 3) the organizational perspective taken by the firm. These elements have also been added to Table 10.1. The desire to address the unsustainable basis of current economic activity is rapidly increasing among regional stakeholders such as businesses. The literature notes that firms are seeking to address environmental and social problems in which they are “intrinsically entangled” (Kennedy, Whiteman & Van Den Ende, 2016). Despite the growing interest in transitioning towards sustainable models, barriers exist that prevent these transitions from successfully or fully occurring. Geissdoerfer (2018) described this business model design–implementation gap as, “the set of challenges that prevent organizations from successfully innovating their business model, due to insufficient follow-up on ideas, lack of implementation of concepts, and failure of businesses in the market.” There is need for critical reflection on the type of barriers a firm faces when transitioning from business as usual to Business Sustainability 1.0, 2.0, and 3.0, and tools to help firms overcome those barriers. For this to occur, knowledge transfer is needed in the entrepreneurial ecosystem.

10.1.2 The role of research centres as catalysers for knowledge transfers in entrepreneurial ecosystems In order to understand barriers to the implementation of sustainable business models in the real-world, universities can play a critical role as catalysers in entrepreneurial ecosystems through university–business collaboration. Through such a collaboration, a two-way transfer of knowledge is possible. Transfers of knowledge in an entrepreneurial ecosystem are often conceived as one-way transfers of knowledge generated in the university exploited by industry in order to create economic, social value, and/or competitiveness (see, for instance, Carayannis, Provance & Grigoroudis, 2016; Ferreira, Raposo, Rutten & Varga, 2013; Ferreira & Carayannis, 2019; Kaklauskas et al., 2018; Wagner et al., 2019). But, as demonstrated in this chapter, the university–business collaboration also transfers real-world knowledge (e.g. about challenges to the implementation about sustainable business models) to research centres which serves to help further theorize these models. about innovations to these models. Twoway transfers of knowledge are needed in entrepreneurial ecosystems to overcome knowledge–action gaps as well as design–implementation gaps. In addition to the transfer of knowledge, when a university–business interface centres around a shared

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Table 10.1: Typology of business sustainability. Business-as-usual Business The Current Sustainability . Economic Paradigm Refined Shareholder Value Management

Business Sustainability . Managing for the Triple Bottom Line

Business Sustainability . Truly Sustainable Business

“Broadening the stakeholder perspective and pursuing a triple bottom line approach. Value creation goes beyond shareholder value and includes social and environmental values. Companies create value not just as a side effect of their business activities, but as the result of deliberately defined goals and programs”

“How can business contribute with its products and services to resolve pressing sustainability issues in their societies? or How can business use its resources, competencies and experiences in such way as to make them useful for addressing some of the big economic, social or environmental challenges that society is confronted with”

Definition

“Typical economic concerns (e.g. access to cheap resources, efficient processes, striving for a strong market position) are pursued to produce economic value in the form of profit, market value or, more generally, shareholder value”

“Even if sustainability concerns are considered in decision making and actions, business objectives remain clearly focused on creating shareholder value”

Concerns

Economic

Three-dimensional Three-dimensional

Starting with sustainability challenges

Value

Shareholder value

Refined shareholder value

Triple bottom line

Creating value for the common good

Inside-out

Inside-out

Outside-in

Perspective Inside-out

Source: Based on Dyllick and Muff (2016).

value (i.e. sustainable transitions), it increases the overall coherence of entrepreneurial ecosystems (Roundy et al., 2017). University–business collaboration in an entrepreneurial ecosystem can take many forms and follow evolving stages of maturity. Johnson (2006 and 2008) categorized the stages of maturity for university–business collaboration and provided examples of activities that could occur at each stage: 1) awareness (e.g. career fairs, interviews), 2) involvement (e.g. advisory programs, research grants, internships), 3) support (student consultancy, curriculum development, workshops and seminar, philanthropic support, guest speakers), 4) sponsorship (research sponsorship, collaborative research

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programs), 5) strategic (e.g. joint partnerships, joint lobbying, business development) (see also: Kaklauskas et al., 2018). While expectations from businesses towards universities are often situated at stages 1 to 4, university–business collaborations on sustainable transitions require strategic collaboration (stage 5). As seen in the case presented in Section 10.2, this type of collaboration requires a large extent of openness and trust among the involved stakeholders. Moreover, complex sustainability challenges require new research paradigms such as participatory, interactive, transdisciplinary, trans-academic, collaborative, and community-based research approaches (Bäckstrand, 2003; Kasemir, Jager, Jaeger & Gardner, 2003). This requires research collaborations among scientists and non-academic stakeholders from business, government, and civil society in order to address issues of sustainability, starting from the co-production of knowledge and the learning-through-doing and doing-through-learning approach. The main objective is achieve, “a virtuous cycle that firstly address[es] complexity with a transdisciplinary approach; secondly, is problem-driven and uses both scientific and local knowledge to resolve contextualized problems; and thirdly, promotes the active involvement of the different stakeholders in a process of scientific co-production” (Orecchini, 2012: 65). The university–business interface offers a new platform for conducting participatory, interactive, trans-academic, collaborative and, at times even, community-based research in an entrepreneurial ecosystem. As seen in the case described in Section 10.2, engagement with local and family-owned firms localized the production and transfer of knowledge. University research centres can play a pivotal role in supporting businesses to bridge the design–implementation gap, which refers to a lack of follow-up on ideas, when ideas are not implemented in the market or fail when they are (Geissdoerfer et al., 2018). University research centres can pinpoint the key elements for successful follow-up and implementation and can offer assistance in reflection upon factors of success or failure in the innovation. In turn, the insights from real-world attempts to adopt sustainable business models yields valuable insights for further innovations of these models and can help to make research accessible and relevant to practice. In the remainder of the chapter, we will describe a case of a university research centre’s interaction with firms on transitions towards sustainable business models in an entrepreneurial ecosystem. From this case, we will distil insights as to how university–business interfaces in entrepreneurial ecosystems can help foster sustainable business model transitions in the real world, and in turn, provide valuable insights to help better refine these models in theory.

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10.2 Transitions towards sustainable business models through a university–business interface: An illustrative case In this section, an example of a university–business collaboration is described to illustrate how such collaborations in an entrepreneurial ecosystem help foster a two-way transfer of knowledge on transitions towards sustainable transitions, specifically, transitions towards sustainable business models. First, the collaboration between the Odisee University of Applied Sciences Center for Sustainable Entrepreneurship (CenSE) and local firms is described. Secondly, how this collaboration leads to two-way knowledge transfer is explored. How knowledge transfers in this context help each side is discussed, and the role of research centres as catalysers in entrepreneurial ecosystems is reflected upon.

10.2.1 The centre for sustainable entrepreneurship (CenSE) The Odisee University of Applied Sciences Center for Sustainable Entrepreneurship (CenSE) is situated in Brussels, the heart of the European Union. As part of a university of applied sciences, the centre straddles the realms of the theoretical and applied. This situates it at the interface of theoretical innovations to sustainable business models, and the real-world context where these models are being applied. Annually, CenSE interacts with an average of 150 local businesses. These interactions vary from intimate consulting relationships, to contact during masterclasses, or seminars on sustainable business models. Through its consulting services, CenSE builds relationships with regionally anchored business partners ranging from small and medium-size enterprises (amongst which, most are family businesses) to large businesses (with an annual turnover of more than 10 billion euros). The research centre also engages with local governments regarding future thinking for sustainable transitions. For example, CenSE engages with the Brussels Regional Governance in relation to their policy on how the sharing economy could contribute to a sustainable future, and the Flemish Regional Governance on future scenarios for sustainable higher education (Flemish Government, 2020). CenSE offers masterclasses on the topics of sustainable business transformation, sustainable HRM, and an International Program Sustainable Management for business leaders. In addition to this, CenSE organizes (with partners from the global south) an International Summer on School Sustainable Management, which brings together international students, business managers, and NGO managers to foster sustainable thinking, increase awareness for sustainable business opportunities, and provide participants with knowledge and the skills to translate sustainable ideas into relevant and viable business initiatives.

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As a centre for sustainable entrepreneurship, CenSE is uniquely situated at the interface of theoretical advances on sustainable business model innovation, and engagement with firms applying these models in the real-world context. From these interactions with firms and organizations (interviews, workshops, masterclasses, consulting), CenSE garnishes insights from real-world experiences with transitions towards sustainable business models (motivations, obstacles, outcomes). In this section, we will describe the experiences of CenSE as an example of how university–business collaboration can lead to the co-creation of insights on innovations in sustainable business models. These insights illustrate how university–business collaborations foster the sort of knowledge exchanges in entrepreneurial ecosystems necessary for solving complex sustainability issues.

10.2.2 Knowledge transfer academia to business: Insights and challenges in the transition to sustainable business models in the real world Providing strategic consultation to business partners enables CenSE to serve as a catalyst for transitions to sustainable business models. Through interviews, workshops, masterclasses, and consulting serves to firms and organizations, CenSE has been able to help support firms and organizations in adopting sustainable business models. These interactions confirm that firms and organizations are increasingly interested in transitions towards sustainable models, but they require support via frameworks, tools, and guidance to address their questions on sustainable business management. Business leaders are not only asking how to facilitate the sustainable business transition but also what corporate sustainability is, and more specifically, what a sustainable business model is and requires. Notable exemplars of outcomes from these interactions include CenSE providing consulting, support, and knowledge transfer on sustainable sea farming, a new business model for sustainable export to Africa and a revised business model for sustainable soup production for a large family-owned supermarket chain; helping entrepreneurs in Lithuania reformulating their business model in a sustainable manner; conveying knowledge on sustainable business model strategies and testing business ideas among Colombian entrepreneurs; exploring new business models together with SMEs dealing with the COVID-19 crisis; and many other examples. Based on these interactions, organizations were able to express barriers they encounter when striving to adopt (more) sustainable business models. In turn, CenSE was able to provide specific support to help overcome these barriers. These barriers correspond to 1) concern, 2) value, and 3) organizational perspective (see Table 10.1). Firstly, it was revealed that the Triple P Model (people, planet, profit) causes fundamental problems prohibiting businesses to move past Business Sustainability

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1.0. The aim to reach a balance between concerns (people, planet, and profit) is subject to external shocks, which force organizations to revert back to and prioritize profit at the expense of the other concerns (hence, forcing the business to fall back on “business as usual”). Firms specifically need support in maintaining a balance of concerns in the face of external shocks (e.g. COVID-19). Through collaboration with business partners, CenSE was able to help firms overcome this barrier by recommending a transition from a reactive business model to a proactive business model. As such, firms would focus on a systemic, pro-active approach to sustainability challenges, with a strong focus on the strategic advantage their sustainable choices could generate for future value creation. Secondly, it was revealed that it is difficult for firms to reconceptualize “value” other than in a traditional economic sense. It was clear through interactions that business partners wanted to pursue further sustainability but moving past a “reined shareholder” notion of value (Business Sustainability 1.0), which is a major barrier. Few of CenSE’s business partners operate in model 3.0. Having conversations about what value constitutes of, how it is created, and for whom, helps businesses transition towards sustainable models. Although businesses realize that resources are scarce, pushing them towards efficient use and circular models (when economically viable), the idea of “borrowed resources” instead of “owned resources” proved very helpful for a change of mindset. This perspective challenges firms to see resources as belonging to the natural ecosystem, and the firm’s role in returning them, preferably with added value for the environment. Thirdly, it was difficult for firms to move past an “inside-out” organizational perspective and adopt an outside-in perspective (Business Sustainability 3.0). Interactions with business partners revealed that business decisions generally do not sufficiently take into account the interests of stakeholders or future generations. Introducing systemic thinking to business partners helped to tackle this issue. Systems thinking takes into account the (eco-)systemic effects of business decisions and contributes to a change in mindset (i.e. from a business-centric to an ecocentric view). Introducing a Systems Thinking Approach helped CenSE’s businesses partners realize, on the one hand, they are just a small part of the bigger whole (i.e. the ecosystem), on the other hand, small adaptations over which they have power may have large (sustainable or unsustainable) consequences for the entire system. Additionally, expanding the decision-making horizon from short-term (often the next 6 months) to long-term (in 10- or 20-years’ time), was helpful in broadening the perspective and taking into consideration the interests of future generations. Scenario Analysis and Backcasting were helpful tools for business partners to adopt this longer-term perspective.

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10.2.3 Knowledge transfer business to academia: Insights and challenges for sustainable business model innovation Interviews and conversations with business managers during masterclasses “Sustainable Business Transformation” revealed that the majority of business partners map their business model as 1.0 or 2.0 but would like to further transition towards 2.0 or 3.0. Interestingly, transitioning from 1.0 to 2.0 was reportedly easier than than moving from 2.0 to 3.0. This is paradoxical, since it would be expected that businesses already on the pathway to sustainability would have an easier task further transitioning. In addition, business partners confided that they experienced several barriers in this transition (i.e., the design–implementation gap). Most reported barriers related to time and budget restrictions; some businesses partners noted the change in mindset as the most difficult barrier to overcome. As stated by one business leader: “Maybe the change towards sustainability 3.0 will happen, but not with the current generation of leaders.” Real-world experiences of implementing existing business models for sustainability revealed the need for further innovations in this field. As described in Section 10.2.1, through collaborations with business partners, CenSE found that: 1) it was difficult for firms to maintain a balance for the concerns of people, planet, and profit, especially when faced with external shocks, 2) it was difficult for firms to move past a “reined shareholder” notion of value, and 3) it was difficult for firms to move past an “insideout” organizational perspective and adopt an outside-in perspective.

10.2.3.1 Balancing concerns Adopting Business Sustainability 3.0 requires a mind shift in which planetary outcomes are the primary focus, followed by societal outcomes, and finally, economic outcomes. This relates to the so-called Doughnut Model put forth by Raworth (2017). The sustainable organization 3.0 should therefore embody a holistic-systemic or ecosystems approach and define corporate sustainability as a model in which planetary and social boundaries limit economic goals since natural capital is an essential prerequisite for economic capital. Dyllick and Muff (2016) propose that the first shift from business-as-usual to business sustainably 1.0 should involve broadening the business (economic) concern. However, based on real-world experience, the Triple P Model (people, planet, profit) causes fundamental problems prohibiting businesses to move ahead. Further theorization on innovations to sustainable business models should take into account that in the real-world, the interchangeability of the 3Ps actually acts as a barrier to transitions towards sustainable business models. The difficulty in finding a proper balance stands in the way of achieving “strong sustainability” (3.0). While early conceptualizations of sustainable business models heavily relied on a

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language of balance (e.g. Elkington, 1998), more realistic iterations are needed, or the feel-good narrative of balance is always at risk of reverting back to economic dominance.

10.2.3.2 Towards a reconceptualization of “value” Firms need support in this reconceptualization of “value.” In the model proposed by Dyllick and Muff (2016), the authors propose reconceptualizing value from “stakeholder value” to “shareholder value,” then “triple bottom line,” and eventually to creating value for the common good. Based on interactions with business partners, we found that Sustainable Business 1.0 is mostly reactive and non-systemic in approach, while Sustainable Business at 2.0 is a strategic, systemic approach. Sustainable Business 2.0 defines corporate sustainability as a balance between economic and ecological/social goals in the present with the intention to evolve towards an ecosystems approach in the future. Business in model 1.0 is reactive, while 2.0 and 3.0 are proactive. In essence, many businesses in stages 1.0 and 2.0 still define “value” as economic value, and social and environmental responsibilities as a burden or cost, or something which is necessary because of changing environmental and customer demands. Further considerations on how to broaden the concept of value to a societal and environmental level is required to help firms overcome this barrier. Without further consideration for how to support firms in their broadening of the concept of value, firms will be stuck in their transitions towards sustainable business models at 2.0.

10.2.3.3 Organizational perspective While Sustainable Business Model 1.0 resembles an absorptive business model, in which shocks and stress (such as environmental shocks or lack of or scarcity of resources) are being addressed when they impact the business. Business Model 2.0, on the other hand, should be adaptive in nature, in the sense that incremental adjustments in anticipation of or in response to change (for example, climate change) are being incorporated into the business model to create future adaptability. This perspective, however, still follows the logic of the “inside-out” approach. In Business Model 3.0, however, businesses are able to create a transformative response, in a sense that they address the root of the problem (e.g. poverty, inequality, and unfair labour conditions), because their choices are led by sustainability at heart. Dyllick and Muff (2016) propose that Sustainable Business Model 3.0 follows the logic of an “outside-in” approach in which a business first looks at the external environment within which it operates and then asks itself what it can do to help

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overcome critical challenges that demand the resources and competencies it has at its disposal. We found that in the real-world, both an “inside-out” and an “outside-in” approach are needed. In our collaboration with our business partners, we found it useful to stretch the inside-out by using the Triple-Layered Business Model Canvas – TLBCM (Joyce & Paquin, 2016). By doing this, we broaden the inside-out approach on three levels: 1) how do I perceive my business on the economic, societal, and environmental level, using the original TLBMC, 2) how do others perceive my business on these levels, by engaging in a stakeholder conversation, 3) how do we perceive the desired state on these levels, now, in the near future, and in the extended future, thereby engaging in an exercise on the purpose and vision of the organization.

10.2.3.4 Strategic positioning of “sustainability” in firms Dyllick and Muff’s (2016) framework for sustainable business model innovations involves movement along the following axes: 1) concerns taken into account by the firm, 2) how “value” is conceived by the firm, and 3) the organizational perspective taken by the firm. Based on our interactions with business partners, we would add another axis to this framework: the strategic positioning of “sustainability” in a firm. In Business Sustainability Model 1.0, sustainability is positioned as a standalone business unit. Moving to Business Sustainability Model 2.0 would entail that sustainability is strategically positioned in the firm – sustainability issues are integrated in business strategy to serve a strategic purpose. In Sustainable Business Model 2.0, the business or organization strategically decides on the pathway towards becoming a more sustainable organization, which definitely has it merits since it helps the firm integrate sustainability in its decision making. The positioning of sustainability in a firm will in turn have an impact on how concerns are balanced, conceptualizations of value, and even the organizational perspective taken, so it is a pivotal part that is being overlook in the current framework.

10.3 Conclusion The university–business interface is a critical space in the entrepreneurial ecosystem for the types of knowledge transfers necessary to tackle complex sustainability issues, such as transitions to sustainable business models. University research centres can play an important role in closing the knowledge–action gap by serving as a catalyst between theoretical development of sustainable business models and real-world implementation. This interface positions research centre in two roles: 1) supporting businesses in bridging the design–implementation gap by offering guidance to select the

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appropriate business model and to resolve conflicts with the current business model and organizational logic, and 2) gaining the insights from real-world attempts to adopt sustainable business models which serves to help further theorize these models. In this chapter we described the experiences of the Odisee University Center for Sustainable Entrepreneurship’s (CenSE) collaborations with business partners to illustrate how research centres can be catalysers in entrepreneurial ecosystems. We employed Dyllick and Muff’s (2016) framework for Sustainable Business Models to make sense of the two-way knowledge transfer. Through interactions it was found that: 1) it was difficult for firms to maintain a balance for the concerns of people, planet, and profit when faced with shocks, 2) it was difficult for firms to move past a “reined shareholder” notion of value, and 3) it was difficult for firms to move past an “inside-out” organizational perspective and adopt an outside-in perspective. We described how the centre was able to transfer knowledge to business partners that enabled them to better overcome barriers in their transitions towards the adoption of sustainable business models. We also described how insights, in turn, are useful for the further theorization and innovation of sustainable business models. As described in the illustrative case above, university research centres are uniquely positioned to serve as catalysts for knowledge transfers in entrepreneurial ecosystems. The complexity of sustainability problems requires new research paradigms and modes of knowledge creation (Bäckstrand, 2003; Kasemir et al., 2003). Within the entrepreneurial ecosystem, the university–business interface is critical for understanding and closing the design implementation gap for sustainable business models. The type of collaboration described in the case involves different actors in the entrepreneurial environment sharing common values (i.e. sustainable business models). Shared values could act to enhance the resilience of the ecosystem (Roundy et al., 2017). While the case presented in this chapter centres around university–business interactions on sustainable business models, it is easy to imagine similar benefits across a variety of topics. Research centres serve an important role in entrepreneurial ecosystems as catalysers for knowledge transfer, and in the co-creation of knowledge with businesses, which is necessary to achieve a shift towards a more sustainable world.

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Schaltegger, S., Lüdeke-Freund, F. & Hansen, E. G. (2016). Business models for sustainability: A co-evolutionary analysis of sustainable entrepreneurship, innovation, and transformation. Organization & Environment, 29(3), 264–289. Upward, A. & Jones, P. H. (2015). An ontology for strongly sustainable business models: Defining an enterprise framework compatible with natural and social science, Organization & Environment, Special Issue: Business models for sustainability: Entrepreneurship, innovation, and transformation, 29(1), 97–123. Volkman, C., Fichter, K., Klofsten, M. & Audretsch, D. B. (2019). Sustainable entrepreneurial ecosystems: An emerging field of research. Small Business Economics, 1–9. doi:https://doi. org/10.1007/s11187-019-00253-7 Wagner, M., Schaltegger, S., Hansen, E. G. & Fichter, K. (2019). University-linked programmes for sustainable entrepreneurship and regional development: How and with what impact?. Small Business Economics. doi:https://doi.org/10.1007/s11187-019-00280-4 Whiteman, G., Walker, B. & Perego, P. (2013). Planetary Boundaries: Ecological foundations for corporate sustainability. Journal of Management Studies, 50(2), 207–336. Yang, M., Evans, S., Vladimirova, D. & Rana, P. (2016). Value uncaptured perspective for sustainable business model innovation. Journal of Cleaner Production. doi:https://doi.org/ 10.1016/j.jclepro.2016.07.102

Ronnie Figueiredo, Raquel Reis Soares, Marcela Castro, and Pedro Mota Veiga

Chapter 11 The Spinner Innovation: Factors for inclusion and advocating in sustainable ecosystems 11.1 Introduction Based on Setor and Joseph (2020), ecosystems emerge when institutions and human individuals interact to influence the formation of career paths even while specific locations are less critical to career success than are the within-individual factors, such as the accumulation of human capital and career choices. In terms of the digital ecosystem context, the reframing of digital companions will only take place when the ecosystem in which they operate is responsibly designed with society-in-the-loop (Morley & Floridi, 2020). There has also been the proposition of a specific academic and research field that would complement the multiplicity of disciplines currently contributing to knowledge service ecosystems (Farrell & Rucinski, 2013). In particular, industry is now continuously adopting open platforms to create and maintain ecosystem innovation. The government’s role has transitioned from regulation and control towards facilitation. Universities have also become proactively engaged in multiple areas, from technology transfers to knowledge cocreation. Companies and their customers have started launching new concepts, R&D, and commercialisation to result in a shared economy (Yun & Liu, 2019). Within this context, we may observe the importance of ascertaining which factors influence inclusion in sustainable ecosystems, especially those based on models of innovation. Hence, our research question becomes: What are the factors for inclusion and advocating in sustainable ecosystems? Our main goal involves identifying, in accordance with the Spinner Innovation Model (2019), the three factors associated with the model that foster inclusion and advocating in sustainable ecosystems. Furthermore, this explains why, in keeping with the different ecosystem services provided to the community, the multifunctional management of forests has acquired an important role over recent years (Riccioli et al., 2020). The proposed methodology incorporates a systematic literature review collected from the Scopus database.

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The organisation of this chapter is as follows: Section 11.2 introduces the Spinner Innovation Model within the scope of a sustainable ecosystem approach; Section 11.3 describes the methodology deployed; Section 11.4 presents the co-author cluster analysis before Section 11.5 details the conclusions, discussion, limitations, and proposals for future research.

11.2 Spinner Innovation: A sustainable ecosystem approach There has long been a perception of innovation as a producer-centred process based on the assumption that profit-seeking incentives constitute the main drivers of innovation (Trischler, Johnson & Kristensson, 2020b). However, in term of sustainable innovation ecosystems, Zhang et al. (2020) propose that such systems should distinguish their climate impacts in order to ensure sustainable ecosystem management. Furthermore, previous studies have reported that sustainable ecosystems are not as successful as they might be in terms of mimicking the behaviours of biological systems (Dave & Layton, 2020). However, in terms of payment for ecosystem services, Ren, Li, Li, Li, and Daily (2020) describe how this means may provide an instrument for reconciling ecological conservation and poverty alleviation. As Suganthi (2020) confirms, such approaches may be part of a solution when taking into account how large scale production and consumption have played havoc on the ecosystem over the years. One example of ecological conservation emerges in how the blue economy became synonymous with generating wealth from ocean-related activities while still protecting and supporting marine ecosystems (Phelan, Ruhanen & Mair, 2020). In addition, the response of ecosystem services to rural–urban transitions is critical to any urban planning that aims to achieve sustainable urban development, in particular for coastal and island cities where economic development and the maintenance of ecosystem services enter into conflict (Xu, Jiang, Huang & Wang, 2020). This furthermore encompasses the reason the multifunctional management of forests has acquired an important role over recent years (Riccioli et al., 2020) in keeping with the different ecosystem services provided to the community. Another example stems from smart cities, acting as a modern reality in an increasingly digitised and fast changing world and, hosting multidimensional, multilayered, and interconnected career ecosystems, posing a number of challenges for the development of sustainable careers (Curşeu, Semeijn & Nikolova, 2020).

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Career ecosystems incorporate complex structures in which a myriad of forces cause the players within – both local, city, and national authorities as well as individuals – to respond to economic and social constraints (Guo & Baruch, 2020). On the other hand, the industry ecosystem represents an important step, we would argue, in understanding the career disadvantages faced by women and in designing strategies for change (Cooper, Baird, Foley & Oxenbridge, 2020). Based on Setor and Joseph (2020), ecosystems in which institutions and human individuals interact and influence the formation of career paths, locations are less critical to career success than the within-individual factors, such as human capital accumulation and career options. According to Turpin and Shier (2020), community-based human service organisations employ a variety of strategies to deliver effective services and support vulnerable groups. In terms of the digital ecosystem context, the reframing of digital companions will only take place when the ecosystems they operate in are responsibly designed with society-in-the-loop (Morley & Floridi, 2020). There have also been proposals for a specific academic and research field to complement the multiplicity of disciplines that contribute to knowledge on service ecosystems (Farrell & Rucinski, 2013). In general, according to Calderón-Hernández, Jiménez-Zapata and Serna-Gomez (2020), there is a need for universities, given the trend towards knowledge societies and globalisation, to interact with innovation, industrial development, and international competition. Indeed, any entrepreneurial university defines and nurtures virtuous processes of knowledge sharing that necessarily require universities to reconfigure their traditional educational programs and approaches in order to create favourable contexts for student entrepreneurship and able to support them in processes evolving from idea generation to idea development, idea prototyping, and commercialisation (Secundo, Mele, Del Vecchio & Degennaro, 2020). In addition, taking into consideration their contributions to the combined performance of education, advanced research and the networking of knowledge, universities have gained recognition as knowledge-intensive institutions and environments susceptible to fostering human capital development, innovation, and entrepreneurship (Ierapetritis, 2019). Indeed, clusters play a fundamental role in establishing a model for innovation capacity knowledge transfers, interconnecting the institutional, scientific, and business participants. Such a model needs adapting to the specific sector and enterprise characteristics and relies on an interconnecting structure, decentralised to a greater or lesser extent in accordance with the respective features (Paiva, Domingues & de Andrade, 2016).

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In particular, the adoption of open platforms is now widespread in industry in order to create and maintain ecosystem innovations. The government’s role has in the meantime evolved from regulation and control towards facilitation with universities also becoming proactively engaged in multiple areas, from technology transfers to the co-creation of knowledge. Companies and their customers have started to establish new concepts, R&D, and commercialisation and resulting in an increasingly shared economy (Yun & Liu, 2019). The Spinner Innovation Model (see Fig 2.1) serves to connect universities, government and industries within an ecosystem to foster economic development, especially in the small- and medium enterprise (SME) sector, through applying three dimensions: knowledge creation, knowledge transfer, and innovation. This resembles the three interacting axes of a fidget spinner with knowledge intensive business services (KIBS) located at the centre of economic development (Figueiredo & Ferreira, 2019). The Spinner Innovation Model divides KIBS into p-KIBS (professional KIBS), t-KIBS (technological KIBS), and c-KIBS (cultural KIBS), (Figueiredo & Ferreira, 2019). In turn, the Spinner Innovation Model approach arises from the perspective of KIBS as building and maintaining relationships with firms (service providing sector) able to promote innovation. Innovation is hereby perceived as resulting from the interaction of private and public knowledge to ensure the delivery of knowledgeintensive solutions (Figueiredo, Ferreira, Silveira & Vilarinho, 2019). This furthermore enables the acceleration of processes designed to change mind sets and bring about organisational transformation in addition to helping better understand the agility of interactions with other actors participating in the ecosystem (Figueiredo & Ferreira, 2019).

11.3 Methodology The study analysis consists of three steps with these explained in further detail in the following subsections.

11.3.1 Background The study involved the application of a systematic literature review according to the methodology of Lim, Davis, Tang, Shannon, and Bonfield (2019); Palmatier, Houston, and Hulland (2018); and Dolati Neghabadi, Evrard Samuel, and Espinouse (2019).

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Figure 11.1: The Spinner Innovation Model. Source: Figueiredo & Ferreira (2019)

11.3.2 Analysis process The first step deployed a search for keywords in a single database, Scopus, to gather the relevant sources in the literature. This step incorporated articles published in English approaching the subjects of “Service Sector,” “SMEs,” “KIBS,” “Knowledge Creation,” “Knowledge Transfer,” “Innovation,” and “Sustainable Ecosystem.” The following keywords served to identify the number of such articles published over a decade in order to trace the evolution of the literature. We applied the same steps to the categoric variables (see Appendix 11.1).

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The literature review comprises a total of 368 articles selected according to the structured keyword search of the above-mentioned database covering the timeframe 2011–2020 (see Figure 11.2).

Identified relevant articles: 823 Elimination based on titles and abstracts Article retrieved for more detailed evaluation: 455 Do not meet inclusion criteria Articles included in review: 368

Figure 11.2: Literature review flow. Source: Elaborated by the authors

The inclusion criteria for the systematic literature review are the following (Dziallas & Blind, 2018): 1. Available in at least one database or cited in one of the relevant articles; 2. Includes one of the keywords in the title, abstract, or full text; 3. Journal publications; 4. Peer-reviewed articles; 5. Published between 2011 and 2020; 6. Articles in English. The second step involved synthesising the studies selects (see Figure 11.3) based on the Spinner Innovation Model variables (Figueiredo & Ferreira, 2019) in order to categorise 140

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0 Sustainable Sustainable Sustainable Sustainable Sustainable Sustainable Ecosystem and Ecosystem and Ecosystem and Ecosystem and Ecosystem and Ecosystem and SMEs Service Sector Knowledge Knowledge Innovation KIBS Creation Transfer

Figure 11.3: Publication numbers per category (2011–2020).

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those most relevant. In order to undertake further analysis, we generated a Microsoft Excel (Office) database and included the following related variables in accordance with the results identified in the referenced literature. The third step analysed all the data through recourse to the VOS Viewer (Version 1.6.13) and correspondingly applying co-author network analysis (see Figures 11.4-11.8) based it portrayed the clusters and their respective strength. We furthermore carried out in-depth content review to identify the key studies related to the proposed research theme. This analytical phase terminated with the application of mindmup software (MINDMUP).

11.4 Co-author cluster analysis 11.4.1 Sustainable ecosystems and SMEs: Cluster 1 – N (60)

Figure 11.4: Co-author network analysis of 60 publications and their clusters with associated strengths. Source: Elaborated by the authors

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SME dominate the European market and correspondingly responsible for producing the bulk of European Gross Domestic Product. In order to strengthen these companies and the European market as a whole, new methods need introducing to enable these companies to create new value chains (Pöltner & Grechenig, 2010). In addition, Valpreda, Moretti, Segreto, Cappellaro and Brunelli (2018) demonstrate how the presence of SMEs in urban ecosystems constitutes a fundamental new opportunity not only for the design and planning of spaces but also for the planning of smart energy systems and the quality of life in cities. On the other hand Žigiene, Rybakovas and Alzbutas (2019) detail how risk management in commercial processes stands out as among the most important procedures shaping the competitiveness of SMEs, their innovativeness and their potential contribution to global sustainable development goals. The ecosystem around commercial processes thus represents the prerequisite for managing the risks faced by SMEs. Furthermore, Valença and Alves (2017) explain how power and dependence emerge out of partnerships among SMEs when building software ecosystems. Indeed, Soltysova and Modrak (2020) analysed the existing related literature before categorising the sharing economy-based business models in relation to their traditional business peer models. Wozney et al. (2017) convey how the importance of developing and improving the relational capabilities SMEs receives widespread acknowledgement. Therefore, Bolesnikov et al. (2019) study the scope for new and improved business collaborations between a car retailer operating in several European countries and its SMEs customers, currently owning fleets of at least five vehicles, operating across various different sectors. Furthermore, the main underlying concepts, the research roadmap and the results achieved by this initiative undergo a brief discussion in Afsarmanesh and Ollus (2006). In addition, Zorpas (2010) sets out the benefits, disadvantages, motivations and the differences in the international literature in order to define the need for implementing environmental management systems. Hernández-Díaz, Calderón-Abreu, Castro-Gonzáles, and Portales-Derbez (2020) report how the dimensions attaining higher levels of sustainability were global management, impacts, and competitiveness while the dimensions returning lower values were relationships and transparency.

11.4.2 Sustainable ecosystem and service sector: Cluster 2 – N (70) In a recent study, Jerome, Sinnett, Burgess, Calvert, and Mortlock (2019) set out a framework containing 23 principles for delivering green infrastructure (GI). This is

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Figure 11.5: Co-author network analysis of 70 publications and their clusters with associated strengths. Source: Elaborated by the authors

internationally applicable as a set of standards for assessing the quality of GIs and ensuring that their contribution to the quality of life, health and wellbeing of individuals and communities, flood resilient towns and cities, and environments in which nature can flourish and become more viable in conjunction with development. Marine ecosystem services are fundamental to ecosystem-based management aiming at attaining the ecological, economic, and social sustainability of the use of our seas. Within this framework, Bryhn, Kraufvelin, Bergström, Vretborn, and Bergström (2020) developed a model for the assessment of the impacts of marine tourism and commercial fishing on ecosystem services. Additionally, Nesticò, Guarini, Morano, and Sica (2019) put forward a public management support tool aimed at identifying the optimal forestry project location according to criteria that not only consider the financial dimension but also the social, cultural, and environmental facets. Through applying discrete linear programming algorithms, the model undergoes testing through a theoretical case study that reveals the advantages and limitations of the model as well as prospects for future research. Furthermore, Schwanitz, Wierling, and Shah (2017) apply a range of assessment methods and study their usefulness as tools for identifying the trade-offs and comparing sustainability performance levels. Indeed, Beudou, Martin, and Ryschawy (2017) analyse the livestock, cultural, and territorial vitality (dis)services perceived by local actors in two different French territories in order to understand how these services might act as levers for an agroecological transition of livestock. The analysis extended to Muthee, Mbow, Macharia, and Leal-Filho (2018) and their article assessing the extent to which adaptation projects have incorporated ecosystem services as well as the options for their redesigning. The projects they selected fall under the auspices of the National Action Adaptation Programme of West Africa. Additionally, van den Heuvel, Blicharska, Masia, Sušnik, and Teutschbein (2020) set out to identify synergies and trade-offs between different sectors and thereby foster

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the sustainable and efficient usage of resources, particularly in the light of climate change. Furthermore, Wan, D’Amato, Toppinen, and Rekola (2017) investigated the role of ecosystem services in the context of China’s forest sector. In turn, Ingram, van den Berg, van Oorschot, Arets and Judge (2018) examine governance and policy options for sustainability in terms of how ecosystem services are addressed in cocoa, soya, tropical timber, and palm oil value chains with Dutch links. Finally, Hossain et al. (2018) identify priority research questions in the field of biodiversity, ecosystem services and sustainability (BESS), based on a workshop held during the NRG BESS Conference for Early Career Researchers before then comparing these with existing horizon scanning exercises.

11.4.3 Sustainable ecosystem and knowledge creation: Cluster 4 – N (52)

Figure 11.6: Co-author network analysis of 52 publications and their clusters with associated strengths. Source: Elaborated by the authors

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In a recent study, Peña et al. (2020) analyse the implementation of the ecosystem services approach (ESA) in spatial planning in the Basque Country via the co-creation of knowledge. This further makes a proposal for a regional GI to examine the cocreation of knowledge process. In addition, Frantzeskaki and Kabisch (2016) present a comparative assessment of the ways in which policy-science dialogues have achieved the co-production of knowledge about strategic urban environmental governance activities after adopting the cities of Berlin in Germany and Rotterdam in the Netherlands as their case studies. In turn, Somerville (2013) highlights the accomplishments of technical service staff members who successfully engaged co-workers in the selection, implementation, and enhancement of a web-scale discovery service. Furthermore, Striteska and Prokop (2020) identify the combination of innovation determinants in the dynamic innovation strategic model driving creation and sustaining the competitive advantages of innovation leaders in selected European countries belonging to the moderate innovator group. Flotemersch, Shattuck, Aho, Cox, and Cairns (2019) carried out a critical review of the literature on the characteristics of stakeholders and of the environment that influence stakeholder engagement and participation in aquatic ecosystems. Their objectives included identifying those factors that require consideration in designing surveys to help encourage the inclusion of ecological and social beneficial usage data in large-scale water monitoring programs. Falkowski, Martinez-Bautista, and Diemont (2015) evaluated the role of emergy in the creation, maintenance, and transfer of traditional ecological knowledge at the individual and community levels as well as the biophysical and cultural resources supporting this knowledge system. In order to ascertain the paths to creating knowledge, Perera-Valderrama et al. (2020) present the main impacts of the “Southern Archipelagos” project, led by the National Center of Protected Areas of Cuba and funded by the GEF, that studied the trends underlining the indicators of success between 2009 and 2015. Brodhag (2013) considers the role of research universities and how they may best interact with key actors and institutions involved in “innovation ecosystems.” In addition, the role of knowledge management in enhancing the entrepreneurial ecosystem through deploying knowledge management, co-governance, and co-management in high-tech firms underwent study by Bhardwaj (2019). Finally, López-Rodríguez, Cabello, Castro and Rodríguez (2019) assess the ability of social learning to enhance dialogue and understanding within the scope of the ESA to bring about transformative social change in governance practice in the Alboran Marine Basin.

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11.4.4 Sustainable ecosystem and knowledge transfer: Cluster 5 – N (57)

Figure 11.7: Co-author network analysis of 57 publications and their clusters with associated strengths. Source: Elaborated by the authors

The need for sustainable alternatives to industrial farming has led to a revival of interest in traditional agro-ecosystems. Fraser, Frausin and Jarvis (2015) examine the intergenerational transmission of social and physical–technical dimensions of the traditional agro-ecosystem of the Loma people in NW Liberia. The findings underline how sustainability is not simply a physio-technical issue; social and belief issues appear to be far more important in framing behaviour in traditional agroecosystems. Therefore, Luisetti et al. (2014) explore the issue of ecosystem stock and services flow and we put forward recommendations on how to value the stock and flows of ecosystem services via accounting and economic values respectively. In order to achieve this, Pilotti (2018) develops a new model that reaches out to a “global community of creativity” as a bridge between the networks of historic

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territories, which encapsulate the roots of variety for transferring to future generations and between local and global quality in an emergent landscape. Durante et al. (2019) set out a complete research and development process, from the translation of scientific knowledge into an indicator for sustainable forest management through to simplification for assimilation. Furthermore, Ariza-Montobbio and Cuvi (2020) provide a multi-criteria methodology to identify, characterise, select, and evaluate adaptive co-management and its constraining and enabling conditions for effectiveness in ecosystem-based adaptation actions implemented between 2011 and 2015. Sustainable usage and the allocation of aquatic resources, including water resources, require the implementation of ecologically appropriate technologies, efficient and relevant to local needs. However, Agboola (2014) reports that success also requires the provision of localised capacity in order to manage technology through knowledge empowerment in rural communities situated within a framework of clear national technology development priorities. Dzierzbicka-gowacka, Nowicki, Janecki, Szymczycha, and Pieckiel (2018) approach the FindFish Knowledge Transfer Platform project, launched to provide solutions for the challenges facing commercial fisheries. The result demonstrates the benefits to commercial fisheries of deploying knowledge transfer platforms and marine environment numerical forecasting systems in their decision-making processes. Adamson (2020) considers a generic resource-harvester model with delayed ecosystem knowledge and predictive behaviour applied by the harvesters. He conveys how delays in the spread of information about the resource level may destabilise the bioeconomic equilibrium in the system and induce either harvesting cycles or resource collapse.

11.4.5 Sustainable ecosystem and innovation: Cluster 6 – N (124)

Figure 11.8: Co-author network analysis of 124 publications and their clusters with associated strengths. Source: Elaborated by the authors

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Cassidy and Resnick (2020) evaluate the contribution of the value co-creation perspective in exploring strategy making in a complex retail high street ecosystem. In addition, Tai, Xiao, and Tang (2020), based on analysis of the social- economic-natural compound ecosystem (SENCE), construct a vulnerability evaluation indicator system (28 indicators) for coal mining cities. In addition, Trischler, Johnson, and Kristensson (2020a) conceptualise the diffusion of user innovations from a service ecosystem perspective. With the focus on sustainable innovations, this evaluates service ecosystems evaluated along with other systemic innovation concepts to put forward a possible theoretical basis for explaining the first adoption and diffusion of user innovations. However, Henry, Bauwens, Hekkert, and Kirchherr (2020) discuss how research on circular business models has mainly focused on the circular approaches adopted by incumbent firms while the contributions of newly established firms (the authors term these “circular start-ups”) have largely gone overlooked. The current literature on business model innovation is moving away from linear models and highlighting critical factors of success such as knowledge, creativity, and innovation. However, Madsen (2020) proposes a perspective both on how these issues relate to the components of business models and how they interconnect with surrounding networks and ecosystems while acknowledge how this still requires further research attention. To provide a definition for digital entrepreneurship ecosystems by highlighting the integrated digital-output and digital-environment perspectives, Elia, Margherita, and Passiante (2020) describe the collective intelligence approach before then adopting a definition for a descriptive framework that identifies the distinguishing genes of a digital entrepreneurship ecosystem. Indeed, George, Merrill, and Schillebeeckx (2019) explore how digital technologies are helping to address the great challenges surrounding climate change and nurturing sustainable development before proposing a research agenda that raises new questions for entrepreneurship, business models, and ecosystems as well as new ways of thinking about trust and institutional logic. However, Thomaz and Catalão-lopes (2019) analyse the different perspectives towards mentoring of the main actors in the social entrepreneurship ecosystem in Portugal making recourse to interviews and focus groups and qualitative data analysis to this end. Neves, Neto, and Aparicio (2020) note the impacts of open data on complex ecosystems and their crucial facilitating role in generating and analysing contextual and actionable data designed to understand, manage, and plan the smart city. Mohiuddin et al. (2020) analyse value co-creation through sustainable strategic alliances amongst commercial and not-for-profit organisations in Bangladesh. Their results suggest that strategic alliances constitute service-ecosystems that facilitate the emergence, engagement, and evolution of social innovation that eventually drive value co-creation through sustained and successful social innovation.

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11.5 Conclusion and discussion This chapter strives to convey the importance of the three factors making up the Spinner Innovation Model for the inclusion of companies in sustainable ecosystems. The Spinner Innovation Model can interconnect with ecosystems including universities, government and industries contributing to economic development, especially for the SME and services sector, by applying three dimensions: knowledge creation, knowledge transfer, and innovation (Figueiredo & De Matos Ferreira, 2019). Based on a systematic literature review, this study identifies five clusters of articles (SMEs, services sector, knowledge creation, knowledge transfer and innovation) and a total of 49 different network factors, with some interconnected (Table 11.1, Figure 11.9). The most relevant factors for SMEs belonging to sustainable ecosystems are the following: transparency; designing new value chains; urban ecosystems; risk management; new business models and new management models; relational skills; collaborations; and scripts. As regards the service sector cluster, the factors referring to competencies for inclusion in sustainable ecosystems are: the existence of support tools, particularly for ecosystem-based management; the ability to implement company culture based approaches; undertaking adaptation projects, leveraging synergies and establishing evaluation methods. In the case of the Spinner Innovation Model components, the factors of the knowledge creation capacities required for inclusion in sustainable ecosystems are: the cocreation and co-production of the knowledge process; the engagement of human resources; monitoring programs; the knowledge system and technologies; and establishing methods for evaluating indicators of success. In terms of knowledge transfer, the necessary factors for sustainable ecosystem involvement are: the existence of physical-technical systems and appropriate knowledge transfer technologies; the capacity for technology management and quality management; predictive behaviours; and undertaking methods for evaluating indicators of success. Finally, in terms of the network factors linked to innovation, the following stand out: the co-creation of value; the diffusion of innovations and open innovation; linear models of innovation; implementing circular business models; establishing and maintaining sustainable strategic alliances; the entrepreneurial orientation towards digital technologies; and putting innovation assessment methods into practice.

11.5.1 Implications This study provides a basis for researchers to further examine and develop an even more robust perspective and a more integrated view of the importance of the factors

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Table 11.1: Factors influencing inclusion in a sustainable ecosystem. Sustainable ecosystem factors Independent Dependent

Network factors

Authors/references

SMEs

Create new value chains; urban ecosystems; risk management; software ecosystem; economy-based business models; relational capabilities; collaborations; roadmap; environmental; management systems (EMS) and transparency.

(Pöltner & Grechenig, ); (Valpreda et al., ); (Žigiene et al., ); (Valença & Alves, ); (Soltysova & Modrak, ); (Wozney et al., ); (Bolesnikov et al., ); (Afsarmanesh & Ollus, ); (Zorpas, ); (HernándezDíaz et al., ).

Service sector

Principles; ecosystembased management; support tool; assessment method; cultural approach; adaptation projects; synergies; trade-offs and role of ecosystem services.

(Jerome et al., ); (Bryhn et al., ); (Nesticò et al., ); (Schwanitz et al., ); (Beudou et al., ); (Muthee et al., ); (van den Heuvel et al., ); (Wan et al., ).

KIBS

Not available

Knowledge creation

Co-creation of knowledge process; knowledge coproduction; engaged coworkers; innovation determinants; monitoring programs; knowledge system; indicators of success; innovation ecosystems and co-governance.

(Peña et al., ); (Frantzeskaki & Kabisch, ); (Somerville, ); (Striteska & Prokop, ); Flotemersch et al. (); Falkowski et al. (); (Perera-Valderrama et al., ); Brodhag (); (Bhardwaj, ).

Knowledge transfer

Social and physical–technical dimensions; value stock; local and global quality; indicator of sustainable; ecosystem-based adaptation; appropriate technologies; capacity to manage technology; knowledge transfer platforms and predictive behaviour.

Fraser et al. (); (Luisetti et al., ); (Pilotti, ); Durante et al. (); (ArizaMontobbio & Cuvi, ); (Agboola, ); Nowicki, Janecki, Szymczycha, and Pieckiel, (); Adamson ().

Sustainable ecosystem

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Table 11.1 (continued) Sustainable ecosystem factors Independent Dependent

Network factors

Authors/references

Innovation

Value co-creation; indicator system; diffusion of user innovations; circular business models; linear models; digital entrepreneurship ecosystem; digital technologies; main actors; open data; sustainable strategic alliance.

Cassidy and Resnick (); (Tai et al., ); (Trischler, Johnson, & Kristensson, ); (Henry et al., ); (Madsen, ); (Elia et al., ); (George et al., ); (Thomaz & Catalão-lopes, ); Neves et al. (); Mohiuddin et al. ()

Source: Elaborated by the authors

associated with the three dimensions of the Spinner Innovation Model for the inclusion of companies in sustainable ecosystems. These factors are susceptible to returning measurable impacts on the capacities of companies to obtain competitive advantages. By proposing a set of new network factors, the present study adds and extends the previous research that examines these issues independently of the Spinner Innovation Model. This study also contributes to the existing body of knowledge by pointing out predictive factors for the inclusion of a company in sustainable ecosystems. Hence, this study approaches the dimensions of the Spinner Innovation Model within the context of sustainable ecosystems rather than the focus generally adopted in the literature on innovation ecosystems. From the perspective of practical implications, the results of this study reinforce the view that the skills encapsulated in the creation and transfer of knowledge and innovation included in the Spinner Innovation Model, for both SMEs and service companies, constitute very significant indicators of the success of inclusion in sustainable ecosystems. Expanding the manager/owner focus on the Spinner Innovation Model returns a better understanding of the orientation appropriate to integrating into sustainable ecosystems as well as to creating an organisational culture of cooperation. SME and service company managers/owners must therefore place greater emphasis on developing the competencies included in the Spinner Innovation Model to help meet the sustainability requirements that have now become an inevitable component of organisational sustainability for all organisations with a vision for their own futures.

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Figure 11.9: Factors influencing inclusion in a sustainable ecosystem. Source: Elaborated by the authors

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11.5.1.1 Limitations and future research There are different definitions for network factors and lacking in any consensus in terms of the dependent variable, “sustainable ecosystem.” One issue arises from the application of a more judicious, perhaps more quantitative, approach to validating the construct presented in the network factor table. Future research may employ statistical techniques, along with various measurements of validity and reliability, in order to generate a complete data collection instrument for this important construct with practical implications. We strongly recommend that future research in this field, sustainable ecosystems, develops the list of factors presented in this study, with structural equations providing the most appropriate approach. Various different suggestions clearly emerge for developing empirical research on the Sustainable Ecosystem and KIBS topics. We thus contribute to this literature by proposing a set of 49 network factors applicable to assessing the extent of inclusion in sustainable ecosystems, taking into consideration the relationships ongoing among the five clusters, or rather among the five networks connected to the sustainable ecosystem. Funding: FCT – The Foundation for Science and Technology/ UID/GES/04630/2020

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Appendix 11.1: Variables Qualitative Variables (Categorical) Independent Dependent

Papers Keywords

SMEs

 (TITLE-ABS-KEY (sustainable AND ecosystem) AND TITLE-ABS -KEY (smes) OR TITLE-ABS-KEY (small AND medium AND enterprises) AND TITLE-ABS-KEY (small AND medium AND enterprise) OR TITLE-ABS-KEY (sme))

Service Sector

 (TITLE-ABS-KEY (sustainable AND ecosystem) AND TITLE-ABS -KEY (service AND sector)) AND (LIMIT-TO (ACCESSTYPE(OA))) AND (LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, )) AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (EXACTKEYWORD, “Ecosystem Services”) OR LIMIT-TO (EXACTKEYWORD, “Ecosystem Service”)) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMIT-TO (SRCTYPE, “j”))

KIBS

 (TITLE-ABS-KEY (sustainable AND ecosystem) AND TITLE-ABS -KEY (kibs) OR TITLE-ABS-KEY (knowledge-intensive AND business AND services) OR TITLE-ABS-KEY (knowledge AND intensive AND business AND services))

Knowledge Creation

Sustainable Ecosystem

 (TITLE-ABS-KEY (sustainable AND ecosystem) AND TITLE-ABS -KEY (knowledge AND creation)) AND (LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO ((PUBYEAR, )) AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMIT-TO (SRCTYPE, “j”))

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(continued) Qualitative Variables (Categorical) Independent Dependent Knowledge Transfer

Papers Keywords  (TITLE-ABS-KEY (sustainable AND ecosystem) AND TITLE-ABS -KEY (knowledge AND transfer)) AND (LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, )) AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMIT-TO (SRCTYPE, “j”))

Innovation

 (TITLE-ABS-KEY (sustainable AND ecosystem) AND TITLE-ABS -KEY (innovation)) AND (LIMIT-TO (PUBYEAR, ) OR LIMITTO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, ) OR LIMIT-TO (PUBYEAR, )) AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (SUBJAREA, “BUSI”)) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMITTO (SRCTYPE, “j”))

Total



Source: Authors

Part III: Universities and entrepreneurial activities

Thomas Lauvås and Ola Edvin Vie

Chapter 12 Boundary spanners enabling knowledge integration for sustainable innovations in university–industry research centres 12.1 Introduction The heightened awareness of societal sustainability challenges highlights the need for an increased effort towards – and better understanding of – developing sustainable innovations. One of the measures taken by governments to foster sustainable innovations is the facilitation of university1–industry collaboration (UIC). One of the predominant policy responses in the EU and the United States to increase UIC is the establishment of university–industry research centres. These centres are often interdisciplinary in nature, seeking to overcome specific challenges in particular industries (Gulbrandsen, Thune, Borlaug & Hanson, 2015; Villani, Rasmussen & Grimaldi, 2017) or tackling some of the “grand challenges” facing humanity (Hessels, Wardenaar, Boon & Ploeg, 2014). The research centres promote knowledge and technology transfer and innovation (Boardman & Gray, 2010) and have two main goals: producing academic research and developing innovations (Chai & Shih, 2016; Gulbrandsen et al., 2015; Ponomariov & Boardman, 2010). Partly due to their different knowledge bases, the collaboration process between university and industry partners in research centres is seldom without challenges (Perkmann, 2017). Exploiting external knowledge from universities is far from straightforward due to significant institutional, technological and knowledge boundaries between companies and university partners (Bruneel, D’este & Salter, 2010; Galán-muros & Plewa, 2016). Many companies are therefore unable to integrate the knowledge stemming from the universities’ research findings to develop innovations (Galán-muros & Plewa, 2016), and the literature on UIC offers limited advice on how to handle these challenges (Bruneel et al., 2010). This chapter examines this challenging process through the concept of knowledge integration, which is the combination of “specialized but complementary knowledge” (Tell, 2011, p. 27). This chapter will examine knowledge integration processes in university–industry research centres on the individual level and assess how strategies frame these processes for sustainable innovation. We focus on two roles: the academic

1 In line with Perkmann and Walsh (2007), we use the term “university” to include all types of public research organizations, which are research organizations that are predominantly governmentfunded, i.e. universities, public research laboratories, research institutes, etc. https://doi.org/10.1515/9783110670219-013

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centre director and the industry partners’ contact persons, who are responsible for knowledge integration and facilitating sustainable innovations in the research centre. Hence, we ask: How do centre directors and industry partners’ contact persons contribute to knowledge integration in university–industry research centres? The research question is examined through a longitudinal study of six Centres for Environment-friendly Energy Research (CEER).2 The CEER scheme aims to develop expertise and promote sustainable innovations by focusing on long-term research in selected areas of environmentally friendly energy in close collaboration with prominent universities and industry partners (research Council of Norway, 2008). The research centres were deemed appropriate as cases because their long-term financing makes them suitable for longitudinal studies of knowledge integration. By adding to the few longitudinal studies of research centres (Lind, Styhre & Aaboen, 2013; Rass, Dumbach, Danzinger, Bullinger & Moeslein, 2013), this chapter makes three distinct contributions. First, it illustrates the organizational dynamics of knowledge integration (Tell, 2011) that underlie UIC (Perkmann & Walsh, 2007). Second, it demonstrates the importance of middle managers as boundary spanners (Nonaka, 1994). Third, these contributions relate to how knowledge integration processes are influenced by the strategies of the research centre and the industry partners and how this is connected to the allocation of resources (von Krogh, Nonaka & Rechsteiner, 2012). These findings have important implications for companies considering UIC, academic researchers leading research centres and policymakers promoting sustainable innovation by supporting such collaborations. The chapter is structured as follows. In the next section, we present our theoretical framework. Thereafter follows a description of our research methodology and empirical data collection. We then present the empirical case study upon which this chapter is based and submit our analysis and discussion. Conclusions and implications regarding knowledge integration in research centres are proposed in the final section.

12.2 Theoretical framework In this chapter, we draw on two key, interrelated theoretical concepts – knowledge integration and boundary-spanning – to explore UIC in research centres. We start with an introduction to how knowledge is developed in such centres.

2 More details are provided in Section 12.3.2: Case selection. See the description of the scheme at the Research Council of Norway (http://www.forskningsradet.no/en/Funding/FME/1215006638765).

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12.2.1 Knowledge development in university–industry research centres In the context of the growing importance of knowledge and innovation for sustainable development, the effort of uniting universities and industry has become a major concern for policymakers (Ranga, Debackere & Tunzelmann, 2003). Since the 1970s, many policymakers have supported more proactive and increased interactions between universities and industry (Cohen, Nelson & Walsh, 2002; Mowery & Sampat, 2005). Following this development, university–industry relationships have been extensively studied in recent years (Bodas Freitas, Geuna & Rossi, 2013; Gulbrandsen, Mowery & Feldman, 2011). Research has emphasized the advancement of higher productivity as one contribution of UIC through facilitating knowledge integration between academia and industry, thus enhancing national innovation performance (Bishop, D’este & Neely, 2011). This is mainly done by giving companies access to fundamental knowledge and the opportunity to conduct high-quality research (Hussler, Picard & Tang, 2010; Laursen & Salter, 2004; Raesfeld, Geurts, Jansen, Boshuizen & Luttge, 2012), two factors that lead to sustainable innovations (Jakobsen, Lauvås & Steinmo, 2019). Although research centres have existed for decades and have become one of the predominant policy responses to stimulate UIC (Ponomariov & Boardman, 2010; Styhre & Lind, 2010), the understanding of these complex organizations is inconsistent and limited (Gulbrandsen et al., 2015; Ponomariov & Boardman, 2010). Several types of organizations labelled research centres exist, and different categories of research centres have been suggested (Smith, 2012). We follow the definition by Styhre and Lind (2010, p. 910), who defined a research centre as a “joint venture between the university, industry and governmental funding organizations, identifying some domain of research where industry and academy can benefit from collaborating.” The effort of combining universities and industry partners in research centres is an attempt to connect two distinct and specialized knowledge bases. Yet, incentive structures in universities and industry are contradictory, often ascribed to a dichotomy between the opposing logics between long-term research and the academic publication system and industrial commercialization (Perkmann & Walsh, 2007). Companies and universities are therefore considered to be unnatural collaboration partners (Hasselmo & Mckinnell, 2001), especially since it is difficult to simultaneously leverage academic journal articles and develop sustainable innovations (Ahuja, Lampert & Tandon, 2008; Smith, 2012). The challenge of integrating diverse knowledge stems from the inherent tension in the division of labour, which is the trade-off between the superior task efficiency of specialization against its inferior coordination and integration properties (Postrel, 2002). In this chapter, we apply the concept of knowledge integration to better understand coordination problems and how differentiated knowledge can be effectively integrated (Grant, 1996).

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12.2.2 Knowledge integration and its boundaries The fact that innovations in general, and sustainable innovations in particular, are created at the boundaries between different disciplines or specializations (Leonardbarton, 1995; De Marchi, 2012) elucidates the importance of managing knowledge across boundaries within and between organizations (Carlile, 2004). Scholars have recognized the value of boundary spanning, where crossing organizational boundaries for knowledge has a higher impact on subsequent technological evolution within and beyond the domain of the firm than exploration within the firm (Rosenkopf & Nerkar, 2001). Other scholars have identified the challenges of transferring knowledge across boundaries (e.g. Szulanski, 1996; von Hippel, 1998). This demonstrates that the integration of specialized knowledge is difficult and that integration processes are not always effective (Grant, 1996; Okhuysen & Eisenhardt, 2002). Hence, there are boundaries between organizations and their representatives that both foster and restrain innovation collaboration (Carlile, 2004). If the collaborative practices are to function well enough to develop sustainable innovations, these boundaries must be overcome (Bruneel et al., 2010; Steinmo & Rasmussen, 2016). One method is using intermediaries in innovation processes. This implies hiring and/or using external institutions to support companies in their innovation activities (Gassmann, Daiber & Enkel, 2011; Howells, 2006). Although we know that the actors involved in the knowledge integration process are important and that more proximate actors may build a more fruitful collaboration (Hansen, 2014; Steinmo & Rasmussen, 2016), few studies have examined the actual collaboration between research centre directors and the firms’ contact persons, who operate and mediate between the firm and the universities. To benefit from UIC, companies should coordinate the knowledge of the individuals that represent the companies in the collaboration (Johansson, Axelson, Enberg & Tell, 2011). In the case of sustainable innovation, this is important to combine expertise from various disciplines. We follow these lines of research on the individual level, exploring how firms, their contact persons and the centre director engage in UIC and overcome challenges to foster sustainable innovations through knowledge integration. The integration process is influenced by existing knowledge integration capabilities within the firm, defined as “the attributes which enable integration to be performed” (Berggren et al., 2011, p. 9). However, it should be noted that these roles also indicate the existence of boundary spanning.

12.2.3 Middle managers as boundary spanners The role of middle managers is to integrate the top-down and bottom-up approaches into a middle-up-down management (Nonaka, 1994, p. 30). While accepting the right for top management to articulate broad visions and strategies, middle managers

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translate these guiding principles into concepts and frameworks that are relevant for frontline workers and lower-level managers. At the same time, middle managers help to formulate the tacit knowledge that workers possess and act as catalysts for knowledge creation for sustainable innovation by being central in sharing and transforming explicit and tacit knowledge throughout the company. Their role is to bring people with specialized knowledge and different team affiliations together to share and develop new knowledge of sustainability. Thus, middle managers act as boundary spanners, defined as “links between a unit and its environment” (Haas, 2015, p. 1034), by connecting people externally and within the organization. The similar importance of middle mangers was also described in a later articulation of leadership in organizational knowledge creation by von Krogh and Nonaka (2012). Their literature review demonstrated a clear tendency to focus on top management, and thus centralized leadership, as opposed to the importance of middle managers and more distributed leadership (von Krogh et al., 2012, pp. 251–252). Second, while distributed leadership activities act as catalysts to developing new knowledge by making tacit knowledge more explicit and connect and encourage participants to share knowledge, centralized leadership provide assets to realize new knowledge; design and implement systems, rules, and procedures; and formulate visions and strategies to connect the existing operation with new knowledge. As demonstrated by Nonaka (1994), different levels in the organization have different roles and responsibilities for developing new knowledge. The lower level of the organization possesses a tremendous amount of tacit knowledge, top management develops explicitly formulated visions and strategies, and middle managers act as boundary spanners and integrators between the other layers.

12.3 Methodology 12.3.1 Research design We conducted an inductive, longitudinal, qualitative study to gain in-depth insights of how industry and university partners integrate knowledge in university–industry research centres working on sustainability. A multiple-case study of six research centres was used to build a multilevel model of knowledge integration in research centres (Yin, 2009). To obtain a precise account of the specific collaborations, the research centre was the unit of analysis, relying on viewpoints from firm and university partners.

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12.3.2 Case selection The research question was addressed through a dataset of six Norwegian technological CEERs operating from 2009 to 2017. The six centres focused on CO2 storage, bioenergy, zero-emission buildings, offshore wind energy (two centres), and solar cell technology. Hence, most research centres were based in new, immature industries. The centres had multiples sources for financing, where the Research Council of Norway contributed up to 50% of the annual budget and industry and university partners contributed approximately 25% each. Each centre had an annual budget of approximately 30 million NOK (Research Council of Norway, 2008).

12.3.3 Data collection The main dataset consisted of 91 interviews with industry and university partners from the 6 research centres, collected in 2 interview rounds (2013 and 2015). Secondary data, including the initial project description, evaluation reports, and annual reports for the research centres, were collected to prepare for the data collection process and to improve the authors’ understanding of the context of the study (Alvesson & Sköldberg, 2009). The centre directors or centre managers were initially contacted for an interview and permission to study their CEER. After this interview, snowball sampling was conducted by asking for the most involved university and industry representatives, as actively participating actors are the ones who drive the development of research centres (Jarvenpaa & Valikangas, 2016; Mora-valentin, Montoro-sanchez & Guerrasmartin, 2004). The interviews were retrospective and semi-structured, covering themes like the respondents’ work background, the initial stages of planning of the centre activities, the rate of involvement and the respondents’ expectations at that stage. Further, the interview covered their experiences with the collaboration processes, innovation activities, suggestions for improvements and their views on future collaboration. Though this process, the informants’ narrative views were obtained (Gephart, 2004). By considering the different perspectives of the informants, we designed and relied upon two separate interview guides: one for industry employees and one for university scientists. With an emphasis on situational details unfolding over time, we obtained an in-depth description of the collaboration processes (Gephart, 2004). The interviews were always conducted with two or more researchers to minimize interviewer bias.

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12.3.4 Data analysis A voice recorder was used during the interviews, which made it possible to transcribe the interviews verbatim for further analysis (Alvesson, 2011). Primary or raw interpretations were made before, during and after the interviews and later in the research process. These raw interpretations inspired, developed, and reshaped theoretical ideas during the research process (Alvesson & Sköldberg, 2009). A qualitative analysis software (NVivo 10) was used to code and categorize the data. The coding began with a careful reading of the interviews line by line, naming and coding the empirical material. We focused on what the university and industry partners experienced as barriers or enablers of knowledge integration in the research centres. During this process, we determined which conditions are necessary for knowledge integration at the individual level in the research centres and how strategies frame these activities. We discussed the coding procedure extensively to increase the rigour of the analytical generalization of the empirical data. In the coding process, we used inductive codes and followed Gioia et al.’s (2013) method. First, we identified similar codes and clustered them in first-order categories before searching for linkages among the categories (Saldaña, 2013), which led to the development of second-order analytical themes (Nag & Gioia, 2012). Triangulation of the data sources was applied by comparing the interview data with secondary data, including reports, newsletters, press releases and websites. Next, we reviewed the literature once more to identify theoretical concepts that could explain and elaborate upon the findings (Eisenhardt, Graebner & Sonenshein, 2016) to contribute to theory development (Miles, Huberman & Saldaña, 2014). The coding structure for this chapter is provided in Figure 12.1.

12.4 Empirical findings The findings are presented in three sections. First, we present how the research centres were typically organized. Then, we present the findings regarding knowledge integration activities at the individual level. Finally, the industry partners’ involvement in knowledge integration at the strategic level is described.

12.4.1 Organizing the CEER research centres As part of the CEER scheme, the Norwegian government’s Research Council provided clear recommendations for how the research centres should be organized so that they could generate sustainable innovations. This resulted in very similar organizational arrangements among the research centres.

Figure 12.1: The coding structure. Source: Authors’ own elaboration.

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The participants came either internally from universities or externally from industry companies or policy organizations (called industry partners). The number of university partners ranged from three to eight, and the number of industry partners ranged from five to twenty. All research and industry partners had representatives in the general assembly, which is the main decision body that meets annually. The general assembly is responsible for approving new partners and deciding on changes to the executive board after the initial agreement drawn up in the consortium agreement. The board is responsible for approving allocation of the budget to the various research activities outlined in the research centre application. It also approves the annual report and all research implementation plans and follows up on the sustainable innovation development process. The industry partners held the majority in the executive boards and typically the chair position as well, meeting at least twice a year. The centre director was usually the most important driving force in the research centres. Normally he/she would be highly involved in the formulation of the research centre application, including the vision, goals, and scope of the research activities. The centre director often had the responsibility of recruiting research and industry partners and managed the day-to-day operationalization in the research centre. The goals and deliveries of the research centre were formulated in the application, where the research activities had been organized in different research areas or work packages (WP), with one researcher serving as the WP leader. Much of the budget was allocated to specialized research groups expected to deliver research on the topics described in the application. These research groups were often so specialized that it would be difficult to move them from one WP to another. Together with the centre director and other administrative centre resources, the WP leaders constituted the centre management team, meeting at least six times a year. Each industry partner would normally have one individual acting as the contact person. This contact person would usually be informed about the various meetings and activities in the centre. Sometimes the contact person also held a position on the board, but the industry partner could also be represented by another individual from the firm. In some of the research centres, one researcher acted as the research sponsor for an industry partner, with expectations of regular communication with the contact person. In principle, each WP should be filled with active researchers collaborating with the industry partners to achieve the goals set by the various deciding bodies (Lauvås & Steinmo, 2019). However, variations in how well the different WPs functioned, even within the same research centre, were significant.

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12.4.2 Enabling knowledge integration at the individual level to achieve sustainable innovation 12.4.2.1 Time and space for knowledge integration The challenge is if and how the industry partners have the capacity to be integrated [in the research centre]. It is a matter of how we communicate together. – University partner

This quote illustrates that, on the individual level, it can be challenging to achieve effective knowledge integration, as this requires significant time and interaction. University and industry partners explained that they often need 3–4 years to learn how to integrate their knowledge effectively through interaction. As one industry partner stated, We do not always know what we should ask for; that is why we need to collaborate with someone with top competence [university partners], because we [the industry] does not always understand the questions. We must interact with someone . . . However, if they [the university partners] stay ‘too far’ within their offices with their top competence, we would not call them up, because we do not know how to use them. – Industry partner

It takes time for the industry partners to figure out what they should ask for and what the university partners can deliver knowledge input on, and they need time to interact and work together to solve issues. This is time-consuming and demanding because of the lack of time available to hold meetings within the research centre. Although most of the industry partners emphasized that physical meetings are very important, they struggled to find time to prioritize these meetings. The industry partners further highlighted that physical meetings create better discussions than by using different types of information and communications technologies. The discussion of further use of research results is considered far more effective in person than via phone or email.

12.4.2.2 Developing mutual understanding A lot of interaction is needed to understand the tacit knowledge of each partner, which is necessary to enable the creation of sustainable innovations. During formal meetings in the research centre, some industry partners discovered knowledge gaps that they had not managed to fully articulate. The university and industry partners in the CEER scheme have, to varying degrees, managed to carry out knowledge integration at the individual level. In some of the research centres, the university partners have primarily been concerned about and focused on conducting research on their own to create academic knowledge disseminated through academic presentations, publications, and reports. All of these documents and publications are available for all parties on an intranet page for each research centre. However, many industry

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partners do not refer to this page, and some do not read published materials at all. Consequently, some university partners wondered whether the industry partners have the capacity to be involved. A quote by a university partner elaborates this: We write rather detailed publications, but a lot of partners do not read these. They need to get this knowledge input in another way. Even though the industry partners generally did not read published works, little was done in some research centres to mend this gap in the first three to four years. This indicates that for some research centres, the transfer processes have not been effective, resulting in little knowledge integration with the industry partners and limited development of sustainable innovations. Some of the research centres adopted the role of research sponsors, with one university researcher being responsible for day-to-day contact with the industry partners. The feedback from the industry partners in these centres was unanimously positive. However, some industry partners manged to take advantage of the published academic work: The research centre serves as a ‘filter’ that filtrate research results to us. In this way, we [the industry partner] avoid being the only one that “translate” external research results into manageable and relevant knowledge for the company. The research centre does the first “screening” of academic work, and that has been a big advantage for us. – Industry partner

In other research centres, some university partners felt that industry partners shared to little information, making it difficult to understand and plan for research that meets the industry partners’ needs: “Some industry partners have a lot of related research questions and activities, but of which they don’t inform or discuss with us . . . that information could create important synergies, important for building a strong knowledge base in the centre.” Such companies were supposedly afraid of exposing trade secrets. Consequently, some university partners did not have enough insight into the industry partner’s strategic challenges, which made it difficult to conduct research activities that were strategically important for the industry partners and that could develop sustainable innovations. This created a negative spiral of collaboration, because if the activities were not perceived as strategically important, it was much more difficult to involve industry representatives and get them to show up at meetings and contribute their knowledge. However, in the centres with high levels of industry participation, a more positive atmosphere with higher levels of knowledge integration was observed, as a university partner stated, “I believe the centre meetings has an important role for increased sharing of knowledge in [sustainable energy area] in Norway.” An industry partner affirmed this statement: “Seminars, workshops and active discussions are really valuable in increasing the knowledge in the whole industry.”

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12.4.2.3 Enabling conditions for knowledge integration Successful knowledge integration involves the combination of industry employees and university scientists collaborating to develop new knowledge for sustainability. As demonstrated, getting the two groups to collaborate was dependent on their mutual understanding of each other’s needs and ways of working. This takes time and usually requires face-to-face meetings. Face-to-face meetings could be organized either as a centre-level event like the annual conference, or at a more local-level event with fewer participants organized by different WPs. Both events helped increase the mutual understanding between industry employees and university scientists, at a more overall or specialized field of knowledge respectively. The centre director could encourage WP meetings to bridge the gap between university and industry employees, and he/she could also help with translating their various needs. Connecting the right people was usually done through a collaboration between the centre director, who has a good overview of the university researchers, and the industry contact person, who knows the employees in his/her organization. The contact person had a similar role in involving the right people from their organization and helping to translate research publications, sometimes jointly with the research sponsor. However, the industry employees usually worked in a day-to-day operationalizing strain in their own organization, making them short on time and in need of prioritizing the most value-adding activities for sustainability.

12.4.3 Enabling knowledge integration at the strategic level to achieve sustainable innovation 12.4.3.1 Securing focus and resources In the initial phases, most of the industry partners were rather passive, with a “waitand-see” attitude. Consequently, many industry partners did not influence the research centre application or the first annual research activity implementation plans. This made the initial research activities decoupled from the strategic areas of importance to many of the industry partners. This indicates a mismatch where the type of boundary faced does not match the type of process used (Carlile, 2004). Consequently, some of the industry actors expressed worry that the publication measurement system prevailed, with few incentives for the university partners to interact with the industry. Hence, many of the industry partners were disappointed with the results flowing out from the research centre: “We do not find that much of the publications, and the knowledge produced [in the centre] is relevant, but we also have realised that we need to be more engaged to get more relevant outputs [from the centre].”

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The most prominent reason for the lack of involvement was a lack of resources and/or time. It should also be noted that in the more successful cases, more than one person from the industry partner worked towards the research centre. The contact person received a lot of information and invitations to activities in the research centre, but if he/she did not have the time or resources to involve others, many opportunities were missed. This quote from an industry partner provides an example: “We had no time; we had just invested [a large amount of money] in our factory. The investment seized all our organisational resources.” Many informants acknowledged that their own effort and time was needed to gain the necessary benefits from the collaboration. For some industry partners, the internal strategic objectives could also be very challenging: “I struggle to manage a very operative organisation, and at the same time keeping a focus on research activities.” However, not all industry partners were passive at the start or experienced dissatisfaction with the collaboration and the results flowing out from the research centre. Companies with their own research divisions and well-defined research strategies were able to influence the research areas and targets. One industry partner actually left the research centre due to shortcomings in their own research strategy, but after an extensive internal process that led to a reformulated strategy, they re-joined the centre. Another industry partner was deeply involved from day 1: We joined the research centre because it is of strategic importance for us. . . and we discussed with the centre management . . . what we saw as important research areas . . . and the centre has produced a lot of knowledge . . . of which some are implemented into our [firm] operations.

12.4.3.2 Aligning interests and changing strategies As the process continued, most of the companies recognized the possibilities inherent in the research centres. With that understanding, some industry partners proposed research areas of strategic importance for their firms’ sustainability. The limitations of the research centres’ structure then emerged, as the research activities and WPs were largely fixed to uttered dissatisfaction for some industry partners: “The research centre was established five to six years ago, and the research focus was set. However, since then, the environment that we [the industry] operates in has changed completely . . . Therefore, the research focus could have been more flexible.” The industry partners could influence the research activities at two levels. First, the board could prioritize the research activities and other strategic directions. Although the overall research focus was already defined in the research centre application, there was some room to include new areas of research if the budget resources were available. However, there was a considerable degree of variation in the flexibility of the strategic research focus between the different research centres, as indicated by an industry partner: “If a firm requests or demands specific research

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activities, the centre board would discuss, and recommend if the centre management should carry this out or not.” Second, industry partners could influence the research activities at the WP level through more informal communication with university researchers at an individual or group level. Defining a relevant research activity for the industry partner usually started with communication between the contact person from the industry partner and the centre director, often when they were recruited as partners but also on a more ongoing basis. Sometimes it was straightforward: “In the process of enrolling the research centre, we discussed our research needs with the centre manager, and he thought it would be doable.” Sometimes it was a lengthier process: After we enrolled [the research centre] . . . we attended the annual conference. After that, we had some discussions with the centre director, before attending other events, such as WP meetings . . . Over time, we understood what kind and type of research that were taking place in the centre . . . At a seminar, we presented our firm, and the centre management grasped what was important to us. Next, together with the centre director, we looked into how the existing research activities could be altered to increase the relevance for us. – Industry partner

Central in these processes was the centre director, who was responsible for the formalities in the research centre, such as the budget, annual reporting to the research council, and calling meetings for the general assembly, the board and the research management team. Often, the centre director had the best overview of the active researchers in the centre as well as the greatest general understanding of the needs and challenges facing the various industry partners. The centre director, together with contact persons from the industry partners, was highly involved in the formulation of the overall research activities and the formal strategic discussion in the board but also connected the industry partners with the right university scientists. However, there was a need for the centre director to find a balance of attuning the different needs between the university partners and industry partners: “There is quite a lot of feedback on what the industry want us to focus on. Then the case is if this is something the university partners actually have competence on, and if they want to focus on this issue. This is an interactive . . . and dynamic process.”

12.4.3.3 Influencing the boundary conditions There were some obstacles to initiating the collaboration. Because the research centre and the university scientists are measured by the number of articles they publish, they are not inclined to spend much time on activities that are not relevant to the publication process or activities defined in the research plan, like being a research sponsor or producing summaries for policymakers. The centre director could advocate for the university scientists to be more engaged with the industry partners. Site visits, meetings, serving as a research sponsor and doing more hands-on technological development

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work were common in the research centres. However, the latter is dependent on the flexibility in the research activities in the research centre. It was not uncommon for all funding to be distributed to the different university partners without having much room for moving resources. Additionally, some industry partners were satisfied with just donating money for research without draining too much of the time resources of their employees. The industry partner would give permission to spend more time if the activity was within their strategic focus and in line with the strategic research focus of the research centre. Many of the contact persons were part of the boards of the research centres or were at least present at the annual general assembly. Part of the formal structure and responsibility of the board was to give advice on the planned research activities. These activities were mainly decided by the research focus chosen in the research centre application and organized in different WPs led by university scientists. However, we observed that some research centres were flexible enough to adapt their research plans after discussions with the board and the centre director.

12.5 The important role of boundary spanners enabling knowledge integration and sustainable innovations Based on these data, we highlight a few key elements. In Figure 12.2, we have placed the university partners on the left and the industry partners on the right. We have highlighted the academic centre director and the different contact persons from the industry partners. Successful knowledge integration at the individual level between university scientists and industry employees, which is necessary to develop sustainable innovations, depends on an enabling context with mutual understanding and sufficient time to work together (Lauvås & Steinmo, 2019). The centre director and the contact persons can facilitate the collaboration, but both depend on securing the necessary resources. The centre director should remind scientists to broaden their focus to more than just publications, while the contact persons need acceptance from the top management in their firm to engage employees to work towards the research centre. In cases where the strategy of the industry partner does not fit the strategy of the research centre, the academic centre director together with the board would come up with suggestions and make necessary changes to make the research relevant for the industry partner. Failing to adjust could result in a dissatisfied industry partner who is more reluctant to spend their resources on the collaboration, which may eventually lead to firms dropping out of the research centre (Gray, Lindblad & Rudolph, 2001).

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Figure 12.2: Boundary spanners enabling knowledge integration and sustainable innovation. Source: Authors’ own elaboration

The central boundary spanners are the centre director and the firms’ contact persons, who connect people and strategies through formal and informal meetings. Their main task is to influence the boundary conditions to align different interests and secure resources to enable knowledge integration at the individual level. The empirical material we have presented highlights the role of the centre director and the contact persons as boundary spanners within the context of UIC for sustainable innovation. The existing literature on knowledge integration has mainly focused on the integration of knowledge held by individuals (Berggren, Bergek, Bengtsson & Söderlund, 2011) without considering how strategies frame and influence the opportunities for knowledge integration. We have therefore referred to the works by Nonaka (1994) and von Krogh et al. (2012), which emphasize how strategies formulated by top management contribute to securing resources for developing new knowledge. Based on the research of Nonaka (1994) and von Krogh et al. (2012), we can recognize the dual function of the academic centre director and the contact persons as offering both centralized and distributed leadership. The latter is visible when they assist with the knowledge creation process by making tacit knowledge explicit, and the former is apparent when they adapt formulated strategies and secure resources for knowledge collaboration. Our findings support the importance of middle managers in knowledge integration and creation and their role of integrating people and strategies to enable knowledge integration for sustainable innovations.

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This chapter is an initial answer to the call for in-depth case studies and inductive analyses of knowledge integration and sustainable innovation as a collaborative effort (Tell, 2011). Figure 12.2 shows many of the elements that are necessary for knowledge integration in UIC and depicts many of the underlying processes and activities that are necessary to accomplish knowledge integration and sustainable innovations. The figure can also be read as an illustration of the difficulties in achieving valuable collaborations, the necessary interplay with actors at several levels and the need for interaction and communication that facilitate knowledge integration (Okhuysen & Eisenhardt, 2002). Hence, our study adds to the few longitudinal studies of research centres (Lind et al., 2013; Rass et al., 2013) by illustrating the organizational dynamics of knowledge integration (Tell, 2011) underlying UIC (Perkmann & Walsh, 2007). Figure 12.2 expands existing research in knowledge integration literature by illustrating the knowledge integration process as a multilevel phenomenon in the specific context of technology-based innovation (Berggren et al., 2011) aimed at achieving sustainability.

12.6 Conclusion Sustainable innovation often depends on getting specialists from different fields to work together to integrate and create knowledge (De Marchi, 2012). This chapter shows some of the obstacles that such collaboration raises and some of the elements that connect the obstacles. We have emphasized the role of the centre director and the contact persons as boundary spanners who integrate people and strategies. As demonstrated in the literature review, the links between university–industry and innovations have been extensively studied. However, our study contributes to a growing need to understand the organizational dynamics underlying these relationships and how to better utilize UIC (Perkmann & Walsh, 2007) to develop sustainable innovations. Although attempts have been made to develop frameworks that resolve the challenges of knowledge integration in UIC, a more detailed understanding of how problems with collaboration and coordination can be improved is needed (Johansson et al., 2011). Our chapter extends prior research on UIC and knowledge integration in research centres by clarifying several elements that either help or hinder the integration of diverse knowledge in research centres. We show that knowledge integration is a multilevel phenomenon conducted by individuals (Berggren et al., 2011) but dependent on the strategies of both the research centre and the industry partners. In particular, we provide valuable insights of how academic centre directors and firms’ contact persons can collaborate to secure favourable conditions and necessary resources for more successful knowledge integration between university and industry that may lead to sustainable innovations.

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12.6.1 Implications Although participating in research centres may be rewarding for the development of sustainable innovations, it is often challenging for industry partners to take full advantage of the knowledge created in the research centres, particularly if the new knowledge is diverse from the companies’ well-established knowledge bases (Howells, Ramlogan & Cheng, 2012). Companies’ ability to integrate knowledge within the firm, as well as between the firm and external actors such as universities, has a major influence on companies’ innovative capability (Lazonick, 2005). Our findings indicate that to fully take advantage of the knowledge developed within the research centres and to increase the potential for sustainable innovations, companies should focus on knowledge integration at the strategic and individual levels. Without a strategy that fits the research centre, it is more difficult to secure the necessary resources to actively participate in the collaboration. Choosing a contact person with the right competence, networks, and time to be the catalyst for knowledge integration is essential, especially since many firms often involve only one person to work with the centre, as indicated by Santoro and Chakrabarti (2002). University scientists should consider the centrality of the academic centre director in uniting the interests of all partners. The centre director should thus have excellent scientific knowledge, including a good overview of the field, but also be well attuned to the needs of the industry partners. Allocating sufficient funds to arranging physical meetings at both the centre level, like annual conferences, and shorter and more focused events at the WP levels to promote mutual understanding is also important. When establishing the research centre, it is vital that the board be capable of handling continuously shifting strategic needs, securing room for flexibility in budget allocation and efficiently utilizing university scientists as resources. Recognizing the importance of the industry partners’ strategies early in the process should be prioritized because industry partners will be reluctant to spend more resources if there is not a good fit. Hence, contact should be made with industry partner management, representing both distributed and centralized leadership (von Krogh et al., 2012) to secure the necessary resources for performing the actual knowledge integration.

12.6.2 Final remark We would like to emphasize that industry partners can benefit from participating in UIC, even without having all the components in place. It is possible to achieve some knowledge integration through collaborative research activities that occur daily on the individual level without having aligned strategic interests, without flexible research plans in the research centre, without a centre director with good overview, without an active contact person, without mutual understanding, without someone

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to translate scientific publications and without having much time to collaborate, but it will be more challenging to do so. Knowledge integration is a complex process in UIC, but it is eased with key persons acting as boundary spanners to promote favourable boundary conditions through doing both centralized and distributed leadership. Together with mutual understanding, these favourable boundary conditions align different interests and secure resources to enable knowledge integration contributing to sustainable innovations.

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Chapter 13 An entrepreneurial ecosystem support model in the digital era: Crowdfunding 13.1 Introduction In the last century, the importance of entrepreneurial activities has gained increasing importance as the free-market economy has been adopted by more developed/ developing countries. In the first stage, there are many difficulties to be overcome in order to realize entrepreneurship activities. These difficulties mean having sufficient social capital, financial capital, advertising and distribution channels, and the ability to meet countries’ legal requirements. At this stage, not every individual can overcome these limitations autonomously. Besides, in order for individuals to perform their entrepreneurial activities properly, it is not possible to talk about the government support. During this period, large organizations overwhelmingly carry out entrepreneurial activities. These organizations dominate the market share nationally and internationally. For this reason, it can be said that large-scale organizations have the hegemony of having the financial and human resources needed to carry out entrepreneurial activities, physical facilities, and business relationship network power. This hegemony has created a structure that provides the market hegemony of large-scale enterprises that develop under the supervision of the public authority that constitutes the traditional business model. However, it cannot be said that this structure has a character suitable for the “Let do and let pass” cornerstone idea for the enterprise activities, which is the basic principle of the market economy. Therefore, in order for the market economy to function properly and the global economy to develop steadily, it has been necessary to remove the obstacles to the realization of the enterprise activities that are against the nature of the system. To eliminate the aforementioned obstacles to business activities, the globalization process was initiated first. In this way, it can be said that it is aimed to eliminate the customs policies, national legal practices, logistics barriers, and restrictions on financial resource circulation between countries. Certainly, in this process, countries have started to offer public support to entrepreneurs to support entrepreneurial activities to gain an advantage in the global market economy. It can be stated that these supports are primarily directed towards realizing the continuity of entrepreneurial activity through universities, which have adopted the mission of establishing social benefit. There may be a desire to exploit the potential for social development, stable economic development, and the emergence of innovative ideas with global competitiveness, together with the commercialization of scientific knowledge. In line with this request, it is understood that https://doi.org/10.1515/9783110670219-014

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the potential benefits of academics, educated young university students, and graduates who have received higher education are primarily being tried to be unlocked (Chang, Yang & Chen, 2009; Grimaldi, Kenney, Siegel & Wright, 2011; Rothaermel, Agung & Jiang, 2007; Shane, 2004). For this reason, many different supports have started to be provided with public support to remove the obstacles in front of entrepreneurship activities with an institutional structure that integrates research centres within universities. Governments have determined the commercialization frame of scientific research and the types of support by establishing their legal basis (The United States General Accounting Office, 1998). In doing so, the framework of patent and licensing procedures on international rights acquisition and its basic support in the commercialization of scientific knowledge can be determined (Jain, George & Maltarich, 2009). Moreover, some autonomous structures have been developed to serve under the corporate identities of universities to enable the academic, student, or external participating entrepreneurs to transform their scientific knowledge into a successful commercial product. These autonomous corporate service structures are technology transfer offices, incubation centres, and techno parks (Grimaldi et al., 2011; Siegel, Waldman & Link, 2003). In this way, a business model in which mutual gain and acceleration can be the subject created. In other words, scientific activities can lead to new entrepreneurship activities that bring about the emergence of new scientific research questions to overcome the problems encountered in the process of meeting new needs. However, ensuring this reciprocal cycle’s health, continuity, and stability is based on having some crucial elements. In other words, there are important factors that affect the commercialization of findings that express scientific value into a product that expresses a value and the functionality of the support units developed by universities within the framework of corporate identity. These factors are the product and service features to aim for a developed, targeted market, the university’s institutional mission and vision, the business model, and financial support structure. There are many scientific articles focused on commercialization strategies and entrepreneurs in the literature (Chebo & Wubatie, 2020; Duval-Couetil, Huang-Saad & Wheadon, 2020; Duval-Couetil, Ladisch & Yi, 2020; Fallah, Hamedani & Hassanzade, 2020; Hassan, 2020; Jenson, Doyle & Miles, 2020; Sachani, 2020; Zarringhalam, 2020). However, after digital transformation era, the potential of crowdfunding is increasing to develop a financial business model structure in both of practice and scientific works. This book section aims to overcome this deficiency in the literature and propose a new financial business model for the success of the enterprises that will take place in techno parks based on blockchain technology. In accordance with this study’s purpose, it consists of the following sections: commercialization of scientific activities in the literature, factors affecting the success

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of commercialization activities, blockchain technology, successful commercialization model based on crowdfunding, and results/recommendations.

13.2 Sustainable entrepreneurship, commercialization, and crowdfunding models in the literature Sustainable entrepreneurship is the process of providing solutions to the market failures causing environmental degradation. Different from social entrepreneurship, this process aims to generate profit for the entrepreneurs (Dean & McMullen, 2007, pp. 51–58). Hence, it can be concluded that the aim of the sustainable entrepreneurs is to produce goods/services to develop solutions to the environmental challenges while making profit at the same time. Developing the entrepreneurship definition of Venkataraman (1997), Cohen and Winn (2007, pp. 35) argued that different from the mainstream entrepreneurs, sustainable entrepreneurs also consider the environmental consequences in doing their business. The goal of searching for economic opportunities while contributing to the sectors in striving to become more environmentally and socially sustainable is the focus of sustainable entrepreneurs Hockerts and Wüstenhagen (2010, p. 482). Shepherd and Patzelt (2011, pp. 137–138) asserted that sustainable entrepreneurship is the process of exploring economic benefit for investors and entrepreneurs while contributing to the sustainability of the planet. Though sustainability is thought to be related to the large-scale firms’ effort to improve their carbon footprint (Rodgers, 2010, p. 125), it is also claimed that small businesses may stand up against climate change, environmental degradation, loss of biodiversity, and pollution (Cohen & Winn, 2007; Dean & McMullen, 2007). In this way, small businesses seem to contribute to sustainable economic development. Sustainable development is closely related to economics, environment, politics, ethics, and culture (Sachs, 2014). Sustainable development, which started to emerge at the beginning of the 1980s, has its roots back to the 1950s. In the 1950s, the main objectives of many economies were growth and productivity, while the concept of income inequality remained on the agenda in the 1960s and 1970s. In this period, an equitable growth model based on social benefits came to the fore. In the early 1980s, sustainable growth came to the fore with the realization that environmental degradation was one of the main obstacles behind economic growth. In other words, it has been seen that sustainable growth needs to be evaluated and analysed in three different dimensions as economic, social, and ecological (Munasinghe, 1993, pp. 1–2). These three dimensions as planet, people, and profit are also at the focus of sustainable entrepreneurship (Bell, 2012, p. 15).

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Universities may also promote sustainable entrepreneurship and development by supporting knowledge spillovers (Wagner, Schaltegger, Hansen & Fichter, 2019). Universities may mediate students, academic staff, and researchers to increase their entrepreneurial skills, support the establishment of new ventures, and encourage the emergence of sustainable new business models. In this way, universities can both act as an intermediary and undertake the task of being a centre where information dissemination takes place. Today, it is seen that some universities develop education and cooperation policies that will support sustainable entrepreneurship and sustainable growth. For this purpose, universities such as The University of Vermont and University of Groningen open graduate programs related to sustainable entrepreneurship, and universities such as University of Sussex, University of Edinburgh, University of St. Andrews, and University of Exeter also carry out graduate programs related to sustainable development. On the other hand, commercialization is another important affective fact to foster innovation and economic development at both the regional and national levels. It has increasing important mediator success role to transfer know-how and technology transfer from company to customer, and company to company in the new knowledge-based economic model. In this context, universities take on a mission to transform the information they obtain as a result of scientific research activities into commercialized products. In order to fulfil this mission, they have created an ecosystem called techno centres together with public and private sector stakeholders. Academic entrepreneurship activities can be carried out in these ecosystems. Before 1980, startup entrepreneurs had activities aimed at commercializing the information they obtained as a result of scientific research. However, only a limited number of successful activities have taken place in the USA due to the lack of effective mechanisms for transforming scientific knowledge into commercialized products. On the other hand, there has been an increasing trend of the research-based commercialization activities based on the Bayh-Dole Act since the 1980s. The federal government conducted a study on this problem at that time. As a result of this research, it has been determined that the lack of a public policy and legal basis for a process that covers all commercialization activities causes problems. So, every agency follows their procedures to manage these activities and this creates a long time between invention and commercialization. It has also been determined that there is no effective incentive for firms to commercialize academic results (The United States General Accounting Office, 1998). In this sense, in 1980, US Congress attempted to enhance knowledge and technology transfer (KTT) activities between university and industry through legislation called the Bayh-Dole Act (Audretsch, Bozeman, Combs, Feldman, Link, Siegel, Stephan, Tassey & Wessner, 2002). Various changes have been proposed and accepted in Congress on the information and technology transfer system. Thus, they proposed a standard patent policy among federal agencies and universities have been allowed to have patents from federal research grants. In this way, it can be said that the USA has achieved many

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successful results in KTT with the implementation of Bayh-Dole Law. Thus, universities had the opportunity to negotiate more flexibly in licensing. With this regulation, thousands of new companies and jobs have been created that aim to develop innovations to encourage economic and social development. In this way, it can be said that a large number of ecosystems (techno parks) that will allow information and technology transfer can be created. Furthermore, within the last three decades, the USA became attained leadership in many high-technology sectors such as biotechnology, electronics, engineering, and environmental technologies (The United States General Accounting Office, 1998). On the other hand, KTT model in the USA could be a good example for many governments from different countries in which technology transfer offices (TTOs) have been established. These offices have aimed to lead university–industry collaboration activities such as licensing, joint R&D, contract research, and consultancy. In developed countries, many breakthroughs have been acquired using these offices (Siegel et al., 2003). In addition to TTOs, many universities have appropriated an area (mostly called Technology Development Zone) in the university campus for the collaborative firms and spin-offs to foster university–industry interaction and entrepreneurship. These areas provided many exceptions and advantages of being in a technology-based cluster. The definition of the university–industry technology transfer in the literature is that “a university scientist who engages in the commercialization of the results of his/her research, largely by patenting and/or setting up a business” (D’Este, Mahdi & Neely, 2010, p. 2). The TTOs aim to provide supportive activities such as patenting, licensing, startup creation, and university–industry partnerships for commercializing academic scientists’ innovations as entrepreneurs (Cantaragiu, 2012; Grimaldi et al., 2011). While licensing is faster and less risky than firm establishment, setting up a company can generate higher revenue than licensing can provide. As finding a licence is not an easy task, as a young company, competing in the market requires much more resources and effort than finding a licensee. Because of the pros and cons of each method, nascent academic entrepreneurs need to consider a number of factors when choosing between licensing and starting a firm (Etzkowitz, 2003; Krabel & Mueller, 2009; Mansfield & Lee, 1996).

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13.3 Which factors can affect the commercialization success activities in the ecosystem of universities after crowdfunding developments? It is possible to mention many factors that can impact the successful transformation of a business idea into a commercial product. These factors are the product and service features to aim to develop the targeted market, the university’s institutional mission and vision, the characteristics of entrepreneurs, and the financial business support model structure. In this section, each variable factor is discussed in detail, and its relations with the successful realization of the commercialization process are discussed in period before and after crowdfunding. First, the effects of product and service features intended to be commercialized in the crowdfunding platform on success is being discussed. In most cases, successful commercialization of innovations entails additional assets from distribution channels to after-sales support. While most young firms cannot access these capacity capabilities and assets easily, established firms in the market have already owned the necessary complementary assets to commercialize the product (Gans & Stern, 2003; Teece, 1986). Since the lack of complementary assets will severely harm the commercialization process, this property of the innovative product should be considered by the academician while assessing its commercialization strategy. In such cases, licensing or selling the technology, if it is possible, may be a better choice due to the necessity of high-cost complementary assets in the case of a spin-off founding. My interviews with academicians also provide evidence for this proposition. One of the academic entrepreneurs AEs states that “some customers ask for the guarantee and after-sale technical support for our product, but we do not have enough resources and distribution channels to provide such support for customers while established companies already have such services.” This issue demonstrates that scientists should analyse the complementary assets required for the commercialization process before assessing the commercialization strategy. In the case of high-cost complementary assets necessity, being a sub-contractor or making licensing agreements with established firms can be first considered. Second, another factor is the targeted market character. The potential demand and competition in the market are important factors in terms of commercialization strategy. While small markets mostly do not warrant new company creation, big markets may be managed by large companies (Lundvall & Borrás, 2005). Entrepreneurs and TTOs should compare licensing offer and potential income that can be created through their own company. Since the company creation also means competing in the market, the competition intensity and the market structure should also be considered by academic entrepreneurs. In the market where competitive established firms exist, cooperation alternatives should also be assessed by spin-offs. However, if there is a potential to create a niche market with no rivals yet, starting a company may provide higher returns than licensing. To sum up, market conditions

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and customer preferences should be considered before determining the commercialization route. On the other hand, mission and vision in university is the third important factor to gain achievement in the commercialization process. According to Shane (2004), differences in university policies, TTO strategies, and other university-based factors severely influence its spin-off company creation and technology transfer performances. In this manner, we propose that the commercialization strategies of academic entrepreneurs are affected by university-oriented issues. Third, the structure and the policies of a university TTO highly lead to the commercialization path of academicians. Since academicians should also accomplish their academic duties, they severely need TTO’s assistance in research commercialization activities (Chang et al., 2009). Therefore, we can claim that the most crucial university-level factor influencing academicians’ commercialization choice is the tendency and expertise of TTOs in commercialization activities. For example, according to Shane (2004), TTOs which have more experience in the firm formation and have more resources for commercialization activities generate more spin-offs because the company formation is a more complex, costly, and time-consuming activity compared to licensing. Shane (2004) has claimed that the number of spin-offs varies across universities, especially due to the different policies, licensing strategies, and other characteristics that TTOs adopt. He takes attention to six factors stimulating spin-off formation: – allowing exclusive licensing, – permitting equity investments in spin-offs, – offering leaves of absence for inventors who wish to found companies, – permitting spin-offs to use university resources to develop technology, – paying lower royalties to inventors, – promoting spin-offs that have access to pre-seed funds. In this sense, it is argued that the technology transfer office’s support and experience severely affect academicians’ commercialization decision and success. Second, the research field, namely academic discipline, is also crucial for choosing the right commercialization strategy (Grimaldi et al., 2011). While some disciplines, such as chemistry and biotechnology, are more proper for licensing, the ICT sector has certain specific characteristics that make it easier to establish a company. There are many studies in which conducted interviews with academic entrepreneurs and TTO experts provide support for this proposition, and additionally, we have seen that the academic quality of a university also affects academicians’ commercialization performance. According to Shane (2004), university reputation makes it easier to find inventors for academicians’ inventions. As a result of his comprehensive research in MIT spin-offs, he detected that the university’s academic quality and institutional superiority facilitate spin-offs. Furthermore, the academician and the university’s network capacity are also essential for the research commercialization activities. If an academician can easily find a colleague

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to collaborate on a complicated research problem, they can progress in more assured and rapid studies. The fourth factor is the character of entrepreneurs. The entrepreneurial attitude plays a vital role in assessing the right commercialization strategy. Since creating a firm entails entrepreneurial abilities and desire, spin-offs are mostly occurring when the inventors are keen to find a new company (Shane, 2004). Nelsen (1991) refers that a spin-off company starts at MIT only when a professor wants to do that. Otherwise, activities that should be performed by a firm owner would be assessed as boring and time consuming by the academician. Also, one of the necessary and facilitating factors in university–industry joint projects is the trust between parts. Cultural differences and insufficient trust between academicians and industry members positively affect the projects (Santoro & Saparito, 2003). High levels of trust between partners trigger sharing valuable knowledge and make successful commercialization easier (Inkpen & Tsang, 2005; Ring & van de Ven, 1992). Especially in licensing agreements, university–industry interaction occurs, and parts may need to work together to achieve the product’s success. Therefore, low trust between the university and industry members makes it difficult to find a proper licensee for the innovative product. Entrepreneurs in scientific researchers who do not believe that the industry will market its innovation successfully would be more likely to start their own company. Industry members who believe that academicians do not understand industry expectations will not tend to make licensing agreements. The final effective factor in the commercialization process’s achievement is the structure of the financial business model. In the commercialization process, it is important that the entrepreneur can provide financial support, which is the lifeblood, and use it in the most beneficial way. It can be said that the financial support system cannot operate in a sustainable manner based solely on state supports. In addition, only having a limited financial business model within national boundaries may prevent the enterprise idea from becoming a global born enterprise. Therefore, it would be appropriate to have a financial business model that allows especially international angel investors to invest in ventures at the right time. Therefore, there is a need for a system that can enable international information exchange and help minimize transaction costs in money transfer. It can be said that an intermediary system can be developed that will provide the aforementioned opportunities thanks to the enormous development in information and communication technologies, and infrastructure. This system is called crowdfunding with the blockchain. It may be said that serious evolution on the success factors for commercialization success in the crowdfunding through blockchain system can be achieved. One of the main obstacles behind development of the crowdfunding system is to convince investors. In order to convince the investors, the entrepreneurs themselves and their team members should have high social capital in a limited time period and in a limited promotion environment as a communication channel. For this reason, it is very important that the training programs that universities have created

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with the mission of raising entrepreneurs are designed in a way that contributes to increase their social capital skills. Certainly, those who are in the ecosystem of universities with an entrepreneurial mission will have an advantage in achieving success in crowdfunding. In addition, entrepreneurs in universities’ techno parks will be a part of a culture within the ecosystem. In this way, social capital forces can be much stronger than normal. Blockchain technology is discussed in detail in the next section.

13.4 Blockchain technology OECD defines blockchain as a shared ledger transfer mechanism between counterparties that is not under the control of any central registry authority. Although blockchain technology has come to the fore with bitcoin, it is actually a technology that can contribute to the change in the way that companies do business in many different industries (OECD Blockchain Primer, 2019, pp. 1–2). As it is seen in the Figure 13.1, there are some basic concepts of blockchain. Among them are node, ledger, and hash. A node is an independent system that guarantees the validity of the transfers and keeps records of the blockchain as a whole. Ledger, on the other hand, is expressed as a system that can have a decentralized structure that records the transaction history (Yaga, Mell, Roby & Scarfone, 2018, pp. 1–13). The technology that converts the detailed information in each block to abstract information by securing with cryptographic methods is called a Hash system (Say, 2019, pp. 18–19). Blockchain can be seen as distributed digital ledgers, in which each block containing separate information is connected to each other blocks that come before it by cryptographic technology. As new blocks are added to the system, it becomes more difficult to make changes on the previous blocks; thus, the security of the system increases even more (Crosby, Nachiappan, Pattanayak, Verma & Kalyanaraman, 2015, p. 11). It is thought that one of the main reasons why blockchain has created many innovative and new business models is the security it provides. The benefits of blockchain technologies can be examined under four headings. First of all, blockchain is stated to increase transparency. In addition, it provides security in the ecosystem, reduces the cost of compliance between the parties, facilitates delivery with automatic control mechanisms, and reduces audit costs. Public, consortium, and private blockchain are the three main types of blockchain technology. A public blockchain is the main type for everyone willing to join the system. In the private blockchain, the processing of data is only under the authority of a private group, while the use of the resulting data for reading purposes can be private or open to the public. Consortium blockchain has a structure between private and public blockchain (PWC, 2017, pp. 9–15). Blockchain technologies may be used in different

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Figure 13.1: The system of blockchain. Source:OECD Blockchain Primer (2019, p. 2).

business industries, such as (The Scientific and Technological Research Council of Turkey, 2020): – Banking – FinTech – Money transfers – Creating and storing documents – E-Commerce and payments – Stocks and stock exchanges – E-notary – Peer-to-peer borrowing and distributed structured credit systems – Donation systems and micropayments – Cloud computing and secure cloud storage Blockchain is the core technology for the ones in financial and non-financial industries. In other words, there are many successful financial and non-financial applications of

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blockchain. The majority of the blockchain-based financial technologies are related to the well-known cryptocurrency, bitcoin. Though bitcoin is a well-known application of blockchain technology, there are a number of other related financial applications such as NASDAQ Private Equity, Blockstream, Bitshares, Coinsetter, Medici, and Augur. There are also non-financial applications of blockchain in the notary industry. In the notary industry, authenticity of a document can be verified with the ease of blockchain technology without the necessity for any centralized institution. Stampery, Blacknotary, Crypto Public Notary, Proof of Existence, and Ascribe are the main examples of blockchain-based notary systems. In addition to the blockchain-based notary system, this technology is also used in storage and Internet of things (IoT) decentralization processes (Crosby et al., 2015, pp. 13–18).

Figure 13.2: Blockchain decision tree. Source: Wüst and Gervais (2017, p. 3).

The fact that blockchain may be used in many sectors does not necessarily require all firms to use this technology in their business model. As it seen in the Figure 13.2, Wüst and Gervais (2017, p. 3) developed a systematic decision tree which enables companies to improve their awareness of whether they need blockchain or not. It is seen in this decision tree that blockchain technology is not necessary for companies that do not need data storage, having a single writer system, or using an online TTP mechanism. Moreover, blockchain is of no use for the ones where all writers are trusted. Blockchain is a technological innovation that increases the interaction

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between parties, especially on platforms where trust in the third party can be problematic. When evaluated in this respect, the blockchain emerges as a system that may change the interaction methods in society from beginning to end. The blockchain technology also significantly increases the security of data storage processes that contain private information and increases transparency between the parties (Wüst & Gervais, 2017, pp. 1–3). Though this technology mediates the rise of innovations not only in finance but also in other disciplines, it is now well known as the technology behind cryptocurrencies such as Bitcoin. Nakamoto, the founder of Bitcoin, defined Bitcoin as a digital signature chain. In terms of Bitcoin, blockchain can be considered as a system that enables the secure transfer of crypto-currencies between the parties without the need for any central registry authority and enables the secure recording of the transfer (Deloitte, 2019, p. 4). Although bitcoin and blockchain are used to express the same concepts in some sources, blockchain is essentially the underlying technology for the cryptocurrencies such as Bitcoin. The transparency and control of financial and non-financial assets are facilitated, and the efficiency of fund transfer increases with the ease of the advantages provided by blockchain technology. Blockchain technology is not considered as a new technology, but it can be evaluated as an innovation that occurs with a combination of existing technologies. With cryptography applications of blockchain technologies based on distributed ledger technology, secure fund transfer is possible without a central registration authority. The brokerage costs caused by the companies that mediate the fund transfer, the ineffectiveness, and insecurity problem in the system that arise as a result of this can be eliminated by blockchain technology (OECD Blockchain Primer, 2019, pp. 1–2).

13.5 Crowdfunding and sustainable entrepreneurship Microfinance and microcredit practices that have emerged to prevent income inequality form the basis of crowdfunding. Microfinance is considered an alternative financing tool, especially for those who have limited access to finance or cannot afford financing costs. Donations, sponsors, or loans as financing sources provided by microfinance also form the basis of the crowdfunding (Weinstein, 2013, pp. 428–431). Financing a project or a new business idea by a group of people is defined as crowdfunding. Contrary to the indirect financing methods, funds can be transferred from those with excess funds to those with fund deficits without intermediaries (Onur & Degirmenci, 2015, p. 1). Crowdfunding provided by donation or investment has shown significant progress in financing new business ideas, especially after the global financial crisis in

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2008. Accordingly, limited access to finance, which was brought to the agenda by the crisis in 2008, especially for new enterprises, paved the way for crowdfunding, especially in developed countries. Legal regulations based on transparency, the widespread use of the internet and social media, tight links with other entrepreneurship activities, and the existence of online platforms that can be controlled by legal regulations are the factors that significantly increase the development of crowdfunding in developed countries (World Bank, 2013, p. 8). Basically, there are two different types of crowdfunding models: non-financial and financial crowdfunding platforms. In the non-financial return model, no return is offered in return for funding. Non-financial return models may be grouped under three main groups as donation, pre-selling, and reward crowdfunding. The donation crowdfunding model is mainly based on the voluntary participation of the funders and it is mostly used to finance projects of social benefit such as cancer research. In the pre-selling crowdfunding model, funders obtain the opportunity to receive a prototype of the product or limited use of the service before other users, depending on the number of funds provided. In the reward crowdfunding model, funders receive a symbolic reward regardless of the amount of fund backed. Financial crowdfunding may also be grouped under two main groups as equity- and lending-based crowdfunding. In the equity-based crowdfunding model funder may receive profit/revenue, directly receive the equity, or may go into an obligation-based model. In the lendingbased crowdfunding model, peer-to-peer lending is essential. In other words, creators borrow from the crowd investors without any intermediary (European Commission Report, 2013, pp. 4–7). It is known that the fund raised by crowdfunding can reach large amounts. An example of this is the Trampoline Systems software company receiving 1 million pounds with crowdfunding (Schwienbacher & Larralde, 2010, p. 3). Crowdfunding has basically three dimensions as creators, funders, and platforms. The creators are those who aim to raise capital with crowdfunding with the advantage of lower cost and access to more information. On the other hand, funders transfer their funds for five different purposes. The first incentive is to be able to reach investors in different investment alternatives with crowdfunding. Second, with crowdfunding, funders may get the right of early access to new products or services since these funders provide financial support before the product is an event developed. Third, funders may consider crowdfunding as a membership to a social platform and choose to invest for this purpose. In addition, funders may transfer funds just for a desire to support a business idea or to contribute to the launch of a specific product. Finally, crowdfunding can be preferred by the creator’s family and friends to make the transaction official (Agrawal, Goldfarb & Catalini, 2013, pp. 10–15). There is no need for any intermediary in the crowdfunding market, and those who offer the crowdfunding service are defined as platforms (Rocholl, 2016, p. 3). These platforms enable funders to meet with creators with the ease of information technology (Mihaela, 2017, p. 66). Among these platforms, “sellaband.com” is the leading one, which was launched in 2006. On this platform, funds were raised from

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the singer’s fans for the release of a music band, and a free CD was sent to his fans as a reward. With this method, $3 million was raised from 65,000 fans in three years, and these raised funds were used to finance 4,000 singers (Schwienbacher & Larralde, 2010, p. 4). Today, there are several fundraising and crowdfunding platforms such as Gofundme, Indiegogo, Kickstarter, Fundly, JustGiving, and Facebook. These platforms mainly depend on a revenue-based model and typically charge a platform or payment fee or both of them at the same time (Crowdfunding, 2020). Some of the crowdfunding platforms and fees are presented in Table 13.1. Table 13.1: Some of the crowdfunding platforms and fees. Platform

Total amount of raised capital (billion $)

Supporters (million)

Platform fee (%)

Payment fee (%)

Gofundme







. + $.

Indiegogo

.





. + $.

Kickstarter

B





. + $.

Fundly

.

N/A

.

. + $.

JustGiving

N/A



Nonprofits: 

Facebook

N/A

N/A

N/A

Personal: . + $.

Source: https://www.crowdfunding.com/

The latest statistics by P2Pmarketdata indicate that the worldwide crowdfunding market reached $304.53 billion in 2018. Peer-to-peer lending-based crowdfunding is recorded as $251.35 billion in 2018 (P2Pmarketdata, 2020). Considering the fact that a significant proportion of entrepreneurs need small amounts of funding in the traction and validation phase of their commercialization process, it can be concluded that crowdfunding is a viable alternative for entrepreneurs’ financing. The most critical stage in the crowdfunding process may be fundraising either by donation or by investment. At this stage, sustainable entrepreneurs can gain a significant advantage over mainstream entrepreneurs. Accordingly, it may be easier and faster for the projects that can benefit the society and/or provide solutions to environmental degradation to obtain funds, especially through donations in crowdfunding. From this point of view, crowdfunding supports sustainable entrepreneurship and increases its effectiveness. In doing so, crowdfunding also supports commercialization of sustainable entrepreneurship and sustainable development. In addition, crowd funders may possibly be interested in engaging in activities of developing sustainable products/services since crowd funders are closely related to the entrepreneur’s sustainable business idea and its development process. Therefore, investors not only provide financial support but also suggest creative business ideas to develop sustainable products/services.

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13.6 The successful commercialization model based on crowdfunding In terms of entrepreneurship, crowdfunding is a financing alternative based on the principle of small amounts of fundraising from a number of individuals (Mollick, 2014, p. 2). Crowdfunding is a fundraising alternative empowered by a group of individuals excluding traditional financial institutions/traditional investors such as banks, venture capitalists, or business angels. In the crowdfunding system, entrepreneurs present their business plans and the returns, if any, of those who invest in this business idea to investors via a web-based platform (Mitra, 2012, pp. 68–71). Crowdfunding is an alternative fundraising for the early-stage financing of entrepreneurs and innovative business ideas in developing countries where laws, regulations, and technological infrastructure are successfully adjusted for the rise of this alternative financing technique. It is possible to raise funds between $1000 and $1 million with crowdfunding, which has shown a significant development with the effect of the global financial crisis that burned down in 2008. Today, crowdfunding can be an important financing alternative in meeting the early-stage financing needs of companies (World Bank, 2013, pp. 14–16). The funding gap of startups may be filled by venture capital, angel financing, or bank loans (Beaulieu, Sarker & Sarker, 2013, p. 2). However, the 2008 global financial crisis has limited the funds available to SMEs (Economist, 2013). In this manner, crowdfunding is a vital financing alternative, especially for startups.

Figure 13.3: Crowdfunding and funding lifecycle. Source: World Bank (2013, p. 16) Crowdfunding’s Potential for the Developing World Report.

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As shown in Figure 13.3, donation-based crowdfunding is an eligible financing alternative to raise funds less than $50,000 for the firms at the idea/inception and prototype/proof of concept funding lifecycle whereas debt- or equity-based crowdfunding fills the funding gap of startups. Access to finance is the primary financial problem faced by entrepreneurs. Crowdfunding contributes to capital formation, especially for small ventures which do not have access to angel finance and venture capital investments (Mitra, 2012, p. 70). Crowdfunding aims to raise funds using social media tools and support new projects or new ventures with these funds. Crowdfunding first emerged in Australia, the United Kingdom, the Netherlands, and the USA, but quickly became popular all over the world in response to the financing gap generated by the global financial crisis in 2008. The arts and entertainment industries, where legal regulations are more flexible, have been the focus of crowdfunding at the beginning. Over time, crowdfunding based on simple donations or gifts evolved to a vital financing alternative for entrepreneurs and a loan or equity investment alternative for crowds. The development of equity crowdfunding is slower than the development of debt crowdfunding in both developed and developing countries. For instance, there are legal restrictions on equity crowdfunding in the USA. Legal arrangements in India and Turkey do not allow companies to provide equity in return for a small gift and reward-based equity crowdfunding (Bruton, Khavul, Siegel & Wright, 2015, p. 12). Ordanini, Miceli, Pizzetti, and Parasuraman (2011, pp. 444–445) argue that crowdfunding basically has three counterparties. Accordingly, entrepreneurs willing to raise funds constitute the first part of the crowdfunding system. As another counterparty, crowdfunding platforms are also essential for this system. A crowd of investors financially supporting the ideas instead of supporting the entrepreneurs by purchasing their products is also another counterparty. Investors may also provide non-financial support through suggesting ideas, co-producing the final product, or evaluating the alternatives. Ordanini et al. (2011, pp. 444–445) evaluate crowdfunding platforms as an essential part of the crowdfunding ecosystem. However, blockchain technology enables crowdfunding systems to run successfully without any online platform. Moreover, the centralized nature of crowdfunding platforms increases unilateral risks in the system and poses a threat to the security of the system. Blockchain technology, on the other hand, removes this threat, increases the security of the system in doing so, and, at the same time, provides a cost-cutting transformation. Moreover, blockchain technology may also improve the efficiency of the crowdfunding system and transform the process, as it is presented in Figure 13.4. Unlike other investors, crowd funders are closely related to the entrepreneur’s business idea and its development process. Entrepreneurs’ particular project may also be supported financially and non-financially by other entrepreneurs with the ease of blockchain technology. From this standpoint, it is highly possible that crowd funders are also interested in engaging in activities of developing the product or services

Investor N

Investor 3

Investor 2

Investor 1

Universities

Science and Tech. Parks

Government

12

TTOs

1

Sust. Entpre.

2

Sust. Entpre.

Sust. Entpre. 3

Sust. Entpre. N

Sustainable Economic Development

Ecological Benefit

Social Benefiit

Sustainable Products/Services

Financial Development

Startups

IPOs

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Figure 13.4: A conceptual commercialization model based on crowdfunding forsustainable entrepreneurs. Source: Elaborated by the authors

281

Crowd of Sustainable Entrepreneurs

Crowd of Investors

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especially related to sustainable development. Awareness of the social issues and environmental sensitivity seem likely to increase the fundraising potential of the projects targeted on this issue. Accordingly, raising funds through crowdfunding can be quicker and easier for sustainable entrepreneurs than for mainstream entrepreneurs. In addition, therefore, investors not only provide financial support but also suggest creative business ideas to develop sustainable products/services. However, the centralized nature of crowdfunding platforms constitutes an obstacle to the functioning of this new efficient system. On the other hand, the decentralized nature of blockchain technologies may enable a large number of investors to securely transfer funds to the system, as well as make it possible for many investors to work on the same project at the same time securely. As sen in Figure 13.4, besides providing financial support, a crowd of investors may contribute to the development of the products/ services via blockchain based crowdfunding system. In this system, universities may also transfer knowledge to the sustainable entrepreneurs securely and TTOs may also promote commercialization of sustainable entrepreneurs using blockchain. Government may play as an audit authority in this model and also provide financial and non-financial support for the commercialization of sustainable entrepreneurs. With the increase in the number of startups producing green products and services, the number of green companies listed on the stock exchange may increase and new investment alternatives for green finance may arise which in turn promote financial development and sustainable development. These green products/services possibly also provide sustainable development.

13.7 Conclusion and recommendations Crowdfunding is one of today’s new-generation financing techniques. Crowdfunding is especially vital in terms of bringing idle small funds to the economy. Gaining a corporate identity for entrepreneurs is an important criterion in terms of their innovation potential. In order for entrepreneurs to have a corporate identity, their financing needs must be met. Many startups with creative business ideas fail simply due to the low access to finance. The failure of creative business ideas negatively affects the growth potential of an economy and the competitiveness of that economy. The obstacles to the commercialization of new business ideas can be removed via solving financing problems of the startups. Access to finance is one of the main problems entrepreneurs in the commercialization of their business ideas. The startups have to be financed especially by equity capital when they are first established. Generally, these startups are unable to use bank loans and therefore many startups fail at the establishment stage. At this stage, crowdfunding can financially support the survival of businesses. In this way, crowdfunding contributes to the commercialization of new business ideas. Thanks to the

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advantages provided by crowdfunding, startups can receive funds without enduring any financial burden. However, the currently functioning crowdfunding system has some important shortcomings. In order for crowdfunding to take place, it is inevitable that platforms mediate between new initiatives and funders are needed. However, the fact that these platforms pick the entrepreneurs among other ones according to their criteria and require a fee for their intermediary activities can negatively affect the effectiveness and efficiency of the system. Therefore, it is necessary to make improvements in the existing system and contribute to the commercialization of innovative business ideas. Blockchain technology, which is an innovative technology although it is not new, can make an important contribution to the commercialization of business ideas. Blockchain technology, removes unilateral risks in the system, increases the security level of the system, and provides a cost-cutting transformation. When considered financially, blockchain technology can increase the access to finance of new startups. Blockchain technology not only increases access to finance through crowdfunding but also contributes to the improvement of the non-financial processes of the startups, thus enabling the commercialization process. Considering the fact that a significant proportion of entrepreneurs need small amounts of funding in the traction and validation phase of their commercialization process, it can be concluded that crowdfunding is a viable alternative for entrepreneurs’ financing. Sustainable entrepreneurs can gain a significant advantage over mainstream entrepreneurs in receiving funds since crowd funders may possibly be interested in engaging in activities of developing sustainable products/services. Therefore, investors providing financial supports possibly give non-financial supports such as creative business ideas. With blockchain technology, non-financial supports such as experience, knowledge transfer, and market analysis required by startups may be provided. However, the centralized nature of crowdfunding platforms constitutes an obstacle to the functioning of this system. However, the decentralized nature of blockchain technologies may enable a large number of investors to securely transfer funds to the system, as well as make it possible for many investors and other entrepreneurs to work on the same project at the same time securely. In doing so, entrepreneurs may gain profit while providing solutions to the social issues and environmental degradation. In this way, many business ideas that cannot be successful alone can be developed with different perspectives, and the commercialization process of business ideas can be shortened in terms of time and the number of commercialized projects can be increased while developing the social and environmental benefits. In other words, with blockchain technology that ranks first in information security, the effectiveness of crowdfunding can be increased and significant advantages can be achieved in terms of commercialization and the same time social problems and environmental degradation may be removed. In doing so, sustainable development may also be enhanced. In this manner, the combination of crowdfunding and blockchain technology both increases the efficiency of the system and increases the contribution to the economy.

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Chapter 14 Steering productive entrepreneurship activities in emerging markets: The role of the university 14.1 Introduction The important role that entrepreneurship plays in steering economic growth has over the last decades been reiterated in academic discourse (e.g. Audretsch, Belitski & Desai, 2018; Naude, 2017; Opute, 2020). Within the discourse on that importance is the logic that entrepreneurship is a tool for combating unemployment and poverty (e.g. Ahmed & Nwankwo, 2013; Iwu & Opute, 2019; Opute, 2020). While entrepreneurship has the potential to contribute significantly to economic growth, including also combating unemployment and poverty, realizing that potential would hinge largely on entrepreneurs knitting their acts to achieve productive entrepreneurship. Towards that productive target, entrepreneurship discourse has underlined the importance of taking a holistic viewpoint in the study of entrepreneurship. Thus, scholars argue that in order to fully maximize the economic growth contribution of entrepreneurial activity, giving due attention to the entrepreneurial ecosystem is pertinent (e.g. McAdam, Harrison & Leitch, 2018; McAdam, McAdam, Dunn & McCall, 2016; Spigel & Harrison, 2017). Supporting the advocacy for more effort towards enhancing the understanding of the entrepreneurial ecosystem, Motoyama and Watkins (2014) point to the need for knowledge on the evolution of an ecosystem over time. According to the entrepreneurial ecosystem foundation, a community’s entrepreneur support network, which involves firms and related institutions that make up the ecosystem, is a critical success factor of the firm. In other words, the entrepreneurship ecosystem viewpoint underlines the importance of network embeddedness. Utilizing such social networks and support institutions would facilitate business growth through reduction of transaction cost, business opportunities creation, knowledge sharing, and research and training (educational institutions; e.g. Motoyama & Watkins, 2014; Turkina, 2018). As noted by Stam and Spigel (2017), entrepreneurial ecosystems imply a set of interdependent actors and factors that interact and function in a way that productive entrepreneurship is enabled within a territory. Simatupang et al. (2015, p. 393) not only reinforce the importance of alignment in entrepreneurship ecosystems but also identify three core stakeholder levels: “an effective entrepreneurship ecosystem depends on the integration of activities of various stakeholders at three different levels, namely the strategic level (policy making), the institutional level (support institutions), and https://doi.org/10.1515/9783110670219-015

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the enterprise level (entrepreneurs and business entities).” When active, entrepreneurship ecosystem allows private, public, and social actors to come together in different ways to support the development and growth of entrepreneurship. Recognizing the importance of this theoretical premise and inspired by the aforementioned research gaps, this chapter focuses on entrepreneurship ecosystem with a particular attention on the support institutions’ stakeholder level and the important role of universities as critical factors in driving productive entrepreneurship and sustainability. In particular, emerging economies form the focus of this chapter because it has been documented in the literature that such economies often struggle with maintaining a sustainable entrepreneurship ecosystem owing to several factors including poor governance systems, dilapidated infrastructure, poor funding systems, and absence of (and inadequate) enterprise education (e.g. Iwu, Eze, Opute, Dongo & Dongo, 2021; Iwu & Opute, 2019; Iwu et al., 2019). In the conceptual framing of this chapter, we forward a support institutions viewpoint that embodies stakeholders that aid and work with universities towards enabling universities to play an active part in driving productive entrepreneurship and sustainability. In other words, this chapter considers not only universities but also government as a policy and funding partner, and private sector actors as funders for universities towards enabling research and teaching curricular to aid productive entrepreneurship. Thus, the focus in this chapter relates to the entrepreneurship education initiatives that universities can implement, as well as private sector and government facilitating role to enable university initiatives to effectively contribute to productive entrepreneurship. This theoretical framing draws from recent literature (e.g. Iwu et al., 2021; Iwu & Opute, 2019) which argues that: (1) government’s failure to play its facilitating role and inadequate private sector participation are core reasons for inactive entrepreneurship in emerging economies, and (2) more research should be carried out in this area. As explained earlier, the core objective of this chapter is to underline the role of universities as central drivers of entrepreneurship education that would contribute to productive entrepreneurship and economic sustainability. Towards achieving that objective, this chapter is advanced further thus. Next, we advise the methodological approach used in putting this chapter together. This is followed by our explanation of an emerging market to enable the reader to understand the conceptual premise for this study. Thereafter, the role of universities in entrepreneurship education is presented. Subsequent to that, and in line with the support institutions viewpoint forwarded in this chapter, the role of government and private stakeholders as facilitators of universities’ entrepreneurship education towards boosting productive entrepreneurship is explained. In the concluding section, the conclusions, future research recommendations are underlined.

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14.2 Method We put this chapter together using the descriptive literature review method. This method is commonly associated with papers of this kind where efforts are made to identify, assess, and fuse all materials that are relevant to a given topic with the aim of critically appraising previous related research. According to Paré, Trudel, Jaana, and Kitsiou (2015) and Rumrill, Fitzgerald, and Merchant (2010), the value of this review method is in its capacity to methodically assemble, from a body of knowledge, materials that express varied views yet offering tangible benefits to the study of a phenomenon. We equally acknowledge the methodological precedence of scholars like King and He (2005), Bragge, Relander, Sunikka, and Mannonen (2007), Grant and Booth (2009), and Yang and Tate (2012) who averred that this method assisted them in sourcing useful resources for their research projects. To derive more from this method, we did not limit ourselves to only journal articles but included technical reports, published and unpublished monographs even though Podsakoff and his colleagues (2005) are of the view that only journal articles can be considered as authentic science. Additionally, cognisant of the debate around the necessity of entrepreneurship (Asitik, Sharpley, & Phelan, 2016; Iwu et al., 2019; Owens & Tibby, 2014), entrepreneurship education and relevance of universities (Audretsch, 2014; Gamede & Uleanya, 2019; Naude, 2017), and how universities can benefit from the government and the private sector (Etzkowitz & Klofsten, 2005; Gianiodis & Meek, 2020; Herrera, Guerrero & Urbano, 2018), we believed it was prudent to include materials that we thought had some relevance to the object of the chapter. Thus, we first identified resources we considered relevant to the project; thereafter, we distilled them according to how closely linked they were to the focus of the chapter.

14.2.1 Emerging market: Conceptualization Before dwelling further on the central theoretical focus of this chapter, it is important to engage with the discourse on what constitutes an emerging market. Taking this approach is important, not only to allow the reader to gain some understanding of conceptual perspectives on what an emerging market is but also to specify clearly the conceptual premise of emerging market in this chapter. Furthermore, taking this approach is also essential to allow the reader to fully understand the importance of the conceptual framing of this chapter which was strategically contextualized bearing in mind critical support institution factors that impede entrepreneurship development in the emerging market setting. Numerous definitions of an emerging market have been offered by scholars and global institutions. Offering a broad perspective, Kuepper (2016) views an emerging market as one that is making progress towards industrialization and rapidly improving both economically and politically. Borrowing some indices distilled by the World

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Bank as characteristics of an emerging market, Burgess and Steenkamp (2006) describe emerging market as one whose market has not reached the level of a developed one. Described by HSBC (https://investorfunds.us.hsbc.com/investing-inemerging-markets/content/what-are-em.fs), an emerging market refers to nations “with large populations and ample supplies of key natural resources such as oil, metals, and timber.” In a perspective that connects to aspects of Khanna and Palepu’s (2000) description of an emerging market, Blomberg (2013) presents a list of the top 20 emerging markets. As noted by Khanna and Palepu (2000), it is appropriate to say that an emerging market is “not there yet” because it has poorly functioning institutions. A common assumption in the literature on emerging markets relates to the fact that emerging markets are characterized by ailing institutions and experience socioeconomic difficulties. Towards improving economic capacity of emerging markets, scholars call for closer attention to that setting (e.g. Iwu & Opute, 2019; Kuepper, 2016). This chapter is a response to that call, and in doing that, this chapter embraces the emerging market viewpoint in Iwu (2018) which aligns with the conceptualization forwarded in past literature (e.g. Burgess & Steenkamp, 2006; Khanna & Palepu, 2000; Kuepper, 2016) that suggests that emerging markets, like a developing economy, can attain considerable industrialized standing if it offers innovative prospects or significant investment by both local and international investors. Leveraging empirical insights from emerging market literature (e.g. Iwu et al., 2021; Iwu & Opute, 2019) which underlines lack of institutional support as a critical impeding factor of productive entrepreneurship in South Africa, this chapter embraces the entrepreneurship ecosystem foundation to contribute to emerging market literature, with an intent towards achieving productive entrepreneurship in the emerging market setting. Within the productive entrepreneurship viewpoint in this chapter is the need for sustainability, a critical target, given the fact the aim is to steer entrepreneurship activity that would impact economic growth, and combat unemployment and poverty. The concept of sustainability is therefore summarized next.

14.2.2 The concept of sustainability Several efforts have been made to define the concept of sustainability. Overall, in these efforts the concept has been linked to diverse but uniquely interconnected parameters, namely: social, economic, and environmental. For instance, one finds that in debates related to the environment and socio-economic issues, sustainability takes centre stage (Chichilnisky, 2011; Jenkins, 2009; Kuhlman & Farrington, 2010). From the viewpoint of environmental development scientists, sustainability is described as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (WCED 1987: 43). Merriam-

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Webster dictionary (2006) defines sustainability as something that has the capacity “of, relating to, or being a method of harvesting or using a resource so that the resource is not depleted or permanently damaged.” In a contribution in the Berkshire encyclopaedia of sustainability, Jenkins’ (2009: 380) defines sustainability as the “capacity to maintain some entity, outcome, or process over time.” Putting together the Merriam-Webster (2006) and Jenkins (2009) viewpoints, one can argue that sustainability refers to having considerable resources to keep an entity, process, or outcome going. This description suggests that something which must be accessible and available consistently should have inexhaustible resources. By extension, therefore, it suggests that to sustain an entity, process or outcome requires the availability of the resources on which it relies for continued existence. Ultimately, associated to the notion of sustainability includes being accountable, thinking ahead, and introducing management practices that are innovative and able to positively influence society and those who live in it (Chichilnisky, 2011; Kuhlman & Farrington, 2010). So, “the practical challenge of sustainability is to find specific ways to pursue those distinct goals that conform to their mutual relation” (Jenkins, 2009: 380).

14.2.3 Support institutions viewpoint of entrepreneurship ecosystem In considering the necessity for steering productive entrepreneurship activities in emerging markets, the entrepreneurial ecosystem framework of Mazzarol (2014) comes to mind. This is because it encapsulates enterprise development; sustainable businesses; and economic growth viewpoint of many researchers (e.g. Aminova, Mareer & Machado, 2020; Isenberg, 2011, 2016; Mubarak, Yusoff, Mubarik, Tiwari, & Kaya, 2019). Mazzarol’s (2014) entrepreneurial ecosystem framework highlights elements that form the support institutions premise forwarded in this chapter thereby depicting a relationship that is shared amongst those elements as they steer productive entrepreneurship activities in emerging markets. In describing sources of sustainability for entrepreneurship activities in emerging markets, one can advance a nuanced analysis of the connection among the elements in Figure 14.1. For instance, drawing from Valerio (2015), the promotion of inclusive economic growth requires the prioritization of entrepreneurship development which means that governments should encourage investments in entrepreneurial activities through a funding practice that allows for easy access to finance, creation of networks that offer mentorship, and other assistance as well as instituting a culture that values entrepreneurship. In galvanizing entrepreneurial mindset, Mazzarol (2014) further argues for a morally sound government to develop policies that work, as well as appoint ministers who can “play a critical role in fostering enterprise and innovation.” In other words, to expect meaningful economic growth

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Government policy Regulatory framework & infrastructure

Local & global markets

Human capital & workforce

Enterprise development; Sustainable businesses; Economic growth

Education & training

Funding & finance

Culture

Universities as catalysts

Mentors, advisors & support systems

Figure 14.1: Entrepreneurial ecosystem. Source: Adapted from Mazzarol (2014).

through entrepreneurship necessitates some strategic elements such as the development of sustainable physical infrastructure, removal of unnecessary red tape which hampers the registration of business, sourcing finance, and access to business education. Basically, to bring about productive entrepreneurship in emerging markets, universities will productively serve as catalysts for development along with government as support institution and the private sector as facilitators.

14.2.4 Universities and entrepreneurship education Accelerating socioeconomic development is critical to the sustainability of any economy. For many researchers (e.g. Hahn, Minola, Van Gils & Huybrechts, 2017;

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Herrington & Coduras, 2019; Khoase, Derera, McArthur & Ndayizigamiye, 2020) entrepreneurship is a key ingredient of socio-economic development. Its relevance is furthered by continuous engagement either as a program or as a module in universities or colleges. In understanding the role of a university within the larger entrepreneurial ecosystem, it is necessary to examine the following questions: – Are universities training anyone to become entrepreneurs? – How is entrepreneurship taught? Do the teaching styles support learning of entrepreneurship? – What’s the best method of teaching entrepreneurship? – What do we expect of our graduates of entrepreneurship? An important part of an entrepreneurial ecosystem is the university (Mazzarol, 2014). Universities are established to produce the necessary manpower for the economic development of a nation (Iwu, Mandyoli, & Nxopo, 2018). With levels of unemployment escalating, the question arises as to whether a university certificate is relevant. At the same time, many argue that universities are not to blame rather the dwindling economies of emerging markets (Hill, 2018; Prince, Halasa-Rappel & Khan, 2018). There is also the school of thought (e.g. Iwu et al., 2018; Owens & Tibby, 2014) that companies are not hiring because graduates do not possess the relevant skills they require. Hindle (2007), Hahn et al. (2017), and Tarekegne and Gelaneh (2019) thus believe that what stands on the way of gainful employment of graduates is not necessarily the qualification they obtain from universities rather that universities are not sufficiently training students to become entrepreneurs. The argument here is that instead of training students to become job seekers, the university should thoroughly impart entrepreneurial skills to students. To understand whether universities and or colleges are training anyone to become an entrepreneur is challenging. This is because there are several factors to consider before offering a meaningful answer. Ying’s study (2008) found that training students to become entrepreneurs is one thing but getting them to take up an entrepreneurial career is another that requires the involvement of industry through the offering of internships, including bursaries and scholarships (Ojeifo, 2013; Gamede & Uleanya, 2019; Dzomonda & Fatoki, 2019). Several other researchers share Ying’s viewpoint. These include Myrah and Currie (2006), Carlsson (2005), and Trivedi (2014) who believe that a legitimate and practical educational mechanism should also involve practicing entrepreneurs. Essentially, practising entrepreneurs (the private sector) should form part of the curriculum development process of entrepreneurship departments. There is also the view that students’ attributes are critical to understanding whether the efforts of a university to train students to become entrepreneurs can yield the necessary outcome. Ying (2008), for instance, questions students’ attitudes, commitment, and mentality and whether they bode well for the training in entrepreneurship. This perhaps suggests that to meaningfully achieve entrepreneurial outcomes

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during training, a student must have the willingness to pursue entrepreneurship on graduation (Tarekegne and Gelaneh, 2019). There is also the question of whether these qualities can be nurtured during entrepreneurship education. In this case, one should be assessing the quality of lecturing, the methods used in teaching, and the learning of entrepreneurship. Enhancing this view, Iwu et al. (2019, p.10) noted the “importance of ensuring an appropriately designed curriculum and competent lecturing team.” Some other researchers have also found similar attributes to contribute to the uptake of an entrepreneurial career. Ying (2008) noted the necessity of personal characteristics such as self-drive, creativity, commitment, a sense of direction, and the ability to manage risk. Iwu, Muresherwa, Nchu, and Eresia-Eke (2020) found similarly that risk-taking and creativity added to the likelihood of an entrepreneurial career. The teaching of entrepreneurship from the early schooling stage can also make a difference. Ying (2008) and Tengeh, Nchu, and Iwu (2015) agree that teaching and learning entrepreneurship requires a fundamental review. Ying noted that colleges and universities start should start differentiating between teaching entrepreneurship as a subject and or as a course. Therefore, one should ask the important question: is teaching entrepreneurship as a subject enough to lead to an entrepreneurial career? Ying goes further to suggest that “teaching entrepreneurship as a subject means sharing knowledge with students . . . [whereas] teaching it as a course should entail actual opportunities for students to set up businesses” (p. 48). Additionally, argue the likes of Bennett (2006) and Tan and Ng (2006), those who teach entrepreneurship should also have industry experience because they are likely to bring their experiences of what worked and what has not worked for students to consider when deciding on setting up a business. To advance the teaching of entrepreneurship, some key value paths are necessary.

(1) The sustainable development goals These sustainable development goals (SDGs) not only serve as reminders of what we should be doing to better our world, it also serves as tools to teach. Goal #14 for instance looks at life below water. What does it say to us? This signifies that opportunities abound in an interdisciplinary method of teaching entrepreneurship. Beyond this, one can also start looking at the SDGs as aligning to the goals of entrepreneurship. Goal #8 addresses decent work and economic growth and resonates with the ideals of entrepreneurship (Basarud-Din & Zainal, 2020; Moya‐Clemente, Ribes‐Giner & Pantoja‐Díaz, 2020; Ribeiro-Duthie, 2020). Perhaps universities can start focusing on how these goals may influence our curriculum?

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(2) Global Entrepreneurship Monitor reports Since 1999, the UK-based Global Entrepreneurship Monitor (GEM) has been researching entrepreneurship ecosystems around the world. The report is put together by a consortium of teams primarily associated with top academic institutions in various countries. Important points to note here include the benefits of using the reports and the case studies in them to enrich teaching and learning of entrepreneurship.

(3) Any discussion of an entrepreneurial ecosystem includes the university Mazzarol’s (2014) entrepreneurial ecosystem framework depicts a relationship among the conditions that foster economic prosperity and wealth creation. So, in galvanizing an entrepreneurial mindset, Mazzarol (2014) argues for a morally sound government to develop policies that work, as well as personnel with integrity who can “play a critical role in fostering enterprise and innovation.” Mazzarol further argues that the promotion of inclusive economic growth requires the prioritization of SME development which means that governments should encourage investments in SMEs through a funding practice that allows for easy access to finance, the creation of networks that offer mentorship and other assistance as well as instituting a culture that values entrepreneurship. In other words, to expect meaningful economic growth through investment in entrepreneurship development necessitates some strategic elements such as the development of sustainable physical infrastructure, removal of unnecessary red tape which hampers the registration of business, and sourcing finance. Thus, steering entrepreneurial activity to achieve emerging market growth also requires the support of government and the private sector.

14.2.5 Government as a support institution In line with the understanding that universities cannot effectively function as catalysts for development in an emerging market, it is equally necessary to explore the important roles of government in an emerging market towards steering entrepreneurial activity to achieve economic growth. These roles include the provision of sustainable infrastructure, removal of unnecessary red tape, supporting entrepreneurial activities with requisite finance, and the promotion of the concept of entrepreneurial university. Ideally, universities ought to be entrepreneurial entities that focus on social change and economic growth. As catalysts for development, universities work with well-educated people who facilitate knowledge transfer, contribute to the creation

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of new ventures, and sustain the competitiveness of established firms and organizations (Etzkowitz & Klofsten, 2005; Klofsten et al., 2019; Saxenian, 1994). Reaching the status of a dynamic entrepreneurial university that commits to true innovation and other elements within the entrepreneurship ecosystem takes time (Miller & Acs, 2017; Herrera et al., 2018; Huang-Saad, Fay, & Sheridan, 2017) often with universities facing survival and future development challenges many of which are situated within national governance issues and university leadership (Leih & Teece, 2016; Gianiodis & Meek, 2020; Klofsten et al., 2019). Regarding university leadership, it is critical to point that university leadership must marry “strategic thinking and capabilities development” to be able to project the university towards a competitive status to allow it achieve entrepreneurial and innovative objectives over time (Leih & Teece, 2016). To attain this status requires a university leadership that that has dynamic organizational capability in the delivery of an entrepreneurial agenda (Klofsten et al., 2019; McAdam, Miller, & McAdam, 2017). The other role of government in driving entrepreneurship development is the provision of sustainable infrastructure. Infrastructure is commonly noted to include transport, communications, power generation, water supply, and sanitation facilities, educational and health-care facilities (Development Bank of Southern Africa, 1998). In the 2015–2016 GEM report, infrastructure was identified as one of the major factors that reduce the accruable value of entrepreneurship to South Africa’s economy (Kelley, Singer, & Herrington, 2016). This finding drives home the point that investing in infrastructure is an important means of promoting economic growth (Makhathini, Mlambo, & Mpanza, 2020). Insufficient investment in infrastructure will debilitate entrepreneurial efforts towards poverty reduction and improving socio-economic development (Asitik et al., 2016). In short, infrastructure must be available to support enterprise development, sustain the business, and generally improve economic growth. The shortage of and or presence of dilapidated infrastructure takes its toll on entrepreneurship uptake, profitability, survival, and benefits of economies of scale. In other words, an important mechanism for sustaining and growing an economy is infrastructure because it facilitates “the production process, enabling access to basic services such as health care and education, and promoting government community relations through policy development” (Iwu, 2018: 216). Additionally, it is fair to add that investment in infrastructure stimulates job creation, unlocks business opportunities, and consequently local economic development. An area for concern though and one in which the government has a significant role to play, in this drive for entrepreneurship development is the growing frustration of new businesses wishing to formalize their operations (Khoase et al., 2020; Nieuwenhuizen, 2019). Bezanilla, García-Olalla, Paños-Castro, and Arruti (2020) report that factors that support the entrepreneurial university development include, legal and administrative regulations including government policies. For emerging markets to derive benefits from entrepreneurship, they must reconsider the

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burdensome nature of business formalization processes. Even though complying with laws governing business registration including tax, value-added tax is a necessary condition for business development and management, Herrington and Coduras (2019) argue that complying with laws and regulations is easier to bear if such laws and regulations are diligently administered and devoid of corruptive practises. The responsibility for enhancing entrepreneurship sustainability and growth in emerging markets is not domiciled within the premises of government alone. It lies with both public and private agencies. Keeping in mind that most entrepreneurial activities result in a small and or medium enterprise (Iwu, 2018), several public private partnerships were set up to provide financial support to entrepreneurs. These include the National Small Business Support Agency, the Small Enterprises Finance Agency, and the Small Enterprise Development Agency. Gwija, Eresia-Eke, and Iwu (2014), note that these attempts by the government of South Africa to improve SME sustainability and growth has been let down by severe reputation allegations of financial impropriety, and instability of the agencies. The National Small Business Support Agency for instance is mandated to “expand, coordinate, and monitor the provision of training, advice, counseling, and any other non-financial services to small businesses following the National Small Business Support Strategy” (Ntsika, 2003). If this agency was efficient, perhaps the result would be seen in a fewer number of closures of SMEs. Khosa, Dube, and Nkomo (2017); Odongo and Kyei (2018); and Ntoyanto (2016) agree that a common criticism that has dogged government support agencies is that they fail to monitor recipients of funds. Substantial results can be achieved if the government not only provides funds but also monitors and evaluates the performance of the businesses it supports. The National Youth Development Agency is tasked with providing oversight to the burgeoning businesses of the youth. Ntoyanto (2016) notes a lapse in surveillance, leading to the collapse of several business ventures. If a proper monitoring system is in place, it would then ensure that a recipient, for instance, who is struggling could easily be identified and the necessary mitigating action taken to rescue their business. In this case, the recipient may receive help timeously reducing the likelihood of collapse. Interestingly, the South African government has created a new department for small business development that caters to the needs of small businesses, including incubation hubs, networks, capital, and other forms of support. It is hoped that this department creates the necessary environment for SME sustainability and growth as well as mutually beneficial public–private partnerships.

14.2.6 Private sector as facilitators The Triple Helix concept suggests that universities should be in constant contact with governments and the private sector to achieve both regional and national

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development. In this regard, the realization of an entrepreneurial university draws from the support of the private sector for funding The private sector should be process facilitators and program implementers (Altenburg, Lundvall, Joseph, Chaminade & Vang, 2009). This is because quite often, governments do not have wherewithal to effectuate some of their programs (Kyalimpa, 2013). Therefore, it is necessary to promote entrepreneurship and private sector engagement in order to drive technological innovation and increase employment figures and subsequently productivity. Within this premise, it is not surprising to find the private sector delving into projects as part of their corporate social responsibility in support of government programs. These may include investments in capacity building to support innovation through the award of research grants for research and development, and or beefing up infrastructure (Audretsch, 2014; Klofsten et al., 2019; Pugh, Lamine, Jack, & Hamilton, 2018). In short, the private sector can play complementary roles to government. Around the world, the private sector is regarded as a key stakeholder in economic development, for its role in national income growth through job creation and innovation (Avis, 2016; Gamede & Uleanya, 2019; Klein & Hadjimichael, 2003). In the developing countries, the private sector provides around 90% of both formal and informal employment, delivering critical goods and services as well as contributing to the growth of tax revenues and the efficient flow of capital (Avis, 2016). Despite the avowed contribution of the private sector in the development of the economies of emerging markets, their contribution continues to receive deep-seated criticisms owing to government’s inability to create policies that encourage and enhance the development role of the private sector (Klein & Hadjimichael, 2003). The role of the private sector in economic development is not the focus of this chapter rather the authors are keen to offer some key points on how the private sector can facilitate entrepreneurship development in emerging markets through universities serving as catalysts for development. First, for universities to achieve their role of generating technology transfer (through, e.g. patents, spinoffs, and startups; Audretsch, 2014), the private sector can guide universities in this regard by modernizing the local economy and steering it towards new and emerging activities (OECD, 2019). This can be achieved through the setting up of research chairs, bursaries, and scholarships. The private sector can also support collaborative research, contract research and consulting and practitioner networking and capacity building of entrepreneurship departments (Pugh et al., 2018) as torchbearers in the march towards an entrepreneurial university. Unlocking entrepreneurial potential among university students through bursaries and scholarships can help transform the non-existent culture of entrepreneurship (Ojeifo, 2013; Gamede & Uleanya, 2019; Dzomonda & Fatoki, 2019). Literature is replete with claims of entrepreneurship generating new firms and businesses, which in turn create the much-needed employment. Within this premise, a call has been persistently made (see Audretsch, 2014; Pugh et al., 2018) for technology

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transfer offices (TTOs) in universities to galvanize relations with the private sector. Doing this, facilitates universities’ achievement of both regional and national economic status including the reaping of financial benefits from such engagements. As stated earlier, excessive government regulations can stifle innovation and business formalization. TTOs may experience hiccups in processing patents and the setting up of startups. Therefore, simplifying regulations may advance the notion of the private sector as a facilitator of strategic entrepreneurship development.

14.3 Conclusion and future research directions Premised in the entrepreneurship ecosystem foundation, this chapter forwards a support institution viewpoint that focuses mainly on the role of universities as providers of essential entrepreneurship education as productive entrepreneurship factor, as well as the facilitating role of government and private sector stakeholders towards driving active and impacting university activity. The conceptual framework forwarded in this chapter is summarized in Figure 14.2.

Entrepreneurship Ecosystem Three Levels of Stakeholders Strategic Level (Policy Making)

Support Institutions Level

Enterprise Level (Entrepreneurs & Business Entities

Entrepreneurship Ecosystem Interaction

Support Institutions (Government, Private Sector, Universities

Private Sector Stakeholders Research Funding, and working closely with Universities to ensure strategic Entrepreneurship Education

Productive Entrepreneurship Universities in their role as providers of Entrepreneurship Education

Entrepreneurship

Government Policy, Research Funding, and exchange of strategic information to aid Universities

drives economic growth combats unemployment combats poverty

Figure 14.2: The conceptual framework for optimizing the support institutions tool in ensuring entrepreneurship education that aids productive entrepreneurship. Source: Authors.

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The conceptual viewpoint of entrepreneurship ecosystem forwarded in this chapter towards optimizing the role of universities as entrepreneurship education providers is one that offers a critical pathway to achieving productive entrepreneurship that would contribute to economic growth and sustainability in emerging markets that are known to lack necessary infrastructures and government facilitation. From a theoretical point, the conceptualized framework offers a viewpoint that contributes to knowledge in the entrepreneurship ecosystem domain as well as economic growth literature. This chapter also contributes to knowledge from the point of advancing research (see directions for future research). Emerging markets are known to have marginal socio-economic development. Broader economic growth leads to socio-economic development with entrepreneurs playing the pivotal role of creativity and innovation. Entrepreneurs engage in creative and innovative entrepreneurial activities towards contributing economic growth. Indeed, it has been argued that effectively productive, entrepreneurship can drive a snowballing economic growth impact (Opute, 2020). Achieving such economic outcome is the desired impact of entrepreneurship activity. Achieving that however is a tall order. For emerging markets to steer entrepreneurship activities effectively to drive economic growth and combat increasing unemployment and poverty, all key support institution stakeholders must effectively play their supporting role. This support institutions’ role must be fulfilled towards ensuring enabling resources such as infrastructures, funding, education, and training and development opportunities. This is the central argument forwarded in this chapter. Integrating the powerful and inherent networks and undertakings among the government, private sector, and universities is of critical importance in driving strategic entrepreneurship education to facilitate productive entrepreneurship. To fully optimize economic growth impact of entrepreneurship in the emerging market setting, universities in that context must play a significant role. As knowledge repositories and major capacity building agents, universities can significantly drive entrepreneurship uptake propensity and productive entrepreneurship outcome through provision of entrepreneurship education option as well as ensuring strategically tailored entrepreneurship education curricula. To fully optimize the entrepreneurship education impact, the delivery of entrepreneurship education should include both taught and practical experiential elements (Iwu et al., 2019). A further support institution point that must be borne in mind in the effort to steer productive entrepreneurship in the emerging market setting relates to the significant facilitating role that the government plays. This government facilitating role, which has been referred to as an intervention mechanism (Iwu & Opute, 2019), could take the form of a direct facilitation of the efforts of entrepreneurs by the government or by the government exercising its facilitating function of enabling the entrepreneurship education schemes of universities. In line with the conceptual framing of this chapter, the latter facilitating option is considered in pinpointing the recommendations in this chapter. Governments in the emerging market

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setting should work closely with universities and support them financially and with technical information as well as top qualified personnel with professional research and pedagogical experience to drive strategic entrepreneurship education. Equally, the government should fund infrastructures and facilities that would aid universities in adequately skilling entrepreneurs. The third component in the support institutions viewpoint forwarded in this chapter relates to private sector support. Getting the entrepreneurship chains to function effectively and drive economic growth in the emerging market requires active private sector involvement in the efforts of universities to steer strategic entrepreneurship education. A critical tool that universities use in enhancing strategic learning and informing policy is research. Private sector stakeholders in emerging markets could also engage in funding universities’ research initiatives.

14.4 Directions for future research This chapter forwards an entrepreneurship ecosystem viewpoint that underlines the important role of support institutions in steering economic growth and sustainability impacting strategic entrepreneurship education. Towards enhancing knowledge in the areas of entrepreneurship ecosystem, entrepreneurship education and economic growth and sustainability, and in the emerging markets in particular, future research should empirically explore the conceptual framework forwarded in this chapter. Utilizing quantitative or qualitative approaches or a combination of both, researchers could explore each of the components of the support institutions considered in this chapter, and or explore their intersections in the drive for economic growth and sustainability in the emerging market setting. Heterogeneity in marketing practices (e.g. Opute & Madichie, 2017) and national culture (e.g. Opute, 2012) are core factors that differentiate economies. Given that fact, exploration of different emerging market settings is pertinent towards enhancing the understanding of entrepreneurship ecosystem dynamics and outcome differentiators in various emerging markets.

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Brendan Dolan, Caroline McGregor, and James A. Cunningham

Chapter 15 Medical device scientists’ influence on research impact within entrepreneurial ecosystems: A systematic literature review 15.1 Introduction One of the increasing requirements of publicly funded scientific research is that it benefits entrepreneurial and innovation ecosystems economically, socially and from a sustainability perspective. Through large-scale public sector entrepreneurship programmes, governments attempt to stimulate and support entrepreneurship and innovation ecosystems to realize economic and societal benefits (Cunningham & Menter, 2020; Ferreira, Fernandes, & Kraus, 2019; Kuratko & Menter, 2017; Leyden & Link, 2015). Some of these public sector programmes are designed and focused on sustainability issues while others are not. Scientists in the principal investigator (PI) role play a central part in capturing value from publicly funded research at the micro level (see O’Kane, Zhang, Cunningham, & Dooley, 2020) and by taking a proactive approach can drive social innovation outcomes (Carl, 2020). In capturing economic, technological, and social value, PIs rely on the support they receive from their host entrepreneurial university or research centre (Dolan, Cunningham, Menter, & McGregor, 2019). While some recent studies have focused on systemic or macro-level analysis of the university role in entrepreneurial ecosystems to bring about social change (Bedo et al., 2020), including, for example, the influence of university business incubators for economic development (Nicholls-Nixon, Valliere, Gedeon, & Wise, 2021), few have focused on the individual scientists within the micro-level. Against this background many debates arise from this ever-expanding “impact agenda,” and yet little is known about how this phenomenon is understood and approached from the micro-level perspectives of PIs. For example, do they really consider sustainability issues when it comes to research impact? As the leaders of publicly funded research projects, and as a key actor within entrepreneurial ecosystems, PIs have significant influence to create and advance scientific knowledge. As such, PIs have the opportunity to influence the potential and eventual broader impacts of their research, beyond traditional scientific impact metrics most commonly associated with research translation, for example, publications, citations (Cunningham, 2019). However, in scoping the literature, particularly systematic reviews, there appears to be a lack of empirical evidence to confirm this assumption that PIs, or even scientists in general, can affect the broader impacts of their research at a micro level, particularly with respect to https://doi.org/10.1515/9783110670219-016

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sustainability. This is of particular consequence for the field of medical device research, with its prime relevance in addressing the societal needs of an aging global population and the global burden of noncommunicable diseases. To commercialize medical device research requires PIs to develop and engage with entrepreneurial ecosystem stakeholders and part of this pathway is a transitional route which can scope out multiple impact avenues, including sustainability. To this end, the focus of this chapter is to examine systematically the available literature from peer-reviewed journal articles that addresses the role of the scientist or principal investigator in creating impactful research within the medical device research domain. Due to the large discrepancy in definitions, understandings, and terminology used in relation to impact, as well as within the wide-ranging field of medical device research, there is a need to analyse the current knowledge in this field. As such, this chapter offers a first step towards assessing, from the existing body of knowledge about the medical device research domain, the influence PIs exert on research impacts with a particular focus on sustainability, identifying gaps in knowledge, in addition to outlining some potential future research avenues.

15.2 Literature considerations 15.2.1 Scientists in the PI role There is a growing body of literature emerging on the PI, and the various dynamic and innovative tasks they undertake within an entrepreneurial ecosystem (Cunningham, Menter, & O’Kane, 2018). PI roles and responsibilities have grown substantially as research systems have evolved and expanded (Cunningham and O’Reilly, 2019). Where once traditional scientific and internal leadership roles were the main duties of scientific project leaders, the PI role has broadened to include external management, networking and collaboration activities, and a myriad of other “third mission” tasks, thus giving rise to this emerging field of research on PIs (Foncubierta-Rodríguez, Martín-Alcázar, & Perea-Vicente, 2020). There are many different definitions of what is a PI (Casit and Genet, 2014) and Mangematin, O’Reilly, and Cunningham (2014) suggest that “ambiguities about the PIs’ definitions reflect the tensions about their role and functions.” Attempts to define the PI roles and responsibilities highlight how PIs undertake a variety of heterogeneous duties (Cunningham, O’Reilly, O’Kane, & Mangematin, 2014; Boehm & Hogan, 2014); this role transition poses potential difficulties, as well as opportunities, for example, managerial challenges for PIs (Cunningham, O’Reilly, O’Kane, & Mangematin, 2015). Beyond the traditional academic outcomes associated with impact (Fenney and Welch, 2014) PI research on strategies and actions to create sustainable, impactful research (the focus of this study) is scarce and mainly focus on economic-specific

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impacts, including on PI market shaping capabilities (Mangematin et al., 2014), knowledge brokerage (Kidwell, 2013), strategic posture and behaviours (O’Kane, Cunningham, Mangematin, & O’Reilly, 2015), technology transfer activities (Baglieri & Lorenzoni, 2014), academic entrepreneurship (Cunningham et al., 2017b), lack of commercialization training (O’Kane et al., 2017; O’Reilly & Cunningham, 2017), entrepreneurial opportunity recognition (Romano, Elita Schillaci, & Nicotra, 2017; Cunningham et al., 2019), and commercialization of knowledge (Menter, 2016). Furthermore, some PI-related research has explored the interactions and collaborations that PIs engage in through research and innovation processes, including exploration of boundary spanning activities, and the importance of strong simmelian ties (where two actors are strongly tied to each other and at least one common third party) (Krackhardt & Kilduff, 2002) between quadruple helix actors at the micro level “in order to realize collective and individual value motives” (Cunningham et al., 2018) to create public good from publicly funded science. However, it appears that PI studies to date have not explored how these various attitudes and approaches relate to the broader societal, sustainable impact of their research projects, or looked specifically at medical device research projects.

15.2.2 Research impact As research systems and the level of public funding of research has grown over the last two decades, so too has the requirement to demonstrate the eventual impact of publicly funded research, beyond the traditional scientific metrics associated with past research evaluations, with greater focus on wider societal impact, including sustainability and environmental impacts. National governments are looking to address this change in policy with the design and development of various impact evaluation frameworks, such as the REF (Research Excellence Framework) in the UK and the Canadian Academy of Health Sciences (CAHS) Framework. These evaluative processes are placing increased emphasis on various categories and measures of societal and economic impacts, including commercialization, technology transfer, and outreach impact metrics (e.g. D’Este & Perkmann, 2011). This has put additional pressure on researchers to demonstrate different categories or classifications of impact of their research, from economic to environmental. In particular, scientists who take on the role of principal investigator for large-scale publicly funded projects now need to design, deliver, and demonstrate a broad range of impacts, for a wide variety of stakeholders (O’Kane et al., 2020; Cunningham et al., 2020). However, from scoping reviews of the literature, there appears to be a lack of research on how PIs, or scientists in general, influence the potential or actual impact of their research, in our case specifically, medical device research. The medical device research field is one with great potential to have a positive, rippling effect on society through a variety of impact avenues (health, social, economic,

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environmental, policy, public service, etc.). At a fundamental level, medical device research, by its very nature, should be closer to translation and producing clear, inarguable societal impact. As a field of research, the investment medical device–related research has attracted has grown exponentially in the past decade, furthering the necessity to demonstrate impact beyond traditional scientific measures. From a purely practical viewpoint, research on medical devices is well documented and regulated through translation processes (Markman, Gianiodis, Phan, & Balkin, 2005). There is a trail of patents, licences, policy changes, and documented grants awarded, all of which make probing the field’s leading PIs, and scientists in general, and their influence on the rippling effect of the impacts of their research, highly intriguing. While there is no globally accepted or agreed upon definition for a medical device, for the purpose of this study, we will utilize the Global Harmonization Task Force’s definition (see WHO, 2019). The objective of this systematic literature review is to gain a better overview of literature available on medical device principal investigators’ influence on the broader impact potential of their research (Hunt, Pollock, Campbell, Estcourt, & Brunton, 2018). As there is limited attention to date on this topic, we take a wide definitional understanding of impact, to include any and all impacts for society from medical device research, and as such include any studies that highlight scientists influence on societal, sustainability, and environmental impacts. The aim of a systematic literature review is “to identify, appraise and synthesize all the empirical evidence that meets pre-specified eligibility criteria to answer a given research question” (see Higgins & Green, 2011). Through a review of this type, we can establish objectively, in a more unbiased nature, the extent to which the literature has progressed in addressing our research focus of: how does a principal investigator, or scientists in general, influence/affect potential and actual impacts (beyond scientific impact) from medical device research?

15.3 Methodology Our systematic literature review was carried out following the process outlined by Tranfield et al. (2003), with the purpose to identify any studies that placed a focus on the effect or influence of scientists on the broader impacts of medical device research. In order to develop the research question, the lead researcher became immersed in the extant literature and read an array of papers to become familiar with the conceptual boundaries of the subjects at hand, and to identify keywords, themes, and issues pertinent to the area of interest. The co-researchers, already knowledgeable about this field, provided input and guidance where necessary. An outline of this process is presented in Figure 15.1. Through consultation with expert library staff, the search databases Scopus and Embase were identified as most suitable for this review, due to their focus on

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Setting the research objectives – Examine the influence of Pls on the impact of their medical device reserach – Examine the types of non-scientific impact Pls are focused on – Examine the approaches and strategies undertaken to create impactful medical device research

Defining the conceptual boundaries – Broadly defining impact and societal impact – Broadly defining medical device research and Pls

Setting the inclusion criteria

Search terms

Cover period

– Reserch w/3 impact or research w/3 benefit OR tech transfer OR translation* – Socio* OR social* OR scietal OR economic OR cultural OR “quality of life” – Scientist OR researcher OR clinician OR investigator OR leader – (Biomedic* OR Medic*) AND (device OR tech*)

– From 1999 – Up to and including November 2020

Search boundaries – Electronic databases–Scopus and Embase

Applying exclusion criteria (Two phases – abstract and full article review) – Articles that focused on scientist influence on impact but not in medical device field – Articles that focused on impact of medical device research but no focus on individual level of scientist – Articles that had no focus on non-scientific types of impact

Final result – Analysing 10 peer reviewed articles

Figure 15.1: Summary of the SLR process. Source: Compiled by authors.

the life and health sciences, particularly medical device–related research. Scopus is multidisciplinary in nature, with a strong focus on health-related topics. However, Embase proved ineffective in finding relevant articles to this review, due to its focus on specific medical devices and the technical aspects involved (through searches using relevant search terms, five journal articles were recovered, with none sufficiently relevant to the scope of this study). Therefore, this systematic literature review was carried out using Scopus as the primary database. Using articles found during previous literature searches, alongside suggestions from a panel of medical device research experts, selected keywords were identified

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in relation to the three aspects of this SLR to be used in combination: principal investigator, medical device research, and impact. (See Table 15.1 for explanations on how search terms were decided upon.) Table 15.1: Rationale for search terms. Principal investigator – For articles focusing on the individual scientist or principal investigator, the search terms we chose were “scientist,” “researcher,” “clinician,” “investigator,” and “leader.” These terms were chosen based on key papers relating to medical device research that we had previously identified, and should be close to fully encompassing terms used relating to the individual scientist, research leader, or PI. Medical device research – In deciding on search terms to cover medical device research, experts in the field offered the opinion that it was too complex a subject area to be able to realistically list these terms to form a search string, with medical device research involving collaborations across a wide array of academic disciplines, from biotechnology to nursing studies. After trial and error, we found that the most succinct search term to capture medical device research–related articles was the combination of biomedical or medical with device or technology. From initial pilot searches it was clear that these terms were capturing papers in biomedical engineering, biomaterials, biotechnology and so on. Alongside device and technology, apparatus was also tested as a search term, but did not yield any results. While this is not an exhaustive search of papers relating to medical device research, it does offer an insight into a variety of prevalent disciplines in this field of research. Medical device research – In deciding on search terms to cover medical device research, experts in the field offered the opinion that it was too complex a subject area to be able to realistically list these terms to form a search string, with medical device research involving collaborations across a wide array of academic disciplines, from biotechnology to nursing studies. After trial and error, we found that the most succinct search term to capture medical device research–related articles was the combination of biomedical or medical with device or technology. From initial pilot searches it was clear that these terms were capturing papers in biomedical engineering, biomaterials, biotechnology, and so on. Alongside device and technology, apparatus was also tested as a search term, but did not yield any results. While this is not an exhaustive search of papers relating to medical device research, it does offer an insight into a variety of prevalent disciplines in this field of research. Impact – For the theme of impact, through investigations of keywords from the most relevant articles already discovered, we chose the search terms “impact” and “benefit,” looking for any articles that include these terms within three words of research (e.g. research w/ impact). We also chose to include translational research in the general theme of impact as this concept relates closely to impacts, in that it is focused on moving basic science through to point-of-care applications. Other common concepts related to impact from previous literature are “ technology transfer,” “knowledge transfer,” and “knowledge exchange,” and so these were included also.

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Table 15.1 (continued) As our study was looking specifically at research impacts beyond traditional scientific impact metrics (e.g. bibliometrics, citation count) we included several of the most common terms associated with other forms of research impact or benefit, in an attempt to filter out some of the less relevant articles. To this end, we included the most common terms encountered in other articles relating to non-scientific research impacts; socio-economic, social, societal, economic, cultural, and quality of life impacts. Several other search terms were trialled to include in the search query string in terms of the concept of impact. “Non-scientific,” “impact awareness,” and “impact orientation” did not yield any responses. “University,” “higher education,” and “entrepreneurial ecosystem” were also trialled to include, due to their relevance to our overarching research question, but did not yield any additional relevant responses. Source: Compiled by authors.

Inclusion criteria for this review, decided on through consultation with relevant experts, required that the document be a peer-reviewed journal article in the English language published after 1999, thus covering the past 20 years (see Table 15.2 for input search string). The article search was carried out in November 2020 on SCOPUS using our search terms and criteria, yielding 325 results. Three articles were identified as duplicates and thus removed, leaving articles for further screening. Table 15.2: Search string query used in Scopus search. (TITLE-ABS-KEY (biomedic*ORmedic*)ANDTITLE-ABS-KEY (deviceORtech*)ANDTITLE-ABS-KEY (scientistORclinicianORinvestigatorORresearcherORleader)ANDTITLE-ABS-KEY ((researchW/ impact)OR(researchW/benefit)OR”technology transfer”ORtranslation*)ANDTITLE-ABS-KEY (socio*ORsocial*ORsocietalOReconomicORculturalOR”quality of life”)ANDPUBYEAR > )AND (LIMIT-TO (DOCTYPE ,”ar”)) Source: Compiled by authors.

The next stage of this review involved abstract screening, reading the titles and abstracts of each article, and excluding any articles that did not meet at least two of the three following key inclusion criteria, that the article contained specific focus on: the individual scientist or researcher; medical device or technology research; and translation approaches or strategies for broader impacts of research. The most common reason for excluding articles was due to a lack of focus on any of these three criteria (n = 110), followed by the exclusion of articles due to lack of focus on medical device research. In cases where it was unclear from the abstract that the article was relevant enough for inclusion, the article was not excluded at this point. Following adherence to these exclusion criteria, we found that 60 articles remained for in-depth review of the full article. The full-text versions of the remaining articles

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were then reviewed for eligibility, with 10 articles remaining for inclusion in this study. Full text of the selected articles was entered verbatim into QSR’s NVivo software for qualitative data analysis. NVivo was chosen as this software programme allowed reviewers “to examine the contribution made to their findings by individual studies, groups of studies, or sub-populations within studies” (Thomas & Harden, 2008). As the articles identified through our search were, for the most part, qualitative in nature, and methodologically diverse, a qualitative research synthesis was chosen as the most appropriate approach to analysing the studies, specifically thematic analysis, in order to best integrate findings from multiple qualitative studies (Thomas & Harden, 2008). Through reading and rereading of the included texts in NVivo, after initial coding of relevant sections, these codes were grouped together based on commonalities, and some relevant themes were identified as noteworthy findings from this systematic literature review. These were also reviewed by two co-researchers and the results of the review were categorized under two headings, as outlined below, relating to our overarching research topic. Data extraction was conducted manually and synthesized using thematic synthesis, with specific focus on results involving the influence or role of the individual scientist on the eventual impact from research. Thematic synthesis involves three overlapping stages, carried out using NVivo: the line-by-line coding of relevant text in each article; the organization of these codes into descriptive themes; and the development of analytical themes.

15.4 Findings 15.4.1 Characteristics of the studies Utilizing our search strategy, ten articles were identified as significantly relevant to our research question for further analysis (see Table 15.3). While the search criteria looked for any articles published in the past 20 years (2000–2020) four of the ten articles were published in the past five years. Four of the ten articles undertook a mixed methods, though mainly qualitative, approach, with two articles strictly qualitative in nature, one exploring the results of a specific funding award, one looking at specific clinical case examples, one literature review, and the remaining article an opinion piece offered by an expert in the field. Six of the ten articles involved interviews with scientists/researchers engaged in medical device research, seeking their views on various aspects and approaches to the successful translation of research and the barriers to be overcome. Two articles focused on principal investigators explicitly and two articles focused specifically on scientists and science conducted within a particular country (Mexico and Brazil). Only 1 of the 10 articles had over 100 citations, with 5 of the 10 articles having less than 10 citations (based on Google citations count,

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accessed on 1 December 2020). For the 6 studies involving interviews with scientists, sample size varied widely, from 5 to 30 interviewees. An assessment of scientific quality of the included articles was not carried out, due to the limited number of articles identified. Specific subject areas ranged from stem cell–related research to biomaterials to nanotechnology to genomic research, highlighting the wide-ranging nature of medical device research, across disciplines, from basic to translational science. Similarly, the articles were published in a wide range of science journals. In general, there was a clear lack of commonality in theme amongst the ten articles, in relation to the influence of scientists on the broader impact from their medical device–related research. Terminology related to broader impact, beyond traditional measures associated with scientific excellence, varied from technology transferfocused to broader macro-level societal impact. This heterogeneity is an important finding in itself, suggesting a difficulty in identifying articles that have significant focus on the scientist’s influence on the broader impacts of medical device–related research. Nonetheless, some recurring themes were identified from this systematic review. Through the three stages of thematic synthesis carried out on the selected articles, descriptive themes were developed and grouped together as either facilitators or barriers to impact.

15.4.2 Facilitators of impact This review identified two main themes relating to facilitators of impact: collaboration with relevant stakeholders and identification of relevant stakeholders.

15.4.2.1 Collaboration with relevant stakeholders The most commonly identified facilitator of impactful research across the articles reviewed was collaboration, the importance of, levels of, and issues relating to collaboration for researchers, and, alongside this, the influence of industry linkages, with eight of the ten articles having some level of focus on this theme of industry collaboration or engagement. All ten articles, in the various areas of impact addressed, highlighted collaboration as a key factor in creating impactful research, or in successful translation of research. For example, in their study on improved antimicrobial strategies for medical implants and devices, Grainger et al. (2013) emphasized “the compelling need for better collaboration between these parties to facilitate realistic prospects for assessing such new strategies in patients.” The importance of incentives or supports for scientists to engage in collaboration activities were mentioned in six of the articles, to greater and lesser extents. Three of the articles mentioned specifically the bi-directional benefits for academics and clinicians, industry and other academic partners, “to gain access to resources and facilities” (Medina-

Journal/year

BMC Health Services Research/  Technology in Society/

Clinical Pharmacology and Therapeutics/  A Journal of Integrative Biology/

Journal of Technology Transfer/

Authors and article title

Esmail, R., Hanson, H., Holroyd-Leduc, J., Niven, D. J., & Clement, F. Knowledge translation and health technology reassessment: Identifying synergy

Medina-Molotla, N., Thorsteinsdóttir, H., Frixione, E., & Kuri-Harcuch, W. Some factors limiting transfer of biotechnology research for health care at Cinvestav: A Mexican scientific center

Stewart, S. R., Barone, P. W., Bellisario, A., Cooney, C. L., Sharp, P. A., Sinskey, A. J., . . . & Springs, S. L. Leveraging Industry-Academia Collaborations in Adaptive Biomedical Innovation

Holmes, C., McDonald, F., Jones, M., & Graham, J. Knowledge Translation: Moving Proteomics Science to Innovation in Society

Baglieri, D., & Lorenzoni, G. Closing the distance between academia and market: Experimentation and user entrepreneurial processes

Table 15.3: Articles included in the systematic literature review.

 Mixed – Interviews and supporting data

 PIs

Offers insights into how scientists acting as PI and user can increase impact potential, academic entrepreneurship, and research project management

Highlights need for broader, interdisciplinary knowledge translation strategies to increase impact potential of proteomics science

 Mixed –  scientists Interviews and ethnographic

Biomedical research scientists’ technology transfer experiences, exploring factors, and barriers to commercialization, including need for greater industry collaboration

Case studies of Sanofi-MIT Partnership awards, highlighting benefits of bidirectional collaboration, and adaptable, flexible process

 PIs

 Mixed – interviews and survey

Case studies demonstrating how selecting and applying theories of knowledge translation can facilitate health technology reassessment

Brief overview

 Qualitative – Sanofi-MIT project case projects studies

n/a

 Debate article

Total Methodology Sample citations

320 Brendan Dolan, Caroline McGregor, and James A. Cunningham

Journal of Magnetic Resonance Imaging/ Genome Medicine/  Future Medicine/  Social Science & Medicine/ 

Lee, V. S. MRI: From science to society

Sayres, L. C., Allyse, M., & Cho, M. K. Integrating stakeholder perspectives into the translation of cell-free fetal DNA testing for aneuploidy

McMahon, D. S., Singer, P. A., Daar, A. S., & Thorsteinsdóttir, H. Regenerative medicine in Brazil: Small but innovative

Wainwright, S. P., Williams, C., Michael, M., Farsides, B., & Cribb, A. From bench to bedside? Biomedical scientists’ expectations of stem cell science as a future therapy for diabetes

Source: Compiled by authors.

Biomaterials/ 

Grainger, D. W., van der Mei, H. C., Jutte, P. C., van den Dungen, J. J., Schultz, M. J., van der Laan, B. F., . . . & Busscher, H. J. Critical factors in the translation of improved antimicrobial strategies for medical implants and devices Discussion of extent to which impactful MRI research is prioritized, and factors to consider, including greater collaboration and relevant expertise

Analysis of Brazil’s RM research environment, factors and challenges, including funding and weak linkages between stakeholders

Analysis of scientist–clinician interactions and stem cell science as barriers to translation, within the frame of sociology of expectations

 Qualitative –  scientists interviews

 Highlights need for implementation pathway “stakeholders” mapping and stakeholder analysis for more effective translation

n/a

Analysis of several biomaterial-associated infection cases to identify potential factors of successful translation of medical device research to address relevant clinical issues

 Qualitative –  (incl.  interviews university “experts”)

 Mixed

 Opinion piece

 Qualitative – Clinical cases clinical case of BAI studies

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Molotla et al., 2017) and to integrate expertise from other disciplines and domains for more effective and efficient translation and research impact. For example, Stewart et al., (2016) highlighted how, to support translation “guidance at a critical moment from an experienced industrial partner can be a crucial tool in bringing ideas from the lab to practice.” Similarly, Baglieri and Lorenzoni (2014) observed that the PIs in their study “never work alone: they may have longstanding allies who can aid in prototyping and in the commercialization process.” Coordination and engagement with relevant stakeholders, of course, first require the identification of said stakeholders, in order to then be able to integrate these various perspectives into the research process.

15.4.2.2 Identification of relevant stakeholders Four of the articles referenced and listed academic and non-academic stakeholders involved in the research process as having importance in the effective translation of research “from bench to bedside.” The most common stakeholders identified in this “complex environment” (Grainger et al., 2013) were, perhaps unsurprisingly, industry partners, clinicians, patients and patient groups (end-users), funding bodies, and academics in other disciplines. Interestingly, only one of the ten articles (Medina-Molotla et al., 2017) mentioned the technology transfer office (TTO) as a stakeholder in the research process. These articles suggest that awareness and identification of relevant stakeholders should be viewed as an important step for scientists of medical device research, in order to integrate input from these stakeholders into the design and development of medical devices for greater impact. Other facilitators of impact of research offered in these articles included adequate and consistent levels of funding (e.g. McMahon, Singer, Daar, & Thorsteinsdóttir, 2010), commercialization experience (e.g. Medina-Molotla et al., 2017), and comparative analysis experience (Lee, 2013).

15.4.3 Barriers to impact The two most dominant themes in relation to barriers were found to be commercialization-related issues and collaboration issues.

15.4.3.1 Commercialization-related issues Issues surrounding commercialization processes, such as the complexities involved in patenting medical device–related research and lack of experience or expertise, were highlighted in five of the review articles. For example, McMahon et al.’s (2010) study involving scientists in regenerative medicine in Brazil found, through their

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interviews, a negative stigma perceived by researchers in public institutes associated with patenting and commercialization, with researchers “slow to adopt IP protection of their work” (McMahon et al., 2010). Furthermore, this study highlighted a lack of “experienced contract negotiators” to support biomedical scientists in their commercialization and technology transfer activities. Similarly, a lack of commercialization knowledge and support was highlighted in Medina-Molotla et al.’s (2017) study of biotechnology research for health care in Mexico, including issues related to “patent fees, inability to create spin-off companies, patenting without a commercial vision.” This study also found that the PIs they interviewed had “rarely followed the technology transfer process through to completion,” with only a few PIs well informed on their projects’ commercialization potential and the “typical barriers” involved in technology transfer in Mexico. Two studies also made note of conflicts involved in publishing and patenting of results from their research. Academic entrepreneurial intent was only mentioned in one of the ten articles (Medina-Molotla et al., 2017) as an important factor for commercialization or technology transfer.

15.4.3.2 Collaboration issues Divergent goals of stakeholders, lack of identification of, or engagement with, relevant stakeholders or stakeholder groups, including industry partners, and time constraints were highlighted in some of the studies as potential barriers to impact. Issues surrounding multi-disciplinary collaboration were also identified in three articles as a potential barrier to collaboration for impact, including the increased levels of disciplinary specialization and teams operating “in a silo-ized manner” causing possible problems across disciplines (Holmes et al., 2016). As another example, Wainwright et al. (2006) pointed to the “divide between the different social worlds of medicine and biomedical science” as an issue in collaboration for impact. Similarly, McMahon et al. (2010) identified weak relationships between universities and hospitals as making it difficult for scientists to secure funding for large-scale clinical trials. Another recurring issue or barrier to impact identified from this review was in relation to regulatory agencies; the complex nature of regulatory procedures, financial burden placed by regulatory pressures for clinical validation (Stewart et al., 2016) or the time-consuming administrative burden involved (Grainger et al., 2013). These studies also pointed to increased costs and risks involved in translation in general as a potential barrier to impact. One of the residual findings emerging from this study centres on the high-level understanding of impact. There was a wide array of research impact addressed within the articles. Types of impact, or impact-related activities under investigation in these articles include translation of research “from bench to bedside,” technology and knowledge transfer and translation, commercialization, and economic and market impacts (e.g. IP, patenting activities). “Translation” was the most common term used in relation

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to “impact” within the reviewed articles, with five of the ten articles making some reference to “the translation gap,” or bridging the translation gap or chasm between laboratory and clinic/point-of-care through various translation strategies. Perhaps the most compelling finding from this systematic review is the small number of articles identified from this review that address the influence of the scientist on the impact of medical device research, with no mention or research focus on sustainability. When reviewing article abstracts the most common reason for excluding studies was the lack of focus at the micro level, on the individual scientist, followed by the abstract not offering some evidence of the study being related to medical device research. The lack of specific mention of “medical devices” in research articles was a significant finding from this review, highlighting the rarity in which research on impact in this context is brought to the fore. These findings offer an indication of the difficulties involved in attempting to identify, define, and categorize impact of research, both for academics, and particularly for funding bodies, who are more and more looking to ensure the research projects and PIs they provide funding to will have some real, tangible societal impact for the public who are, circuitously, the true financiers of research.

15.5 Conclusion and future research avenues To our knowledge, this is the first systematic literature review specifically focusing on the influence of scientists on the broader impact of medical device research beyond the more traditional metric of impact associated with publications, and so on. Interpreting the findings, we arrive at some informative conclusions. From the articles identified with a micro-level focus of scientists involved in medical device research, the findings indicate that collaboration between microlevel actors in research translation processes is a key mechanism for creating potentially impactful research. Alongside this, strategies are required to encourage and emphasize collaboration and commercialization for impact amongst the academic community, with industry and regulatory partners, and individual PIs and scientific leaders. There is also a need to identify stakeholders and to include their perspectives in terms of planning for impact from research activities. Carl (2020) argues that PIs can drive social innovations and with respect to promoting sustainability, these stakeholders and the local institutional environment seem to matter in shaping the individual response and adaptation to this agenda. PIs can provide “strong, visionary leadership” within entrepreneurial universities (Nkusi, Cunningham, Nyuur, & Pattinson, 2020) in this regard. Therefore, entrepreneurial universities need to encourage individual actor actions with respect to promoting sustainability, and this needs to become part of their core mission and or values. Our study illustrates the important institutional role universities play in advocating sustainability within and

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beyond their institutional context. Acknowledging the small number of studies involved in this systematic literature review and our narrow focus on the medical device arena, future research is needed to examine if similar findings emerge using other disciplines and what institutional and disciplined based strategies should universities adopt to support sustainability at the micro institutional level. With respect to the facilitators of impact the study identified collaboration with stakeholders and the identification of relevant stakeholders as key themes within this SLR. These findings would suggest that PIs in this domain needs to be in entrepreneurial and innovation ecosystems that are vibrant and predisposed to supporting other stakeholders to create and exploit knowledge (Canter et al., 2020) so as to realize an array of impacts (see Audretsch, Cunningham, Kuratko, Lehmann, & Menter, 2019). Moreover, given the more economic impact focus of ecosystem stakeholders that PIs interact with in this domain it is therefore not surprising that the SLR found no evidence of a focus on sustainability. However, the current impact focus of PIs may change based on the advocacy of stakeholders that influences them to incorporate sustainability as part of their research activities and programmes. The barriers to impact revolve around the exploitation of knowledge through technology transfer and commercialization and some of these issues highlighted in this SLR are similar to studies in other fields (see Gilsing, Bekkers, Freitas, & Van Der Steen, 2011; Kaushik, Kumar, Luthra, & Haleem, 2014; Martyniuk, Jain, & Stone, 2003; O’Reilly & Cunningham, 2017). Consequently, these findings while acknowledging its size would suggest that universities have a role in supporting PIs in how they would incorporate sustainability at the micro level. Within the entrepreneurial university literature the role of TTOs and importance of technology transfer and commercialization has been highlighted (see Guerrero, Urbano, Cunningham, & Organ, 2014; Cunningham et al., 2017a). In this SLR it interesting to note the general absence of reference to university technology transfer offices (TTOs) as a stakeholder in the research process given one of their core missions is to support the exploitation of new knowledge (Fitzgerald & Cunningham, 2016). This is particularly striking given the arena of our study where knowledge exploitation is a key activity and outcome in advancing treatments for end users, that is, individual patients. In fact, the one study that did mention it posited that PIs “rarely followed the technology transfer process through to completion” (McMahon et al., 2010). Furthermore, McMahon et al. (2010) highlighted that there is a lack of “experienced contract negotiators” to support biomedical scientists in their commercialization and technology transfer activities. It is also notable that only one of the ten articles mentioned the role of entrepreneurship in relation to scientists’ influence on research impacts. Acknowledging the study limitations, it highlights that institutional settings may have some further way to go to fostering and supporting technology transfer, not alone advocating sustainability. To this end further research is needed to understand how entrepreneurial universities advocate sustainability and what strategies do they adopt that influences micro-level (individual) behaviours? What incentives are needed to support

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PIs in their pursuance of sustainability outcomes as part of their publicly funded research programmes? An interesting finding from this systematic review of the available literature on scientists’ influence on creating impactful medical device research is the small number of relevant articles that fit the study’s criteria for inclusion. This review is fundamentally important as it illustrates the lack of empirical focus to date on scientists’ effect on broader societal impact of research projects in the medical device field. Alongside this, there was obvious difficulty in accessing relevant articles that relate to the influence of scientists (and even more so PIs specifically), medical device–related research, and broader impacts of research, including sustainability. Further follow-up studies are required to understand why sustainability has not received such empirical focus as in other discipline domains. Is it a case of commonality of understanding of sustainability in this context or is it a reflection that sustainability is not of significant relevance within this field? There is a lack of common terminology in relation to impact and translation for impact, a finding highlighting the need for consensus of understanding and basic, universally agreed-upon terminology. This may be a barrier at micro- and macro-levels with respect to advocating sustainability within an entrepreneurial university setting where there can be predominate focus on commercialization, technology transfer, and entrepreneurship. Within our SLR the literature searches for research impact producing predominantly “journal impact factor”–related articles. This may be a reflection of the norms and traditions of this disciplines that contribute to medical device research. Also, this review found a lack of focus in the literature on medical device–related research as a whole. In some respects this finding is not surprising given the increasing pressures and expectations that universities and individual scientists face in realizing wider impacts beyond more traditional impact metrics (Cunningham and Miller, 2021). One could argue that the structure of the field and the orientation of knowledge creation mean there is less of a focus within discipline journal discourse with respect to research commercialization and the wider impact agenda that includes sustainability. Whilst a focus on how a PI influences impact (beyond the scientific) from medical device research may seem somewhat esoteric, the practical reality of the growing need for scientific scholars to demonstrate impact beyond scientific metrics means research of this nature is more pertinent than ever before. Researchers have to consider the deployment of devices in more frugal and less developed health settings. National vaccination programmes with respect to Covid-19 have highlighted this with respect to developed and developing country contexts. However, the predominant focus seems to be balanced towards the economic impact rather than considering wider societal and sustainability issues. Since such few studies currently exist on this topic it is the ideal time to hone the language and delineate the definitions so future research can build on this study using generally accepted conceptual boundaries. How universities support and advocate sustainability warrants further

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future research at the macro and micro levels and how this in turn influences and contributes to entrepreneurial and innovation ecosystems. The need for medical device researchers to plan, articulate, and demonstrate broader societal impact from their studies is only going to get more pressing in the coming years. This will be accelerated to some degree by mission-orientated research policies being adopted by national governments as a means to realize maximum impact, and address the grand challenges of our times, from public research funding (Mazzucato, 2018). Beyond a solid set of definitions of both “impact” and “medical device research,” what is needed are robust frameworks to assist individual PIs at the micro level so as they can plan the scope of their research, their laboratory or even their field of research and consider how their work can effect change across the spectrum including the societal and sustainability spheres. Scientific research shapes government policy, informs legislation, and can have profound industrial, environmental, and economic effect. However, effectuating such change requires individual PIs to think beyond their immediate sphere of influence and consider how their work may impact society more generally (see Cunningham, 2019). One potential avenue to explore in this regard would be incorporating Bronfenbrenner’s bioecological systems theory (Bronfenbrenner, 2005) into a framework to conceptualize impact across environmental systems, from the micro- and meso-levels of the PI (e.g. fellow academics in their university) to the exo- (e.g. patient groups), macro- (e.g. policy makers), and chrono-levels (changes over time). In terms of achieving broader impact for society, including sustainability, at a more macro-level, entrepreneurial universities and their ecosystems play a vital role in supporting societal and economic development (see Nicholls-Nixon et al., 2021; Nkusi et al., 2020). Work on entrepreneurial ecosystems is advancing, with a clear and important role for universities in the impact agenda, though the nature of this role is a matter of debate amongst academics (Bedő et al., 2020), as is the approach towards entrepreneurial universities (Cerver Romero, Ferreira, & Fernandes, 2021). As suggested from the findings of this study, within these university settings, the PI can affect change through their leadership (Nkusi et al., 2020), none more so than in the critical goal of sustainability research. However, as we have demonstrated in this review, there is a clear lack of research to date on how these important stakeholders of research can influence impact for society, and in particular how PIs can effect the promotion of sustainability research. There is a dearth of micro-level studies on the role of the individual scientist, specifically the PI, in creating societally impactful medical device research. At the outset of this chapter the authors acknowledged the evolving role of the PI and their growing set of responsibilities as key actors in universities and their ecosystems. The need to push the boundaries of research impact further is one such responsibility; it is hoped that this research provides the foundation for PIs, researchers, and scientific educators to embark on a fuller understanding of research impact and adjust their actions accordingly. Overall there is a need for further studies and future

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research to explore how individual scientists within entrepreneurial ecosystems engage with sustainability or what are the barriers they experience in pursuing such an agenda within their research activities. Acknowledgements: The authors wish to thank Dr Emmet Fox for his research support in preparing this chapter and acknowledge the library staff at NUI Galway and academic researchers in the medical device sphere that supported the design of our systematic literature review. In particular the authors wish to thank the CÚRAM Translation Group for their advice throughout. The CÚRAM Principal Investigator Impact: Research in Medical Devices project is funded by Science Foundation Ireland (SFI), cofunded under the European Regional Development Fund under Grant Number 13/RC/ 2073. Limitations: Several limitations should be acknowledged. Due to the complex and much debated nature of impact from research, this systematic literature review was not able to obtain all relevant academic articles on impact and impactful research. One discovery from carrying out this systematic review was that there does not appear to be commonly accepted and used terminology for research impact or more specifically for societal impact. Similarly, the theme of medical device research is often multi-disciplinary, ranging from basic to applied and clinical research, and so some articles involving medical device–related research scientists may not have been captured if they do not explicitly state that their research is related to developing medical or biomedical devices or technologies. This could be due to the fact that much research in the medical device sphere happens so far removed from the eventual design and development of a medical device or technology that it is not categorized as medical device research. Also, with our broad definition for a medical device, any technology related to medical health–related measures was included. We hope that this chapter motivates other studies that examine sustainability at the micro level within entrepreneurial universities across different disciplines. It also affirms that there is a need for further research and empirical studies that examine the role of entrepreneurial university in advocating sustainability.

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Kseniya Sorokina, Paula Odete Fernandes, and Jeyhun Mammadov

Chapter 16 Students’ perceptions of university social responsibility: A cross-cultural comparison 16.1 Introduction Corporate social responsibility (CSR) as a concept has been a subject for debates in the management area for a long time and it has become one of the standard business practices of our time. CSR can be described as a field of management that takes into consideration ethical issues in all aspects of the business. In modern organisations, managers need to know how they could contribute to strategic development and changes. These are the issues addressed by a study of strategic management (Thompson, 2001). Current research work will analyse both concepts of CSR and strategic management as well as separately and at the same time connect these topics. The topic of CSR has been a subject for debates in businesses for centuries. However, this concept was taken more into consideration during the last decades. The modern era of CSR began in the 1950s. According to Garriga and Melé (2004), at that time, the literature tended to refer to the Social Responsibilities (SR) rather than CSR. In the early writings, it was referred to more often as SR than as CSR. This trend was because the age of the modern corporation’s importance and governance in the business sector had not yet arisen or been noted. The publication of Bowen’s (1953) landmark book Social Responsibilities of the Businessman is argued to mark the beginnings of the modern period of literature on this subject. As it is reviewed in Ismail’s article, nowadays, CSR (also called corporate responsibility, corporate citizenship, responsible business, and corporate social opportunity) is a concept whereby business organisations consider the interest of society by taking responsibility for the impact of their activities on customers, suppliers, employees, shareholders, communities, and other stakeholders as well as their environment. This responsibility presents that the organisations have to act following legislation and take initiatives to improve the well-being of their employees and their families as well as for the local community and society at large. CSR can include a different kind of activities, for example, it could be working in partnership with local communities, socially sensitive investment, developing relationships with employees, customers and their families, and involving in activities for environmental conservation and sustainability (Ismail, 2009). The term university social responsibility (USR) is explained as the capacity of Higher Education Institutions to distribute and implement a set of principles, general and specific values aimed at enriching the educational and social challenges of the

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society through four key processes: management, teaching, research and extension (Dominguez, 2009). In the modern world, the term Entrepreneurial Ecosystems (EE) has received increasing attention. Through governments, private enterprises, universities, and communities have started to distinguish the potential of united policies, structures, processes that substitute regional entrepreneurship activities and in the process, can maintain innovation, productivity, and employment growth (Ács, Szerb & Autio, 2015; Foster & Shimizu, 2013; Stam, 2015). According to the Isenberg (2011), Isenberg (2014), and Hechavarria and Ingram (2014), over the last decade, researches, professors, and policymakers have frequently discussed to the environment surrounding entrepreneurial activity as the entrepreneurship ecosystem. Academics and different experts arguing that EE steadily identifies the possible and confirmed vitality that higher education mostly, and universities, play in building and maintaining a growing and thriving EE (Hechavarria & Ingram, 2014, 2018). Following the thinking previously described this research work has as its main objective to understand the perception of CSR in the point of view of the international students that attended the Polytechnic Institute of Bragança (IPB), how they react to the questions on the given topic, do they find the concept vital or not. For that, a questionnaire survey was applied to 200 international students, both Master and Bachelor attendants, from different countries. These students were attending the institution under the Erasmus programme or another programme with which the IPB has protocols, in the academic year of 2018/2019. To meet the main objective of the study, this chapter is structured as follow, following this introduction. In Sections 16.2 and 16.3, some theoretical concepts related to CSR and competitive advantage in EE, and USR will be presented. Section 16.4 explains how this study was conducted and briefly presents the data collection tool and the statistical techniques used for data processing and analysis. Section 16.5 presents the findings obtained for the study and the respective analysis. Section 16.6 draws the conclusions.

16.2 Corporate social responsibility and competitive advantage in entrepreneurial ecosystems Porter and Kramer (2011) link competitive advantage to CSR. They indicated that the association between CSR and competitive advantage is often viewed as promising if social needs, environmental limits and corporate interests are well coordinated within it. It provides value both for the company and society (Porter & Kramer, 2011). If CSR activities do not support their strategies due to the reason of a dynamic market environment, businesses are not able to achieve long-term competitive advantage. Companies that want to achieve a balanced competitive advantage need to

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categorise CSR as a business strategy, moreover, there is a need to protect the business opportunities and power, the internal operational process should be effectively managed to serve the demands of the external stakeholders and groups that put pressure on the corporation (Porter & Kramer, 2011). There are two fundamental outcomes of CSR activities: corporate reputation and organisational commitment, which can become sources of competitive advantage for a company using CSR as a differentiation tool. Positive corporate reputation is a performance indicator, a competition factor and a competitive advantage in itself. The biggest contribution of corporate reputation to businesses is an advantage in sustainable growth and competition. Corporate reputation is very important because it gives the advantage to sell its products for more high prices with fewer prices in the procurement of raw material and intermediate products (Yalçıntaş, 2017). Nicotra, Romano, Del Giudice and Schillaci (2018) identified an EE is a combination of social, political, economic and cultural elements that supports the improvement and progress of advanced start-ups and encourages new entrepreneurs in the market to take such kind of risks as starting, funding, and in helping businesses with a high risk (Nicotra et al., 2018). According to the idea of Isenberg (2011), an EE involves such elements that can be grouped into six areas: conducive culture, facilitating policies and leadership, availability of dedicated finance, appropriate human capital, venture-friendly markets for products, and different institutional and infrastructural supports. According to the research conducted Campanella, Della Peruta, and Del Giudice (2013) universities play a very vital role in giving birth to new prospective entrepreneurs. According to Jack and Anderson (1999), typically, entrepreneurs who before were university graduates not only have artistic abilities, they also have entrepreneurial expertise (Jack & Anderson, 1999). Consequently, based on the results of the study by Mack and Mayer (2016), an EE that is a helpful tool for fresh candidates for entrepreneurship. Here are some vital accepted strategies in generating an EE at the university include (Guerrero, Urbano & Fayolle, 2016): – Curriculum policy. The importance of the curriculum meets the needs of national affordability, as well as the idea and mission of the university in the creation of graduates. To produce graduates who are not just looking for work but graduates who are also able to create employment opportunities, alternatives in vision and mission are needed. Entrepreneurship based curriculum is the main curriculum that will be a measure of the accomplishment of the university in creating highly professional graduates; – Collaboration with other institutions. The University initiates different kind of partnerships with other universities in order to provide suitability for students in foundations of their businesses; – Awarding best entrepreneurship. Students who already have a business may have a chance to receive best entrepreneurship award. This event was held regularly to

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activate the entrepreneurial spirit of the students. The occasion can enhance enthusiasm for the students, but in giving the award there still needs to be a variety of categories.

16.3 University social responsibility Universities, as education institutions, play an essential role in the development and improvement of society by contributing to the well-being of citizens. It is important to examine, considering the social responsibility of universities with a large number of stakeholders (students, institutions, government, employees, companies, local community) how these institutions establish the mission, objectives, and strategic actions which are oriented at meeting these expectations. Reason, Ryder, and Kee (2013) noted that CSR is a wide concept and focuses not only on the company’s obligations towards society. Educational institutions especially universities are socially responsible for bringing up the change till the origins of the society. The importance of developing social and personal responsibility in universities and colleges is not a new concept. Already in the 1940s, for the same authors, there have been studies which emphasise the importance of raising awareness among the students, faculty, universities and educational institutions administration. The principles of CSR are not new to the education sector, and universities worked for a long time to the benefit of society by educating new generations and engaging in community services (Dima, Vasilache, Ghinea & Agoston, 2013). According to Plantan (2002, p. 65), “universities can provide the platform for community services as universities build bridges internationally, serve as national gateways for the sharing and dissemination of knowledge, and influence society through the ideas and values shaped by the humanities and liberal arts.” Accordingly, universities have to deal with more directions rather than concentrating only on teaching and research. Human and Social development could be a good example to deal with. That is, universities should be socially responsible to the local society by strengthening relationships between universities and the communities within which they operate (Alshuwaikhat & Abubakar, 2008; Haden, Oyler & Humphreys, 2009). According to the words of McTighe and Musil (2012), as cited in the work of AlKhoury, Bolkart, Fechter, and Alhamali (2015), the knowledge which is given to the students in the university is not only about technical skills but also to make students socially responsible so that they might not engage in activities known to improve civic knowledge and skills at acceptable rates, even with increased attention on community engagement (Al-Khoury et al., 2015). Based on what was explained in the previous paragraphs and section and not losing the purpose of the study, the following research hypotheses were established:

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H1: Independent variables are influencing the participation in social activities; H2: There is an association between school-of-students variable and pre-excising knowledge about social responsibility; H3: There is an association between the level of participation in social activities and Sociodemographic variables (age, gender, and degree of the student); H4: There is a positive correlation between the statements that schools are an important factor for students’ development and that professors from home/IPB institution motivate students to grow beyond themselves; H5: There is a difference between males and females regarding the perception of USR.

It should be noted, and for the first research hypothesis, the independent variables are: “Home University help me to develop personal and social responsibility” (Q3); “University motivates me to participate in the community” (Q5); “I do actively participate in offers from university” (Q15); “I believe my university respects its commitment to the community and plays role in the social responsibility of the survey” (Q18). And the dependent variable is “I usually participate in social activities” (Q8).

16.4 Methodology and methods 16.4.1 Sample and data The study was carried out by a higher education institution, the IPB. This institution has an average of 2,300 international students from different countries every year. Given the diversity of cultures, it is relevant to know the students’ perception of social responsibility issues by comparing the home institution with the host institution. In order to collect the information from each international student, it was decided to follow a quantitative research and to use a questionnaire as a data collection tool. The questionnaire consists of the questions on how USR practices are implemented in a specific university. The questionnaire contains two parts: questions about personal data (age, gender, country where the respondent is from, home university, degree of studies, and school of education in order to have a general profile of each participant) and the second part is a collection of information on USR. The second part of the questionnaire consists of 20 questions. All the questions from the second part are based on five points Likert scale from 1 (strongly disagree) to 5 (strongly agree). The questionnaire can be found in Appendix 16.1. Questions for the survey were derived from the article named “Students social responsibility initiatives and impact on University Performance: An Empirical Study from Lebanon” written by the authors Al-Khoury et al. (2015). The population of the study was the students were attending the institution under the Erasmus programme or another programme with which the higher education institution, IPB, has protocols, in the academic year of 2018/2019, both master and bachelor

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programmes. The questionnaire was sent by the IPB’s international relations office to all students as well as the usage of the social network Facebook. It was used in the Google Forms that was conducted during April–May of 2019. In total, 200 questionnaires were received, a response rate of 8.7%. For measuring the reliability of the questionnaire, it used the internal calculated Cronbach’s alpha consistency (Smith & Albaum, 2013): α>0.9 – can be concluded that questionnaires reliability is very good. 0.9 >α>0.8 – can be concluded that questionnaires reliability is good. 0.8> α>0.7 – can be concluded that questionnaires reliability is reasonable. 0.7> α>0.6 – can be concluded that questionnaires reliability is weak. α