Regional Helix Ecosystems and Sustainable Growth: The Interaction of Innovation, Entrepreneurship and Technology Transfer [1st ed.] 9783030476960, 9783030476977

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Studies on Entrepreneurship, Structural Change and Industrial Dynamics

Luís Farinha Domingos Santos João J. Ferreira Marina Ranga  Editors

Regional Helix Ecosystems and Sustainable Growth The Interaction of Innovation, Entrepreneurship and Technology Transfer

Studies on Entrepreneurship, Structural Change and Industrial Dynamics Series Editors João Leitão University of Beira Interior, Covilhã, Portugal Tessaleno Devezas University of Beira Interior, Covilhã, Portugal

The ‘Studies on Entrepreneurship, Structural Change and Industrial Dynamics’ series showcases exceptional scholarly work being developed on the still unexplored complex relationship between entrepreneurship, structural change and industrial dynamics, by addressing structural and technological determinants of the evolutionary pathway of innovative and entrepreneurial activity. The series invites proposals based on sound research methodologies and approaches to the above topics. Volumes in the series may include research monographs and edited/contributed works. More information about this series at http://www.springer.com/series/15330

Luís Farinha  •  Domingos Santos João J. Ferreira  •  Marina Ranga Editors

Regional Helix Ecosystems and Sustainable Growth The Interaction of Innovation, Entrepreneurship and Technology Transfer

Editors Luís Farinha Polytechnic Institute of Castelo Branco and NECE—Research Center in Business Sciences Castelo Branco, Portugal João J. Ferreira University of Beira Interior and NECE— Research Center in Business Sciences Covilhã, Portugal

Domingos Santos Polytechnic Institute of Castelo Branco and CICS.NOVA Castelo Branco, Portugal Marina Ranga European Commission—Joint Research Centre in Seville and University of Warsaw (Poland) Seville, Spain

ISSN 2511-2023     ISSN 2511-2031 (electronic) Studies on Entrepreneurship, Structural Change and Industrial Dynamics ISBN 978-3-030-47696-0    ISBN 978-3-030-47697-7 (eBook) https://doi.org/10.1007/978-3-030-47697-7 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

 Regional Helix Ecosystems and Economic Growth��������������������������������������    1 Luís Farinha, João J. Ferreira, Marina Ranga, and Domingos Santos  The Role of Universities in Building Dense Triple Helix Ecosystems in Sparse Regional Environments������������������������������������   11 Maria Salomaa, Liliana Fonseca, Lisa Nieth, and Paul Benneworth  Between Good Intentions and Enthusiastic Professors: The Missing Middle of University Social Innovation Structures in the Quadruple Helix������������������������������������������������������������������������������������   31 Paul Benneworth, Jorge Cunha, and Ridvan Cinar  Why Do Publicly Funded Firms Find the University More Useful to Innovate Than Others? Can We Accomplish the RIS3 Target?����������������������������������������������������������������������������������������������   45 Joana Costa  Applying Regional VRIO Model to Island Regions: An Evaluation of RIS3������������������������������������������������������������������������������������   67 João Lopes, José Oliveira, and Paulo Silveira  Implications of Urban Sustainability, Socio-ecosystems, and Ecosystem Services ����������������������������������������������������������������������������������   85 José G. Vargas-Hernández, Karina Pallagst, and Justyna Zdunek-Wielgołaska  Regional Innovation Ecosystems: Tuning the Regional Engine’s Helix Through Smart Specialization����������������������������������������������  107 João Lopes, João J. Ferreira, Márcio Oliveira, Luís Farinha, and José Oliveira Regional Industrial Restructuring ����������������������������������������������������������������  125 Jan Ole Rypestøl

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 The Role of Clusters in the Smart Specialisation Process: The Case of Inovcluster in Portugal��������������������������������������������������������������  147 Teresa Paiva, Cláudia Domingues, Luis Farinha, and Marina Ranga  Incubation: Does It Make a Difference After Graduation? Analysis from Portugal������������������������������������������������������������������������������������  159 Daniel Ferreira Polónia, Jorge Cunha, and Tiago Leite

Regional Helix Ecosystems and Economic Growth Luís Farinha, João J. Ferreira, Marina Ranga, and Domingos Santos

Abstract  An infinity of regional innovation models has been described in the literature over the last few decades, with emphasis on innovative means, new industrial spaces, industrial clusters, industrial districts, regional clusters, learning regions or more recently, high-tech areas, clusters of knowledge-based industries, regional innovation systems, as well as innovation networks. A complementary approach, the Triple Helix model, suggests that territorial competitiveness largely depends on the trilateral relations between companies, government and universities that emerge in regions inserted at different stages of development. More recently, the term Quadruple Helix introduced the fourth dimension, represented by civil society, including at this level the end user (consumers, local associations, etc.), with an institutional role in the creation and dissemination of knowledge for innovation and development. Massively, the Triple Helix model and the regional innovation systems model have been exploring the territorial dimension of innovation in a concerted way. The Regional Helix concept recognizes the contribution of both schools to improving the understanding of the relationship between territories, innovation and competitiveness. In this approach, universities play a key role in building dense ecosystems of competitiveness in sparse regional environments. The ability to align endogenous resources and capabilities with territories’ smart specialization strategies is now a new priority. It is important to ensure new levels of urban sustainability, green growth strategies and consolidated levels of health and well-being. There

L. Farinha (*) Polytechnic Institute of Castelo Branco and NECE—Research Center in Business Sciences, Castelo Branco, Portugal e-mail: [email protected] J. J. Ferreira University of Beira Interior and NECE—Research Center in Business Sciences, Covilhã, Portugal M. Ranga European Commission—Joint Research Centre in Seville and University of Warsaw (Poland), Seville, Spain D. Santos Polytechnic Institute of Castelo Branco and CICS.NOVA, Castelo Branco, Portugal © Springer Nature Switzerland AG 2020 L. Farinha et al. (eds.), Regional Helix Ecosystems and Sustainable Growth, Studies on Entrepreneurship, Structural Change and Industrial Dynamics, https://doi.org/10.1007/978-3-030-47697-7_1

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is an urgent need to clarify the role of clusters in boosting these ecosystems of regional sustainable competitiveness, realizing what direction to follow in terms of regional industrial restructuring. Keywords  Triple Helix · Quadruple Helix · N-Tuple Helix · Regional Innovation Ecosystems · Regional Helix Ecosystems

Over the last three decades, there has been a shift towards the understanding of innovation as a socially constructed mechanism based on the accumulation of knowledge through a continuous and interactive learning process (Cooke, 2008; Shearmur, 2011; Tura & Harmaakorpi, 2005). This paradigm change was related to a better a deeper understanding of innovation dynamics, which do not follow a linear trajectory, according to the science-push model or the demand-pull approach, but derive from an interactive and systemic cooperation among key stakeholders. At the same time, an increasing focus on regional or territorially based aspects of innovation has developed, complementing the national level of innovation performance analysis, which can provide several inaccuracies if applied to specific regional contexts because of the heterogeneity and frequent lack of coherence between regions in many nation states, especially large and developing ones. Territorially based complexes of innovation and production have been increasingly seen as privileged instruments to harness and recreate knowledge and intelligence across the globe (Koschatzky, 2003). The accumulated knowledge that production systems develop, because they are incorporated in locally based institutions and in a generally non-mobile workforce, tend to perpetuate certain competitive advantages, but, although proximity matters, what really is important for the upgrading of the competitive edge of localized production systems and resource creation is organizational proximity (Carlsson, 2005; Fujita & Krugman, 2004; Kirat & Lung, 1999; Shearmur, 2011). A plethora of regional innovation models have been used in the literature, ranging from earlier ones, such as innovative milieus (Aydalot, 1986), new industrial spaces (Storper & Scott, 1988), industrial clusters (Porter, 1990, 1998), industrial districts (Bagnasco, 1977), regional clusters (Saxenian, 1994) and learning regions (Morgan, 1997), to more recent ones, such as high-tech areas (Keeble & Wilkinson, 2000), clusters of knowledge-based industries (Cooke, 2002), regional innovation systems (Asheim & Coenen, 2006; Cooke, 2002; Doloreux & Parto, 2005) and innovation networks (Rycroft, 2003). A complementary approach, the Triple Helix model developed by Etzkowitz and Leydesdorff (1997), suggests that territorial competitiveness largely depends on trilateral relationships among companies, government and universities that emerge in regions at different stages of development and with different inherited socioeconomic systems and cultural values. As regions seek to create a self-reinforcing dynamic of knowledge-based economic development, the three institutional helices

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are each undergoing an internal transformation, even as new relationships are established across institutional boundaries, creating hybrid organizations such as technology centres, knowledge transfer offices and other bridging and intermediary organizations. The new networks within a region, established by means of concerted triangular interactions, may allow the emergence or renewal of high-tech clusters and the creation and organization of new industrial sectors. As Ranga and Etzkowitz (2008) point out, there has been a shift from an earlier focus on innovation sources confined to a single institutional sphere, whether product development in industry, policymaking in government or the creation and dissemination of knowledge in academia, to the interaction among these three helices as the source of new innovative organizational designs and social interactions. This shift entails not only various mechanisms of institutional restructuring of the sources and development path of innovation but also a rethinking of the orthodox models for conceptualizing innovation, including innovation systems (national, regional, sectoral, technological, etc.). More recently, the term Quadruple Helix has been used, where the fourth dimension is represented by the civil society: the end user (consumers, local associations, etc.) enters the equation and becomes directly involved in the innovation process. The Quadruple Helix also refers to the roles played by civil society as the fourth institutional player in the creation and diffusion of knowledge for development (Carayannis & Campbell, 2012). With the inclusion of other institutions and key actors in this process, the game has continuously expanded, so that some authors talk about an N-Tuple-Helix (Leydesdorff, 2012) and academics, policymakers and technologists paid increasing attention to networks of knowledge, technology and innovation embedded in the triple, quadruple or multiple helix dynamics associated to the reinforcement of the university’s third mission (society engagement) to achieve a global competitive advantage (Brown, 2016; Peris-ortiz et al., 2016; Carayannis, Grigoroudis, Campbell, Meissner, & Stamati, 2018; Predazzi, 2012; Rolfo & Finardi, 2014). The Triple Helix model and the regional innovation systems model have been widely used to explore the territorial dimension of innovation. The of Regional Helix concept recognizes the contribution of both schools to improving the understanding of the relationship among territories, innovation and competitiveness: on the one hand, the regional innovation systems model focusing on key territorial characteristics such as the flux of knowledge and innovation production, and on the other, the Triple Helix affirming the centrality of the hybridization of elements from university, industry and government to generate new institutional and social formats for the production, transfer and application of knowledge. A closer integration of the two models could bring new opportunities to create convergent analytical and methodological paths that allow for a further research enrichment of the debate about territorial competitiveness and sustainability. More recently, the smart specialization concept and the theoretical constructs developed around it have been also applied to the exploration of regional innovation dynamics, in an underlying belief that smart specialization strategies make smart regions smarter (Carayannis et al., 2018; Markkula & Kune, 2015). Furthermore, open innovation and entrepreneurship have also been increasingly examined in the

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context of regional innovation ecosystems and have been recognized as central factors for sustainable competitiveness (Asheim, Smith, & Oughton, 2011; Hall, Daneke, & Lenox, 2010; Huggins & Williams, 2011). This book contains nine chapters that explore recent trends in the regional innovation ecosystems literature from the perspective of innovation and entrepreneurship embedded in the Quadruple Helix concept. The nine chapters cover a wide range of aspects related to regional innovation and entrepreneurship and contribute to both regional science and territorial competitiveness research by adopting an entrepreneurial perspective on the dynamics of regional innovation ecosystems. The book provides an important starting point for exploring the role of smart specialization and regional stakeholder interaction in regional innovation systems and regional economic growth. This allows the establishment of a new recognized area of research on the effect of entrepreneurial incorporation on regional innovation systems through the dynamics of Regional Helix, explained through the collaborative dynamics of regional stakeholders. The chapters included in this book show the diversity of research approaches and methodologies, which demonstrates there is a burgeoning literature on regional innovation ecosystems associated to the quadruple helix dynamics, through regional innovation and entrepreneurship initiatives.

1  Overview of Chapters The first chapter entitled “Between Good Intentions and Enthusiastic Professors: The Missing Middle of University Social Innovation Structures in the Quadruple Helix” highlights the role of universities in stimulating social innovation and, in particular, through the development context associated with quadruple helix dynamics (interaction between academia, business, political decision-making and civil society). It approaches the institutional logic of higher education institutions (HEIs) from a community engagement perspective, its entrepreneurial capacity and the resulting benefits. The article aims to understand if this entrepreneurial logic reinforced through the connection with the community explains the resistance of universities to develop social innovation activities. Through two case studies applied to two universities committed to supporting social innovation, the authors note the existence of a “missing medium” between enthusiastic managers and committed professors, undermining the corresponding performance associated with social innovation, developed around the spheres of the quadruple helix concept. The second chapter entitled “Regional Innovation Ecosystems: Tuning the Regional Engine’s Helix Through Smart Specialization” aims to analyze the theoretical evolution of the concepts of triple, quadruple, quintuple and N-Tuple Helix, associated with the dynamics of the interaction among different stakeholders present in the regional innovation systems. It develops a systematic review of the literature through a bibliometric analysis of research in these areas of knowledge. Using the Web of Science database, from a total of 378 articles, 4 clusters were identified

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in the literature to highlight: collaborations and innovation in research and development, Entrepreneurial Activity at the Entrepreneurial University, Triple Helix and Quadruple Helix dynamics in regional innovation systems. The chapter also foresees further exploratory studies to be carried out in the future, for a better understanding of the typologies and interactive density among the different regional stakeholders, in an orientation towards innovation and entrepreneurship. The third chapter entitled “Why Do Publicly Funded Firms Find the University More Useful to Innovate Than Others?: Can We Accomplish the RIS3 Target?” focuses on the failure of the territorial innovation policy, leading to smart specialization (RIS3) becoming the holy grail of European cohesion. It argues that regional sustainable growth optimizes resource use, increases efficiency levels, creates competitiveness and respects the environment. It adds that inclusive growth contributes to strengthening social and territorial cohesion and shows that the relevant interactions between universities and other institutions are highlighted in the production and diffusion of knowledge according to the regional competitive advantages, of greater importance to the less favoured regions. In addition, the ability to transform and exploit knowledge can determine the success of innovative performance. As a result, the university’s leading role in innovation, coupled with its poor connection with the business community, generally results in a poor contribution to the competitiveness of the regions. This is coupled with poor political support to ensure positive discrimination in line with smart specialization policies. The new policy framework is reviewed, bringing a reformulation of traditional incentives to innovation, financing and subsidy strategies. The results presented were based on a panel of companies located in a moderately innovative region in Portugal. The fourth chapter analyzes the implications that urban sustainability, ecosystems and ecosystem services have on the basis of designing urban green growth strategies. It uses the analytical method, based on the theoretical and conceptual reviews of the literature on the topics under analysis, from a qualitative analysis. The study reveals that urban sustainability and environmental performance integrate biodiversity and socioeconomic ecosystems to provide better quality ecosystem services, supported by green infrastructure design. It concludes that ecosystem services and human well-being can suffer serious irreversible declines if sustainability is not based on the biodiversity of social ecosystems, green infrastructure and natural capital. The fifth chapter asks “What Roles Do Universities Play in Sparse Environments in Building Triple Helix Relationships, Stimulating Regional Innovation Processes?”. It argues that universities and local authorities increase collaboration with the private sector, but change comes through a complex “spiral” model where the internal and external dynamics of the parties are influenced. It adds that cultivating partnerships based on regional success stories requires a lot of work from all parties involved. It clarifies that the role of universities is to link informal and functional relationships to more formal strategic relationships, so that universities maximize their stability and minimize their exposure to volatility. It further argues that regional partners can play different roles in encouraging universities to undertake these internal integration activities, reinforcing the role of triple helix interactions to

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ensure long-term regional change. Government can play a regional leadership role by encouraging university leaders to recognize the research strength of their academics; regional companies can create collectivities to engage with academics and build a critical mass of interaction activities. The study, restricted to five universities in sparse regions, provides a nuance of the original model—that of universities playing a role of “tertius gaudens” in relation to government and industry actors to facilitate the development of regional innovation assets. The sixth chapter entitled “Incubation: Does It Make a Difference After Graduation?—Analysis from Portugal” looks at the context in which 2 regional and university incubators were created and from which 32 companies were formed. Results for these companies are compared to the same set of results for 32 unincubated companies, totalling 64 startups, all created in the same time period (2007–2011). The 64 technology companies are located in the Aveiro region, in Portugal. The results of the study revealed that there is no significant difference from overall business results; however, incubated companies behave differently than non-incubated companies in terms of productivity of their intangible assets, allowing dependence and openness to foreign markets. The seventh chapter sought to answer two research questions: (1) How do regional industries restructure? and (2) What is the role of ecosystems in such processes? Regarding the first question, the study reveals that regional industrial restructuring must be understood as a process initiated by the exploration of an identified opportunity. These opportunities may be incremental or radical in nature, and the most potent opportunities are radical ones. It is further shown that not all identified opportunities focus on profit; rather, some focus on changing smart specialization strategies, improving systemic factors and creating an increase in collective value. The study also reveals that opportunities are exploited by two possible types of entrepreneurs—namely, enterprise-level entrepreneurs and system-level entrepreneurs. The former initiate processes of organic change, while system-level entrepreneurs initiate planned processes. In addition, regional industrial change processes benefit from close collaboration between the two types of entrepreneurs. Regarding the second research question, the study reveals that the regional context influences all stages of the process. Following the RIS literature, the study authors classified regional ecosystems as being organizationally dense and diverse, organizationally dense and specialized or organizationally thin. It is argued that thin ecosystems are more prone to industrial extension, while denser and more diverse ecosystems were better placed to promote the creation of new industries or for their renewal. The eighth chapter entitled “The Role of Clusters in the Smart Specialisation Process: The Case of Inovcluster in Portugal” argues that clusters are a major driver of a region’s competitiveness and economic growth. It also highlights the importance of a smart specialization strategy that is capable of ensuring the involvement of clusters as stakeholders of a regional innovation ecosystem. Through a case study, this chapter illustrates how Inovcluster, an agri-food cluster located in the Central Portugal region, geared towards SMEs and microenterprises, operates

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within the research and innovation strategies for smart specialization (RIS3), promoting the regional competitiveness of business of its associate members. The ninth article entitled “Application of the VRIO Regional Model in Island Regions: An Assessment of RIS3” aims to assess stakeholder perceptions of the areas of smart specialization strategy (RIS3) defined for two Portuguese island regions in Portugal (Madeira and Azores). This study follows a quantitative methodology by applying questionnaires to regional stakeholders. The study follows the VRIO regional model applied to these island regions. The survey results revealed that stakeholder perceptions are not the same as their policymakers regarding the areas of smart specialization defined in the RIS3 of the region to which they belong. The authors sought new contributions to the literature, with the aim of assisting policymakers in defining regional strategies by advising and measuring regional performance.

2  Conclusion and Agenda for Future Research The so-called Regional Helix approach seems to constitute a step forward towards a broader understanding of the evolving dynamics of local and regional economies. It is not a substitutive but an enriching complementary theoretical, analytical and policy tool to both the regional innovation system rapport and the helix perspective. There is yet a need for more intense theorization and empirical research that can carry new insights into this domain of the cause-effects relationships between entrepreneurship, innovation and local and regional economic dynamics. It thus allows for an understanding of the territorial innovation performance through the lens the systemic public-private dialectics, giving a special focus to the governance mechanisms that support the upgrading of regional competitiveness profiles. The different book chapters also converged on the argument that the function of knowledge creation can no longer be predominantly situated at the R&D system. Other protagonists, such as user innovators, customers, new intermediaries, collaborative innovators and citizen innovators, make considerable contributions especially to the newly acknowledged innovation types, such as low-tech innovation and social innovation but also to more orthodox product, process, organizational and marketing innovation. There is now a call for reshaping policy instruments, not steered mainly by a RD&I logic but more focused to stages downstream, nearer to the market needs and towards concentrating on the mechanism that allow for the emergence and fertilization of an entrepreneurial culture. To each territory is associated one only particular helix, and this, which might seem a handicap in terms of policy design, is, in fact, the enriching never-ending challenge that territorial restructuring rises to the regional science research community. Each territory has its own history and economic, social and technological trajectories which have to be acknowledged. This book leaves a legacy of an open academic posture: not closed to mainstream theories but rather open to empirical

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observation and experimentation, to the dialogue with other scientific disciplines, to action in the civil society and to our responsibility in a rapidly changing world. The link between academic conceptualization and policy formulation, besides the substantial progresses that have been achieved along the last three decades, still, often, remains weak and ambiguous. There is, thus, an obvious need for more intense theorization and empirical research that can carry new and more consistent insights into this domain of the cause-effects relationships between entrepreneurship, innovation and local and regional economic dynamics.

References Asheim, B., & Coenen, L. (2006). Contextualising regional innovation systems in a globalising learning economy: On knowledge bases and institutional frameworks. Journal of Technology Transfer, 31, 163–173. Asheim, B. T., Smith, H. L., & Oughton, C. (2011). Regional innovation systems: Theory, empirics and policy. Regional Studies, 45(7), 875–891. Aydalot, P. (1986). The location of new firm creation: The French case. In New firms and regional development in Europe (p. 105). Bagnasco, A. (1977). Tre Italia: La Problematica Territoriale Dello Sviluppo Economico Italiano. Mulino: Bologna. Brown, I. D. (2016). The chemical bond in inorganic chemistry: The bond valence model (p. 27). Oxford: Oxford University Press. Carayannis, E. G., & Campbell, D. F. J. (2012). Mode 3 knowledge production in quadruple helix innovation systems (pp. 1–63). New York: Springer. Carayannis, E.  G., Grigoroudis, E., Campbell, D.  F., Meissner, D., & Stamati, D. (2018). The ecosystem as helix: An exploratory theory-building study of regional co-opetitive entrepreneurial ecosystems as quadruple/quintuple Helix innovation models. R&D Management, 48(1), 148–162. Carlsson, B. (2005). Innovation systems: A survey of the literature from a Schumpeterian perspective. In H.  Harmusch & A.  Pyka (Eds.), The companion to neo-Schumpeterian economics. Cheltenham: Elgar. Cooke, P. (2002). Knowledge economies clusters learning and cooperative advantage. London: Routledge. Cooke, P. (2008). Regional innovation systems: Origin of the species. International Journal of Technological Learning, Innovation and Development, 1(3), 393–409. Doloreux, D., & Parto, S. (2005). Regional innovation systems: Current discourse and unresolved issues. Technology in Society, 27, 133–153. Etzkowitz, H., & Leydesdorff, L. (1997). Universities in the global knowledge economy: A triple Helix of academic-industry government relations. London: Cassell. Fujita, M., & Krugman, P. (2004). The new economic geography: Past, present and the future. Papers in Regional Science, 83(1), 139–164. Hall, J. K., Daneke, G. A., & Lenox, M. J. (2010). Sustainable development and entrepreneurship: Past contributions and future directions. Journal of Business Venturing, 25(5), 439–448. Huggins, R., & Williams, N. (2011). Entrepreneurship and regional competitiveness: The role and progression of policy. Entrepreneurship & Regional Development, 23(9–10), 907–932. Keeble, D., & Wilkinson, F. (2000). High-technology clusters, networking and collective learning in Europe. Aldershot: Ashgate. Kirat, T., & Lung, Y. (1999). Innovation and proximity: Territories as loci of collective learning processes. European Urban and Regional Studies, 6(1), 27–38.

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Koschatzky, K. (2003). The regionalization of innovation policy: New options for regional change? In G. Fuchs & P. Shapira (Eds.), Rethinking regional innovation: Path dependency or regional breakthrough? London: Kluwer. Leydesdorff, L. (2012). The triple Helix of university-industry-government relations. In E.  Carayannis & D.  Campbell (Eds.), Encyclopedia of creativity, innovation, and entrepreneurship. New York: Springer. Markkula, M., & Kune, H. (2015). Making smart regions smarter: Smart specialization and the role of universities in regional innovation ecosystems. Technology Innovation Management Review, 5(10), 7. Morgan, K. (1997). The learning region: Institutions, innovation and regional renewal. Regional Studies, 31, 491–503. Peris-ortiz, M., Farinha, L., Ferreira, J. J., & Fernandes, N. O. (Eds.). (2016). Multiple Helix ecosystems for sustainable competitiveness. Cham: Springer. Porter, M. (1990). The competitive advantage of nations. New York: Free Press. Porter, M. (1998). On competition. Boston: Harvard Business School Press. Predazzi, E. (2012). The third mission of the university. Rendiconti Lincei, 23(1), 17–22. Ranga, L. M., & Etzkowitz, H. (2008). Creative reconstruction: Towards a triple helix-based innovation strategy in Central and Eastern Europe Countries. In M.  Saad & G.  Zawdie (Eds.), Theory and practice of triple Helix model in developing countries (Issues and Challenges). London: Routledge. Rolfo, S., & Finardi, U. (2014). University third mission in Italy: Organization, faculty attitude and academic specialization. The Journal of Technology Transfer, 39(3), 472–486. Rycroft, R. (2003). Technology-based globalization indicators: The centrality of innovation network data. Technology in Society, 25, 299–317. Saxenian, A. (1994). Regional advantage: Culture and competition in Silicon Valley and route 128. Cambridge, MA: Harvard University Press. Shearmur, R. (2011). Innovation, regions and proximity: From neo-regionalism to spatal analysis. Regional Studies, 45(9), 1225–1243. Storper, M., & Scott, A. (1988). The geographical foundations and social regulation of flexible production complexes. In J. Wolch & M. Dear (Eds.), The power of geography. London: Allen & Unwin. Tura, T., & Harmaakorpi, V. (2005). Social capital in building regional innovative capability. Regional Studies, 39, 1111–1125.

The Role of Universities in Building Dense Triple Helix Ecosystems in Sparse Regional Environments Maria Salomaa, Liliana Fonseca, Lisa Nieth, and Paul Benneworth

Abstract  University-industry-government relationships driving regional innovation are often discussed by using the shorthand of the ‘triple helix’, referring to any arena where these partners come together. This rapid expansion of the idea’s use risks it becoming a ‘policy concept’ whilst potential tensions of collaboration can be ignored. Instead of ‘happy family stories’ of well-functioning regional partnerships, we seek to explore how triple helix mechanisms may stimulate regional innovation systems in places that have traditionally not had a long history of collaboration. Whilst universities are often dominant drivers of innovation in these ‘sparse’ regional innovation ecosystems, they may not be fit to respond to the identified regional needs. We address this by using empirics from five regions with relatively sparse triple helix environments and present evidence on the ways in which the universities have sought to play the role of tertius gaudens—honest broker—helping to address the stalemates that emerge between partners with very different goals, norms, values and intentions around regional innovation. We identified several processes through which universities can play this role and thereby contribute to densifying sparse innovation environments, increasing agglomeration and diversity whilst helping to address the tensions and problems that densification brings. Keywords  Regional innovation systems · Peripheral regions · Entrepreneurial universities · University regional engagement · Innovation barriers · Institutional diversity M. Salomaa (*) Lincoln International Business School, University of Lincoln, Lincoln, UK e-mail: [email protected] L. Fonseca Department of Social, Political and Territorial Sciences, University of Aveiro, Aveiro, Portugal L. Nieth Center for Higher Education Policy Studies, University of Twente, Enschede, The Netherlands P. Benneworth Høgskulen på Vestlandet, Bergen, Norway © Springer Nature Switzerland AG 2020 L. Farinha et al. (eds.), Regional Helix Ecosystems and Sustainable Growth, Studies on Entrepreneurship, Structural Change and Industrial Dynamics, https://doi.org/10.1007/978-3-030-47697-7_2

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1  Introduction It has become increasingly common to talk about university-industry-government relationships stimulating innovation using the shorthand of the ‘triple helix’. In Europe, the terminology has been used to refer to any arena where these partners come together to stimulate better cooperations. But the rapid expansion of the idea’s use risks it becoming a ‘policy concept’ (Böhme & Gløersen, 2011), something that creates consensus by hiding disagreement. In effect, triple helix collaborations are agreed to be good despite different visions of what constitute good relationships and specifically obscuring tensions in arising collaborations between public, private and civil society partners. In the original triple helix model (THM) of Etzkowitz and Leydesdorff (2000), the underlying mechanism was the tertius gaudens, the honest third party, helping to address the stalemates that emerge between partners with very different goals, norms, values and intentions around regional innovation. In much of what is written about triple helix partnerships, there is a risk that these tensions are ignored and the mechanisms by which they are addressed shift into the background behind ‘happy family stories’ of well-functioning regional partnerships (Lagendijk & Oinas, 2005). We bring these two trends together to explore how triple helix mechanisms build up in places lacking long histories of collaborative relationships between partners and therefore lack the experience in addressing these problems. We focus on places with ‘sparse’ regional innovation ecosystems, where a university may be a dominant innovation driver but without necessarily meeting regional partners’ expressed needs. Although all partners would benefit from denser interaction, these mismatches between partners’ capacities and goals inhibit building closer relationships and thereby addressing these mismatches, trapping the regions in a sparse triple helix vicious circle. We therefore ask the research question: “what roles do universities play in sparse environments in building up triple helix relationships stimulating regional innovation processes?”. We use empirics from five regions with relatively sparse triple helix environments where universities played leading roles in attempting to build up relationships between triple and quadruple helix partners.1 Applying the empirical material to the conceptual framework derived, the chapter presents evidence from these five regions on the ways in which the universities have sought to play this tertius gaudens role, of the honest broker, to address the tensions that can arise, specifically using their global connections to help build better local interactions. The chapter identifies several processes through which universities can play this role and thereby contribute to densifying sparse innovation environments, increasing agglomeration and diversity whilst helping to address the tensions and problems that densification brings. This chapter therefore helps understand the ways in which universities can help build more fertile innovation and entrepreneurial ecosystems, thereby contributing to driving regional growth and wellbeing. 1  The Quadruple Helix refers to the fact that civil society organisations can be considered as a distinct sector of regional innovation networks and therefore deserve their own separate inclusion.

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2  Literature Review 2.1  The Problem of Sparse Innovation Environments Solving the innovation challenge in ways that produce socially equitable as well as economically efficient solutions requires understanding how innovation processes occur. This is particularly applicable to peripheral regions that face materially different challenges to those of the most successful regions from which examples are most frequently drawn (Eder, 2019). Whilst diverse sets of challenges for these groups of regions have been identified by various authors (for an overview see Nieth and Benneworth (2018)), Tödtling and Trippl (2005) highlight that peripheral regions lack structural density, with insufficient actors to achieve critical mass; old industrial regions may become ‘locked-in’, incapable of creating new pathways or interactions, resulting in “ties that bind” (Grabher, 1993). These challenges have been addressed in practice in weak(er) regional innovation ecosystems in diverse ways. One approach can be linking the peripheral region to urban areas on a national or even international scale (Eder, 2019; Isaksen & Karlsen, 2013). Firms and universities can become important regional actors using international contacts to facilitate knowledge exchange and learning. Isaksen and Karlsen (2013) even argue that ‘less emphasis [should be placed] on the endogenous development capacity’ of the region, with other geographic scales (national, international) potentially being equally important for innovation. These approaches nevertheless assume that a region has assets, actors and capacities that are sufficiently attractive to external partners to develop these wider linkages.

2.2  The Triple Helix Approach The THM conceptualises the partnering of regional actors for boosting regional innovation capacity (Etzkowitz & Leydesdorff, 2000; Leydesdorff & Etzkowitz, 1998), focusing on the interactive innovation dynamics between three main cooperating actors: industry, government and university. Bilateral relationships concatenate and drive their regional innovation environments forward, in a heuristic of a helical model of overlaid and reciprocal exchanges (sometimes depicted to resemble the DNA double helix). In its initial formulation, its tryptic form was proposed in consideration of emerging tensions and contrasts stemming from dualistic collaborative arrangements. In the introduction of a third element, cooperative actor relationships could be better managed. The THM was developed from a relatively limited set of paradigmatic cases (e.g. Silicon Valley), assuming a spontaneous emergence of these cooperative links and the development of functional regional partnerships (Lagendijk & Oinas, 2005). However, the original model saw that conflict was a potential driver of innovation: in the tertius gaudens mechanism, the ‘third who benefits’, this refers to a third

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party that can work to create balance and address emerging tensions when otherwise productive innovation relationships founder. This third party would act as an ‘honest broker’, moderating these different intentions, values, goals and norms between actors, mediating rigidities and compensating for any absences, enabling the potential of those innovation relationships otherwise held back by those tensions. The THM of various stakeholders is part of a much wider family of Territorial Innovation Models (Moulaert & Sekia, 2003). The triple helix model is similar— although not identical—to concepts of ‘regional innovation coalitions” (Benneworth, 2007), ‘regional innovation networks’ (Rodrigues & Teles, 2017) or ‘multi-level partnerships’ (Morgan & Nauwelaers, 2003), all-encompassing the idea of different stakeholders coming together and providing potential solutions to varied problems (Wilgaard Larsen, 2017). Whilst the idea of partnerships becoming regional ‘possibility-­making machines’ (Åkerstrøm Andersen, 2008) is attractive, it obscures the fact that different partners have different aims, motivations, desires and goals. Harmonious and uncomplicated cooperation in ‘happy regions’ (Lagendijk & Oinas, 2005) cannot be seen as the status quo, as a variety of stakeholders ‘each with their own assumptions, ideas, goals and expectations’ (van Drooge & Spaapen, 2017, ‘7 Discussion & Conclusion’, para. 1) need to be aligned, whilst facing different tensions (Nieth, 2019). In this chapter we combine these two literatures to ask whether these regional partnerships can drive densification processes in these sparse innovation environments, thereby addressing an important lacuna in the literature: moving beyond thinking of sparse innovation environments in terms of processes that operate in successful/dense innovation regions. We specifically address the role of different actors in triple helix partnerships snd how they play different roles to address tensions and create new innovation assets. We ask the research question: ‘what roles do universities play in sparse environments in building up triple helix relationships that stimulate regional innovation processes?’.

3  Methodology and Case Studies 3.1  Methods To answer this research question, this study comparatively analyses five universities in sparse innovation environments across varying national and regional contexts: the five universities are all located in sparse innovation environments and all have actively sought to manage their contributions to regional development. The study draws on desk-based research and data from a total of 194 semi-structured interviews, split as following throughout the case studies: 35 interviews in Satakunta (FI), 36 in Lincolnshire (UK), 40 in Twente (NL), 38 in Aalborg (DN) and 45 in Aveiro (PT). These were conducted between 2017 and 2019 with academics, local authorities and other relevant stakeholders (e.g. businesses, intermediary and civil

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organisations) exploring how universities contributed to supporting regional innovation and entrepreneurial co-operative environments. Questions addressed engagement activities and collaborative projects of relevance undertaken with external stakeholders, emerging tensions and opportunities and the effective or foreseen impact these had on the region and the institutions involved. Interviews were recorded, transcribed and translated into English where applicable.

3.2  Cases The University of Aveiro has played an active and relevant role in the entrepreneurial ecosystem of Aveiro (NUTS III) and Centro region (NUTS II), evidenced in previous studies (Fonseca, 2019; Rodrigues & Teles, 2017). Despite its location in a less developed region, it benefits from a unique lagoon setting in the Portuguese coast and its positioning between the major metropolitan areas—Lisbon and Porto—creating opportunities to develop its innovative assets in the areas of environment, agri-food, ICT and others related to the local industry. UA has boosted regional innovation by engaging in inter-institutional collaborations with both big, medium and small businesses but especially with its continued work with local (municipalities) and regional government (intermunicipal community of Aveiro and Centro region’s commission) in the support of development initiatives, like the incubator network, the science park and the technological platforms. The University of Twente has been contributing to the regional innovation environment through diverse channels, such as teaching entrepreneurship courses, as well as contributing to regional strategy platforms and supporting a start-up/spin-­ out system which encourages students and researchers to contribute to regional development (mainly in the high-tech sector). Established in 1961, it was created with the aim to revitalise the regions lagging industry and create a knowledge-based environment that would attract students, researchers and companies alike. It has been working with governmental actors such as the 14 municipalities of Twente, cities (especially Enschede and Hengelo) and the Twente region as well as with industrial partners and societal stakeholders (Nieth, 2019). The region and the university have been focusing on expanding as well as supporting high-tech related projects, activities and sectors. Aalborg University, opened in 1974 after active lobbying of diverse regional interest groups, is situated in the most Northern part of Denmark and combines 11 municipalities. The city of Aalborg constitutes the centre of the region, with the university and much of the industry being located there. Since its creation the university has been an integral part of the regional innovation ecosystem through its active involvement in joint initiatives and platforms (especially internationally known clusters). At the same time, AAU has adopted the problem-based approach for teaching, learning and research, allowing active interaction of students (and to a lesser degree also academics) with the private and public regional stakeholders. The regional industry, which is heavily based on SMEs, used to be dominated by

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traditional and labour-intensive industries, counts on more growth-oriented knowledge industries today. University Consortium of Pori, coordinated by the new Tampere University,2 is a network of three Finnish universities. Altogether, there are six university consortia scattered across the country in more peripheral regions otherwise lacking access to HE. UC-Pori is located in the Satakunta region in the Southwest of Finland, where the former Tampere University of Technology has offered degree studies in engineering since the late 1980s. It was officially established in 2003, and later on the position of the university consortia was legitimised in 2009 (Ministry of Education and Culture, 2009) to reinforce the societal role of higher education. Currently, the UC-Pori contributes to building a regional innovation ecosystem not only by increasing the local skill level with local access to higher education but also by engaging with regional authorities in policy design and evaluation processes and supporting local SMEs through ERDF funded activities (Salomaa & Charles, 2019). It is active in all regional priority sectors such as energy production, offshore process industry, ports and logistics. University of Lincoln, located in the rural region of Lincolnshire in North East of England, has had a strong regional mission since its establishment in 1996. Since then, it has expanded rather quickly and become an important driver of regional development, especially through intensive collaboration with regional authorities (Salomaa, 2019). UoL has strived to support regional economic growth by focusing on large-scale, collaborative infrastructure initiatives such as the establishment of Lincoln Science and Innovation Park together with the Lincolnshire Co-Op to attract more large-scale companies to the area. It has also sought to serve the local job market by providing tailored degree education, e.g. in engineering but also increasingly in other local priority sectors, namely, in agri-food and food manufacturing, through National Centre for Food Manufacturing at the Holbeach campus and the Lincoln Institute for Agri-Food Technology at the Riseholme campus.

4  T  he Dynamics of University Collaboration Activities in Sparse Innovation Environments In this chapter we focus on a set of concrete collaborative projects that fulfilled our criteria in that they involved actors from all three sectors, represented an increase in the density of the regional innovation environment, and actors played different roles in each of these sectors. Four of the cases represent efforts to create density by the development of new networks between different partners, the network for sustainable business development and Matchmaking Schemes in North Denmark, Aveiro’s Network for Innovation and Collaboration and health sector and robotics collaboration in Pori. Four of the cases involved developing specific physical infrastructures  University of Tampere and Tampere University of Technology merged in January 2019.

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for improved collaboration, the living lab for lighting in Aveiro, rural campuses and technology hubs in Lincolnshire and Enschede’s smart city infrastructure in Twente. A final example was the University of Twente’s Professional Doctorate of Engineering scheme, P.D. Eng., which contributed to raising high-level innovation skills in the region.

4.1  N  etwork for Sustainable Business Development (North Denmark) The Network for Sustainable Business Development (NSBD) is a collaboration between various municipalities of North Denmark, local business centres, Aalborg University, a local energy firm and several companies (Kommune, n.d.), aimed at managing different activities in the area of green and sustainable development. The municipality of Aalborg, which secured the network’s initial developments, was already engaging actively within the field of sustainability and has been ‘recognized as a pioneering municipality for crafting local authority commitment to sustainability initiatives’ (Normann, Johnsen, Knudsen, Vasström, & Johnsen, 2017). Today, the network is managed by two municipalities, Aalborg and Hjørring, with a secretariat involving actors from municipalities, university and different technological experts. It is primarily financed by municipalities but also received some EU Structural Funds, and—reflecting the national priority for green and sustainable development in Denmark—there have also been national funds. A NSBD researcher claimed that the idea to create the network emerged in 2008 as a result of an ongoing between researchers at Aalborg University and their municipal counterparts. A project participant noted that this initiative was a ‘a very collaborative effort between the three main partners’ (public, private and university) aiming to create tasks and benefits for everyone: the municipality drove the ‘environmental rationality aimed at monitoring and adjusting operational practices in polluting industries’; the university acted as knowledge specialists promoting technical advancements (Normann et  al., 2017). A member argued the network was important for experience and knowledge transfer: building up the capabilities of the municipality, and teach the people how to transform from being regulators to being advisors or dialogue partners. We are upgrading both the industry but also the public organisations.

4.2  Matchmaking Scheme (North Denmark) Aalborg University (AAU) and the North Denmark Region created a new coopetition infrastructure in 2007/2008 seeking to facilitate cooperation with the existing business infrastructure in the region, particularly in the region’s remoter rural areas

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and with SMEs. The original idea of this Matchmaking Scheme was creating new access points for university knowledge, one of the scheme’s initiators describing this as a ‘no wrong door policy’ (Nieth & Benneworth, 2019). The project was constructed to match regional needs, thereby ensuring funding from the regional Growth Forum, the body distributing European and national economic development funds. The new scheme involved two elements: the first was a matchmaking secretariat responsible for project management and organising matchmaking activities and the second were the ‘matchmakers’. There were three varieties of matchmaker created to stimulate knowledge exchange and build up new connections: internal matchmakers (academics and managers from different faculties), external matchmakers (employees of municipalities, business associations or similar institutions) and student matchers (individuals facilitating connections between students and regional businesses). These matchmakers were identified and connected to each other, and as they were usually well connected, this extended many small networks into a large consolidated arrangement with more perspective of partners’ different interests and needs. The secretariat also organised ‘municipality tours’, and project fairs were initiated, creating new ways for engagement between researchers, students and companies. More recently, new university management decided to refocus the programme as part of a rationalisation of all university knowledge exchange arrangements, partly reflecting national policy shifts in Denmark, shifting the focus to student-business connections.

4.3  Network for Innovation and Competitiveness (Aveiro) The Network for Innovation and Competitiveness (Rede para a Inovação e Competitividade, RIC) was established in 2008 as a 1-year partnership between Águeda municipality (in Aveiro region), UA and its Águeda polytechnic school and firms and entrepreneurial associations. Funded by the EU’s regional innovative actions programme, RIC’s creation was a purposeful ‘introduction of the triple helix model into the political discourse’ in Aveiro region (Rodrigues & Melo, 2013, p. 1681), following a belief that this arrangement would help boost local competitive capacity and innovative dynamics. The proposal was driven by the Mayor of Águeda’s generally recognised innovative mindset. In turn, UA regarded RIC as an opportunity to implement its regional engagement discourse. Entrepreneurs and firms were enticed by the prospect of accessing and developing innovation assets. More than 100 ideas were proposed (CMA, 2009) although most were rejected due to their impracticality or lack of innovativeness. Six developed into projects, of which the Lighting Living Lab (LLL) was the most notable (see below). Whilst RIC produced few tangible results, it represented the first step to connecting actors and legitimising the inclusion of academic resources in development efforts in Aveiro region. This was profited in future projects and experiments (see, e.g. Fonseca, 2019), including the RunUp network which sought to create more competence networks linking universities and local sectors (habitat, mobility, culture and tourism)

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in Águeda. National recognition for the RIC led to further similar projects including the Urban Network for Innovation and Competitiveness (RUCI) encompassing all 11 of Aveiro’s municipalities.

4.4  Lighting Living Lab (Aveiro) The Lighting Living Lab (LLL) emerged out of the RIC and demonstrates the way that the network drove substantive collaboration between different stakeholders in Águeda. The mayor of Águeda first initiated the notion of the LLL in 2006/2007 in articulating the desire of creating ‘an association to create open innovation’ in lighting, one of his municipality’s most important industries (70% of Portugal’s lighting industry are located in Águeda). The concept of a living lab was then relatively innovative, and close cooperation between the public, private and research sector persuaded actors to undertake the experiment. From the outset, the municipality served as ‘the main testing environment’ for new lighting solutions, with citizens involved ‘to explore the social and behaviour implications of the new technologies and co-design new solutions’ (World Bank and ENoLL, 2015). The initiative sought to address regional problems of high energy consumption and local companies’ competitive challenges such as intense local competition along with technological challenges incorporating digital electronic technology in diverse lighting products. The LLL’s main activities involved organising conferences and workshops, technology development and demonstration, joint participation in exhibitions, joint development and implementation of projects and (research) studies. The university was an important partner as a knowledge provider but also serves as a neutral connector between the different, sometimes very conflicting stakeholders. More recently, challenges such as financing, severe competition between the companies and a failure of the university to develop industry-specific training have led to a significant slowdown in LLLs’ activities.

4.5  U  C-Pori’s Collaboration with Healthcare Institutions (Pori) The University Consortium of Pori (UC-Pori) launched several projects together with local healthcare institutions supported by the Satakunta Regional Council and European Regional Development Funds. The consortium was extensively funded by the city council, and researchers felt that that wanted to ‘give something back to the community’. These initiatives were built on individual connections, as UC-Pori researchers were required to actively search for partners to find ways to contribute to regional priority sectors (e.g. Salomaa & Charles, 2019). One project sought to assist healthcare professionals using mobile robots with specific functions targeted

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to elderly people with memory illnesses. The researchers had contacted a local healthcare institution to explore how robotics could be applied in elderly care and the challenges they faced in their daily activities. One issue was that dementia patients easily get lost and need constantly assistance, for example, in navigating out of their room. A set of such repetitive tasks were identified with healthcare professionals and then partly automated, with engineers developing a mobile assistance robot to assist the demented patients. The researchers also invited local businesses to take part in the pilots and creating a new ecosystem through implementing open-­ source software. A second project together with local hospitals aimed to assist surgery patients discharged from the hospital through gamification. In this case, researchers developed a game that measured whether patients understood the instructions for treatment during home-based convalescence. Both these pilots, producing academic outputs as well as new healthcare innovations beyond regional boundaries, were also potential steppingstones towards larger, international research projects.

4.6  Rural Campuses Riseholme and Holbeach (Lincoln) The University of Lincoln (UoL) aimed to support regional priority sectors, notably agri-food, by establishing satellite campuses located in more rural areas of Lincolnshire. The Holbeach campus, previously a satellite campus of an agricultural college, officially joined UoL in 2002 with a strong support from the local government. The campus subsequently grew rapidly increasing collaboration with local industries (Salomaa, 2019). Following the UoL takeover, the Holbeach campus provided ‘a higher level of technical science based skills that the industries didn’t have before’ an access point for agricultural industries to academic knowledge, alongside helping researchers with relevant expertise for the food sector, such as life and computer science, to better engage. Since 2008, the Holbeach campus hosted the National Centre for Food Manufacturing (NCFM) offering apprenticeships and short courses for food industry employers, as well as state-of-the-art R&D facilities used by both local and bigger international food producers, e.g. Nestlé and Heineken. Following the NCFM’s opening, UoL has been actively working with regional partners to develop the food sector (Salomaa, 2019). In 2016, the Lincoln Institute for Agri-Food Technology (LIAT), located at the Riseholme campus, was established to coordinate and enhance UoL’s contributions to food production and agriculture. Collaboration between LIAT, School of Science and NCFM secured large-scale projects from both national and European funding sources, notably in agri-robotics, where UoL’s management identified a possible strategic opportunity: ‘when you think about the alignment with the regional need and the agricultural sector, and our understanding of where the technological maturity is, we could see agro-robotics would become a bigger thing’.

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4.7  Lincoln Technology Hubs (Lincoln) Lincolnshire County Council (LCC) has used European Regional Development Funds (ERDF) to deliver business support programmes: one such initiative sought to encourage local SMEs to apply cutting edge technology by showcasing modern technology in ‘Digital Hubs’ located throughout Lincolnshire. These would demonstrate how modern technology, for example, motion capture cameras, could be applied in manufacturing processes, such as fault detection in production lines. As LCC lacked capacity to operate the equipment and hubs; they were contracted to third parties, with one being located at the University of Lincoln. University personnel contacted LCC during LCC’s search for partners, suggesting that UoL could host a hub: I think I submitted a proposal to them to say what kind of equipment we’d want and what kind of support we would offer companies in return for that equipment, in return for the council investing in us.

There were originally five hubs across Lincolnshire, but a review saw this reduced to three as not all hubs were performing equally well: the UoL hub was perceived as running smoothly having engaged with more businesses than expected. One LCC interviewees noted: ‘the university uses the hub in a more advanced way I would suggest, tending to use it in a more in-depth-way with businesses looking for technological support’. The problem for the university was in persuading academics to engage with the project as the funds only cover capital investment, the UoL interview noting: ‘I have to work sometimes on some goodwill and I have to do quite a bit of persuading to help to get people engaged with this’. However, the collaboration through UoL Digital hub has been beneficial for all parties: it has generated PhD research projects and long-term knowledge transfer partnerships with regional partners.

4.8  Smart City (Twente) In Twente, the municipality of Enschede has adopted the smart city concept in the hope of stimulating the creation of new knowledge resources, attracting funding and promoting international cooperation. Several initiatives have emerged, led both by the municipality and other major regional institutions. The Smart City Enschede project was started in 2017 by the municipality, involving companies, residents and knowledge institutions, proposing Enschede as ‘a city where entrepreneurs can test and demonstrate their new concepts, products and services in an open field lab’ (Novel-T, 2019). Simultaneously, the University of Twente (UT) launched its own Smart City Initiative, in close cooperation with Enschede’s municipality and, later, with the province of Overijssel. Despite UT’s initiative being predominantly focused on internally coordinating strategic interdepartmental research and education activities and funding attraction on smart city topics, these two initiatives intersected to

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generate projects involving both UT and the municipality. The UT’s Smart Campus project sought to create a living lab for advanced technologies involving other institutes and local companies. There has also been a focus on involving civil society actors in these partnerships, with one project addressing flooding in a city district using citizens to self-measure and report local groundwater levels. These initiatives were relatively small and lacked longer-term, deeper impacts, in part because of financial pressures. One interviewee noted: ‘Despite smart city being very important, there is hardly capacity or money to really make it successful’. Therefore, albeit a strategic focus area of UT, smart city is not a priority area within the regional strategy, hindering its development and upscaling.

4.9  UT’s PDEngs (Twente) The University of Twente (UT) created a professional doctorate in engineering (PDEng) to raise local skill levels through a practically oriented training programme targeting the needs of industry partners, supported by the Cluster Smart Industry East Netherlands project partly funded through European Regional Development Funds (ERDF). There were lengthy discussions with local stakeholders on smart industries and manufacturing, with UT staff preparing an ERDF bid proposing to transfer scientific knowledge on smart industries to local SMES via 18 individual research projects. Another project motivation was identifying mechanisms to use a long-term ongoing training programme to bring together different regional actors more closely together, particularly business partners. The ERDF subsidy cover half the training costs paid by companies, although most PDEng candidates are university employees because that is most cost-effective for the companies. Because firms had no previous experience in accessing ERDF programmes or PDEngs, thus the whole process seemed rather daunting to firms, slowing their recruitment onto the programme, despite the university employing that recruitment to a third party. To facilitate this, regional funds paid for the university to employ PDEng candidates to work on projects of local relevance where there are no identified funding companies, thereby contributing to raising high-level skills in the field of smart industries and manufacturing.

5  Discussion In this paper, we are asking the question of ‘which roles do universities play in sparse environments in building up triple helix relationships that stimulate regional innovation processes?’. We are specifically interested in the ways in which universities become involved in projects that have wider benefits other than being purely bilateral knowledge transfer activities. Rather, the focus is on the sharing of knowledge assets that also help other companies to access innovation resources. Although

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universities are not necessarily interested in generating a profit from their activities, collaborative innovation must nevertheless make sense from their own perspective, and they must derive advantages from it. It is clear from these examples that in regions with sparse innovation environments, there are challenges for universities in participating in these collective activities. In the nine examples presented above, universities have had to play their regional roles in rather different ways to address these issues and ensure that they can benefit from undertaking those activities.

5.1  U  niversities’ Roles in Stimulating Triple Helix Collaboration in Sparse Innovation Environments One of the main issues identified was that, where universities were interested in stimulating new industries and adoption of new technologies, there were not always regional partners capable of absorbing this knowledge to create new industries and improve competitiveness. What emerged in the examples was, in the case of a mismatch or tension between universities and firms, that the role for government was to help foster interaction by attuning interests and objectives for greater potential. This can be clearly observed in the case of the LLL initiative in Aveiro, where there were both knowledge assets in the university and a set of lighting firms. The intense competition between the companies and within their markets meant that there were no attractive propositions for the university to engage with individual companies, but the LLL initiative created a set of activities, often further subsidised, which helped the university and companies to build up their linkages. We here see one possible tertius gaudens mechanism, namely, purposeful mobilising actors’ voices and aligning different stakeholders through networking activities to create links for further collaboration and even pilot projects. A second issue that arises here is that universities in more peripheral areas sometimes face a rather marginal existence. Therefore, external collaboration and societal contributions are regarded internally as a form of existential risk: a badly loss-making collaboration could potentially threaten the continuity of the HEI activities. In the case of rural campuses, support and demand from government can help stimulate the university to prioritise—or value—engaging with regional industrial partners. In Pori’s case, local authorities provide substantial financial aid to the UC-Pori campus, which partly steered researchers towards bilateral interaction between local industries and public sector actors. In these collaborations, UC-Pori sought to develop pragmatic solutions to other parties’ problems, alongside seeking external funding to support those activities. In this case, it is the university that the government partners must cajole to undertake regional engagement, again with the same potential results of building up incidental relationships some of which then concatenate into more long-lived and sustainable regional innovation activities. In the case of Pori, we also denote the exercise of agency by researchers, rather than institutional leadership. This second tertius gaudens mechanism could be

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considered as government enrolling university capacities to persuade university leaders to embrace engagement more systematically. A third issue arose in the lack of well-expressed regional demands from partners for knowledge resources, making it hard for government to steer those activities strategically. What we see in both the cases of the Matchmaking Scheme and the rural campuses of UoL is that the universities undertook efforts to make their offer clearer to firms. Part of this involved better coordinating their internal knowledge resources, such as linking allied sectors such as manufacturing and computer science to food technology—as in the UoL case. But this also involved creating linkages outwards, from the university to business contacts, to create pathways by which potentially interested business partners would be made aware—by matchmakers—of the existence of these concrete pathways into the university. In this case, the universities’ agency helped resolve tensions between government and business, where there were no instruments that government could use to steer firms towards collective behaviours. In the Matchmaking Scheme, there was even the explicit involvement of matchmakers from the local municipalities to stimulate collective innovation activities. This third mechanism is the activity by the university to mobilise pathways to business users that then allowed government to steer policy to better aid businesses. Another variety of this mechanism was evident where universities helped articulate the needs of sophisticated industrial sectors to government, encouraging government to use their strategic tools and resources to better support those sectors. Three examples showed universities and businesses working together to create a dynamic set of innovation activities, with these sectors then becoming adopted by regional governance partners as priority sectors. UoL’s rural campuses helped identify a high-technology future for the agricultural sector by linking it to automation and company science technologies; UC-Pori used its links to local healthcare providers to mobilise an open-access cluster of robot developers which reinforce robotics’ role as a strategic priority sector within the region. In the case of the LLL in Aveiro, the successful ‘triple helix’ collaboration—although initiated by the Mayor of Águeda—was able to win national recognition and become distinguished at the regional level. This also applies to the National Centre for Food Manufacturing located in the Holbeach campus (UoL), successfully bringing together university knowledge and local businesses through strategic collaborations, whilst mobilising national and international companies. The fourth mechanism is therefore that universities and firms work together to win external resources, in this case often European Structural Funds, that represent a recognition of those sectors’ innovative potential and which then see them becoming stronger in regional strategic agendas. A final mechanism is in the role that universities can play in providing a sense of continuity to partners and provide an ongoing search and matching facility for complementarities between partners. In a sparse environment where resources are difficult to access and develop, the potential to build the concentration of certain capacities by bringing actors together whose assets can complement the needs of the others is an important step to systematising, potentiating and making innovation processes more effective. This is evident in the Aveiro cases of RIC and LLL as well

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as the case of the NSBD, where knowledge from the university, administrative and financial resources from the municipality and needs, ideas, contacts and experiences from businesses and citizens combined to originate wider benefits. The third party, as can be the university or the municipality in these cases, creates a kind of system of deferred exchange, i.e. providing assets without expecting an immediate return on investment. Thus, whilst complementarities can imply a mutually beneficial transaction, particularly in the case of sparse environments, there seems to be a need to have a stakeholder that can envision long-term effects and generate the effort to fulfil that potential. Whilst creative turnover was relatively weak in the RIC case, possibly characteristic of the element of sparseness in such environments, the capacity to generate a degree of permanence of value is thus desired in the tertius gaudens.

5.2  K  ey Barriers to Constructive Triple Helix Relationships in Sparse Innovation Environments The cases also provide some interesting insights into some of the issues that universities face in functioning constructively in triple helix partnerships in sparse innovation environments; we here identify four main issues. Firstly, universities are very complex actors and engage in these triple helix partnerships in various ways, as strategic leaders through to a kind of surreptitious individual interaction. Secondly, these elements do not interact in a straightforward way, in that researchers remain important in the delivery of the benefits, and strategic frameworks, on their own, are not enough to align universities towards delivering regional contributions. Thirdly, there is an issue of scale in these triple helix activities, in that it is possible to mobilise small activities, but it is much harder to then build those up into something that has a more general regional benefit. Finally, these change processes are extremely long-term, whilst the short-term benefits are not always evident or can even be costly, so there is the issue of who can persuade universities to engage for persistent regional good. It is not clear to us whether these problems are a function of the sparseness, for example, that the issue of the complexity of universities as actors is less material in denser innovation environments where there are more actors in general. But nevertheless, they seem to serve to constrain the contributions the universities can make to these dynamic forward-moving partnerships. The first issue regarding universities’ organisational complexity cements that the activities that support the triple helix development do not necessarily always originate at the leadership level of the university, nor is it that the university leaders lead in solving tensions in triple helix relationships. These issues were observed in the case of UC-Pori, where the roles of the ‘honest broker’ were played by researchers and not the university as an institution. This means that universities lack a single set of interests and goals and, in turn, can undermine developing relationships with other actors through this process of attuning divergent interests. Diverse projects in North Denmark were dependent on the network of matchmakers. However,

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university (and student) matchmakers were restricted in their capacity to connect external partners to their own networks. What they could not always provide was an access to ‘university networks’ more generally, because they negotiated their participation based on their immediate contacts’ interests—interests that were not necessarily those of other academics elsewhere in the university. Likewise, the PDEng programme was designed by a single individual within the UT, and although it could have potentially served to create engaged studentships across the university, its alignment to those particular university interests hindered its diffusion across the institution. The second issue is that there are constructive relationships between different elements of universities allowing support to be demonstrated for regional activities, but these are not always available when regional partners demand them. With the case of UoL’s technology hub, a highly committed individual can autonomously initiate projects with potential long-term effects, but it is not always possible for universities to align their strategic, infrastructure and academic interests in all potential opportunities. University managers may resist engagement—as an existential risk—or prioritise other areas, such as teaching or research quality, and unless engagement contributes to those, engagement cannot achieve an internal institutional traction. University managers are also far more exposed to the exigencies of other kinds of policy-making, so although the matchmakers in Aalborg were generally satisfied with the role of the scheme, a change at the national level meant that it was necessary to reconfigure the whole scheme internally. This leads to the third problem, which is that it is not simple to upscale from the basis of individual successful projects in the university to a situation where the university contributes more generally constructively to regional collaborative projects. The PDEng addressed one particular long-standing problem that firms and government had been unable to address: that of high-level skills for smart industries and manufacturing. But, despite creating a new accreditation structure, it was difficult to use that PDEng mechanism to create new pathways for all regional partners to access applied high-level skills within the university. One approach noted here is the creation of dedicated strategic spaces, such as Riseholme and Holbeach campuses, the LLL in Aveiro or the Lincolnshire Technology hub, in which universities are committed to invest in these sites that have a wider regional benefit. But this simply promotes a small activity to become strategically important by increasing the dependence of the university on that activity. It does not find ways to upscale and make more open-facing the universities’ knowledge activities that could potentially create regional benefit. The final issue relates to both the preceding issue of upscaling as well as the role universities may play in providing a long-term source of stability for complementarities in innovation actors and resources. Whilst small projects may have a very clear cost-benefit logic for universities at the individual level, universities, as much as other actors, may find it difficult to see a profitable way to stimulate engagement more strategically in the present, in order to produce longer-term regional benefits that will ultimately strengthen the university. Universities face urgent pressures on their resources and may therefore lack the freedom to systematically prioritise

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regional collaboration activities except in those conditions where they are organised as these stable, economically sustainable projects. This risks universities overlooking the informal interactions that their employees have with other triple helix actors and hinder concatenating them to achieve the upscaling.

6  Conclusion This chapter has asked the question of ‘what roles do universities play in sparse environments in building up triple helix relationships stimulating regional innovation processes?’. We have traced out a set of triple helix partnerships and relationships in five different sparse regional innovation environments and are able to identify the ways in which universities might constructively contribute to improving regional innovation environments. In all these different kinds of relationships, universities and local authorities increase collaboration with the private sector, but the changes emerge through a complex ‘spiral’ model where both internal and external dynamics of the parties influence one another (Rodrigues & Melo, 2013). In these cases—where there is not a ‘natural’ critical mass of interaction as a consequence of this sparseness of interaction—existing connections may slowly build sustainable mechanisms to improve the density of the innovation environment. However, nurturing these partnerships into regional success stories requires a lot of work from all parties (Wilgaard Larsen, 2017), as they tend to be fragile and dependent upon the present support environment (Åkerstrøm Andersen, 2008). A challenge for universities is in linking informal, functional relationships to more formal, strategic relationships in ways that allow universities to maximise their stability and minimise their exposure to volatility. Regional partners can play different kinds of roles to encourage universities to undertake those internal integration activities that can help with the upscaling of triple helix activities to drive these longer-term processes of regional shift. Government can play a regional leadership role, encouraging university leaders to acknowledge their academics’ research strength; regional firms can create collectivities to engage with academics to build up a critical mass of interaction activities. In some cases, where necessary, governmental partners may even directly subsidise university leaderships, so they permit their academics to take the risk and create regional contributions responding to business needs. Although universities may have complex internal dynamics, our paper suggests that the tertius gaudens principle may apply to these tensions within the university, with external partners helping university internal actors to resolve their tensions and to align strategic priorities with the activities being delivered by their knowledge workers. We acknowledge that this is a relatively small study of five universities in sparse regions, using research that has been repurposed from other studies to provide a retrospective comparative dimension. This constraint demands a degree of modesty in the claims that we make, and we were unable to claim that the repertoires that we find universities playing are universally present or represent a best practice for

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universities seeking to maximise their triple helix contributions. Concomitantly, we note that the study provides a nuancing of the original model—that of universities playing a tertius gaudens role with respect to government and industry actors to facilitate developing collective regional innovation assets. There is a range of different repertoires and barriers to be observed here. In some cases, the role of the university is as one of the partners who become trapped through tensions with another, and it is the third partner that plays the honest broker role. In other examples there is more of an orchestration, as solving one problem between partners leads to a development and new tensions between different partners, with the necessary roles shifting as the innovation environment becomes denser. And it is this modified innovation model that is our contribution regarding the understanding of triple helix relationships in regional innovation contexts, as a diverse and dynamic process between actors with diverse internal and external interests. This issue of the role of internal diversity in shaping triple helix dynamics is not something currently addressed in the literature, and we contend that more reflection is needed to ensure that triple helix approaches retain their analytic salience and applicability to understanding contemporary regional innovation-based economic development processes.

References Åkerstrøm Andersen, N. (2008). Partnerships: Machines of possibility. Bristol: Policy Press. Benneworth, P. (2007). Leading innovation: Building effective regional coalitions for innovation. Research Report from the National Endowment for Science, Technology and the Arts (NESTA). Böhme, K., & Gløersen, E. (2011). Territorial cohesion storylines: Understanding a policy concept. Spatial Foresight Briefing 2011:1. Luxembourg. Retrieved from https://www.spatialforesight.eu/files/spatial_theme/spatial/publications/Brief-2011-1-111025.pdf Eder, J. (2019). Innovation in the periphery: A critical survey and research agenda. International Regional Science Review, 42, 119–146. Etzkowitz, H., & Leydesdorff, L. (2000). The dynamics of innovation: From national systems and “Mode 2” to a triple Helix of university-industry-government relations. Research Policy, 29, 109–123. Fonseca, L. (2019). Designing regional development? Exploring the University of Aveiro’s role in the innovation policy process. Regional Studies, Regional Science, 6, 186–202. Grabher, G. (1993). The weakness of strong ties: The lock-in of regional development in the Ruhr area. In R. Boschma & R. Martin (Eds.), The embedded firm: On the socioeconomics of industrial networks. London: Routledge. Isaksen, A., & Karlsen, J. (2013). Can small regions construct regional advantages? The case of four Norwegian regions. European Urban and Regional Studies, 20, 243–257. Kommune, A. (n.d.). Network for sustainable business development [online]. Nørresundby. Retrieved August 28, 2019 from www.xn%2D%2Dcenterforgrnomstilling-gjc.dk/in-english/ network-for-sustainable-business-development Lagendijk, A., & Oinas, P. (2005). Proximity, external relations, and local economic development. In A. Lagendijk & P. Oinas (Eds.), Proximity, distance, and diversity: Issues on economic interaction and local development. London: Routledge. Leydesdorff, L., & Etzkowitz, H. (1998). The triple helix as a model for innovation studies. Science and Public Policy, 25, 195–203.

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Ministry of education and culture. (2009). Universities Act 558/2009. In: Ministry of education and culture, F. (ed.). Retrieved from https://www.finlex.fi/en/laki/kaannokset/2009/ en20090558_20160644.pdf. Morgan, K., & Nauwelaers, C. (2003). A regional perspective on innovation: From theory to strategy. In K. Morgan & C. Nauwelaers (Eds.), Regional innovation strategies: The challenge for less-favoured regions. London: Routledge. Moulaert, F., & Sekia, F. (2003). Territorial innovation models: A critical survey. Regional Studies, 37, 289–302. Nieth, L. (2019). Understanding the ‘strategic black hole’ in regional innovation coalitions  Reflections from the Twente region, Eastern Netherlands. Regional Studies, Regional Science, 6, 203–216. Nieth, L., & Benneworth, P. (2018). Universities and neo-endogenous peripheral development: Towards a systematic classification (2). In P. Benneworth (Ed.), Universities and regional economic development - Engaging with the periphery. Abingdon: Routledge. Nieth, L., & Benneworth, P. (2019). Regional policy implications of the entrepreneurial university–lessons from the ECIU. In A. D. Daniel, A. Teixeira, & M. Torres Preto (Eds.), Examining the role of entrepreneurial universities in regional development. Normann, R.  H., Johnsen, H.  C. G., Knudsen, J.  P., Vasström, M., & Johnsen, I.  G. (2017). Emergence of regional leadership–a field approach. Regional Studies, 51, 273–284. Novel-T. (2019). Smart city enschede [online]. Retrieved September 19, 2019 from https://novelt. com/en/services/smart-city-enschede/ Rodrigues, C., & Melo, A. I. (2013). The triple Helix model as inspiration for local development policies: An experience-based perspective: The triple helix model and local development in Portugal. International Journal of Urban and Regional Research, 37, 1675–1687. Rodrigues, C., & Teles, F. (2017). The fourth Helix in smart specialisation strategies: The gap between discourse and practice. In S. D. O. Monteiro & E. Carayannis (Eds.), The quadruple innovation Helix Nexus: A smart growth model, quantitative empirical validation and operationalization for OECD countries. New York: Palgrave Macmillan. Salomaa, M. (2019). Third mission and regional context: Assessing universities’ entrepreneurial architecture in rural regions. Regional Studies, Regional Science, 6, 233–249. Salomaa, M., & Charles, D. (2019). The university third mission and the European structural funds in peripheral regions: Insights from Finland. RUNIN Working Paper Series, 07/2019. Tödtling, F., & Trippl, M. (2005). One size fits all? Towards a differentiated regional innovation policy approach. Research Policy, 34, 1203–1219. van Drooge, L., & Spaapen, J. (2017). Evaluation and monitoring of transdisciplinary collaborations. The Journal of Technology Transfer, 1–15. Wilgaard Larsen, P. (2017). Delineating partnerships from other forms of collaboration in regional development planning. International Planning Studies, 22, 242–255. World Bank & Enoll. (2015). Citizen-driven innovation. A guidebook for city mayors and public administrators. In J. Eskelinen, A. García Robles, I. Lindy, J. Marsh, & A. Muente-Kunigami (Eds.). Washington, DC: World Bank & European Network of Living labs (ENoLL).

Between Good Intentions and Enthusiastic Professors: The Missing Middle of University Social Innovation Structures in the Quadruple Helix Paul Benneworth, Jorge Cunha, and Ridvan Cinar

Abstract  This chapter considers the role of universities in stimulating social innovation, and in particular the issue that despite possessing substantive knowledge that might be useful for stimulating social innovation, universities to date have not been widely engaged in social innovation activities in the context of Quadruple Helix developmental models. We explain this in terms of the institutional logics of engaged universities, in which entrepreneurial logics have emerged in recent decades, that frame the desirable forms of university-society engagement in terms of the economic benefits they bring. We ask whether institutional logics could explain this resistance of universities to social innovation. Drawing on two case studies of universities sincerely committed to supporting social innovation, we chart the effects of institutional logics on university-supported social innovation. We observe that there is a “missing middle” between enthusiastic managers and engaged professors, in which four factors serve to undermine social innovation activities becoming strategically important to HEIs. We conclude by noting that this missing middle also serves to segment the operation of Quadruple Helix relationships, thereby undermining university contributions to societal development more generally. Keywords  Universities and social innovation · Universities’ third mission · Institutional logics · Quadruple Helix · Social innovation upscaling · Entrepreneurial universities

P. Benneworth (*) Department of Business Administration, Western Norway University of Applied Sciences, Bergen, Norway Center for Higher Education Policy Studies, University of Twente, Twente, The Netherlands e-mail: [email protected] J. Cunha ALGORITMI Research Center, University of Minho, Guimarães, Portugal R. Cinar Department of Social Political and Territorial Sciences, University of Aveiro, Aveiro, Portugal © Springer Nature Switzerland AG 2020 L. Farinha et al. (eds.), Regional Helix Ecosystems and Sustainable Growth, Studies on Entrepreneurship, Structural Change and Industrial Dynamics, https://doi.org/10.1007/978-3-030-47697-7_3

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1  Introduction There has been a recent trend in Triple Helix studies to extend the original frame of reference to incorporate more classes of actors that have distinctly different dynamics. Although Leydesdorff (2012) posited that there might be any number of additional classes in the helix model, one reading that has achieved a widespread popularity is that of the Quadruple Helix involving societal groups in knowledge creation processes (Rodrigues & Teles, 2017). These groups’ dynamics and logics are very different to government, industries and universities, representing a fourth helix. Their presence can create new opportunities but also new tensions, and the ways in which these relationships play out help define the dynamic potential of the emerging Quadruple Helixes. The Triple Helix approach has been criticised for failing to deal with tensions between partners and the role this plays in driving forwards place-specific developments (cf. Benneworth, Amanatidou, Edwards Schachter, & Gulbrandsen, 2015). In this chapter we contend that any serious Quadruple Helix approach needs to consider these tensions to understand how including social partners affects regional helix processes. We specifically focus on the role played by the Quadruple Helix in stimulating social innovation to solve the Grand Challenges, increasingly articulated in terms of the United Nations Sustainable Development Goals (Beynaghi et al., 2016). We explore the Quadruple Helix dynamics between universities and societal groups, one of the most critical relationships in social innovation. A key process in social innovation is that of upscaling, where a novel concept is implemented in one place, and then transformed to allow it to be implemented in other contexts, allowing what may originate as a local “life hack” to contribute to solving the SDGs at the global scale. Although universities have a huge potential to contribute their knowledge and other assets to social innovation, a recent inventory of social innovation in Europe highlighted how underdeveloped and one-dimensional these contributions were (Howaldt, Kaletka, Schröder, Rehfeld, & Terstriep, 2016). There appear to be barriers that stop them making this contribution and allowing the Quadruple Helix to function efficiently to mobilise these solutions. We explore this failure by focusing on universities’ internal norms, value systems and ways of working, conceptualised as “institutional logics”. An institutional logic frames how the organisation and its members interact with external actors. We ask the overall research question: how far can unproductive QH relationships between universities and social groups be explained in terms of resistant institutional logics of university that fail support the upscaling of social innovation?

We present a conceptual framework detailing social innovation and upscaling and the university institutional logics concept. We explore this framework using data gathered from two universities which have sought to place societal engagement as a core institutional mission and trace the effects of institutional logics on this effort. We identify four effects from that institution logic, discouraging individuals, disconnecting social sciences and humanities researchers (SSHRs),

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instrumentalising SSHRs and undermining an interdisciplinary actionable knowledge base. We propose a framework for understanding institutional logic effects on damping social innovation as a consequence of a “missing middle” between engaged and active researchers and enthusiastic and supportive managers and highlight potential effects this has on segmenting the regional Quadruple Helix.

2  U  niversities Contributions to Social Innovation: An Institutional Logics Approach Social innovation emerged to explain how organisations can shape socio-economic outcomes using technologies to meet needs in an improved way. Social innovation has become part of the contemporary policy rhetoric in terms of the Quadruple Helix, collaborative activities between universities, government, business and societal partners (Leydesdorff, 2012). There is great policy interest in harnessing the Quadruple Helix potential to help deliver socio-economic development goals. One QH relationship that has yet to operate effectively is that between universities and societal partners, which we conceptualise universities’ internal reasoning approaches resisting social innovation as an organisational goal, operating as “institutional logics”.

2.1  Social Innovation as a Quadruple Helix Process In the last decade, the idea of social innovation emerged studying processes by which the empowerment of people and communities allowed important social problems and challenges to be addressed (e.g. social exclusion, climate change, mass urbanisation, migration, rising inequality in income distribution) (Edwards-­ Schachter & Wallace, 2017). The UN Sustainable Development Goals reflect a realisation that a class of societal problem require urgent attention in these decades for humanity to survive into the twenty-second century. Ackoff (1999) styled these Grand Challenges “multidisciplinary messes”, not technological problems amenable to technocratic solutions, but requiring introducing new organisations, behaviours and social systems. Social innovation is one approach researchers used to understand the kinds of innovations necessary to address these challenges without becoming excessively focused upon the development of new technologies. Considerable effort has been devoted to producing definitive definitions of what social innovation is and how it works (e.g. Benneworth & Cunha, 2015; Howaldt & Schwarz, 2010; Mulgan, 2007; Phills, Deiglmeier, & Miller, 2008; Pol & Ville, 2009), as well as to propose a model to explain the process of social innovation (e.g. Benneworth & Cunha, 2015; Mulgan, 2006; Neumeier, 2012; Westley, Patton, & Zimmerman, 2006). However,

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as highlighted by Spila, Echeverría and Unceta (2016, p. 10), social innovation is “part of the emerging subjects in innovation studies, where there is still a lot of work to do: conceptual terms, and the development of process and impact indicators”. Benneworth, Lawton Smith and Bagchi-Sen (2015) note that the hunt for a precise definition has hindered developing understandings of the phenomena described by social innovation. We note that it seeks to describe change not adequately explained by associated technology development processes, but in which social change and agency are substantive elements in the process. The basis of the Quadruple Helix is that social systems and actors also participate in collaborative interactions with other kinds of knowledge actors to shape development trajectories and stimulate innovation. The under theorisation of social innovation alluded to above is also evident in the way that QH notions have been deployed in practice, a set of normative statements that civil society should be involved in innovation processes alongside universities, business and governments that constitute the first three helices, without challenging the essentially technological nature of Triple Helix Innovation. But a more objective perspective on civil society actors would start from the unique nature of each helix in their context, and the ways in which this character shapes the interactions, both positively and negatively. Indeed, the involvement of social actors in innovation processes is not straightforward, not least because social actors often achieve their coherence in response to a struggle against something, and that something is often against actors in other helices, such as the state or business (think of planning authorities or property developers in an anti-gentrification social movement). These social movements may be extremely creative, but they are not necessarily the economic transactional pathways present that allow partners in other helices to contribute productively (Moulaert, Swyngedouw, Martinelli, & Gonzalez, 2010). Academics may find themselves participating in social movements pursuing social innovation goals whether in their capacities as knowledge producers, as responsible academics or as engaged systems. But what there is not is an obvious set of structural relationships which allow the social innovation to move from this original (potentially contested) context and allow other communities facing similar situations to benefit from that innovation.

2.2  The Importance of Upscaling to Social Innovation The critical issue here is then in understanding how a single solution to a social problem in a particular context can move and achieve wider systemic effects. Benneworth and Cunha (2015) argue social innovation achieves a system-level effect when single activities are upscaled, move beyond their original context and are applied in other contexts more systematically. They regard the social innovation process as comprising two loops involving a series of interlinked stages. The first set of stages form the creating loop, giving rise to an innovative idea; and the second set

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of stages form the upscaling loop, where context-specific activities are encoded allowing its implementation in other contexts, broadening the innovation’s effectiveness. The creating loop involves three elements. The first is that after a social problem has been identified, a solution emerges that can solve that problem (idea generation stage). Secondly, it is then necessary to plan implementation, creating a safe space for experiment and investment to put that plan into action (creation of experimental space stage). Thirdly, applying the plan in a single instance provides an opportunity for the social innovator to assess whether the idea is feasible and actually works (demonstrator stage). The upscaling loop involves making the same innovation work in a different context. The first element is once a successful demonstrator is created, it then becomes necessary to decide whether and how the social innovation is to be scaled (decision to expand stage). Secondly come mobilisation activities where supportive structures and “pilot teams” further develop and improve the innovative solution beyond the original context (support coalition stage). Finally, that solution is implemented in new settings, places or circumstances, requiring its transformation (from a small scale) and codification (of the solution) to enable scalability (codification stage). Benneworth and Cunha (2015) note barriers at each stage may hamper upscaling and diffusion of social innovation practices and/or initiatives. We here highlight five general classes of barriers to upscaling, which is also shown in Fig. 1: 1. Limited representativeness (Dijk, de Kraker, & Hommels, 2018, p. 4; European Commission, 2017, p. 39): results developed in a single context are so context-­ specific they cannot be spread more widely. 2. Actors in local political economies have such different power relationships that dynamics achieved between actors in one place are not replicated elsewhere (Bergman, Markusson, Connor, Middlemiss, & Ricci, 2010; Harrison & Laberge, 2002). 3. Social innovation is strongly dependent on tacit knowledge in the absence of extensive technology developments and is difficult to translate into replicable routines (Ambrosini & Bowman, 2008; Spila et al. 2016).

Barriers to upscaling:

• Limited representativeness • Context dependency • Inertia of stakeholders • Tacit dimension / hidden innovation • Resource availability

Social entrepreneurship Agency of social innovation Allows to overcome those barriers

• Social innovator as agent of change • Co-creation and collaborative action • Involvement of stakeholders • Capacity building • Structured form of organization / Social enterprise • transfer of knowledge

Fig. 1  The exercise of agency and the upscaling of social innovations. Source: authors’ own design based on Benneworth and Cunha (2015)

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4. There are not always robust underpinning business models for social innovation activities securing necessary economic investments allowing codification, translation, and enrolment (Bergman et al., 2010). 5. Finally, upscaling of those projects might be hampered by stakeholder resistance, even where there are potential positive benefits, that longstanding traditions embedded in the reasoning of key stakeholders influence current decisions to prevent more radical changes (Dijk et al., 2018).

2.3  Universities, Social Innovation and Institutional Logics Universities are complex organisations formed by different disciplinary groups, subunits and structures that may carry different belief systems and values (Pinheiro, Langa, & Pausits, 2015). Since the American Bayh-Dole Act in 1980, universities mobilised resources to drive technological innovation and commercialise research findings (Popp Berman, 2012), stimulating intensive industry collaboration with firms as universities’ main stakeholders and business engagement, the main mechanism to stimulate regional development. In this period, start-ups, licences, patenting and technology transfer offices became the “rule of the game”; engineering and technology disciplines became the main departments through which universities engaged regionally. Engineering and technology disciplines thus found themselves at the heart of entrepreneurial university discussions and adapted to this changing environment more rapidly and intensively than the rest, thereby becoming powerful in many institutions. The role of social sciences in the entrepreneurial university model was downplayed, whilst humanities were largely forgotten. Since 2010 universities are expected to go beyond traditional third mission contributions, address grand societal challenges and turn civic (Cinar, 2019), demanding collaboration with different partners (civil society organisations, different segments of population) and different engagement behaviours. Institutional logics represent belief and value systems as well as habituated behaviours embedded in institutions (Thornton & Ocasio, 2008). What is collectively considered as an appropriate and legitimate organisational practice guides individual behaviours; a single organisation may have one dominant institutional logic (Scott, 2008) or multiple institutional logics that coexist (Reay & Hinings, 2009). An institutional logic approach has proven useful for explaining university behaviour (Thornton & Ocasio, 2008). Given the differences between technological and social innovation, and the way that technological innovation became incorporated into universities by creating new behaviours, routines and structures, one explanation for the relatively weak involvement of universities in social innovation might be that universities’ institutional logics hinder upscaling of social innovations.

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3  Methodology and Introduction to Case Studies In this chapter, we are concerned with understanding the effects that institutional logics have on the capacities that universities have to facilitate the upscaling of social innovation. To address this, we utilise an exploratory qualitative research design with multiple case studies. We select two universities, namely, the University of Twente (UT) in the Netherlands and University of Aveiro (UA) in Portugal. We present findings from these two universities with a history of being entrepreneurial and engaged universities and with whereby innovation has been at the heart of their regional missions. Both are a member of a the European Consortium of Innovative Universities (ECIU), which recently won recognition from the European Commission as one of 17 Consortia funded under the “European University initiative”.1 More recently, they have articulated the common desire to stimulate social innovations in both universities and creating regional social benefits. We are not seeking to criticise these two institutions; rather their genuine efforts to stimulate external engagement and social innovation provide a rather fertile laboratory where it is possible to meaningfully explore the ways that institutional logics play out. Both universities are relatively young technical universities in peripheral regions, with faculties in areas such as engineering, sciences and medicine focused on solving problems. We interviewed 36 (19 in UT and 17 in UA) key actors for social innovation: university staff involved in social innovation projects and senior managers such as current and previous rectors, vice-rectors, along with academics in various disciplines. We asked about their experience of engaging with social innovations, individual and institutional challenges these actors face and why some social innovation initiatives did not continue to achieve longer-term sustainability. The interviews were later transcribed and analysed. The analysis first foregrounds the dominant institutional logics regarding social innovation in both universities in Chapter “Why Do Publicly Funded Firms Find the University More Useful to Innovate Than Others? Can We Accomplish the RIS3 Target?” in the form of relatively short sketches of these industrial logics (further detail on these cases is presented in Cinar, 2019; Cinar & Benneworth, 2020). We then reflect on challenges in upscaling of social innovations, being careful in imposing the institutional logic concept. We are careful not to ascribe direct agency to institutional logics; rather we use them as a leitmotif of common directionality allowing various outcomes integrated indicating how universities have upscaled social innovations.

 https://ec.europa.eu/commission/presscorner/detail/en/IP_19_3389 accessed 1 December 2019

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4  The Institutional Logics of Twente and Aveiro This chapter explores two institutions that have always been strongly oriented towards society, that orientation evolving through time reflecting changing circumstances and evolving higher education policy frameworks. The University of Twente (hereafter UT) was established in 1961 with a mission to remedy the declining textile industry in its geographical vicinity, namely, in the cities of Enschede and Hengelo in the east of the Netherlands. UA was established in 1973 with a technical orientation and mission to revive declining industrial structure in Aveiro Region and tackle socioeconomic and environmental problems such as contamination in Aveiro Lagoon. Both universities have had recent success with social innovation, at the strategic level, and also developed substantive infrastructures to support its promotion, such as the DesignLab at Twente or the ID+ DESIS Lab in Aveiro, both of which seek to promote design thinking as a way of mobilising solutions to societal problems drawing on university research. UT was created as the third Dutch technical university with both a national mission to create a skilled workforce for post-war construction and growth as well as the specific regional mission outlined above. UT has a long history in regional engagement through mobilising its technical capabilities and later via technological innovation and industry collaboration. From the early 1980s, its influential rector Professor Harry van der Kroonenberg envisioned an entrepreneurial future for the university, and UT intensified its industry collaboration, start-ups and spin-offs creation, technology transfer and research commercialisation. Since then, contributing to regional development has become associated with technological innovation, and most institutional resources have been provided for technological innovation. In 2010, UT changed its slogan from the “entrepreneurial university” to “high-tech human touch” reflecting this increasing technological innovation focus. In 2018, the University Board adopted a policy note on being the socially responsible university, with five main research themes, support for creative students, a summer school, an activity programme for secondary school pupils and the Living Smart Campus programme bringing different groups onto the university campus (Cunha & Benneworth, 2020). The UA was created at a time of an expansion of Portuguese higher education created part of a cohort of higher education institutions and universities to specifically assist democratising Portuguese society, supporting this by creating a technologically skilled workforce. This specific mission has casted several roles for engineering and technological disciplines such as environmental engineering, material engineering and chemistry. UA’s understanding of regional contribution was mainly shaped around traditional third mission practices such as contract research, industrial collaboration, technology transfer, encouraging start-ups and spin-offs. Although collaboration with municipalities increased since 2007, this was driven in no small part by the availability of European Structural Funds, and internally much resource was mobilised to intensify firm collaboration and technological innovation. The Department of Communications and Art has been a key player in trying to

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mobilise more social innovation activities within the university. The recent inauguration of the Creative Science Park in 2018 was part of ongoing efforts to expand its physical space to accommodate a higher number of firms alongside hosting community engagement and social innovation activities. These histories have led to an institutional logic that we here characterise as “technological innovation and business engagement” becoming ingrained in both universities. This logic shares a belief system that tackling grand societal challenges is possible with advances in technological innovation and increasing collaboration with businesses. As a result, firms became both universities’ primary external stakeholders with civil society achieving little institutional purchase. This logic emerged in both universities since the 1980s, with the sciences (mostly technological) and engineering disciplines being first in both universities to adhere to this particular logic and have played a key role in its continuation. Social sciences shared some values and practices to some extent and were able to adjust to this logic when they framed the characteristics of their respective subdisciplines to respond the changing environment of “technological innovation and business engagement”. Humanities have had the most difficulty in functioning under this logic, and their adherence has been relatively limited. We now turn to address how this logic of “technological innovation and business engagement” hinders upscaling of social innovations in both universities.

5  I nstitutional Logics and Social Innovation in Twente and Aveiro In this paper we are concerned not to give direct agency to our theoretical constructs, which have been adopted as a way of understanding the tendencies and common directions arising within universities as a result of the organisational structure necessary to hold together these complex organisations. We do not claim that universities’ “institutional logics” have prevented the orientation towards social missions. The message is more nuanced: the ways these technological universities embraced social innovation, and how they are perceived as contributing to solving the Grand Challenges, are shaped, constrained and influenced in various ways by factors that appear to have their origins in this shared institutional logic of “technological innovation and business engagement”. We highlight four areas where this institutional logic appears evident in terms of similar impacts on the universities’ capacities to support upscaling of social innovation from incidental activities into institutionally supported missions. This institutional logic’s headline effect was in creating a “missing middle” in how the two universities engaged with social innovation. Both universities had both grassroots action (by individual researchers) and institutional commitment to social innovation, a range of institutional activities and strategies formally committing the universities to promoting social innovation. Institutional leaders could illustrate

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their strategies when asked by reference to good social innovation projects that had achieved a degree of institutional embedding, such as the Sheltersuit Foundation at UT or three social innovation projects at Aveiro each with their own characteristics. They also pointed to their respective Design Thinking Laboratories (DesignLab and ID+ DESIS Lab). But these different levels did not connect effectively, something that hindered the upscaling of social innovation projects into expandable solutions with coalitions ready to implement elsewhere. We identify this in four common effects. Firstly was that social innovation was dependent upon individuals and their willingness to participate, as an Aveiro researcher noted: The problem is that if a social innovation project requires expertise on a let’s say a specific material and there is only 1 person who developed expertise specifically on this material and he rejects collaborating, then this is a big challenge. You need to find another person from another university etc. (Aveiro)

At the same time, it was also clear that the respective institutional logics provided negative signals to those individuals considering social innovation. The overall calculus made it extremely difficult to find a researcher who was willing to engage with social innovation projects for a sufficiently long time to produce results with the potential to influence the institutional strategic agendas. Even if one social innovation was successful, there was a tendency for it to become framed as being less institutionally valuable, with one researcher at Twente noting: If your social innovation idea has a business dimension and if it is technology related, you will definitely find support here. But if it does not, you will have difficulty in implementing here. I would not say impossible but really difficult. For sure.

The second effect was a technological framing on project composition within the university. Both universities held a general belief that technical knowledge could provide answers to social questions and that social scientists and humanities scholars were not a necessarily condition for a successful social innovation activity. As an interviewee at the culture and language department of Aveiro related: They [other disciplines] think of us as a ‘paper and pencil department’. We are detached from all of this [regional engagement and social innovation]. Disconnected from this discussions.

The net effect was to give social innovation the character of a demonstration project rather than as a social network; the solution of the technical project was then followed by the moving on to address other problems in other projects. Social innovation projects in both institutions did involve social sciences and humanities researchers, but these did not achieve the necessary institution profile to become influential, because their social benefit outcomes were framed as being less important than economic growth and technological progress. The third effect was related to that, which was there was a tendency to involve social sciences and humanities researchers in a rather instrumental way, primarily providing societal acceptance rather than understanding the human dynamics and societal values in the project. In Twente, one researcher reported:

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If technological faculties here even consider working with us, they do so in a very very instrumental fashion by saying we got new technologies and we all the time discover there is societal resistance. Can you come up with the tools to persuade these people?…[O]ur impact on society would be far greater if we did not start with technological knowledge but we start with societal challenges in this region. (quoted in Cinar & Benneworth, 2020)

In Aveiro, where political governance structures are somewhat convoluted, the role of social sciences scholars was often in helping technological project teams negotiate these messy structures and achieve political support for their activities. The final element relates to the characteristics of the necessary actionable knowledge base. Project teams for social innovation formed out of individuals within disciplinary structures who came together and retained their disciplinary orientation, leading to the creation of multidisciplinary knowledge. However, upscaling typically required interdisciplinary knowledge to be created, outside of those specific disciplines. This meant that at the time of upscaling, there was a general feeling amongst all the researchers that the questions to be asked were potentially interesting but not interesting to their specific disciplines and outside their area of expertise. At the very moment that social innovations were reaching a readiness to be upscaled institutionally, with a strong entrepreneur ready to drive that upscaling, they found themselves losing their organisational legitimacy as participating academics regarded those social innovations as being less intrinsically interesting as vehicles for solving scientific problems.

6  Discussion and Conclusion In this paper we have sought to understand how far can unproductive Quadruple Helix relationships between universities and social groups be explained in terms of resistant institutional logics of university that fail to support the upscaling of social innovation. We have done this with an exploratory piece of research in two universities that have made serious efforts to embed social innovation within their institutional mission, and in which there have been some impressive efforts, and interesting infrastructure created to attempt to support social innovation activities. But at the same time, we have identified a “missing middle”, as a result of the two institutional logics, that has four effects, discouraging individuals, disconnecting and instrumentalising social sciences and humanities researchers and undermining the emergence of an interdisciplinary actionable knowledge base. This seems to provide a useful heuristic that explains how university institutional logics might serve to restrict and channel university participation in Quadruple Helix activities, and in this final section, we reflect briefly on the implications that this has for our understanding of regional helix models. Our first observation is that the missing middle segments the social innovation university into senior managers and engaged researchers. Each of these two groups tends to have relationships with different kinds of Quadruple Helix partners: whilst university managers have relationships with policy-makers and with strategically important businesses, it is only

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the researchers that have connections to the civil society partners (the researchers also have connections to businesses). This is illustrated in Fig. 2, which places this missing middle in the context of Quadruple Helix relationships. The overall effect here is to create a two-tier Quadruple Helix, with at its core the strategy-makers, universities, their strategic business partners and policy-makers and much more peripherally civil society actors. Even where managers sincerely desire to make the university a place for upscaling social innovation, there are a set of forces that interconnect and prevent social innovation becoming important to the university, because of the importance of technological innovation relationships with business. We here see a resonance with a higher education literature that points to the idea of “mission overload” in universities (De Boer, Enders, & Leisyte, 2007) when confronted with many parallel goals and targets; this would imply that social innovation is important but not quite enough so to be “strategically important” to the HEI. We acknowledge that this is a short and synthetic book chapter that is seeking to make a relatively large contribution, and we recognise we must be cautious with the claims we make in this volume. However, given the scale of the apparent under-­ involvement of universities in social innovation and their huge capacity to contribute identified by Howaldt et al. (2016), we believe our framework provides a useful heuristic for understanding the weak relationships that form between university-­ civil society pairs in Quadruple Helix relationships. We see here that the “missing middle” effect serves to segment the Triple Helix and undermine the involvement of civil society. On the basis of this model, we might also speculate that this segmentation serves to hold back civil society partners from participating in the strategic core of the Quadruple Helix relationships; this might in turn explain the general

Fig. 2  The missing middle of university social innovation and upscaling. Source: authors’ own design

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difficulties in involving civil society partners in regional strategy processes organised around Quadruple Helix partnerships. This has clear implications for theorising and making policy for the Quadruple Helix, and we contend a much better understanding is required of the segmentation effect if regional helix relationships are to serve territorial development processes that address the societal challenges of the twenty-first century. Acknowledgments  This paper draws on work supported by the European Commission’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie action grant agreement No. 722295, the RUNIN Project. This work was also financed by the Portuguese Foundation for Science and Technology, under Project PTDC/EGE-OGE/31635/2017. Any errors or omissions remain the authors’ responsibility.

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Why Do Publicly Funded Firms Find the University More Useful to Innovate Than Others? Can We Accomplish the RIS3 Target? Joana Costa

Abstract The failure of the deterritorialised innovation policy addressing the regions based on the “one-size-fits-all” policymaking made the Research and Innovation Strategies for Smart Specialisation (RIS3) become the Holy Grail of the European cohesion. This policy strategy is part of a multilevel framework, which encompasses national and regional vectors harmonising transversal strategies and combining different aspects to generate a consistent policy mix. This growth strategy will reinforce the existence of an innovative and knowledge-based society, which aims to raise welfare, promote responsible practices, modernise economic activity and spread prosperity. Sustainable growth will optimise the use of resources, boost the efficiency levels, generate competitiveness and respect the environment. Inclusive growth will promote social and territorial cohesion which is sought after in the convergence policy, which has slowed down the pace after the financial crisis. The development of regional competitive advantages will rely on the establishment of relevant linkages between the Academia and the private institutions in knowledge creation and transfer. In this vein, the University is expected to play a central role, facing important challenges and requiring transformations, mostly in the case of less favoured regions. Productivity raise, construction of comparative advantages, market consolidation and profit maximisation, required to avoid the obsolescence of firms, will rely in the prosecution of innovative activities. Despite being risky, these activities are sought by firms as a source of economic performance increase, being the building blocks of a profit maximisation strategy. The velocity at which innovation occurs will differ among industrial sectors due to their singularities along with other firm structural characteristics, still, those who perform innovative activities are more prone to achieve higher standards of turnover growth and profits. The organisational compeJ. Costa (*) DEGEIT, Universidade de Aveiro, Aveiro, Portugal INESC TEC, Porto, Portugal e-mail: [email protected] © Springer Nature Switzerland AG 2020 L. Farinha et al. (eds.), Regional Helix Ecosystems and Sustainable Growth, Studies on Entrepreneurship, Structural Change and Industrial Dynamics, https://doi.org/10.1007/978-3-030-47697-7_4

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tences concerning human capital, knowledge absorption, accumulation and diffusion will enhance the innovation capabilities, thus generating advantages. In this path, Universities will be determinant as they may leverage the success of the ­entrepreneurial innovativeness throughout the provision of relevant knowledge, productive techniques and methods. Absorbing, transforming and exploiting the general knowledge provided by the University will be the firms’ incumbency which will reflect the speed and the success of the individual’s innovative performance. Considering the reinforced role of the Academia as a knowledge producer and therefore inside the innovation process, the existence of incipient connections with firms will be unbearable. What enables and hinders University-firm linkages is, so far, overlooked in the literature demanding for the comprehensive analysis, in particular the causes of its failure, and the accurate policy mix that overcome the situation is vital for a successful RIS3. The singularities of this policy framework require redirection of the tools and actions to be taken such as incentives, grants, loans and subsidisation strategies. Empirical results shed light to the significant difference observed in the classification of the University as a source of information for innovation between public monies recipients and other firms. Among public funding beneficiaries, the Academia is an important source of knowledge to draw upon; conversely, for the other firms, it seems of poor importance the knowledge conveyed in the contact. In general, firms fail to consider the University as a relevant source of information for innovation, which seems to be incompatible with the establishment of smart specialisation strategies. These unexplored connections, which pledge the success of the present innovation policy, and reinforce the importance of its appraisal to fully understand the determinants of University-firm linkages and its connection to public subsidisation, encompassing the identification of the most effective beneficiaries. The econometric estimations, relying on the CIS, were run considering a panel of firms operating in Portugal, which provides the empirical evidence for a moderate innovation milieu which is poorly done so far as most of the studies focus on innovation leader. The findings reinforce the existence complementarities among policy instruments and highlight that new avenues of research should explore other policy instruments such as open innovation frameworks. Keywords  University-firm linkages · Public funding · RIS3 · Hurdle panel data

1  Introduction Governments and institutions worldwide have in the agenda global, inclusive and sustainable growth. Analysing this problem is vital as it is believed to be the promoter of long-term income generation, improvements in the living standards and a guarantee of a future to the planet. Given the climate and energetic changes faced,

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it is central emphasise the importance of public investments in higher education, research and development and technology to boost responsible innovation, productivity and competitiveness gains. Strengthening the communication channels between Universities and firms is central for the accomplishment of these objectives, and the public policy needs to promote the accurate ecosystem for its development. The success of these projects anchors on the existence of an encouraging political, financial, fiscal and social accommodation of innovative actions. Collaboration among Universities and firms over the last decades has been fertile, generating an important part of the disruptive discoveries; research findings arising from University are introduced to the market by firms or even through licensing or start-ups. Academia plays an ascertained role in knowledge transference to the private sector, reinforcing its centrality in knowledge creation and diffusion (Schartinger, Scjibany, & Gassler, 2001). Detailing the importance of the University in connecting science and technology raises the odds of policy success; as a consequence it is passionately discussed by academics, policymakers and the civil society. Over the last decades, this connection was framed using different theoretical frameworks such as the “National Systems” (Edquist, 1997; Freeman, 1987; Lundvall, 1992; Nelson, 1993), “Mode 2” (Gibbons et  al., 1994), “Regional Innovation Systems” (Cooke, Uranga, & Etxebarria, 1997), the “Triple Helix” (Etzkowitz, Webster, & Healey, 1998), the “Mode 3”, “Quadruple Helix” and the “Quintuple Helix” (Carayannis & Campbell, 2009, 2010), and all of them have underlined the increasing centrality of the Academia in the innovation process. Nowadays, five major players are included in the framework: university, government, industry, civil society and ecosystem. Even though, the common ground of the frameworks is considering that players are continuously interacting, feeding bidirectional communication and contributing to knowledge creation and diffusion, with Universities being the ignition sources of knowledge production. Complex problems such as inclusive growth, climate emergency and environmental sustainability require multidimensional commitments and political challenges therefore requiring the collaboration between Universities and industry as single institutions cannot deliver solutions or regulations at the pace required. The linkage between Universities and firms should be mediated by other institutions and sets of parallel interactions, to enhance the effects of knowledge production. So, the overall effect of knowledge production arising from Universities on industrial innovation is hardly quantified (Salter & Martin, 2001). Innovation is increasingly related to the ability to absorb external information, knowledge and technologies; therefore, modern innovative strategies rely in cooperative R&D inside and outside the value chain (Arora et  al., 2001; Fritsch and Lukas, 2001; Tether, 2002; Veugelers, 1997). Throughout the innovation process, governments usually intervene to overcome constraints; still, firms are expected to innovate and vowed accountability for the support received. Public funding will be determinant for technology development, although requirements to access grant

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monies can impact the ability of nascent firms to grow and unlock subsequent funding (Perez, 2013). On the one hand, Universities and firms can leverage the gains of knowledge transfer by means of technology transfer offices which may help firms to scale up their projects at a higher pace, by providing the know-how about the access to funding; on the other hand, more knowledge flows need to be enabled. Enhancing these communication channels requires a holistic view about the entire innovation ecosystem (Debackere & Veugelers, 2005). The RIS3 gained momentum in a place-based economic shift relying on three pillars: smart, sustainable and inclusive growth. Thus, empirical evidence provided by the CIS data depicts a scenario in which Universities are a frail source of knowledge for innovative activities according to European firms (Laursen & Salter, 2004); this may demand adjustments to be done concerning policy design to meet the requirements of this collaborative policy framework. Smart growth will promote a knowledge-based economy, which is expected to transversally modernise the economic activities, improving welfare. Sustainable growth will increase the efficiency levels in the use of resources, generate competitive advantages and preserve the environment. Inclusive growth will promote social and territorial cohesion, vastly promoting the convergence which is believed be slowing down in the recent years. This policy package is a multilayer strategy for countries interconnecting national and regional components and coordinating transversal strategies and different aspects to generate a cohesive policy mix. The construction of the RIS3 relies on the contribution of the elements of the helix, which means that firms, Academia, governance of the civil society and the ecosystem being responsible for the creation and implementation of the framework thus promoting knowledge creation and diffusion. Firstly, it relies on the identification and exploitation of regional capabilities being the foundation of knowledge creation and diffusion, the emergence of synergies consequently adding value to the productive chains. Secondly it develops measures and policy instruments to promote accurate public intervention. The Portuguese strategy comprises 5 structural targets and 5 thematic axes comprising 15 strategically smart priorities; in doing so, the authorities expect to meet the requests of the RIS3 and successfully develop the framework in the country (European Commission, 2014). A systematic empirical analysis of firm’s perception of Universities as sources of information for innovation activities is critical to bring a broad comprehension about new alliances between industry and Academia to be made in the near future. Economic downturns reduce financial availability inside organisations; when facing constraints, activities with uncertain outcomes such as innovation will be withdrawn. Therefore, smarter sources of information for innovation will determine the continuity of the innovation cycles. Moving outwards seems to be the most accurate solution for firms, even though requiring organisational transformations. Important research carried in the past (e.g. Abramovsky, Harrison, & Simpson, 2007; Cassiman, Veugelers, & Zuniga, 2008; Grimpe & Hussinger, 2013; Laursen & Salter, 2004) when analysing University-firm linkages point towards the

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importance of the source independent of the complexity of the connection. In this vein, addressing the determinants of University-firm connections will provide policymakers accurate information about the enabling and hindering factors to establish the RIS3 foundations. Therefore, the target of the chapter is twofold: firstly identify which firms in the industrial fabric find use in the Academia as a relevant source of knowledge and secondly to connect the existing policy instruments (public funding) with the helix elements and evidence complementarities among them. It is of worth mentioning that the econometric specification chosen is a Random Effects Hurdle Panel, which combines a Random Effects binomial logit and a Random Effects Ordered Logit in a time span, permitting a more precise understanding of both whether or not using the University combined with the appraisal of its importance as a source of knowledge. This model allows addressing the determinants of the connection and in particular the quantification of the role of public funding as an enhancer of this linkage. The remainder of the chapter is structured as follows: Sect. 2 includes a literature background on the role of Universities as knowledge producers for innovative activities and the role of the Helix along the innovative process. Section 3 will discuss methodological topics, model construction and the validation of main hypothesis in test. Section 4 presents the econometric estimation and the result discussion. Section 5 concludes and designs policy recommendations.

2  Literature Background 2.1  U  niversities as Knowledge Producers and Its Connection to Firms Universities exist to produce and diffuse knowledge in the broadest sense of the term, for the particular characteristics of the good they produce; they cannot rely on private initiative. Private agents behave as maximisers, channelling investment to profitable endeavours. Basic knowledge is not interesting to private initiatives as in the short term it will not be profitable. However, if researchers only focus on applied research, granting the money value of their efforts to their sponsors, no disruptive findings will be achieved with devastating consequences in terms of the technological progress, and efficiency levels will be drawn in stagnation. Some third party needs to align the private and the societal interests promoting sustainable development. During the second half of the twentieth century, worldwide governance channelled efforts to reinforce their NSIs under the strong belief that the interaction among these elements would create “engines of growth” (OECD, 1996). Dynamic capabilities consist on the ability to build up, combine and reinvent internal and external competences to move forwards in changing environments (Mowery & Sampat, 2004). Firms rely on external sources to overcome their

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weaknesses thus absorbing the emergent technological opportunities (Cohen & Levinthal, 1990; Rosenkopf & Nerkar, 2001). Relying upon external sources of information and the success of the innovative processes that are positively correlated (MacPherson, 1997), higher number of interactions and diversity of players will raise the propensity to successfully innovate (Laursen & Salter, 2004). The acquisition of external knowledge may assume different shapes: human capital (graduates, scientists or engineers) (Zucker, 1998), strategic alliances (Mowery, Oxley, & Silverman, 1996; Rosenkopf & Almeida, 2003) or informal networks (Liebeskind, Oliver, Zucker, & Brewer, 1996; Rosenkopf & Tushman, 1998). The firms’ competitive advantage will rely on its dynamic capabilities which comprise a dynamic understanding of the organisational processes and the factors of production, which is assumed to be “path dependent”. The impact of the past in present decisions is in one hand desirable as it can create virtuous cycles, but in the other, it may deter changes due to its cost and complexity (Teece et al., 1997). Launching connections inside and outside the productive chain generates relevant flows of knowledge which will feed the innovation process (e.g. suppliers, clients, competitors; moreover, Universities, public and private laboratories and government agencies also bring important apports through basic knowledge (Jewkes & Stillerman, 1958). Product design and development will be reinforced with supplier collaboration (Wasti & Liker, 1999; Nellore & Balachandra, 2001). New product ideas will arise from the civil society, in a consumer-driven model (Gemunden, Heydebreck, & Herden, 1992). Conferences, firm associations and publications also provide relevant contributions and share common experiences (Cohen, Nelson, & Walsh, 2002; Mansfield, 1991). Innovative initiatives developed by R&D intensive firms will rely on internal sources, consumers, top management and marketing. Furthermore, of establishing strong ties with formal sources of knowledge such as Universities, public and private R&D laboratories, they will raise the pace and the propensity to develop successful innovations (Deiaco, 1992; Gemunden et al., 1992). The empirical analysis carried will consider the sources of information for innovative activities organised in a similar way to the CIS framework.

2.2  University-Firm Collaboration Universities play a major role in knowledge production and diffusion which has been growing over time. The different innovation frameworks evidence this role underlying the importance in terms of teaching, researching and more recently enterprising (e.g. Etzkowitz & Leydesdorff, 1997; Freeman, 1987; Gibbons et al., 1994; Lundvall, 2007). To some extent, all firms are technology-based (Freeman, 1982), so knowledge-­ intensive industries address the generation, dissemination and the application of some technology. As technological requirements of firms differ, some sectors

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classified as being low-tech, mid-tech or high-tech (e.g. Pavitt, 1984), which reinforces the role of knowledge creation on industrial innovation (Mansfield & Lee, 1996). The NSI, firstly described by Freeman (1987), placed Academia in a central role, explaining the interaction among institutions in both the public and the private sector as generating, upgrading and diffusing new technologies. Later, Lundvall (1992) proposed a framework based in interactions in the production and diffusion of knowledge with economic value. The efficiency of the connection will determine the innovative performance; and the institutions and their competencies will foster the technological progress (Nelson & Rosenberg, 1993; Patel & Pavitt, 1994). Inside the system of innovation, there are different institutions which develop and diffuse new technologies, produced with or without interaction; inside these networks governments create and implement actions to heighten innovation. Therefore, the interconnectedness of the institutions to develop, use and transfer the existing knowledge will reshape new technologies (Metcalfe, 1995). These frameworks address innovation as a systemic, interactive and evolutionary process, whereby new products and processes are brought into economic and social use throughout the activities of the network and mediated by institutions and policies (Hall, Yoganand, Sulaiman, & Clark, 2003).

2.3  Public Policy to Promote Innovation The outcome of the innovative process is, by nature, uncertain. Additionally it is time consuming, cumulative and collective. Given the specific characteristics mentioned, very few private investors will afford its expenses, bear high risks and wait the long run and cumulativeness required. Moreover, the non-excludability of knowledge as an asset demands for the involvement of public and private sources (Mazzucato, 2013). Normally, the theoretical frameworks propose government interventions when market failures are identified; in the case of basic research, the intervention becomes evident due to non-rivalry, non-excludability and non-­ diminishability, added to positive externalities underlying the knowledge generation. Therefore, technological revolutions require public funding. On the contrary, applied research seems to be attractive enough to grasp the attention of private investors; still, in most cases it is too risky, requiring the attention of complementary funding carried by different institutions. Hereby, the public sector is central in the promotion of basic and applied research and in the provision of early-stage high-­ risk finance to innovative companies willing to pursue investments (Perez, 2013). Given the maximisation strategies of the private agents, with no public funding, it would be impossible to pursue research in any field that could not prove itself useful to profit generation. Under this paradigm, most of the research in humanities and social sciences would be neglected as their focus relies in basic research, which is to some extent non-profit. Appraising the University as a corporation, focusing in the intrinsic values of the commodities produced will withdraw the immaterial

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research prioritising in problem-solving approaches, this path may undermine the ethical freedom of the academy and pledge the disruptive solutions that randomly occur during the creative processes. Identifying what source of finance is relevant to support innovative activities is urgent, given the role of innovation in the present political debate. Issues such as climatic urgency, sustainability and energetic crisis require a societal change; and the innovation policy can be either an enhancer or a detractor. Policymakers must have the full awareness about the importance of the attribution of funds in the promotion of knowledge creation and diffusion (European Commission, 2011). Given the urgent needs to change the technological paradigm towards the sustainability mindset, the circular economy concepts and the global waste reduction, public funds need to finance the new highly risky ventures, which are capital intensive, to speed up the pace of the technological progress (Mazzucato & Semieniuk, 2016). At present, there is a great variety of alternatives to increase the availability of finance for innovation and improving the access conditions, the regulation while maintaining an operational system in which property rights taken for guaranteed, accurate contracts, bankruptcy processes are agile will reinforce the role of institutions. In what concerns the availability of finance, the provision of funds to innovative firms can be performed either via grants or public venture capital or by means of financial vehicles such as the funds-to-funds model, loan guarantee schemes or provision of tax credits to early-stage investors. Besides, providing networks of business angels, running investment readiness programs for entrepreneurs and investors, creating accelerators and incubators or establishing credit mediation services will enhance the development of innovative actions. A very good understanding of the incentives of bureaucrats, politicians, financial intermediaries and innovative firms is crucial when designing new schemes as otherwise the public monies can be misused. The following table (Table 1) describes the importance attributed to each innovation source (through a multinomial scale of response) by the firms; the three columns presented comprise the responses of the same 1099 firms along the three biennia included in the research (CIS 6, CIS 8 and the CIS 10)1.

2.4  Research Hypotheses The research aims to address whether or not firms use the Universities as sources of knowledge for the innovation process and its importance; moreover, the model allows the identification of the structural characteristics enabling the establishment 1  The period of analysis is 2004–2010 as in subsequent editions of the CIS the question addressing the linkage to other sources of innovation did change or was completely removed. Over this period the question and the measurement were exactly the same allowing precise comparisons.

n % Suppliers n % Clients n % Competitors n % Consultants and n Private labs % Universities n % Government labs n % Conferences n % Scientific n journals % Firm associations n %

Low or very low 28 2.5 88 8.0 111 10.1 178 16.2 151 13.7 132 12.0 144 13.1 180 16.4 190 17.3 223 20.3 High and Medium very high 206 434 18.7 39.5 336 208 30.6 18.9 247 248 22.5 22.6 266 94 24.2 8.6 165 94 15.0 8.6 117 74 10.6 6.7 88 32 8.0 2.9 258 121 23.5 11.0 283 80 25.8 7.3 195 49 17.7 4.5

Source: Author’s computation based on the panel (CIS 6, CIS 8 and CIS 10)

Other sources

Institutional sources

Market sources

Internal sources

Source Inside the firm

Not used 40 3.6 76 6.9 102 9.3 170 15.5 298 27.1 385 35.0 444 40.4 149 13.6 155 14.1 241 21.9

CIS 6

Table 1  Innovation sources and their importance for the observed panel

Not used 47 4.3 62 5.6 93 8.5 180 16.4 258 23.5 367 33.4 443 40.3 166 15.1 160 14.6 232 21.1

CIS 8 Low or very low 38 3.5 111 10.1 121 11.0 175 15.9 179 16.3 164 14.9 164 14.9 217 19.7 248 22.6 256 23.3 High and Medium very high 228 449 20.7 40.9 376 213 34.2 19.4 232 316 21.1 28.8 282 125 25.7 11.4 223 102 20.3 9.3 162 69 14.7 6.3 108 47 9.8 4.3 274 105 24.9 9.6 285 69 25.9 6.3 218 56 19.8 5.1

Not used 38 3.5 71 6.5 81 7.4 140 12.7 204 18.6 299 27.2 390 35.5 135 12.3 151 13.7 221 20.1

CIS 10 Low or very low 45 4.1 96 8.7 104 9.5 189 17.2 205 18.7 182 16.6 187 17.0 211 19.2 235 21.4 246 22.4 Medium 208 18.9 355 32.3 226 20.6 266 24.2 202 18.4 167 15.2 103 9.4 249 22.7 274 24.9 218 19.8

High and very high 439 39.9 208 18.9 319 29.0 135 12.3 119 10.8 82 7.5 50 4.5 135 12.3 70 6.4 44 4.0

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of the link and its strength, in particular the ceteris paribus effect of public funding in this connection. The major questions to be answered are (1) Which firm characteristics’ enhance the probability to contact?; (2) Does the innovative strategy affect the connection propensity?; (3) Are the beneficiaries of the public monies more prone to recognise utility in the knowledge produced by the Academia? Over the last decades, Universities are considered in the literature as knowledge producers, promoting economic growth, being as a consequence considered as relevant sources of information for innovation (e.g. Feller, 1990; Hausman, 2012; Henderson, Jaffe, & Trajtenberg, 1998; Mohnen & Hoareau, 2003; Van Looy, 2009; Veugelers & Del Rey, 2014). Being, frequently, responsible for patent licensing or geographic knowledge spillovers (e.g. Duch, García, & Parellada, 2008; Hughes & Kitson, 2012; Laursen & Salter, 2004; Mansfield & Lee, 1996; Monjon & Waelbroeck, 2003). As a consequence, the dependent variable will be measure of contact establishments and its degree of importance. Firms’ structural characteristics such as the firm dimension, the R&D expenditures, the technological regime, the having and open innovative strategy and the use of public funds among others will influence the probability to draw upon Universities. R&D expenditures expectably result in innovative activities; as a consequence, they seem to be a good proxy for appraising the importance of innovation in the managerial strategy. R&D expenditures mirror the scientific and technological competences of the firm (Markusen, Hall, & Glasmeier, 1986). According to Cohen and Levinthal (1990), there is a complementarity between private and public R&D; therefore, higher expenses in internal R&D will raise the odds of drawing upon the public. Albeit, the prominence attributed to the Universities is doubtful; only a small number of firms find this source of information as relevant to their innovation (Klevorick, Levin, Nelson, & Winter, 1995). The existing literature is not consensual as in some cases R&D expenditures and reliance upon Universities is positively related (Cohen and Levinthal (1990), Mohnen and Hoareau (2003) and Laursen and Salter (2004)); in other cases, it seems to be unrelated (Salter & Martin, 2001). Still, it seems reasonable to accept that firms spending their own resources to innovate may find of interest to grasp information from Universities to save time and money in their innovative activities. Hypothesis 1: R&D Intensity Raises the Probability of Connection with Universities The seminal work of Pavitt (1984) appraises the economic sectors concerning their technological intensity and highlights the existence of different categories connected to different innovation strategies; firms operating in sectors considered as high-tech are expected to be more prone in performing innovative activities. Inside the same sector, it is expectable to find a similar knowledge base, even though the accurate use of internal or external knowledge may generate leveraged results (Saviotti, 1998). In sum, firms included in sectors considered as high-tech are more prone to rely on external sources of knowledge to develop innovative activities.

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Hypothesis 2: Technological Intensity Raises the Probability of Using the University as a Source of Knowledge for Innovation The effectiveness of the conventional policy instruments such as funding and grants to promote R&D is not overwhelmingly proved, given the failure in observing the effort and the measurement of the result. Indeed, R&D has asymmetric information as full awareness of the effort put by the agent is impossible. Moreover, the identification of the role of Universities in this process is unclear. Providing financial grants for collaborative research grasps an important part of public budget for funding innovation compared to the amount attributed to the establishment of industry-science collaborations. It is certain that public funding is effective tool to overcome the insufficiency of internal finance or the inexistence of venture capital; hence it is proved that it crowds out the private (David, Hall, & Toole, 2000). Still, the empirical evidence reinforces the positive effects of public granting in R&D intensity or in patent activity (Cerulli, 2010). Consequently, firms which draw upon public monies will invigorate their R&D departments which will rely on Universities to speed up their innovative projects. Hypothesis 3: Firms Relying on Funds Are More Prone to Connect with Universities According to the innovative strategy, firms will adopt a different interaction profile with the NSI. The use of external sources of knowledge is connected to open innovation strategies; under this framework, firms will absorb knowledge emerging from the ecosystem to develop innovative activities. Some  firms opt for developing the innovative activities under a closed framework, developing the innovative projects relies upon internal R&D resources. When opting for an open innovation strategy, managers can decide to develop connections with other stakeholders inside the production chain or any of the players in the NSI, thus diversifying the provenience of the knowledge used to develop their innovative activities. The agents inside the production chain, such as clients or suppliers, will determine the regularity and the intensity of innovative activities (von Hippel, 1988). The user community is considered as a very important source of ideas to develop innovations (Carayannis & Campbell, 2009; Gemunden et al., 1992; Salter & Martin, 2001). In the same vein, Laursen and Salter (2004) identified “openness” (count of different sources of innovation used by the firm to perform its innovative activities) as a measurement of broadness in terms of source diversity in the innovative process. An open innovative strategy will encompass a larger number of sources and will find them very important to innovate. As a consequence, open firms will have a higher probability to draw upon Universities and find this source as being very important. Hypothesis 4: Having and Open Innovation Strategy Raises the Probability to Draw Upon Universities Firms opting to perform innovations are forced to develop a wide variety of competences to implant the process. So, they need to reinforce the capabilities to back up

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the connections with the sources of innovation for external knowledge absorption and obtain the physical and human capital embedded in their new ideas (Morgan, 1997). Innovative projects are a multilayer platform with multiple interactions with external institutions, relying upon different sources, among which one can find Universities and Research Laboratories (Gibbons et al., 1994). Additionally, connections with other external players are part of the quotidian actions to develop innovations as well. The concept of “absorptive capacity” was firstly proposed by Cohen and Levinthal (1990) to describe the ability to exploit knowledge arising from the outside, which is central to perform innovations. The identification of technological opportunities depends on the ability to integrate existing knowledge arising from external interactions. Innovative firms must be able to adopt and adapt the existing knowledge according to their needs; therefore modern firms will be compulsorily connected to external source of knowledge. As a result, innovative firms should have an increased probability to use the University as a source of information for the innovative activities. Hypothesis 5: Innovative Firms Are More Likely to Draw from Universities

3  Methodology and Data Collection 3.1  Panel Description Given that the analysis relied on a time series perspective, three editions of the CIS were included, allowing the appraisal of a 6 years’ time span. To establish accurate comparisons, the option was a balanced panel, which means that only those firms traceable in all the editions were kept; as a consequence, the sample includes 1099 firms observed during 6 years. The exploratory variables comprise firms’ structural characteristics, human capital and innovation strategy. The importance of Universities as sources of information for the development of innovative activities can be empirically tested by observing the connection in a twofold way: firstly the probability of use and secondly the degree of importance. The role of the Academia in terms of knowledge production and diffusion, and its centrality inside the NSI, has been extensively debated, and it has gained relevance under the RIS3 implementation. However, most of the industries tend to undervalue the role of this player, mentioning some hindering factors in the communication.2

 Detailed variable description and descriptive statistics of the variables can be found in Appendix 1.

2

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3.2  Summarisation of the Hypotheses in Test The present research posits five hypotheses to answer the research questions and provide empirical support for its debate and the design of a policy package aiming at reaching the RIS3 objectives. The table below (Table 2) summarises the hypotheses in test and brings the results based on the econometric estimations better described in Sect. 4.

3.3  Theoretical Model Specification The firms’ strategic behaviour encompasses several vectors; here the analysis will focus in the choice of using the University as a source of knowledge to innovate. The multinomial response provided is to some extent sequential, as, in a first stage, the firm can either rely on the University or not to innovate, and, given that it establishes connections with it, the source can be considered of poor, medium or high importance. Given that the nature of the transition from not using to use with a poor importance is very different from the transition from poor to medium importance and from medium to high, econometric specification to accommodate the nature of the scale is the “hurdle model”. This econometric model simultaneously estimates two steps, as it considers primarily, a binary choice model (Random Effects binomial logit): whether or not to rely on Universities; and, secondly an ordered model (the Random Effects Ordered Logit), comprising the degree of importance of the source, given that it was chosen as a collaboration in the innovative activities. At once, the model considers the choice of using, regardless of the importance against not using, followed by the hierarchy of importance. It is a “modified count model in which two processes generating the zeros and the positives are not constrained to be the same” (Cameron & Trivedi, 1998). Table 2  Investigation hypotheses Hypothesis Description [H1] R&D intensity raises the probability of connection with universities [H2] Technological intensity raises the probability of using the university as a source of knowledge for innovation [H3] Firms relying on funds are more prone to connect with universities [H4] Having and open innovation strategy raises the probability to draw upon universities [H5] Innovative firms are more likely to draw from universities Source: Author’s composition according to the literature

Result Not supported (inverse direction) Not supported Supported Partially supported Not supported (inverse direction)

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The hurdle estimation, in the first part, separates the zeros from the positive concretisations by means of a binomial logit model with a binary outcome. Concretely, the hurdle is crossed if the firms mention the University as a relevant source of knowledge for innovation; followed by, the conditional distribution of the positive outcomes (being of poor importance, medium importance or high importance) is determined by a truncated at zero ordered logit model (Mullahy, 1986). The log likelihood function is the sum of the log likelihood for the binomial and the ordered model. This function is separable, respectively, to the parameters in estimation; so, hurdle models are considered as the sum of two independent models (McDowell, 2003). Using the hurdle model is a conceptual improvement, aiming to provide a fine-­ tuning quantitative approach in determining the relevance of Universities as sources of information for innovation. The model seems to be more precise in the quantitative measurement as the decision of use differs from the attribution of a degree of importance, as in the first, we have a state transition and moving towards the second or the third rank is far more a qualitative choice.

3.4  Operationalisation of the Dependent Variable As previously mentioned, the dependent variable measures the importance attributed by firms to Universities as a source of information for innovation. In the CIS, a specific question is posed to the firms to appraise this topic, and data collection outcomes a 0-1-2-3 scale. Scoring 0 is given when the firm finds the use of the University irrelevant (does not use it); the 1-2-3 scale highlights the gradation of importance of the source, following an ascending order.

4  Econometric Estimation In the estimation below (Table 3), the dependent variable is the multinomial which illustrates the connection with the University. The effect of size is captured by means of two dummy variables to appraise the marginal effect in the probability of use compared to the benchmark (small firms). Another control is belonging to an economic group, which expectably raise the probability of opening to the University. The technological regime is appraised by means of two dummy variables, capturing the increased probability of medium and high-tech regimes compared to low-­ tech. In a similar vein, another explanatory vector measures the effect of R&D intensity. Next, the effect of the innovation strategy is assessed throughout being or not an innovator independent of the innovation type performed in the period. Other variables were included to control the effect of the innovation strategy such as being a persistent or an occasional innovator.

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Table 3  Econometric estimations

Size_medium Size_large Group Tech_intens_med Tech_intens_high Innovative Funds Openness RD_intensity Turn_growth_rate Education_intensity Occasional_innov Persistent_innov External_know_act Training_act Industry Services

Estimation results Logit Ordered logit Pr (use) Pr (low) Estimate Estimate 0.228 −0.074 0.650∗∗∗ −0.140∗∗ 0.059 −0.025 0.093 0.074 0.194 0.056 −5.576∗∗∗ 0.545∗∗ 0.555∗∗∗ −0.133∗∗∗ 0.373∗∗∗ −0.022 −0.016∗∗∗ 0.004 −7.89 × 10−5 −2.32 × 10−4 0.241∗∗∗ −0.074∗∗∗ −0.564∗∗∗ 0.013 −0.004 −0.022 0.151 −0.077∗∗ −1.279∗∗∗ −0.033 −0.327 0.052 −0.761 0.148

Pr (medium) Estimate 0.040 0.077∗∗ 0.014 −0.041 −0.031 −0.299∗∗ 0.073∗∗∗ 0.012 −0.002 1.272 × 10−4 0.040∗∗∗ −0.007 0.012 0.042∗∗ 0.018 −0.028 −0.081

Pr (high) Estimate 0.033 0.063∗∗ 0.011 −0.033 −0.025 −0.246∗∗ 0.060∗∗ 0.010 −0.002 1.048 × 10−4 0.033∗∗∗ −0.006 0.010 0.035∗ 0.015 −0.023 −0.067

Source: Author’s computation based on the constructed panel considering the CIS 6, CIS 8 and CIS 10

Furthermore, the role of the human capital in this propensity will be measured by the educational intensity, as the literature highlights the connection between absorptive capacity and the existence of qualified labour force. The open innovation strategies will be addressed by means of a multinomial variable counting the different external sources of knowledge in use by the firm which should enhance the probability to rely on the University. In what concerns the importance of “using public funding to innovate”, which is an important proxy to answer the research question, the evidence highlights the positive effect  of being a public funding recipient on the propensity to rely on Universities to develop innovative activities. The following table illustrates the estimation results. Column 2 provides the estimates for the marginal effects of the explanatory variables in the probability of using the University (first part of the hurdle—logit model); columns 3, 4 and 5 show the estimates for the ordered logit which describe the degree of importance attributed to the source given that it is used by the firm. In what concerns size, the empirical evidence does not sustain significant differences between medium sized organisations compared to the small; conversely, being a large firm, compared to the small raises the probability to use the University as a source of knowledge. Additionally, large firms have a lower probability to find

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the University of poor importance and an increased probability to identify this source as being of high importance (6.3 pp). The technological regime fails to be significant in both the choice and the degree of importance of the University as an innovation source. As a consequence, for the Portuguese case, there is no evidence that high-tech firms are more prone to rely on the Academia to develop innovation, compared to low-tech. This fact is perhaps due to the fact that those firms prefer to develop innovation based on the internal sources of knowledge. The innovative strategy, proxied by being an innovator in general (independent of the type of innovation), presents an opposite sign from what was expected, in this vein, innovative firms are less prone to use the Universities that their non-innovative counterparts. Additionally, innovators are more prone to find this source as being of poor importance and, coherently, less prone to find it of high importance when compared to those which do not innovate. This result is surprising and deserves further analysis as it evidences that those entrepreneurial initiatives that innovate are more critical with the Academia, withdrawing the communication with this important part of the NSI.  Inexistent connections between innovators and Universities will jeopardise the implementation of the RIS3. In what concerns R&D intensity, the results evidence significance in the first part of the hurdle and insignificance in the second. Hence, higher R&D intensity decreases the probability to rely on the University. In the same vein, rising the degree of openness enhances the probability to use the University as a source of knowledge by 37.3  pp; still this variable is insignificant to explain the degree of importance attributed to the University to grasp knowledge for innovation. The use of public funds is a conventional policy instrument to leverage innovation among firms. Normally it is evaluated as a direct effect, which means the effect of grants in innovation. Still, relying on the new innovation policy mixes implies a combination of instruments to raise the efficiency levels of the public monies. As a consequence, our aim is to measure the involvement in multiple instruments to construct a smart innovation strategy. This estimate analyses the effect of drawing from public funds in the probability of using the University as an external source of knowledge. The results show that, in the first stage, firms that rely on public funds are 55.5 pp more prone to use the Universities compared to their non-beneficiary counterparts. Then, in the second stage, those that use the public monies have a decreased probability to find the University of poor importance (13.3 pp) and do find the University as an important source of knowledge more often than others (6 pp). These results do evidence that public grant recipients are far more involved with the Academia than other firms; hence, to some extent grants and connections appear as complements in the innovative strategy. This finding reinforces the advices of the RIS3, in which the quintuple helix is connected with solid ties, sharing knowledge throughout bidirectional relations. Moreover, it seems that the public funds are combined with other strategies, leveraging the potential outcomes of the expenditures in funding private research;

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this coexistence is a synonym of a smart application of funds and goes along with the expectable outcomes of the smart specialisation strategies.

5  Conclusions and Policy Recommendations The smart specialisation policies challenged the NSIs to interact more consistently promoting synergies among all participants and taking advantage from the embedded competences and the comparative advantages of the different regions. Accomplishing the policy targets requires adjustments in the different regions by the different actors; University-firm linkages are to some extent paradoxical connections as there is common agreement on their desirability, despite being less frequent than aimed. The Universities claim the lack of interest of the entrepreneurial community in their projects and the impatience for forthcoming results; on the contrary, firms allege the long waiting time, the abstraction of the results and the lack of interest of the academics in problem-solving projects. Academia has always been the engine of knowledge creation and diffusion, with free thinkers moved by genuine academic interests and curiosity rather than economically sustainable targets. Wasting the potential of this actor among the quintuple helix will pawn the success of any innovation model, so promoting the connection between Universities and firms can streamline the implementation of the new policy packages as well as the regional development. The empirical evidence does reinforce that the firms’ structural characteristics do influence the propensity to interact with the University. Firms with larger dimension and with higher degrees of skilled workers are closer to the Academia and find a higher importance on the knowledge spillovers emerging from the contact. This is perhaps due to the absorptive capacity mentioned in the literature. In our case, the technological regime along with the economic sector does not influence the propensity to establish links; this result is quite controversial as in most cases there is a positive discrimination of high-tech sectors which do not identify gains emerging from this connection. A reflexion must be made as these sectors are not rarely regional strategic vectors, given the preconceived belief about their importance as anchors. More attention should be put in the fact that innovative firms have a lower propensity to interact with the Universities, standing alone in their innovative ventures and multiplying the resources employed in the promotion of technological progress. Policymakers should address why do those firms get away from the University and when contacting this player, why do they find its knowledge as irrelevant. The results obtained for the impact of public funds are promising as the grant beneficiaries seem to be closer to the Academia. These firms identified the importance of the University as a source of valuable information for their innovative activities. Combining both instruments it is expectable that those firms will enhance the odds of success in their innovation process, as they combine external sources of

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finance with external sources of knowledge, leveraging their innovative performance with a lower level of internal resources. Henceforth, policy packages must combine financial with non-financial instruments as producing knowledge inside the University ensures a rapid and symmetric diffusion of novelty combined with a cost and risk reduction during the innovation process. The challenges of the future rely on smarter sustainable production, zero waste and corporate responsibility to promote inclusive and prosperous growth inside the regions. Reinforcing the desirability of approaching the elements of the helix warrants the effectiveness of the process and potentiates the effectiveness of the RIS3. Appendix 1 Descriptive statistics of the variables in use  Variable Tech_intensity Sector Size Group Prod_innov Serv_innov Process_innov Org_innov Mkting_innov Innov_general Expenditures_ RD_intramural Expenditures_ RD_extramural Expenditures_ RD_machinery Expenditures_ RD_others Expenditures_ RD_TOTAL Funds_general Sou_intern Sou_suppliers Sou_consumers Sou_competitors

Description Technological intensity Economic sector (aggregation) Firm size Economic group Product innovation Service innovation Process innovation in general Organisational innovation_procedures Marketing innovation Innovation in one vector Expenditures RD intramural € Expenditures RD extramural € Expenditures RD machinery € Expenditures RD others € Expenditures RD total (RDTOTAL) € Use of funds to innovate Sources of innovation_internal Sources of innovation_supplier Sources of innovation_consumers Sources of innovation_competitors

N Mean Std. Dev. 3297 2.298 0.778 3297 2.329 0.517

Min 1 1

Max 3 3

3297 3297 3297 3297 3297

2.868 0.485 0.362 0.307 0.570

0.748 0.500 0.481 0.461 0.495

2 0 0 0 0

4 1 1 1 1

3297

0.558 0.497

0

1

3297 3297 3297

0.424 0.494 0 0.758 0.428 0 450,951 3,399,602 0

1 1 7.14 × 107

3297

127,791 959,163

0

1.64 × 107

3297

527,456 4,693,380 0

1.27 × 108

3297

59,876 823,605

0

2.37 × 107

3297 1,166,075 6,579,944 0

1.41 × 108

3297 2200

0.189 0.392 2.437 0.830

0 0

1 3

2200

1.962 0.894

0

3

2200

1.998 1.028

0

3

2200

1.469 1.008

0

3

Why Do Publicly Funded Firms Find the University More Useful to Innovate… Variable Sou_consultants

Description Sources of innovation_consultants Sou_universities Sources of innovation_universitie Sou_public_labs Sources of innovation_R&D labs. Sou_conferences Sources of innovation_conferences Sou_journals Sources of innovation_journals Sou_associations Sources of innovation_associations Openness Openness to sources of innovation Turnover growth rate— Turnover_ growth_rate percentage (%) RD_intensity R&D expenditures to turnover ratio Education_ Percentage of the labour intensity force with undergraduate training or more

63

N Mean Std. Dev. 2200 1.209 1.069

Min 0

Max 3

2200

0.930 1.042

0

3

2200

0.673 0.919

0

3

2200

1.479 0.994

0

3

2200

1.370 0.925

0

3

2200

1.107 0.929

0

3

3297

4.914 4.081

0

10

3292

791,578 4.54 × 107 −100 2.61 × 109

3297

4.533 115.682

0

6615.23

3297

2.521 1.557

0

6

References Abramovsky, L., Harrison, R., & Simpson, H. (2007). University research and the location of business R&D. Economic Journal, 117(519), 114. Arora, A., Fosfuri, A., & Gambardella, A. (2001). Markets for technology and their implications for corporate strategy. Industrial and Corporate Change, 10(2), 419–451. Cameron, A., & Trivedi, P. (1998). Regression analysis of count data. New  York: Cambridge University Press. Carayannis, E., & Campbell, D. (2009). Mode 3’ and ‘Quadruple Helix’: Toward a 21st century fractal innovation ecosystem. International Journal of Technology Management, 46(3/4), 201–234. Carayannis, E., & Campbell, D. (2010). Triple Helix, quadruple Helix and quintuple Helix and how do knowledge, innovation and the environment relate to each other?: A proposed framework for a trans-disciplinary analysis of sustainable development and social ecology. International Journal of Social Ecology and Sustainable Development, 1(1), 51–52. Cassiman, B., Veugelers, R., & Zuniga, P. (2008). In search of performance effects of (in)direct industry science links. Industrial and Corporate Change, 17(4), 611–646. Cerulli, G. (2010). Modelling and measuring the effect of public subsidies on Business R&D: A critical review of the econometric literature. Economic Record, 86(274), 421–449. Cohen, W., & Levinthal, D. (1990). Absorptive capacity: A new perspective of learning and innovation. Administrative Science Quarterly, 35, 128–152. Cohen, W.  M., Nelson, R.  R., & Walsh, J. (2002). Links and impacts: The influence of public research on industrial R&D. Management Science, 48, 1–23. Cooke, P., Uranga, M., & Etxebarria, G. (1997). Regional innovation systems: Institutional and organizational dimensions. Research Policy, 26, 475–491.

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Applying Regional VRIO Model to Island Regions: An Evaluation of RIS3 João Lopes, José Oliveira, and Paulo Silveira

Abstract  This research aims to evaluate the stakeholders’ perception on the areas of smart specialization strategy (RIS3) defined for a given region. A quantitative methodology was followed through questionnaires applied to different stakeholders from two Portuguese island regions (Madeira and Azores); regional VRIO model was tested for these regions. The results of the research indicate that stakeholder perception is not the same as its policy makers in the areas of smart specialization defined in the RIS3 of the region where they belong. Our research provides support to policy makers in regional strategies modeling, assessing, and measuring island regional performance. Furthermore, this research suggests measures to conduit the gaps found in the island regions’ smart specialization strategies. Keywords  Smart specialization · Regional development · RIS3 · VRIO · Island regions · Resource-based view

J. Lopes (*) European Business School, ISAG & NIDISAG, Research Unit of ISAG & NECE, Research Centre in Business Science, Porto, Portugal e-mail: [email protected] J. Oliveira European Business School, ISAG & University of Minho & NIDISAG, Research Unit of ISAG, Porto, Portugal e-mail: [email protected] P. Silveira Polytechnic Institute of Castelo Branco & SHERU, Sport, Health & Exercise Research, Castelo Branco, Portugal e-mail: [email protected] © Springer Nature Switzerland AG 2020 L. Farinha et al. (eds.), Regional Helix Ecosystems and Sustainable Growth, Studies on Entrepreneurship, Structural Change and Industrial Dynamics, https://doi.org/10.1007/978-3-030-47697-7_5

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1  Introduction The European Union (EU) faces major economic challenges that require an ambitious and differentiated economic policy for the twenty-first century. This policy is based on more investment in research, innovation, and entrepreneurship. The strategy for Europe 2020 is EU’s response to the economic crisis. Thus, Europe 2020 is a strategic and integrated approach to innovation that maximizes research and innovation potential at European, national, and regional levels (Foray, Goddard, & Beldarrain, 2012; Sabato, Vanhercke, & Verschraegen, 2017; Woronowicz et al., 2017). Challenges are even greater for island regions. These regions have peculiar characteristics compared to other types of regions: they are confronted with similar social, environmental, and economics’ problems, in general, structural nature problems, areas on which they have no control. The characteristics that island regions usually have in common are (1) difficulties in accessing external capital; (2) open and poorly diversified economies; (3) strong exposure to natural disasters and effects resultant from climate change; (4) limited institutional capability; and (5) insularity. Since resources are limited, the sustainable use of these resources is extremely important. Commonly, in island regions, there are a limited number of qualified and available human resources to be dedicated to the stream of specialists in sustainable development. In this sense, regional approaches that reinforce experience and knowledge share, including innovation ecosystems, are of crucial importance (Armstrong et al., 2012; Lopes et al., 2020; Lopes, Farinha, & Ferreira, 2018). Usually, island regions rely heavily on tourism and agriculture as a source of income from labor and commerce. Coastal zones are, in these regions, considered of great importance for economic activity. These regions are still productive areas for a wide variety of living marine resources and a high degree of biological diversity. As so, these resources need to be increasingly enhanced in order to have a positive economic and social impact in those regions (Buhalis, 1999; Johannes, 1998; Lopes, Farinha, & Ferreira, 2018). Having this objective in mind, EU has recently defined regional research and innovation strategies for smart specialization (RIS3). For the implementation of RIS3 to be effective, it is important that many indicators are analyzed in order to help regions in their economic and innovative diversity to distinguish their distinctive territorial characteristics (Lopes, Farinha, & Ferreira, 2018). Regarding the measurement of the companies’ international competitiveness in a given territory, a lack of analysis of the regional characteristics and resources is identified as a gap (Buckley et al., 1990; Coviello et al., 1998; Doyle & Wong 1998; Özçelik & Taymaz 2004; Traill & Silva 1996). It is important to know to what extent the collaborative and networking dynamics are affected by the regional environment of business integration. In order to mitigate the competitiveness inefficiency of the regions and provide policy makers with new decision support tools, the RBV approach is followed in identifying competitive

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strategies and public policies to be implemented in the territories (Lopes, Farinha, Ferreira, & Silveira, 2018b; Mudambi & Puck, 2016). The objective of this research is to test a regional VRIO model on the island regions based on the stakeholders’ perception of RIS3  in the Portuguese island regions (Madeira and Azores; Ioppolo, Saija, & Salomone, 2012; Lopes, Farinha, Ferreira, & Silveira, 2018b), originally designed for organizations. This research aims to support policy makers to shape regional strategies by assessing and measuring island regional performance. Our research starts with the present introduction. In the second section, the conceptual framework is formed, more specifically, cohesion policies measuring the performance of the regions and the resource-based monitoring system and capabilities. In the third section, the used methodology is described. In section four, we disclose the results of the two Portuguese island regions (Madeira and Azores). Finally, we discuss and present the conclusions of the research.

2  Conceptual Framework 2.1  Cohesion Policies Since the economic crisis hit the EU, innovation and cohesion policies at the European level have undergone substantial changes. The “smart specialization” was the new and current paradigm in this respect, which changed the main premises that dominated European regional policy in the last decades. The smart specialization is based on the entrepreneurial process of discovery of the companies of each region. Regional governments play a role as moderators throughout the process. The smart specialization stresses that regions are not able to do everything in terms of science, technology, and innovation action and related policies. Thus, regions need to focus on specific domains which must be carefully chosen. Regions should channel their resources toward the development of smart specialization domains. Nevertheless, regions should still use the process of self-discovery and abstain to imitate other regions (Muller et  al., 2017). Regional strengths and capacities in relation to the European program for research and innovation strategies for intelligent specialization (RIS3) are therefore key (Markkula & Kune, 2015). Hypothesis 1: Stakeholders consider that the domains of smart specialization selected in RIS3 of the region which they are part of are creators of competitive advantage. RIS3 is the most recent version of the reform of the EU Cohesion Policy proposed for the period between 2014 and 2020. RIS3 aims to identify the knowledge in selective “domains,” as well as priorities, in areas/sectors where the region (or a Member State) has a relative advantage that may lead to a competitive advantage (Foray, 2014; Foray et al., 2012; Kotnik & Petrin, 2017; Lopes, Farinha, Ferreira, & Silveira, 2018b). The formulation of RIS3 should be governed by five principles: (1)

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the economic transformation agenda shall include monitoring and evaluation systems; (2) involve stakeholders in the process and encourage innovation and experimentation; (3) support innovation and try to stimulate private sector investment; (4) build on each region’s strengths, competitive advantages, and potential for excellence; and (5) support policy and investments in key regional priority resources (Cebolla & Navas, 2019; Foray et al., 2012). Moreover, RIS3 consists of investigation and knowledge, human capital, industrial and technological capital, as well as the competences of the territories. Therefore, RIS3 emphasizes the role of technology, innovation, and knowledge in socioeconomic development (Camagni & Capello, 2013; Muller et al., 2017; Tiits, Kalvet, & Mürk, 2015). With the implementation of RIS3, it is expected that most developed economies in R&D systems will be able to invest in the creation of new intensive activities with a strong component in science. Observed from another perspective, it is how less authoritative discounts can direct your research and development to areas where they already exist in the implemented industry (Foray, David, & Hall, 2009; Lopes, Ferreira, & Farinha, 2019).

2.2  Measuring the Performance of the Regions Currently, in regional politics, business and cooperation networks are increasingly considered as the key to success. In this sense, R&D cooperation networks, when applied correctly to reality, will lead to new technological projects, which as a rule will positively affect competitiveness (Farinha & Ferreira, 2016; Semlinger, 2008). Some performance monitoring systems have already been developed. However, these systems rely mostly on the Balanced Scorecard (BSC). In the literature, some examples of performance monitoring systems (collaborative BSC, territorial BSC, and regional helix scoreboard) were found. The collaborative BSC concept was developed by Al-Ashaab, Flores, Doultsinou, and Magyar (2011). The collaborative BSC model is a tool developed to measure, track, and improve the impact of conducting collaborative projects between industry and university. This model allows companies to evaluate their open innovation models (Al-Ashaab et al., 2011). Territorial BSC is a strategic tool developed for the regional public sector. This allows the competitive potential of the territorial system to be measured by means of classification. Through the territorial BSC, it is possible to interpret the characteristics of the supply territory using an ad hoc approach and to plan the necessary increase in the functions of the headquarters of the regional public sector and the associated competences. The territorial BSC allows to obtain profit-oriented indicators that are designed to highlight the strategic and economic benefits associated with heritage as it relates to competitiveness. This strategic tool allows the remodeling of local economic systems (Ioppolo et  al., 2012; Lopes, Farinha, Ferreira, & Silveira, 2018b).

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Regional helix scoreboard was developed with the objective of measuring the dynamic interactions of the triple and quadruple helices applied to the regional context. In the regional helix scoreboard, innovation and entrepreneurship initiatives are used as pillars of regional competitiveness (Farinha & Ferreira, 2016). Although there are models to measure regional performance, none of them considers the resources and capacities of each region. In this sense, adapting the resource-based view (RBV) and the “Value, Rarity, Imitability, and Implemented in the Organization” (VRIO) model to regions can bridge this gap (Ayuso & Navarrete-­ Báez, 2018; Barney, 1991; Wernerfelt, 1984).

2.3  The Resource-Based Monitoring System and Capabilities The theoretical perspective of resource-based view (RBV) was created with the objective of developing tools to investigate the position of companies associated with the resources used by them. According to the RBV, companies able to accumulate valuable resources, rare, not easily imitated by rivals (imperfectly inimitable), and not easily bought or sold in (non-replaceable) markets, will gain a sustainable competitive advantage over their competitors (Ayuso & Navarrete-Báez, 2018; Barney, 1991; Wernerfelt, 1984). In this context, companies can be analyzed through their productive resources, identifying the basis for creating competitive advantage (Backman, Verbeke, & Schulz, 2017; Barney, 1991), as well as value to the firm and creating barriers to new companies that can compete as competitors (Grant, 1991; Wernerfelt, 1984). Wernerfelt (1984) defines a resource as anything that can be understood as a strength or weakness of an organization. Resources can be considered the inputs of the organization’s process of operation, such as important equipment, individual employee skills, patents, and finance or talent managers (Grant, 1991). Thus, resources can be seen as advantages; capabilities; organizational processes; attributes; information; and all the intrinsic knowledge gathered by the organization. These resources are monitored by the organization and will enable the implementation of efficient and effective strategies (Barney, 1991). Resources can be classified into five categories: (1) human resources; (2) financial resources; (3) individual resources; (4) physical resources; and (5) organizational resources (Barney & Hesterly, 2007). The capacity is the condition that a set of resources holds to carry out an activity in an integrated manner (Grant, 1991). Resources are the basis of the organization’s capabilities, while capabilities are the essential sources of competitive advantage. Resources and capabilities, when well exploited and linked to market opportunities, become core competencies that will provide the organization with differentiation, making it a competitive advantage. To operationalize RBV, VRIO model emerged. The VRIO appeared to assist the internal analysis of organizations from the

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perspective of resources and capabilities and their impacts on competitive advantage (Barney & Hesterly, 2007). VRIO model integrates four relevant characteristics on the resources that have been explored: (1) valuable; (2) rare; (3) difficult to imitate; and (4) organization (Barney & Hesterly, 2007; Barney & Wright, 1998). From these characteristics it is possible to formulate four questions (Table 1). These characteristics create advantages for the organization on the elaboration of strategies that cannot be replicated in the different contexts (Barney, 1991; Barney & Hesterly, 2007, 2015; Barney & Wright, 1998; Grant, 1991; Wernerfelt, 1984). VRIO model is used for companies to identify their weaknesses and strengths about the internal organization. VRIO model takes into account the potential that each resource or capacity is aiming to improve the competitive position of the organization (Barney, 1991; Lopes, Farinha, Ferreira, & Silveira, 2018a). In this alignment, most of the characteristics indicated above can be applied to regions. Regions have domains of specialization, unique characteristics, differentiated human resources, and different infrastructures. Hypothesis 2: It is possible to apply the VRIO model to the island regions. In these terms, it will be pertinent to develop the RVB theory and VRIO model applied to regions and not only focused on organizations or companies.

Table 1  Resource description and questions Resource Valuable

Rare

Costly to imitate

Exploited by organization

Description Question: Do resources and capabilities allow the organization to explore outside opportunities or counteract an environmental threat? Description: Features and capabilities that enable an organization to design strategies that improve its efficiency and effectiveness, seize opportunities, or minimize threats Question: Are resources currently controlled by a small number of competing organizations? Description: Valuable and common capabilities and capabilities among organizations that give rise to competitive parity (when companies earn normal profits but cannot create an above-average advantage). The number of companies that hold certain features or capabilities must be less than the number needed to create perfect competition Question: Are organizations without resources facing a cost disadvantage in order to obtain or develop them? Description: Resources are only a source of sustainable competitive advantage if organizations that do not already have them cannot easily obtain them Question: Are the company’s other policies and procedures organized to sustain the exploitation of its valuable, rare, and hard-to-imitate resources? Description: They are complementary components because they have limited capacity to develop in an isolated way some competitive advantage, needing other resources and capacities to reach this advantage

Source: Adapted from Barney (1991), Barney and Wright (1998) and Barney and Hesterly (2007)

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3  Method 3.1  Research Approach The approach followed in this research is of a quantitative nature, when the numerical values are produced by counting, measuring, or verifying, thus allowing to discover, verify, or identify symmetrically (or not symmetrical) concepts derived from a theoretical framework (Lopes, Farinha, Ferreira, & Silveira, 2018b). The two Portuguese islands regions (Madeira and Azores) were defined as the unit of analysis. As this is exploratory, the analysis was only applied to the two regions to “simplify” its application. In this sense, and to verify the RIS3 of each region, it was concluded that two questionnaires (one for each region) need to be developed. The applied questionnaire was the same used by Lopes, Farinha, Ferreira, and Silveira (2018b). The questionnaires were previously validated and sent electronically to the stakeholders of each region (municipalities, incubators/technology parks, university/polytechnic institutes, and companies). The answers were collected during the periods from May to July 2017, obtaining a total of 66 validated answers (35 in the Azores and 31 in Madeira).

3.2  Regional VRIO According to Barney (1991), VRIO model was thought and tested in an organizational context. The innovative factor of this process results from the application of the VRIO model to the regional context (Table 2 and Fig. 1). Table 2  Regional VRIO Costly to Valuable? Rare? imitate? No – – Yes

No

–

Yes

Yes

No

Yes

Yes

Yes

Yes

Yes

Yes

Exploited in the Competitive region? implications – Competitive disadvantage – Competitive parity – Temporary competitive advantage No Unused competitive advantage Yes Sustainable competitive advantage

Score UCA or SCA >3,0 >3,0 >3,0

>3,0

>3,0

SWOT Performance category Below Weakness normal Normal Strength or weakness Above Distinctive normal strength and competence Above Distinctive normal strength and competence Above Strength and normal distinctive competence long-term

UCA unused competitive advantage, SCA sustainable competitive advantage Source: Lopes, Farinha, Ferreira, and Silveira (2018b)

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Fig. 1  Regional VRIO. Source: Lopes, Farinha, Ferreira, and Silveira (2018b)

The original VRIO model analyzes the internal performance of resources and capabilities of an organization or company, under the perspective of “valuable,” “rarity,” “costly to imitate,” and “exploited in the organization.” The new model applied to regions (which can also be applied to a country, group of countries, or a city) allows one to know whether a given resource or capacity is effectively exploited in the unit of analysis. Resulting from the applicability of a survey based on a panel of resources and capabilities addressed to stakeholders, by applying a 5-point concordance on the Likert scale (Lopes, Farinha, Ferreira, & Silveira, 2018b), validation is achieved for each resource or capacity when the respective average is higher than three, determining that the region reaches the unused competitive advantage (UCA) or sustainable competitive advantage (SCA) position for that resource or capacity, making it possible to calculate the respective scores and hence their comparison with other regions. The following matters describe the procedure used by Lopes, Farinha, Ferreira, and Silveira (2018b). RIS3 defined by the respective regions (Madeira and Azores) was used for resources and capabilities. On the questionnaire, for all questions a 5-point Likert scale was used, adapted to each question, where 1 corresponds to “no value” or “no rarity” or “easy to imitate” or “nothing exploited by the region” and 5 to “total value” or “total rarity” or “hard to imitate” or “fully exploited by the region.” A value above three was considered as “yes.” VRIO is an initialism for the four questions framework asked about a resource or capability to determine its competitive potential: the questions of value, rarity, imitability (ease/difficulty to imitate), and region (ability to exploit the resource or capability). The unused competitive advantage score (UCA) allows measuring the temporary competitive advantage of a region and makes comparisons with other regions. In any case, this is not a sustainable competitive advantage. When there is a regionally organized competitive advantage, the Score sustainable competitive advantage (SCA) allows us to measure the level of permanent competitiveness of a given region, which can be compared with other regions. The Score UCA, the Score

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SCA, and the Score “RIS” were also created to assist on the interpretation of the results. Regarding the Score “RIS”, it was calculated by adding the mean values of the responses collected to “valuable,” “rare,” “costly to imitate,” and “exploited in the region.” In this way, it is possible to obtain another indication in which the minimum will be 0 and the maximum 20. Thus, it was possible to operationalize regional VRIO (Lopes, Farinha, Ferreira, and Silveira 2018b).

3.3  RIS3 Portugal: Azores and Madeira The goal of RIS3 in the EU is to identify the regional affirmation and differentiation sectors, in the cross-linking of knowledge and the market, reinforcing the capture of value-focused on endogenous resources and the international dimension of regional productions. In Portugal, RIS3 is divided into seven regions: Norte, Centro, Lisbon, Alentejo, Algarve, Madeira, and Azores. However, the present research only covers two of these regions (Madeira and Azores). RIS3 strategies require an integrated, local-based approach to policy design and implementation. Policies need to be tailored to the regional context, recognizing that there are several possible ways to achieve regional innovation and development (ARDITI, 2014; GRRAA, 2014). In this sense, seven differentiating domains of the Madeira region and three of the Azores regions were identified (Table 3).

4  Results Figure 1 along with Table 1 assists in the interpretation of the results. Based on the collected answers, two tables were constructed summarizing the results for the regions of Madeira and Azores (Tables 4 and 5). Table 4 below shows the results of Madeira. As can be seen in Table  4, not all resources and capabilities are considered a “sustainable competitive advantage” in terms of regional competitiveness. In this context, sustainability, management, and maintenance of infrastructures and energy, mobility, and climate change are considered “competitive parity.” Being these two resources and capabilities “competitive parity” considered “normal,” regarding performance, such falls into “strength or weakness” category of the SWOT (Lopes, Farinha, Ferreira, & Silveira, 2018b). The parameters agro-food quality and bio-sustainability are considered “temporary competitive advantage.” Therefore, performance is considered “above normal” and falls into the SWOT category in “distinctive strength and competence.” Resources, sea technologies, health, and wellness are considered “unused competitive advantage.” In this perspective, as in “temporary competitive advantage,” the performance is considered “above normal”; as such, they fall into the category “distinctive strength and competence” of the SWOT. When analyzing the “Score

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Table 3  Smart specialization—Madeira and Azores regions Smart specialization Region strategies Madeira Tourism

Resources and sea technologies

Health and wellness

Agro-food quality

Sustainability, management, and infrastructure maintenance Bio-sustainability

Energy, mobility, and climate change

Azores

Agriculture, livestock, and agribusiness

Fish and sea

Tourism

Scope Through the creation of a critical mass, tourism specialist is guaranteed a structural condition, able to produce knowledge and expand the training offered on the tourist phenomenon Despite the location of Madeira and the recognized quality of the actions and projects developed in recent years, the international expression of ID & I activities in the area of marine science remains very limited. More development is intended in the area of ID & I Health in the region is broadly characterized by operational and management indicators embodied in national and regional publications. The geostrategic situation, climate change, human genetics, and acquired knowledge are essential for the development of a strategic map for health research in Horizon 2020 The food area represents a traditional sector of the economy in this region. It is linked essentially to products of agricultural origin There is a need in this region to create conditions for the proper use, management, conservation, and maintenance of its infrastructures. Focus should be on ecological solutions This domain is characterized transversely with implications in the most diverse areas such as territory occupation and maintenance, health and well-being, education, economy or tourism, etc. Considering the protection of the environment, economy, and life quality in the medium- and long-term perspective, there is great potential for research and development; thus, these are fundamental factors for a sustainable development In this region, the rurality is well evident in the territory occupation, in island’s characteristic landscapes, and in cultural identity. Agriculture, livestock, and agro-industries are particularly important in terms of economic development, income generation, and job creation The economic exploitation of sea resources has been repeatedly pointed out as a “national design.” The fishing industry is the main source of exploration of the sea in the Azores, constituting an important source of income with a great social and economic impact in the region Due to its distinctive natural characteristics, the archipelago of the Azores has a high potential for tourism. Azores has their offer for nature tourism and not for sun and beach component

Source: Adapted from ARDITI (2014) and GRRAA (2014)

3,03 3,06

3,19 2,97

2,94 2,68

3,03 3,00

3,00 3,06

Valuable 4,61 3,90

3,81

3,61

3,48

3,48

3,68

2,74

2,48

2,71

2,94

2,77

Exploited in the region 4,10 2,87

Source: Lopes, Farinha, Ferreira, and Silveira (2018b)

Resources and capabilities Tourism Resources and technologies of the sea Health and wellness Agro-food quality Sustainability, management, and maintenance of infrastructures Bio-­ sustainability Energy, mobility, and climate change

Costly to Rare imitate 3,13 3,55 3,42 3,32

Regional perception

Table 4  Results of Madeira Archipelago

No

No

No

No

No

Competitive disadvantage No No

Yes

No

Yes

No

No

Competitive parity No No

Regional competitiveness

Yes

Yes

No

Temporary competitive advantage No No

Yes

Unused competitive advantage No Yes

Sustainable competitive advantage Yes

3,30

12,48

11,99

11,81

12,71

12,67

Score Score Score UCA SCA “RIS” 3,85 15,39 3,55 13,51

Score

Applying Regional VRIO Model to Island Regions: An Evaluation of RIS3 77

2,94

3,23 3,40

No

No

Yes

3,11

2,80 2,77

No

Regional competitiveness Exploited in the Competitive Competitive region disadvantage parity 3,63 No Yes

Costly to Rare imitate 2,69 3,00

Source: Lopes, Farinha, Ferreira, and Silveira (2018b)

Regional perception Resources and capabilities Valuable Agriculture, 4,06 livestock, and agro-industry Fisheries and 3,94 sea Tourism 4,14

Table 5  Results of Azores Archipelago

No

Temporary competitive advantage

Yes

Unused competitive advantage

Sustainable competitive advantage

3,59

13,71

12,62

Score Score Score UCA SCA “RIS” 13,38

Score

78 J. Lopes et al.

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UCA,” it is possible to verify that the resource and capability factor closer to be considered as sustainable competitive advantage is the resources and technologies of the sea (3.55). The resource and capacity tourism is the unique factor to be considered as a “sustainable competitive advantage.” In terms of performance, tourism fits in the “above normal” category, which falls in the “strength and distinctive competence long-term” (“Score SCA”—3.85) of the SWOT. The resource and tourism capacity is the only one considered as “sustainable competitive advantage”; being so, Score “RIS” was verified. The Score “RIS” is the sum of the four strands (valuable, rare, costly to imitate, and exploited in the region) of each resource and capability that could be classified between 1 and 5, with a maximum of 20. Consequently, with this new indicator, it should be noted that all resources and capabilities are above 10. Sustainability, management, and maintenance of infrastructures is the one that has a lower rating (11.8). On the opposite side, there is tourism at 15.39. Table 5 presents the results for the Azores region. As can be seen in Table 5, no resource and capability is considered a “sustainable competitive advantage.” Agriculture, livestock, agro-industry, fisheries, and sea are considered as “competitive parity.” Therefore, in terms of performance, these resources and capabilities are considered as “normal” and fall into the SWOT category of “strength or weakness.” Regarding tourism, this is considered as “unused competitive advantage.” In this sense, the performance is considered “above normal”; as such, it fits into the SWOT category “distinctive strength and competence.” This resource and capability factor is closer to be considered as sustainable competitive advantage (“Score UCA”—3.59). Although competitiveness is different in tourism, when analyzing the Score “RIS,” the differences are not significant. Agriculture, livestock, and agro-industry have a Score “RIS” of 13.38, fisheries 12.62, and tourism 13.71 respectively. The three resources and capabilities are located above 10, which fits into the upper half of the Score “RIS.”

5  Discussion and Conclusion The present research is aimed to test the regional VRIO model on the island regions. Two hypotheses were formulated: Hypothesis 1—Stakeholders consider that the domains of smart specialization selected in RIS3 of the region which they are part of are creators of competitive advantage; and hypothesis 2—it is possible to apply the VRIO model to the island regions. Regarding hypothesis 1, it is partially rejected. In Madeira region, tourism alone was considered a “sustainable competitive advantage.” However, the resources and sea technologies and health and wellness are

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considered an unused competitive advantage, and agro-food quality and bio-­ sustainability are considered temporary interest advantages. The Madeira region has defined seven areas of smart specialization in RIS3; it is recommended that the position taken on sustainability, management, maintenance of infrastructures, energy, mobility, and climate change be revised. For the various stakeholders, these two smart specializations are not sources of differentiation that may generate a competitive advantage, whether temporary or sustained for the region. RIS3 aims to support new activities and projects at a finer granularity level (Lopes, Farinha, Ferreira, & Silveira, 2018b). We recall that each RIS3 consists on the identification of knowledge in selective “domains” as well as priorities in areas where the region has an advantage (Foray, 2014; Foray et al., 2012). Camagni and Capello (2013) indicate that RIS3 consists of investing in knowledge, human capital, and industrial and technological capital in the territories’ competences (Lopes, Farinha, Ferreira, & Silveira, 2018b). The results of this research suggest that the island region of Madeira should direct its resources in a privileged way for tourism. This domain is the one that will bring the most benefits within the domains identified in RIS3. Secondly, it is recommended that resources should be directed to the areas of resources, sea technologies, and health and wellness. Finally, the agro-food quality and bio-sustainability fields are included. Considering the results obtained in this research, it is further recommended to review the position taken on sustainability, management, maintenance of infrastructures, energy, mobility, and climate. The island region of Madeira may mobilize significant resources in these two areas. It is also recommended that the resources allocated to these two domains be invested in the domains of the sea, health and wellness, agro-food quality, and bio-sustainability so that they may become a sustainable competitive advantage. When looking to the Azores region, none of the three domains identified in RIS3 were considered as a sustainable competitive advantage. However, tourism was identified as an “unused competitive advantage.” The other two areas are in competitive parity. In this region, it is recommended that its resources should be mostly allocated to tourism. Tourism is the domain that is proximate to become a sustainable competitive advantage. As for the other two areas, it is recommended to disinvest. Alternatively, new differentiating domains can be identified in terms of competitiveness, with investment being channeled to them. Azores region has only three smart specialization domains defined in RIS3; it is not recommended to specialize only in tourism. The investment may not be enough to develop the three domains; in this sense, it may not be possible to leverage the selected domains as defined in the current RIS3. The results of the present research indicate that none of today’s intelligent specialization domains will generate temporary or sustainable competitiveness. As so, most of the region’s resources could be channeled only to tourism, with the other two areas of smart specialization in the background. When choosing and selecting some priority domains, micro-innovation systems will emerge which will be heavily supported by the concentration of large resources.

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In this sense, RIS3 raises some issues and poses some risks. Prioritizing certain domains always involves a risk because it involves predicting future development of technologies and markets (Lopes, Farinha, Ferreira, & Silveira, 2018b). In this context, the stakeholders’ perception on the Azores region points out that efforts should not focus only on tourism because if this strategy does not achieve the expected results, the region should also have other alternatives. It is therefore recommended that policy makers should invest more resources on agriculture, livestock, agro-industry, fisheries, and sea domains. Alternatively, the region may consider including other domains in its RIS3 and as soon as possible adjust them to the region’s needs. It is intended that these new domains should be differentiators and generators of competitive advantage. Smart specialization strategies best represent a region’s ability to create new areas of development. Smart specialization strategies can help to develop the discovery process to identify new opportunities. These opportunities can generate new domains of smart specialization. For these new smart domains of expertise and to create sustainable competitive advantage, resources should be allocated to the regions where implementation is expected to occur (Lopes, Farinha, Ferreira, & Silveira, 2018b). This research also demonstrates that the number of areas assigned to the region of Madeira is a somehow excessive as it will disperse many of the available resources. In island regions resources are more limited than in other regions (Meneses, Ribeiro, & Cristóvão, 2012). It is recommended that they specialize in fewer domains. The island region of the Azores is concentrated in fewer areas, but the results indicate that it is relevant for policy makers in this region to rethink the strategy of smart specialization outlined. Investigation should be carried out to identify new domains; alternatively, the strategies outlined to develop these domains should be reviewed. A combination of these two measures may also be performed. Concerning hypothesis 2, it was confirmed. The regional VRIO model was developed. It should be remembered that the VRIO model is originally applied in an organizational context (Barney & Hesterly, 2007). In this sense, it was necessary to make adaptations to the model, so it could be applied to regions. The first challenge that arose to develop this model was to know how resources and capabilities could be applied to the regions. The solution was to use RIS3 since all regions of Europe have been implementing it (Foray, 2016). The present research, although evidencing a pertinent and innovative focus, needs more applications in order to ascertain its applicability to other territorial realities and to gauge on stakeholders’ perception in a broader way. The perception of RIS3 by stakeholders is not as clear as that for policy makers selected or the selective group of invited stakeholders that assisted in these decisions. This research aimed to evaluate the real perception that stakeholders have of each region about RIS3, thus eliminating individual and/or institutional interests. Throughout the conclusion some open research questions have been left that may lead to future research lines, such as “Is it justified that Madeira region has seven smart specialization domains defined in RIS3?” and “What is the motive the Azores only have three smart specialization domains defined in RIS3?” It would also be

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important to evaluate the real impact that each selected domain has in its region, to complement the empirical evidence of this research. In Azores, it would be important to investigate whether there are other areas that can be considered for RIS3. Further aim of this research is to demonstrate solutions to the specific difficulties that island regions have. According to the results obtained, we recommend some suggestions for policy makers. These suggestions can be considered in the formulation of regional island strategies. The small number of responses obtained can be considered as the main limitation to the research. The results cannot be generalized to other regions since the elaboration of strategies cannot be replicated in different contexts (Barney, 1991; Barney & Hesterly, 2007, 2015). In this sense, it is always necessary to apply the regional VRIO model to each particular region. It would be important to further complement this research with qualitative studies in order to understand why the regions have selected these domains and not others and understand how strategies are being implemented in the domains selected for RIS3.

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Implications of Urban Sustainability, Socio-ecosystems, and Ecosystem Services José G. Vargas-Hernández, Karina Pallagst, and Justyna Zdunek-Wielgołaska

Abstract  This paper has the aim to analyze the implications that urban sustainability, socio-ecosystems, and ecosystem services have as the bases to design the urban green growth strategies. The method used is the analytic based on the theoretical and conceptual literature reviews on the topics described. A qualitative analysis offers better knowledge outcomes than a quantitative analysis using models that are based on a limited subset of implications between sustainability, socio-ecosystems, and ecosystem services with strong limitations because uncertainties and ambiguities are difficult to quantify if not impossible. Urban sustainability and environmental performance integrates biodiversity and socioecosystems for the provision of better quality ecosystem services supported by green infrastructure design into the green projects aimed to achieve economic and environmental benefits. It is concluded that the ecosystem services and human well-being may suffer irreversible severe declines if sustainability is not built based on biodiversity of socio-­ecosystems, green infrastructure, and natural capital. Keywords  Urban sustainability · Socio-ecosystems · Ecosystem services

J. G. Vargas-Hernández (*) University Center for Economic and Managerial Sciences, University of Guadalajara, Núcleo Universitario Los Belenes, Zapopan, Jalisco, México K. Pallagst IPS Department International Planning Systems, Faculty of Spatial and Environmental Planning, Technische Universität Kaiserslautern, Kaiserslautern, Germany e-mail: [email protected]; https://www.ru.uni-kl.de/ips/team/kpallagst/ J. Zdunek-Wielgołaska Faculty of Architecture, Warsaw University of Technology, Warsaw, Poland e-mail: [email protected] © Springer Nature Switzerland AG 2020 L. Farinha et al. (eds.), Regional Helix Ecosystems and Sustainable Growth, Studies on Entrepreneurship, Structural Change and Industrial Dynamics, https://doi.org/10.1007/978-3-030-47697-7_6

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1  Introduction Livelihoods, health, and survival of human beings are completely dependent on natural environment and ecosystem services, beyond culture and technology. Urban sustainable development is based on the relationship with its socio-­ ecosystems and urban green growth that provide critical ecosystem services useful to improve human well-being and health while at the same time buffer against natural disasters and disturbances. Urban sustainable planning, design, and development based on the socio-ecosystems and green infrastructure provide complementary benefits and opportunities of ecosystem services to benefit the cities, such as creating habitats, reducing energy demand, retaining storm water and rainwater, reducing runoffs, and creating green spaces for amenities, recreation and education, etc. The socio-ecosystem changes are long-term challenges to urban sustainability because the regime shifts that have an impact on the ecosystem services availability. Growth of urban sustainability based on green infrastructure to develop the socio-­ ecosystems is needed to provide efficient and environmentally friendly technology, giving support to the availability of ecosystem services as the benefits obtained from nature. People moving from rural to urban settlements consume ecosystem services experiencing separation from nature. Changes on urban sustainability are dependent on the availability of ecosystem services, which can be caused by changes in the social dynamics due to cascading and feedbacks changes intensifying the incremental ecosystems and their services changes. Some of this urban sustainability changes can be predictable, but other of these changes in ecosystems and ecosystem services are difficult to reverse given their impact magnitude. To a certain extent, there is a disagreement on the future scenarios of ecosystem services despite that there is agreement on many factors determining these scenarios, among which may include the role of governments at all levels of governance, technology, learning, resilience of ecosystems, etc. This paper intends to address some of the implications involved in this complex and uncertain relationships existing between urban sustainability, socio-ecosystems, and the ecosystem services.

2  Urban Sustainability The new development path supports economic growth, social inclusion and equality, and environmental sustainability, in such a way that ensures the greening of a more inclusive growth. Urban green growth strategies aligned economics with sustainable development to foster environmental sustainable and socially inclusive development. Sustainability emerged from the global development context (Adams and Jeanrenaud 2008; Mebaratu 1998) and is considered as a process or trajectory (Childers et al. 2014). Two pioneering works to sustainability are Design for Human

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Ecosystems and Regenerative Design for Sustainable Development of John T. Lyle (1994, 1999) (McHarg 1969, 1994). The founding concept of sustainability is equity across time and space defined as the capacity to support the quality of life of the current generation without impairing the capacity of future generations to meet their own needs for well-being. That is, the well-being of one person, place, or region should not be at the expense of the well-being of others. The concept of sustainability is based on ecological principles of resource conservation in ecosystems, mass balance in systems, and assimilated resources in allocation trade-offs of closed systems (Gunderson and Holling 2002; Ostrom 2009). Sustainability is the core concept of sustainable ecosystem management aimed to balance human needs in the long-term sustainability of ecosystems. Procedural sustainability is essential to sustainable ecosystem management (Binder et al. 2010). The sustainability goals of an urban area promoting environmental integrity and resilience of the ecosystem, economic feasibility, social inclusion, and cohesion lead to the adaptive processes and a cycle of human institutions (Ernstson et  al. 2010). In short, the sustainability goals of an urban area are the sustainable economic growth, increasing resilience and reducing poverty. The pillars of sustainability are the economic feasibility, social well-being, and environmental integrity. These pillars of sustainability are science-based related with decisions emerging from political power and social relationships and from biological and material systems (Pincetl 2012) addressing biophysical hazards, social vulnerabilities, and institutional inertia (Biggs et al. 2010; Walker et al. 2004). In comparative terms, urban sustainability draws upon resources from a regional contexts and the connections beyond their boundaries with resources and wastes (Rees 2000). Sustainability mechanisms try to reduce the urban negative impacts of the challenges posed by the global climate changes and to open new opportunities of urban development (Symes et  al. 2005). Urban ecosystems are threatened by climate change. Urban ecosystems are vulnerable to climate change and the assessment of factors contributing, and the impact is critical for any natural disturbances and disasters, essential to the ecosystem functioning over time and space. If global greenhouse gas emissions’ rates decline, climate change is less severe and poses less threats to urban socio-ecosystems and natural spaces. Adaptive governance approaches involving learning mechanisms are required for a transition toward greater sustainability and resilience. Economic sustainable development through green growth can be achieved by systemic changes in economic growth patterns. Sustainable and efficient green growth patterns require inclusive green growth. Transition to green economy is the basis for sustainable development supported by inclusive governance approaches and stakeholder’s experiences, knowledge, and initiatives to manage the planning and implementing processes. Transition from green economy toward greening of growth and environmental concerns is a challenge of governance and policy framework. Urban governance approaches may contribute to formulating and implementing strategies and policies and setting the targets aimed to the transition management

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to achieve urban green growth and sustainable development. Unfortunately, at global governance level, the system and practices do not meet the green growth and sustainable development challenges. Implementation of urban sustainable policies must take into account individual, community, and global interests. Deepening into policy solutions, green growth is one path to sustainable development. Green growth may reduce natural resource consumption and alleviate pressure on commodities and mitigate the impacts of adverse environmental effects while fostering economic, social, and environmental resilience. Greening economic growth strategies synergizes economic growth and environmental sustainability and protection characterized by investment in economic activities that enhances the earth’s natural capital and reduces ecological and environmental risks. Green behaviors are conditioned by the individual’s thinking green and expanding at the organizational level as green business (Go green members program 2014). Thinking green in social systems expands to green business and leads to green economy supported by eco-innovative initiatives for an efficient use of natural and environmental resources through engineering and social structures to enhance the capacity to deliver sustainable and inclusive growth (Eco-innovation action plan 2011). Moreover, the concept goes to urban level as green cities and toward green economy which includes all the upper social levels (UNEP 2010). Industrial shareholders and analysts more than government officials in some specific cases are more sensitized to the corporation’s ecological and social footprints and their impacts on sustainable development, climate change, and poverty and recognize the social responsibility in environmental protection. Green and sustainable chemistry are searching for new pathways and catalysts for environmental chemical processing methods, for example, conversion of renewable biomass into other ecological materials. These concerns are going beyond the borders of product life cycles into the natural capital and its economic flows and ecosystem services. Sustainable industrial ecology networks are developed to address sustainable issues and achieve incremental improvements to optimize results in ecological industry. Industrial ecology framework transforms industrial systems in a closed-­ loop ecosystem model with cyclical flows where the waste of one natural resources is recycled as a nutrient of other species. Industrial ecology networks recycle spent biodegradable or durable materials into other new applications. Industrial ecology eliminates the waste by innovation to convert the wastes into useful byproducts. Green sustainable buildings are becoming a sustainable design practice involving materials and energy renewable efficiency, respect for natural environments, utilization of ecosystem services, etc. Some protocols are now used to evaluate the environmental performance of sustainable buildings and campuses. Designs of sustainable systems include requirements to address inherent resilience, sometimes achieved through simplicity reducing failures and disruptions. The sustainability urban plans usually articulate a number of specific targets (Symes et  al. 2005). The way urban spaces are envisaged and shared ultimately impacts the way global changes are addressed. Sustainable urban planning must take into account the occurrence of many variables. Cities are resources destructors

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and polluters and the most disturbing environment sustainability in direct or indirect manner (UN 2007). However, the urbanization processes expose the dual effects on sustainable environment. The United Nations endorsed the New Urban Agenda in 2016 to the implementation of new paradigm in urban planning, building, and managing aimed to achieve a sustainable development goals. New urban ecosystems can be constructed where industrial business, shopping centers, have been before and have demolished under an urban ecological restoration projects to restore the original natural systems. The inputs of this New Urban Agenda are the identification of critical issues and challenges, structural policy priorities and constraints, and knowledge of valuable resource to urban actors involved on sustainable housing and urban development. The new urban development is supported by government incentives implemented to generate benefits in the sustainable green development. Some targets of sustainable urban plans include green resources conservation, green energy efficiency, waste management, and reuse and recycle materials (Munier 2011; Platt 2006; Symes et al. 2005). Urban sustainable planning incorporates nature into the urban settlements and tries to preserve the surrounding landscape (Jacobs 1961; Howard 1965; McHarg 1992). Sustainable urban planning investments in green infrastructure and green transport improve urban mobility, public health, and access to markets while reducing emissions, air, and water pollution. Urban ecological rehabilitation, restoration, and re-greening areas in a city planning and design that provide opportunities to build a more climate-resilient urban ecosystems and connect with the development of urban green spaces and ecological systems that contribute to enhance the well-being and health of residents are socially and ecologically desired. Urban and regional sustainable planning policies and supply chain management strategies have an impact on the mobility systems. For example, sustainable urban transportation systems, with shorter journey distances and times, balance urban and regional competitive economic growth, social inclusion and equity development, and sustainable environment, creating more liveable cities. Sustainable mobility is conceived as moving freely and gaining access, communication, and trading and establishing relationships without the sacrifice of essential human and ecological values. At the center of this conception are the evolutions of transportation technologies and their supporting infrastructures (World Business Council for Sustainable Development, Geneva 2002). Sustainable supply chain systems should generate ecological footprint using renewable materials and energies, urban land use, and industrial wastes and emissions management. In acceptable environmental impacts, the rate of resources replenishment is faster than the consumed, and waste does not exceed the capacity of the ecosystem. The interactions between socio-ecosystems, ecosystem services, and urban ecological resilience are potential that requires more attention in urban sustainable planning. Urban sustainable planning and design must consider the connectivity of urban landscapes to land uses and its socio-ecological determination and significance to maintain the socio-ecosystem services and resilience. Cities that integrate

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urban forests, green areas, parks, etc. into biodiversity in urban sustainable planning and design are well positioned in their socio-ecosystem services to become more resilient.

3  Socio-ecosystems The concept of green growth is based on the complementarity of the economic, social, and environmental systems that is socio-ecosystems. The term ecosystem describes the entire system of living organisms in interaction with biotic factors such as air, water, and minerals occupying a given space (Tansley 1935). The socio-­ ecological system concept contends that the integral parts of any ecosystem are the humans, their social and political systems, and the equitable sustainable governance. Ecosystem integrity is the degree to change from their natural state due to the interventions of human. Interventions in socioeconomic systems can be modeled through computer simulations. An ecosystem is a flexible model incorporating the urban systems, social structures, and infrastructure (Burch 1988; Naveh 2000). The metaphorical implications concept of the ecosystem concept stimulates the transdisciplinary dialogue with society, the ecosystem models, and experimental frameworks of reference to urban ecological processes (Felson et al. 2013; Felson and Pickett 2005; McGrath 2013b). Ecosystem ecology focuses on the fluxes and transformations of matter, information, and energy among and cross the ecosystem components. Ecological theory based on socio-ecosystem development and succession is reflected on the system composition of free resources to accumulate and conserve in adaptive cycle traced in system dynamics (Holling and Gunderson 2002) of connectedness, wealth or capital, and resilience. The organismal component of a human ecosystem includes institutional and social arrangements, structures, and interactions, from small to large and from persistent to temporary (Naveh 2000; Pickett and Grove 2009; Ostrom 2005). The study of urban ecosystems integrates physical processes, economic and social factors, nonlinear feedback across a broad range of scales, and disparate process phenomena (Urban Security 1999). Most of urban settlements are located in areas with high levels of biodiversity supporting high ecosystem productivity (Hansen et al. 2004; Ricketts and Imhoff 2003). Biodiversity preservation cannot reduce other kinds of anthropogenic stresses to ecosystems. Biodiversity within species maintains the ecosystem processes with individual species responding to environmental fluctuations (Frost et  al. 1995; Ives et  al. 1999; Cottingham et  al. 2001; Elmqvist et al. 2003; Norberg 2004; Folke et al. 2005). Species acting across a range of space and time scales are an element of ecosystem diversity (Peterson et al. 1998). Biophysical and social adaptive processes are across space in human ecosystems (Cumming et al. 2006) including individuals and institutions through the interconnectivity of organisms in physical environments (Ostrom 2005). Adaptive processes

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are supported by a variety of sources, among which are general ecological theory, biological ecosystem structures, genetic variations and evolution, regulatory population feedbacks, etc. (Gunderson et  al. 2002; Walker et  al. 2004; Scheiner and Willig 2011). Management of the socio-ecosystem feedback has crucial implications on the development of society and human systems (Diamond 2005). Socio-ecosystems can be modified by people which, in turn, its feedbacks change human systems, economies, society, livelihoods, life quality, health, etc. In complex socio-ecological environments, social and ecological change is continuous based in the relationships between people and ecosystems, but the consequences are difficult to predict. A substantive application of landscape and ecosystem ecology to urban ecosystems considers the spatial heterogeneity of structures, flows, and controls across the complexity of the urban socio-ecosystem (Forman 2008), the social and biophysical disturbances in urban structure, process, and change (McGrath 2013a; Shane 2013), and the ecological interactions and connections between infrastructure and biophysical processes in urban areas (Pickett et al. 2008, 2011). Spatial landscape structure and ecosystem processes on landscape designs are relevant for the maintenance of biodiversity through seed dispersal, species movement, and pollination (Hobbs 1997; Kendle and Forbes 1997). Habitat configuration and composition affect the individuals, communities, and populations that inhabit landscape and together with patches complement the natural resources forming ecological functional units among species confined to urban ecosystems (Guerry and Hunter 2002; Quin et al. 2004; Blair 1996; Melles et al. 2003). Biophysical components and processes can have benefits to the urban socio-­ ecosystem functions as it is the case that embedded green areas contribute to the prevention of pollution and higher proportions of trees are associated with low rates of crime (Troy et al. 2012). Green public areas available in the urban context of historic center not always are capable to perform the ecosystem functions. This situation requires other types of private interventions such as urban green roofs and other greening actions to become fundamental elements of the ecological greening network and bring other economic, environmental, and social benefits. Ecosystem networks formed by different stakeholders such as experts, scientists, technicians, managers, citizens, etc., depending upon shared interests, trust, and experiences, they are involved in knowledge creation, technological innovation, social learning, information and communication technologies, etc. (Olsson et al. 2006). It is widely accepted that resource extraction is leading to accelerated reduction of biodiversity and degradation of the socio-ecological system, but conservation takes account human social and economic needs (Grumbine 1994; Szaro et  al. 1998). Industries of natural resource extraction might have appropriate land use and socio-ecosystem protection. Poverty is closely related to ecosystem degradation (Biggs et al. 2004). Some poverty mitigation strategies may increase the pressures on socio-ecosystems and may compromise the benefits. Ecosystem degradation is tolerated by wealthy people despite the increasing demand for cleanup of some

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aspects of the environment (Stern 1998; Khanna and Plassmann 2004; Gergel et al. 2004). Ecological land-use complementation promotes response diversity as referring to diversity of responses to environmental disturbances among in the same ecosystem functions, a critical mechanism for the maintenance of ecosystem processes (Elmqvist et al. 2003). Ecological land-use complementation (ELC) provides the habitat for the species and the landscape complementation function of other ecosystem processes and functions. The urban ecosystems are complex diversity of land uses and vegetative land of any landscapes (Foresman et al. 1997). Urban land use areas have influence on biodiversity and terrestrial ecosystems (Sala et al. 2000) and continue to intensify in urban areas overlapping location of areas that are rich in biodiversity (Ricketts and Imhoff 2003). Ecosystem management is an approach used to analyze all these issues. The concept of ecosystem management is based on the recognition that species interact with each other and with a surrounding environment. According to the Ecological Society of America, ecosystem management have the purposes to protect the entire habitats and the particular species, maintain the native ecosystems, manage resilience and disturbances, and establish buffer areas around core reserves (Grumbine 1994). The sustainable socio-ecological management system takes into account the complex connections of the entire ecosystems to deliver the ecosystem services, balancing the human economic social and cultural needs with the ecosystem sustainability. Ecosystem management applies ecological science to resource management to long-term sustainability of ecosystems and to deliver the essential ecosystem goods and services to society (Chapin et al. 2002). Ecosystem management can be reactive and proactive. A sustainable ecosystem management system must have defined the boundaries, determine components and interactions, use local and traditional knowledge to assess integrity and conditions, evaluate the supply and demand, carrying capacity, thresholds, and tipping points, implement actions, set the levels of extraction, restore degradation, and improve connectivity (Brussard et al. 1998; Slocombe 1998; Tallis et al. 2010). A flexible adaptive management approach to socio-ecological systems has the functions of monitoring, learning, and feedback adjustment of strategies and goals to meet the changing human needs, incorporate new information, and make resilient corrections. Adaptive management deals with complexity and uncertainty of the socio-ecological system (Williams 2011). Environmental management that incorporates risk and cost (Carpenter 2003), under an adaptive approach, accelerates social learning to develop planning, controlling, and evaluating by absorbing uncertainty. In doing so, environmental management creates the capacity to cope with social and ecological change and strategically manipulates the socio-ecological process to get the effective functioning of the socio-ecosystems (Holling 1978). The ecosystem management and governance has an institutional context and regulatory frameworks which condition the recommendations in the management system, the supranational, international, national, and local governance levels, and the place-based ecosystem services management. Ecosystem governance structures

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and functions rest on the diverse sectors and span across the supranational to the most local level practices (Young 2002). Institutional socio-ecosystem determines its management and governance structures, functions, and processes and the ecosystem services (Vatn 2005). The new institutional governance of socio-ecosystems should pursues government regulations to encourage green value and wealth by protecting natural resources and biodiversity with efficiency, innovation, and sustainable budget. Institutional governance capacity of socio-ecosystems is the capacity to transform, adapt, and innovate (Folke et al. 2010; Gunderson 2000; Gunderson and Folke 2011; Olsson et al. 2004). Technological development is shaped and shapes the ecosystem management approaches. Green engineering and design for environment in socio-ecosystems have impacts in design process. In Techno Garden approach, the globalized economy is supported with substantial investments in environmental technology with engineered ecosystems and market solutions to environmental problems (MA 2005a). Interventions are designed decisions in socio-ecosystems. Design and implementation of a portfolio-based approach for managing ecosystems is a challenging task to enhancing system resilience and reducing impacts of human-engineered or natural disasters. The portfolio approach for managing ecosystems may have different strategies and goals applied to specific conditions. The MA scenarios are a tool for analyzing different assumptions, building socio-­ ecosystem decisions and policies, and exploring logical consequences. System design practices are contributing to building sustainable socio-ecological systems and green sustainable development. Green environmental development policies to be effective require a proper mix of financial incentives to strengthen the interventions for the creation and establishment of green infrastructures aimed to overcome the socio-ecosystem crisis. Urban local governments and private companies must invest on green infrastructure if they are concerned with the improvement of the urban environment, the socio-ecosystem, and the quality of life. Innovation and adaptation to change of socio-ecological systems are facilitated by cooperation between stakeholder groups (Chapin et al. 2011). Knowledge and skills acquired and applied to enhance community environmental development and to foster leadership action help to create a more resilient socio-­ ecosystem in the face of disasters and disturbances. Natural ecosystem connectivity is built through greater economies of scale and scope, economic efficiency, social integration and inclusion, and sustainable development approaches. The connectivity of the ecosystem is the element that facilitates effective operation and interactions of the different linked nodes such as greenbelts, greenways, ecological corridors, etc. Ecologists now are supporting the argument that the socio-ecology systems may have multiple equilibria (Holling 1965; Lewontin 1969). After being disturbed, a socio-ecosystem system may have new conditions which set feedbacks preventing to returning to its previous equilibrium of disturbance (Carpenter 2003). A specific equilibrium on an ecosystem with multiple equilibria with the potential to cross thresholds is depending on the set of conditions. Recovering of the entire

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ecosystems requires the support of development partners and nongovernmental organizations. Socio-ecosystem requires protection given the variability of disasters and disturbances (Koch et al. 2009). Self-organization refers to the emergence of ecosystem patterns as the result of evolution of species (Levin 2005). Biodiversity is a form of insurance to buffer ecosystems against losses of species and reduce the functions (McCann 2000; Naeem 2002a, b). The capacity of the ecosystem includes the recovering from management mistakes (Fischer et al. 2006). Ecosystems and social systems are mutually reinforcing having positive feedback in such a way that any change has an impact between each other in a virtuous cycle. Any community well-being improvement leads to the ecosystem protection. The socio-ecosystem has the ability to recover from disasters and shocks, which may be improved by institutional and cultural framework and high degree of social capital and cohesion. Diversified economies with biodiversity-rich ecosystems can have some specific resources more vulnerable to weather shocks where ecosystems can lose species but still maintain the ecosystem performance (Briguglio et  al. 2009; Rose 2007). Specifically, some communities highly dependent on natural resources are more vulnerable. A decrease in biodiversity may tend to reduce ecosystem stability as the decreasing in managerial diversity of a corporation reduces its ability of survival and longevity. Regulations imposed upon industrial activities have impacts of perturbations and disasters upon biodiversity and complex bio ecosystems. Large-scale damage caused by disasters has a high impact on human communities and socio-ecosystems. However, restoration of some ecosystems after disturbances may preset some difficulties. Also, socio-ecosystems can be measured after disturbances (DeAngelis 1980; Neubert and Caswell 1997; Pimm and Lawton 1980). Ecosystems crossing critical thresholds of disturbance or disaster might have difficulties to restore, and in absence of system dynamics, the ecosystem is launched down to unpredictable trajectory. An ecosystem may cross a threshold from one ecological state to another, and the restoration is impeded by biotic such as the invasion of exotic species and abiotic barriers. Changes in landscapes structures and dominant hierarchies of species, loss of native species, biochemical processes, biotic and physical processes and feedbacks, etc. are some conditions that may prevent ecosystems to return to pre-­ disturbance state (Ehrenfeld and Toth 1997; Suding et  al. 2004) and launch it to unpredictable trajectory. Ecosystems can provoke also cascading changes such as the case studied by Brashares et al. (2004) where after the collapse of coastal fisheries has increased the bush meat hunting or the ecological adverse consequences caused by the dry lands now covering more than 40% of the surface of the Earth (MA 2005b). Socio-ecosystems are often more complex to accurately model regime shifts and simulate results. Ecosystem functions provide cultural benefits to urban population. Cities are complicated socio-ecosystems with dynamically interconnected components over space and time (Pickett et al. 2001). Building urban ecological functions for leading to land uses accommodation as the result of the development of knowledge on urban ecosystem functioning and stronger partnerships among urban planner and

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designers, ecologists, landscape designers, urban residents, etc. contributes to improve better urban resilience (Felson and Pickett 2005). Urban socio-ecosystems are assessed in multisector policies and actions using indicators such as urban green infrastructure and green services. Stakeholder’s negotiations should set the goals and indicators. The assessment of long-term sustainable ecosystems requires indicators of ecosystem resilience and health to determine the level of exploitation. The impact assessment of life cycle flows in terms of the ecosystem perturbations. The ecosystem health is based on resilience considered as the ability of maintaining structure and functions under stress, productivity or vigor, organization, or functional diversity (Rapport et  al. 1998). The sustainable ecosystem management requires suitable indicators for management and monitoring to ensure the balance of needs and the prioritized goals in every stage of the model ecosystem structure of biodiversity, functions, quality regulations, and other socioeconomic indicators. Biocomplexity challenges the integrated system evaluation addressing the dynamic web of interrelationships arising when components of the global ecosystems’ biological, physical, chemical, and the human dimensions interact (National Science Foundation 2002).

4  Socio-ecosystem Services Ecosystem services are a concept that integrates strategies for long-term green economic growth and competitiveness with improvement of welfare and poverty reduction by investing in natural capital. The ecosystem services have the objectives to analyze and manage interconnections and modularity in practical specific situations. It is crucial to studying the Identification of the ecosystem services and how they are affected by other states of the system. Ecosystem services are categorized as supporting such as in the case of biodiversity, provisioning, regulating, or cultural (TEEB 2011). Ecosystem services are the ecosystem functions ranging from material goods to non-market services that used, consumed, and enjoyed by humans (Crossman et al. 2013; Gómez-Baggethun et al. 2013). Some ecosystem services are the ecotourism potential, flood control, and water regulation, which can be developed as economic growth, social development, and environmental sustainability strategies. Ecosystem services are also provided by species. Urban farming and agriculture initiatives and urban local-scale greening are efforts that facilitate the provision of ecosystem services such as clean fresh water, air purification, carbon sequestration and storage, temperature regulation, storm water runoff and management, healthy and affordable food production and supply, equitable access to recreation, gardening opportunities, etc. These ecosystem services are locally produced and managed by private, nongovernmental organizations, and local authorities, challenging the densification, decreasing urban land availability.

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The economic analysis based on the ecosystem services includes energy savings and continuity benefits. Green economy capitalizes the economic value on natural capital investments and provides incentives for maintaining its function to secure ecosystem services, saving on green infrastructure development, contributing to the adaptation of the ecosystem, and improving human health and environmental security. Natural capital management is an analysis framework that goes beyond the product life cycle of companies and their economic flows to sustain the ecosystem services. Natural capital provides ecosystem goods and services subject to natural resource management decisions, the growing population, and per capita consumption. The ecosystem services and functions framework unites biodiversity conservation with human health and well-being goals to benefit people living in urban settlements. The maintenance of ecosystem services for the benefit of human health and well-being in urban settlements depends on biodiversity and the state of ecosystems (TEEB 2011), although the connection between biodiversity and human livelihoods is not always clear. Biodiversity supports the ecological structure and functions to produce the ecosystem services. Urban ecosystem biodiversity is integral to functioning and provision of ecosystem services to the population (Gómez-Baggethun et al. 2013). The type of ecosystem can be provisioning, regulating, and cultural and the production, management, regulation, and strategically planning scales of the ecosystem services, at community, city, local, regional, or global level. Management mechanisms are in relationship with the scale of ecosystem service production regulating water supply, storm water quality, etc., enhancing educational and recreational activities supported by ecological functions and processes (New York Restoration Project 2013). Consumed ecosystem services can be produced and managed at multiple spatial scales by an array of government agencies, nonprofits organizations, community groups, and even by private firms. Urban ecosystem services consumption depending on biodiversity over the scale of urban production can be produced in cooperation with other institutions. The sensitivity scales of socio-ecosystems may shift and vary on functions and damages and their abilities and capacities to provide and supply ecosystem services and goods. Ecosystem services are usually produced at local and regional level and at all spatial scales including the global, well beyond the urban boundaries, but provide benefits to residents of the city. However, operationally spatial problems may exist between the locations where the ecosystem services are produced and supplied and the places where they are consumed or demanded. The service providing units (SPUs) noted by Kremen (2005) make reference to the ecosystem type and environmental conditions depending of the scale of production and supporting the ecosystem services. Communities should maintain a flow of valuable ecosystem services taking into consideration socio-ecological changes, disasters, and disturbances. Communities affected by the reduction of natural capital and environmental services are vulnerable to the impacts of disasters and disturbances (CBD and WHO 2015). Ecology for human nature interactions is crucial in urban systems for the production of urban ecosystem services (Bolund and Hunhammar 1999; TEEB 2011; Gómez-Baggethun et  al. 2013). Biodiversity, urban green infrastructure, and

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ecosystem services perform diverse relevant functions, models, and indicators in the urban socio-ecosystem. The ecosystem services framework links urban social and urban ecological infrastructure for the benefits of ecosystems and human beings. A spatial approach to managing ecosystems has a buffer against uncertainties and disturbances ensuring that all the ecosystems provide similar amounts of ecosystem services. More ecosystem services occurring at one time will be trade-offs between services. The ecosystems provide diverse ecosystem services to the community with some impacts (Adger 2000). In the relationship between human beings and nature, feedbacks between human behavior and well-being, ecosystem services, and environmental conditions support the resilience of socio-ecosystems. The resilience of socio-ecosystem is related to ecosystem services. The resilience of ecosystem services is subject to the resilient ecosystems. In other words, ecosystems lead to generate ecosystem services. Socio- ecosystems produce and supply ecosystem services valuable for increasing the resilience. The critical components of an ecosystem service should be determining to measure resilience in relation to the production functions of the ecosystem dynamics. Human-engineered services replace some ecosystem services. Human-­ engineered services may protect some services but may increase other vulnerabilities undermining and eroding the provision of other ecosystem services. Engineering resilience measurement poses several challenges such as the speed of recovery to previous conditions of disturbance depending of the ecosystem service or system component and the type and severity of disturbance. Weakening ecosystem resilience may compromise the ecosystem services requiring human-engineering services to replace ecosystem services. Ecosystem services are related to resilience responding to complex and uncertain systems while focusing on natural disturbances, disasters, long-term stresses, etc. (Ives and Carpenter 2007). Ecosystems are subject to disturbances and disasters leading to declination of ecosystem services. Communities can suffer the loss of ecosystem services and being severely impacted by the disasters and disruption of ecosystems. Diversified economic and social community structures are in better position to cope the ecosystem disasters and disturbances affecting the provision of ecosystem services. Ecosystems lacking resilience are vulnerable to disturbances reducing the production and supply of ecosystem services. To stabilize complex systems, it is necessary to provide a constant flow of ecosystem services increasing system resilience and reducing vulnerability (Gunderson et al. 1995). Resilient ecosystems produce, maintain, and supply stable ecosystem services with a speedy recovery from disasters and disturbances. Ecosystem services recovery and ecosystem restoration are defined in terms of measures to be monitored. Some components of the ecosystem services may recover at different speed such as the case of species diversity. The ecosystem services recovery and ecosystem restoration can be planned using knowledge and monitored. The predictive capability of the ecosystem services and functions is required to restore them. Ecosystems with low level of resilience either restore slowly the provision of ecosystem services and increase resilience or may switch of regime failing

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to return to previous conditions. However, restoration of ecosystem services follows different pattern than the restoration of the ecosystem structure. Ecosystem structures and functions may affect differently from the ecosystem services, and any change in the ecosystem does not necessarily affect the ecosystem services which may have different outcomes after disturbances. Maximizing a specific ecosystem service may be counterproductive to resilience or other ecosystem services. The system resilience maintains the conditions to sustain the provision of ecosystem services which are important contribution to the human well-being. Transitions from landscape to residential developments lead to declining of agricultural land, water quality, and other critical ecosystem services, which are necessary to protect the urban residents. Ecosystem services as products of interacting ecosystems are a governance system. Institutional analysis of ecosystem services focuses on the governance institutions of the ecosystem services, the institutional mismatching, and institutions contributing to the perception of ecosystem services. The socio-ecosystem institutional governance framework conditions the ecosystem services (Norgaard 2009). The multilevel institutional governance system of ecosystem services has a wide range of goals and responsibilities of policy design making and implementation with intricacies in governance structures and overlapping jurisdictions among the international, national, local, and community levels, including non-state actors and civil society. The ecosystem services focus shifts to value the benefits by institutions that identify and allocate them (Norgaard 2009). Studies estimating the monetary value of benefits of ecosystem services conducted by Elmqvist et al. (2015) based on quantification in biophysical units in urban ecosystems analyzed revealed up to US$ 17,772 in profits per hectare per year. Sustainable management of natural capital of ecosystem services to achieve particular value ecosystem and biodiversity values is at the base of management of natural resources supported by a governance strategy and policy on ecological infrastructure. Management strategy and policies of urban ecosystem services may have multiple goals aimed to improve synergy and decrease the trade-offs. Ecosystem services provide the baseline for policy, planning, and management improvements in transitional states for a more sustainable resilient city. Planning and management of natural and human-controlled processes altering the relationships between urban biodiversity and urban ecosystem processes are essential functions to deliver socio-ecosystem services. Ecosystem services, human well-being, competitiveness, and governance are the challenges of the sustainable ecosystem management. Increasing the resilience of the ecosystem services requires the management’s complex systemic dynamics and variability affecting the socio-­ ecosystem. Management addresses the matching between production and consumption of ecosystem services. Ecosystem services are embedded in institutions, relay on landscapes, and are governed by natural resource sector organizations, administrations, and land use planning (Primmer and Furman 2012). Urban government ensures protection of nature and urban ecosystem processes to support the urban ecosystem services. Urban ecosystem services are included in planning, policing, and managing for the

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development of urban resilience. Changes over time and space of the ecosystem functioning is crucial to planning, policing, and management of urban ecosystem services. Complex urban systems designing, planning, and managing require urban ecosystems to become resilient to changes of the system and sustainable management to provide reliable ecosystem services. System design is moving from controllable traditional products and services to unpredictable industrial socioecology systems where economic, political, biological, and ecological threats have become global concerns. Urban green spaces may be undermined by development, planning, and designing processes of existing urban nature, ecological functioning, connectivity, and ability to provide ecosystem services (Yli-Pelkonen and Niemelä 2005). Vacant lots providing ecosystem services require mapping the social needs in high density populated and low-income urban areas that tend to decrease the access to green spaces (Kremer et al. 2013; McPhearson et al. 2013). A small fraction of consumed food is produced in local urban agriculture, while most comes directly from regional farms in a variety of ways, such as the greenmarkets (Gittleman et al. 2010; Cohen and Ackerman 2011) in a promising trends in developing urban ecosystem services. Other trends of producing urban food and providing other ecosystem services are the urban farms, private gardens, community gardens, roof farms, etc. These ecosystem services provided are the habitat to biodiversity, recreation opportunities, runoff retention, socio-ecological support, etc. (McPhearson and Tidball 2013). Practice communities can enhance contacts and exchange knowledge and experiences to design, implement, and evaluate urban green infrastructure, biodiversity,, and urban ecosystem services. Local community groups and nonprofit organizations operating urban farming may use agriculture as a means to provide ecosystem services, economic, educational, and other community benefits (EcoStation: NY Inc. 2013; Farming Concrete 2011; The Battery Conservancy 2012; Added Value 2013). Green local partnerships between local community groups, nonprofit organizations, and agencies involved in planning, designing, and managing policy for the provision of ecosystem services to community residents have a critical role on site project. The institutional policy analysis may determine the distribution of rights to allocate benefits and values from the ecosystem services. The governance complexity and uncertainty of ecosystem services are challenged by assigned rights and coordination of ecosystems’ biophysical structures and functions, the policy formulation and management practices at the different governance levels, and their institutional interplay. One limitation to manage the complexity of the socio-ecological system dynamics is the availability of data useful to know how to restore and increase the resilience of ecosystem services. Management of sustainable natural resources requires market financial incentives and payments for ecosystem services schemes. Payments for ecosystem services is a policy of rights assumed to use and produce ecosystem services, in accordance with rights already determined and governance systems in place. For example, the right to clean water and beautiful landscape may determine the right of a fisherman to a fishing quota. Investing in ecosystem services through the

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payments’ approach does not necessarily a trade-off between the needs of landowners, resource users, and the objectives of other multiple stakeholders. Spatial multi-scale assessment of urban green infrastructure and urban ecosystem services framework can be connected to urban local policies involving a network of organizations to develop and use different governance levels. Evaluation of the structural and functional role of the urban green infrastructure takes into account the contributions on multi-scales urban development and the relationship of biodiversity and urban ecosystem services. Further research on the obstacles for using the ecosystem services framework and the potential solutions to achieve sustainable urban resilience is required. Urban biodiversity influencing ecosystem services production and supply is a process that requires further analysis (Faeth et  al. 2011). Research is required to analyzing inequalities between the urban spatial distributions between supply and demand of ecosystem services.

5  Conclusions The results of this theoretical and conceptual literature review lead to an assessment used to integrate, adapt, conserve, and monitor biodiversity linked to urban sustainability, healthy socio-ecosystem management, and the outcomes in ecosystem services provided to the cities, in such a way that learning from biodiversity changes and enhances the urban ecosystem resilience. Well-functioning and healthy ecosystem services contribute to the sustainability of the city. Urban ecosystems must integrate uncertainty in change into city management processes in delivering sustainable ecosystem services supported by practical techniques. Urban communities can protect their natural resources and spaces, natural landscapes, engineering infrastructure, and urban ecosystems through the use of asset management systems’ techniques for the provision of urban ecosystem services, the operating budgets for their maintenance and support, the integrating value and other several options such as zoning, and rights for land acquisition and development. Urban sustainability and environmental performance integrates biodiversity and socio-ecosystems for the provision of better quality ecosystem services supported by green infrastructure design into the green projects aimed to achieve economic and environmental benefits in energy, water, air quality, transportation and logistics, waste and materials, climate, bioeconomics, etc. Urban ecosystem services benefit population offering opportunities for recreation and education besides clean air and water, flood control, etc. Feedbacks resulting in poor performance of the ecosystem services tend to intensify human modification by creating degradation of socio-­ ecosystems and poverty. Green and natural infrastructure and urban ecosystems should be integrated into asset management of sustainable development programs to ensure the ecosystems’ functions are properly yielded the expected environmental services. In sustainable

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urban planning and development, ecological policies for green interventions are implemented to improve urban ecosystem services quality, the environmental sustainability, and to instrument the measure of the ecological value through the use of ecological indexes. The performance of biodiversity and socio-ecosystem services resilience can be promoted by implementing green infrastructure technology in adaptive management frame under the scenarios which approach to analyze the impact and effects of green policies on urban sustainability. The socio-ecological feedbacks are the base for absorbing uncertainty and ambiguity in different urban sustainability scenarios which pose a unique challenge for regime shifts in ecosystem services. Finally, the ecosystem services and human well-being may suffer irreversible severe declines if sustainability is not built based on biodiversity of socio-­ ecosystems, green infrastructure, and natural capital.

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Regional Innovation Ecosystems: Tuning the Regional Engine’s Helix Through Smart Specialization João Lopes, João J. Ferreira, Márcio Oliveira, Luís Farinha, and José Oliveira

Abstract  The present research aims to contribute for the analysis of the theoretical evolution of the triple helix, quadruple helix, quintuple helix, and multiple helix concepts, embracing the dynamic interaction of different stakeholders in the context of regional innovation systems. Following a preliminary literature review on the subject, it was possible to develop a systematic literature review with a bibliometric analysis of research that addressed the evolution of the triple helix until the multiple helix, into the regional innovation systems perspective. Extensive research was conducted using the Web of Science database between 1990 and 2018, covering a total of 378 articles, generators of 9991 citations. Four clusters were found in the literature for this field of research: R&D Collaborations and Innovation; Entrepreneurial Activity in Entrepreneurial University; Triple Helix Dynamics; and Quadruple Helix in Regional Innovation Systems. New theoretical perspectives

J. Lopes (*) ISAG – European Business School & NIDISAG – Research Unit of ISAG & NECE – Research Centre in Business Sciences, Porto, Portugal e-mail: [email protected] J. J. Ferreira University of Beira Interior and NECE—Research Center in Business Sciences, Covilhã, Portugal e-mail: [email protected] M. Oliveira Polytechnic Institute of Leiria & NECE – Research Centre in Business Sciences, Leiria, Portugal L. Farinha Polytechnic Institute of Castelo Branco and NECE—Research Center in Business Sciences, Castelo Branco, Portugal e-mail: [email protected] J. Oliveira European Business School, ISAG & University of Minho & NIDISAG Research Unit of ISAG, Porto, Portugal e-mail: [email protected] © Springer Nature Switzerland AG 2020 L. Farinha et al. (eds.), Regional Helix Ecosystems and Sustainable Growth, Studies on Entrepreneurship, Structural Change and Industrial Dynamics, https://doi.org/10.1007/978-3-030-47697-7_7

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based on bibliometric analysis and new research paths have been identified, aiming to better understand the regional interaction of stakeholders for innovation and entrepreneurship. Keywords  Triple helix · Quadruple helix · Quintuple helix · Multiple helix · Regional innovation systems

1  Introduction Innovation in companies has been one of the key factors of competitiveness and growth in different markets. The phenomenon of the influence on innovation environments to increase and accelerate the innovation capacity of the organizations within it, besides the contribution to the economic and technological development of countries, has also gained prominence in recent years in the implementation of policies and practices by organizations. The main foundations of this growing movement are not exhaustively sustained in the concepts of national innovation system (Nelson 1993), triple helix (Etzkowitz and Leydesdorff 2000), and open innovation (Chesbrough 2003). University-­ industry-­government relations (triple helix) are strategic to foster the dynamics of innovation. The triple helix model is part of the interaction between three main actors, the university, industry, and government, in order to explain the dynamics of technological innovation. However, this model has evolved over time, and this triad has received new actors that have strengthened the process of generating innovation and knowledge, considering new aspects for sustainable development (Etzkowitz and Leydesdorff 2000). The increasing importance of the triple helix model has led to the emergence of a rich body of theoretical and empirical research to discuss new methods for knowledge creation (Chung and Park 2014). Carayannis and Campbell (2009) propose a revised triple helix model which he calls the quadruple helix. In addition to the university-industry-government, civil society was added. However, sustainable issues led to the need to include the environment as a transdisciplinary framework, which analyzes sustainable development and social ecology, in the first extension of this model (Carayannis and Campbell 2012; Casaramona et al. 2015). Following a preliminary literature review on the subject, it was possible to find the lack of a systematic literature review with a bibliometric analysis of research that addressed the triple, quadruple, fivefold, multiple helix and regional innovation systems. Therefore, the aim of this research is to systematize the studies on Triple, Quadruple, and Quintuple Helix, including recent approaches such as multiple helix and regional innovation systems.

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The present research contributes to the analysis on the theoretical contribution for the triple helix evolution in the regional innovation systems. To perform this bibliometric review, we used the Web of Science database and applied some filters to exclude publications that are not relevant to the research. In this context, we will look at trends in this field, as well as identifying the most important subthemes. We will seek to open new horizons for future publications and to reveal the most relevant authors and journals on this subject. Identifying the authors who produced most articles clearly addresses the important need to understand how, when, and where interest in this topic arose. This approach also points to the studies that served as the basis for building research on the subject under research.

2  Literature Review Nowadays, we can see a substantial increase in the competitiveness between organizations that are aware of the rapid and profound changes that are occurring in society and are increasingly betting on creativity and innovation as means to generate competitive and sustainable advantages over time. According to Etzkowitz (1996), more than ever these changes have brought industry closer to the research being done in universities, whose benefits are revealed through the growing origination of marketable innovations. Networks began to emerge in the late 1990s when successful regions in southern Europe began to experience an economic crisis (Hadjimichalis and Hudson 2006) and underpinned the creation of Regional Innovation Ecosystems (RIS). RIS is understood as a set of public and private interests, formal institutions, and other interacting organizations (Doloreux 2003). Regions can thus become drivers of new ideas and provide opportunities for entrepreneurship as well as the discovery of valuable new knowledge (Huggins and Williams 2011; Ikeda 2008). Nowadays, triple helix model is one of the most popular and accepted metaphors to explain the ability to transform scientific knowledge into technological innovation. The triple helix model was developed in the most important technology centers and parks around the globe. The model suggests that a higher rate of technological development is only possible from the partnership between government, companies, and universities (Etzkowitz and Leydesdorff 2000). The fundamental idea is that technological innovation is only possible when the knowledge developed in universities is channeled to respond to the economic and social demands that private entities and companies analyze, manage, and later commercialize, with the support of public policies aiming to coordinate the development of the potential of sectors and regions and to manage contractual models of partnerships between different actors (including patents). In this perspective actors (governments, industry, and universities) need to increase their interaction to create innovations that contribute to economic development, competitiveness, and social welfare (Coronado et al. 2004; Etzkowitz and Brisolla 1999; Lombardi et al. 2012; Malecki 2005; Ritala and Huizingh 2014).

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Therefore, the triple helix model has emerged as a reference framework for the analysis of knowledge-based innovation systems, emphasizing the multiple and reciprocal relationships between the three main actors in the process of knowledge creation and capitalization (Lombardi et al. 2012). While traditional models of economic growth were focused on capital and labor as drivers of growth, new concepts and models of growth today have incorporated a broader range of growth factors including, but not limited to, growth, human capital, knowledge, innovation, and even intangibles such as entrepreneurship (Audretsch and Keilbach 2008). Hence, the traditional university-industry-government triad was strengthened with new models of knowledge generation, including society (Quadruple helix) and environment (Quintuple helix), as important helixes in the dynamics of innovation. In this regard, Carayannis and Campbell (2009) point out that the Quadruple Helix model adds the perspectives of the media and culture, as well as that of civil society. From this perspective, there is a need for a broad understanding of knowledge production and the application of innovation, and a more integrated public in the dynamics of innovation is required (Carayannis and Rakhmatullin 2014). In this Quadruple Helix, civil society can also be perceived as a user or beneficiary of innovation, acting as a driver of innovation processes. From this perspective, these users or beneficiaries are central to the model and encourage the development of innovations that are relevant to them (Arnkil et al. 2010; Carayannis and Rakhmatullin 2014). The Quintuple Helix emphasizes the natural environments of society to produce knowledge and innovation, contextualizing the approaches defended by the triple helix and quadruple helix models (Carayannis and Campbell 2009; Carayannis and Rakhmatullin 2014). In this model, the environment is considered as the main factor for the preservation, survival, and vitalization of humanity and needs to be inserted in regional development policies and proposals (Carayannis et al. 2012). The fivefold helix can be proposed as a framework for the transdisciplinary (and interdisciplinary) analysis of sustainable development and social ecology (Carayannis et al. 2012). This fivefold helix can then be a motor for new knowledge and innovation in response to environmental challenges, being a broader perspective of socio-­ ecological transformations and natural environments (Grundel and Dahlström 2016). More recently, the concept of multiple helix has emerged. The multi-helix ecosystem model for sustainable competitiveness represents the interaction of different actors (industry-government-university-society) that together promote entrepreneurship. Entrepreneurship must emerge from ideas that emerge through R&D and generate economic, social, and environmental impacts, which in turn develop regional competitiveness (Lopes and Farinha 2018; Peris-Ortiz et al. 2016).

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3  Data and Method Bibliometric analysis of publication and citation data is currently used in almost every country in the world, particularly universities and research institutions. The simplest but useful method for measuring a researcher’s impact is citation counting (Garfield 1972; Nerur et al. 2005), and, among other more complex citation-based indices, the “h” index (Hirsch 2005) became the most popular (Garfield 1972; Nerur et al. 2005). Through the bibliometric studies by subject, it is possible to project the hierarchical structure and weight of each subfield in any discipline. It is even possible to provide knowledge about different positions in the subject fields. Consequently, the classification of journals by various criteria ensures a detailed assessment of the subject level of information science (Özen 2018). For this research, we used the Web of Science (WOS), more specifically the Web of Science Core Collection, published by Thomson Reuters between 1990 and 2018. The data found in WOS is considered to be the central source of information for extensive bibliometric exploration in the social sciences. The data covered in WOS has a high level of acceptance in the academic community and is therefore essential for our analysis. WOS is still the only bibliographic database that normalizes the references cited for each article record throughout the collection, allowing for bibliographic couplings. With other databases, it is usually not possible to make bibliographic couplings (Franceschini et  al. 2016; Liu et  al. 2015; Lopes and Farinha 2019). We used five key search terms such as “Triple Helix,” “Quadruple Helix,” “Quintuple Helix,” “Multiple Helix,” and “Regional Innovation Systems,” based on SCI-EXPANDED, SSCI, A & HCI, CPCI-S, CPCI-SSH, ESCI, CCR-EXPANDED, and IC in 5956 publications; then, only articles and reviews were selected leaving 5225 publications. The restriction to articles and reviews was used because they are considered documents with validated knowledge (Podsakoff et  al. 2005). Other documents (i.e., books, chapters, and conferences) have been excluded due to their limited variability and availability (Jones et al. 2011). It was also selected the Web of Science Categories of Management or Business or Regional Urban Planning or Economics with 393 publications (Lopes et al. 2019; Maziak et al. 1998). Finally, we selected only publications written in English with 378 articles for our analysis (Lopes et al. 2018); the selection process of the articles is summarized in Fig. 1.

4  Findings and Discussion 4.1  Publications and Citations by Year A total of 378 articles and 9991 citations (1996–2018) resulted from the selection criteria applied in the Web of Science Core Collection database; Fig. 2 shows the evolution of publications and citations over the years.

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Database: Web of Science Core Collection

Web of Science Categories: Management or Business or Regional Urban Planning or Economics = 393

Keyword: ‘‘Triple Helix’’ or ‘‘Quadruple Helix’’ or ‘‘Quintuple Helix’’ or ‘‘Multiple Helix’’ and ‘‘Regional Innovation Systems’’

Type of document: Articles and Reviews = 5225

Year of Publication: 1990-2018 = 5956

Indexes: SCI-Expanded, SSCI, A&HCI, CPCI-S, CPCI-SSH, ESCI, CCREXPANDED, IC

Language: English = 378

Fig. 1 Methodology

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Fig. 2  Publications and citations by year

As can be seen in Fig. 2, citations have been steadily increasing over the years, summiting in 2018 with 1681 citations. Regarding the number of publications, it is noted that until 2002 only 11 articles were published (first phase); from 2003 to 2007, 38 articles were published (second phase); and from 2008 to 2018, 329 articles were published (third phase). In the earliest phase, the first article to emerge was “The Triple Helix: Academic-­ Industry-­ Government Relations—Implications for the New  York Regional Innovation Environment” by Etzkowitz (1996). This article addresses the shift to an

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innovation environment on which universities and other knowledge-producing organizations play a sturdier role. Local, regional, and national governments are found to play a more prominent role in industrial policy. Companies are beginning to show their interest in technology development. Strategic horizontal and vertical alliances to develop and market new products are beginning to emerge. It is in this context that the concept of triple helix is born as academia, industry, and government interactions. In 2000, the most cited article on the topic under review was published, The Dynamics of Innovation: from National Systems and “Mode 2” to a Triple Helix of University-Industry-Government Relations, by the authors Etzkowitz and Leydesdorff (2000). In this research, the triple helix is compared with alternative models to explain the current research system in its social contexts. The research evidences that the role of universities in societies is increasingly knowledge-based. On a second phase, academics begin to investigate deeper into the topic, and several articles appear addressing in general the university-industry links (D’Este and Patel 2007; Eun et al. 2006; Parker 2006; Van Looy et al. 2006). In this second phase, the article by D’Este and Patel (2007), “University-industry linkages in the UK: What are the factors underlying the variety of interactions with industry?”, examines the different channels through which academic researchers interact with industry and the factors that influence the involvement of researchers in various interactions. In 2008 and the following years, there was a great interest in the subject by academics. Investigations pointing to divergences between the motivations for the different actors to interact with each other begin to be observed. On the one hand, the industry is motivated by the commercialization that new knowledge can bring to their companies. Nevertheless, despite being appointed to do research and consulting for companies, the real motivation of universities and researchers is research. Regional governments stand apart in this divergence (Ankrah and Al-Tabbaa 2015; D’Este and Perkmann 2011; Hessels and van Lente 2008). It is at this stage that the concepts appear: quadruple helix (Carayannis and Campbell 2009), quintuple helix (Carayannis et al. 2014), and multiple helix (Lopes and Farinha 2018). The quadruple helix allows and emphasizes the coexistence and co-evolution of different knowledge and innovation paradigms: the competitiveness and superiority of a knowledge system are highly determined by its adaptive ability to combine and integrate different modes of knowledge and innovation. The quadruple helix emphasizes the importance of integrating the public perspective based on media and culture. The result is the establishment of an emerging knowledge and innovation ecosystem that is well-configured for the knowledge economy and society (Carayannis and Campbell 2009). Carayannis et al. (2014) examine and compare the dynamics of competition in different contexts, adopting a multilevel approach to help understand and analyze the complex phenomenon of intra-organizational competition. Value creation from an ecology perspective is discussed to improve the conceptualization of quintuple helix. The quintuple helix model brings an additional new perspective to the quadruple helix by adding a new “natural environment” helix. The quintuple helix

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represents a “five-helix model,” where the environment or natural environments represent the quintuple helix (Campbell et al. 2015). The multiple helix ecosystem model for sustainable competitiveness represents the interaction of different actors (industry-government-university-society) that together promote entrepreneurship. This same entrepreneurship must emerge from ideas that, in turn, emerge from research and development and together generate economic and social impacts, leading to regional development and competitiveness. In the end, the model has as its output environmental impacts that aim, for example, to reduce carbon emissions (Lopes and Farinha 2018).

4.2  Most Productive Journals In Table 1 it is possible to observe the journals with the largest number of articles. The 378 articles under review are published in 103 journals. Table 1 exhibits only the journals that have at least three articles published on the theme under research. As seen in Table 1, only the 23 journals with the highest number of publications are displayed. The journal with most articles published is Technological Forecasting and Social Change. The journal has 40 articles published, representing 10.58% of the sample, and has 472 citations. The impact factor is 3815 and falls under Quartile Q1. This journal has two categories at WOS: (1) business and (2) regional and urban planning. The second journal with the most articles published is Research Policy with 39 publications, representing 10.32% of the sample. Note that Research Policy is the journal with the most citations in the field under research (6890). The journal has an impact factor of 5425 and fits into Quartile Q1. Research Policy has only one category in WOS management. The third journal with the most articles published is Science and Public Policy with 31, which corresponds to 8.20% of the sample. With regard to citations, the journal has 305, has an impact factor of 1575, and falls under Quartile Q2. Science and Public Policy has three categories in WOS: (1) environmental studies, (2) management, and (3) public administration.

4.3  Authors and Countries with the Greatest Productivity Next, we will check which authors have the most citations and articles published on the theme under research. This analytical procedure seeks to identify patterns and trends through the research of citations (Ferreira 2011; Lopes et al. 2019). To see exactly which authors and co-authors are most cited in the 378 articles, we provide Table 2, showing the number of articles published and their citations. The 378 articles present 830 authors/co-authors. In Table 2 we will present only the authors who

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Table 1  Journal with the highest number of articles Rank Journals 1 Technological Forecasting and Social Change 2 Research Policy 3 Science and Public Policy 4 Journal of the Knowledge Economy 5 European Planning Studies 6 International Journal of Technology Management 7 R&D Management 8 Technovation 9 Journal of Technology Transfer 10 Technology Analysis & Strategic Management 11 Regional Studies 12 Technology Innovation Management Review 13 Foresight and STI Governance

39 31 26

6890 305 95

22 19

Q1 Q2 Q2

163 357

15 15 13 13

494 564 530 204

2.354 5.25 4.037 1.739

Q3 Q1 Q1 Q2

8 6

101 22

Q1 –

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Asian Journal of Technology Innovation The Annals of Regional Science Entrepreneurship and Regional Development European Journal of Innovation Management Imp Journal

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3.074 Emerging citation Emerging citation 0.706

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1.075 2.928

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Emerging citation 1.855

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International Journal of Innovation and Technology Management International Journal of Innovation Science Journal of Business Economics and Management Local Economy

Emerging citation Emerging citation

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Q1

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Science, Technology and Society

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24

Emerging citation Emerging citation

15 16 17 18 19

20 21

Source: http://apps.webofknowledge.com Source: https://www.scimagojr.com

b

Impact factor Quartile (2018)b Citations (2018)a 472 3.815 Q1 5.425 1.575 Emerging citation 2.101 1.16

14

a

Number of publications 40

Q1 Q2

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-

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Table 2  Most important authors in the literature Author and Rank co-author(s) 1 Etzkowitz, H 2 Leydesdorff, L 3 Gebhardt, C

Number of publications 6 11 1

4 5

Terra, Brc Webster, A

1 1

6 7

D’este, P Patel, P

1 1

8

Hessels, Laurens K. Callaert, J Debackere, K Van Looy, B Gulbrandsen, M Smeby, Jc

2

9 10 11 12 13 14 15

16 17 18 19 20 21 22

Carayannis, Elias G. Van Lente, Harro Campbell, David F. J. D’Este, Pablo Perkmann, Markus Meyer, Martin Klofsten, M Cooke, P Sharif, Naubahar

Author and Citations Rank co-author(s) 3519 23 Ranga, M 2872 24 Zimmermann, E 888 25 Nieminen, Mika 888 26 Auranen, Otto 888 27 Dooley, Lawrence 538 28 Lupton, Gary 538 29 O’Reilly, Caroline 376 30 Philpott, Kevin

Number of publications 1 1

Citations 186 186

2

181

1 1

157 144

1 1

144 144

1

144

2 2 2 1

336 336 336 309

31 32 33 34

Benner, M Sandstrom, U Miller, Kristel Meyer, M

1 1 6 1

131 131 125 122

1

309

35

5

120

10

306

36

1

114

1

296

37

1

114

6

291

38

Mcadam, Rodney Martinelli, Arianna Von Tunzelmann, Nick Park, Han Woo

3

111

2 1

272 270

39 40

Almeida, M 1 De Mello, Jmc 1

107 107

2 1 1 3

210 207 197 187

41 42 43

Eun, Jong-Hak 1 Lee, Keun 1 Wu, Guisheng 1 – –

105 105 105 –

have at least 100 citations. Table 2 will show the 43 authors/co-authors most cited in the theme under analysis. As seen on Table 2, the author with the most citations is author Etzkowitz, H with 3519 citations in six published articles. 24.53% of publications from Etzkowitz, H are in the educational research category. The other categories the author investigates are social sciences interdisciplinary; history; philosophy of science; management; multidisciplinary sciences; sociology; information science; library science; regional urban planning; business; and environmental studies, among others. Secondly, with 2872 citations in 11 published articles comes author Leydesdorff, L. 67.31% of the

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articles published by the author are in the information science library science category. The other categories in which the author publishes are computer science interdisciplinary applications; computer science information systems; management; interdisciplinary social sciences; multidisciplinary sciences; and communication, among others. Regarding the authors with the most published articles, author Leydesdorff, L. is the one who publishes the most on the topic and the second with the most citations. Second is author Carayannis, Elias G. with 10 publications and 306 citations. Author Carayannis, Elias G. has 67.50% of publications in the management category. The other categories that the author publishes are industrial engineering; operations research management science; business; economics; multidisciplinary engineering; and regional urban planning, among others. Concerning the nationalities, the 378 articles contain 68 nationalities of the authors (we did not include the co-authors in this analysis). It was found that the country that publishes the most is the USA with 55 authors, which corresponds to 14.55% of the sample. Secondly, England appears with 53 authors, corresponding to 14.021% of the sample. In third place is the Netherlands with 31 authors, which corresponds to 8.20%. With less than 8%, there are other countries like Sweden, Italy, Finland, China, South Korea, Australia, Germany, Spain, Brazil, and Portugal, among others.

4.4  Cluster Analysis From the 378 publications, 370 are articles, which correspond to 97.88% of the sample. The remaining eight publications are considered reviews, which is 2.12% of the sample. Regarding the themes under analysis, 344 publications address Triple Helix, which corresponds to 91% of the sample. There are 53 publications dealing with Quadruple Helix (14.02% of the sample), 11 publications dealing with Quintuple Helix (2.91% of the sample), 2 publications analyzing Multiple Helix (0.53% of the sample), and 34 publications addressing the Regional Innovation Systems (8.99% of the sample). It is noted that there are 66 publications that analyze more than one topic simultaneously (17.46% of the sample). To understand how the field of research is divided into research groups, the citation networks of the 378 articles were analyzed. VOSviewer excluded 35 publications from the review because they did not yet have any citations. The analysis thus covers 343 publications. Thus, VOSviewer software detailed the reference network and identified the most relevant clusters on the topic. Each color identifies a cluster (Fig. 3). Analyzing the cluster network that VOSviewer has grouped together, we designate the following four clusters: 1 . R&D Collaborations and Innovation 2. Entrepreneurial Activity in Entrepreneurial University 3. Triple Helix Dynamics 4. Quadruple Helix in Regional Innovation Systems

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Fig. 3  Cluster network

As seen in Fig.  3, cluster 1 encompasses 105 articles and is called “R&D Collaborations and Innovation.” Cluster 1 generally includes articles addressing the collaboration of various actors in R&D and innovation activities. In regional innovation networks, the collaboration between different actors is essential for the success of regional development. However, over the years, there have been several difficulties for collaboration by the different regional actors. There are differences in culture, organizational functioning, and incentive mechanisms, as well as different objectives of the various actors involved, making it difficult to create and sustain collaboration (Bjerregaard 2010; Johnson 2008). As the interaction between local businesses and researchers becomes increasingly active, R&D investment from companies, universities, and research institutes has a stronger effect on building the regional innovation system (Jiao et al. 2016). Concerning cluster 2, it incorporates 103 publications and is called “Entrepreneurial Activity in Entrepreneurial University.” This cluster addresses the entrepreneurial university and its activities. Over the past two decades, regional governments have increasingly encouraged universities to encourage and stimulate their students for entrepreneurship. The “entrepreneurial university” is viewed by

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regional governments as a strategy for economic development in an increasingly knowledge-based society (Etzkowitz et  al. 2000). University researchers interact with industry using various channels such as consulting, contract research, joint research, training, and patent or spin-out activities (D’Este and Patel 2007). As more regional actors become involved in entrepreneurial activities, academic publications usually also increase (Van Looy et al. 2004, 2006). Regarding cluster 3, it has 88 publications and was called “Triple Helix Dynamics.” Cluster 3 generally addresses the dynamics of the triple helix. The triple helix (university-industry-government) aims to foster economic development (Etzkowitz and Leydesdorff 2000; Leydesdorff 2000). This approach can be used to understand why high growth sectors are concentrated in specific locations. Differences in formal (i.e., institutional arrangements) and informal networks are usually influenced by wider geographical, political, economic, and social environments (Smith and Bagchi-Sen 2010). The triple helix model has evolved over the years. Currently, the term “Multiple Helix” is already published in the literature. Finally, cluster 4 comprises 47 articles and identified as “Quadruple Helix in Regional Innovation Systems.” In general, this cluster has articles that address Quadruple Helix and Regional Innovation Systems. According to Carayannis and Campbell (2009), the Quadruple Helix (university, industry, government, society) is born from the co-evolution of different paradigms of knowledge and innovation. Quadruple Helix emphasizes the importance of integrating the media and culture-­ based public perspective as well. Therefore, new ecosystems have emerged based on the knowledge society (Carayannis et al. 2018). In RIS, as a rule, the roles of innovation creators in ecosystems are addressed. Actors are vital in the creation of RIS and in the policies of regional governments. The more regional innovation and research activities are well implemented, the more easily companies develop their international connections. Public-private research collaboration and the international connections of these actors are the main determinants of developing a RIS (Lew et al. 2018).

5  C  onclusions, Practical Implications, Limitations, and Future Research Lines Nowadays, RIS is seen as the driving force behind wealth creation for the regions. Consequently, RIS has enabled the creation of regional clusters, where the regional level is coincident with the geographical level. Regions are increasingly implementing innovation policies (Grundel and Dahlström 2016). In this context, the present research aims to systematize the articles about the Triple, Quadruple, and Fifth Helix, Multiple Helix, and regional systems of innovation. We have verified the evolution of publications on the theme under analysis. It was found that the first article appeared in 1996, and until 2018 the interest on the subject by academics has been increasing. In fact, it was found that the vast majority

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of articles were published between the years 2015 and 2018. Undoubtedly, as more articles are published, the number of citations will also increase; in our observation they have reached their peak in the year 2018. We also analyzed the magazines that publish more as well as the most impacting authors on the theme under investigation. The magazines that publish most are Technological Forecasting and Social Change, Research Policy, and Science and Public Policy. The magazine with the most citations is Research Policy. Regarding the most impactful authors, the one with the most published articles is Leydesdorff, L, and Carayannis, Elias G. However, the most cited author is Etzkowitz, H. The network of references identify the most relevant clusters in the subject under investigation, (1) R&D Collaborations and Innovation; (2) Entrepreneurial Activity in Entrepreneurial University; (3) Triple Helix Dynamics; and (4) Quadruple Helix in Regional Innovation Systems. In cluster 1 (R&D Collaborations and Innovation), it was found that it includes articles that address to stakeholder collaboration in R&D and innovation activities. It was also confirmed that there are deficiencies in the collaboration of stakeholders in the regions where they operate. The difficulty of creating sustainable collaboration between different regional actors is since there are few incentive mechanisms, as well as different organizational cultures (Bjerregaard 2010; Johnson 2008). Thus, we leave some questions to be investigated: What are the incentive mechanisms for collaboration that policy makers foster? Are incentive policies for R&D and innovation activities effectively implemented? What types of organizational culture have the most R&D and innovation activities with regional stakeholders? In cluster 2 (Entrepreneurial Activity in Entrepreneurial University), articles on the entrepreneurial university and its activities were written. As so, the entrepreneurial university is regarded by regional policy makers as a strategy for socioeconomic development in an increasingly knowledge-based society (Etzkowitz et al. 2000). In this context, regional actors need to be more involved in activities promoted by universities. The more universities collaborate with the different regional actors, the greater will be the academic publications, and consequently the business activity of the region will increase (Van Looy et al. 2004, 2006). Having said that, we raised some research questions: Is regional entrepreneurial activity affected by the number of academic publications that universities produce? What is the impact that academic publications have on regional actors where universities are located? What is the importance of academic publications in entrepreneurial activity? Cluster 3 (Triple Helix Dynamics) addresses the dynamics of the triple helix in the regions. The triple helix basic premise is the economic development of the regions (Etzkowitz and Leydesdorff 2000; Leydesdorff 2000). The triple helix has been used to understand why high growth sectors are centered on specific locations (Smith and Bagchi-Sen 2010). Therefore, we formulated some unanswered questions to be included in future researchers: How do geographical, political, economic, and social environments influence the dynamics of the triple helix? How to measure the performance of triple helix dynamics? Is the triple propeller a current or outdated model?

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Cluster 4 (Quadruple Helix in Regional Innovation Systems) tackles articles that address Quadruple Helix and RIS. Different regional actors are central to the creation of RIS and the policies of regional governments. Collaboration in research between the public-private sector and the international connections of these regional actors is fundamental to the development of a successful RIS (Lew et  al. 2018). Concerning the Quadruple Helix, it highlights the relevance of integrating the helix of the public perspective based on media and culture, i.e., society. In this new context, new ecosystems have emerged based on the knowledge society (Carayannis et al. 2018). Therefore, some questions should be analyzed in future studies: What is the impact society has on the development of RIS? How do universities provide knowledge to the society where they operate? Does society have an interest in the new knowledge that universities acquired? How relevant is each of the actors in Quadruple Helix to the new knowledge society? If there are triple, quadruple, quintuple, and multiple helix, which one has the most impact on the regions? With the present research, it is possible to verify the main investigation trends in the subject under current and future analysis. This research also expects to clarify the existing literature. Regarding the practical implications, we believe policy makers need to recognize the importance of stakeholders in the socio-economic development of their regions. Regions must be having as premise the increasing collaboration between different regional actors. Local policy makers need to promote and increase incentives for R&D and collaborative innovation. This research has some limitations that are present in any bibliometric analysis. Choosing the database as well as the key search terms may have excluded articles that might be of interest for the present research. The usage of other databases may have presented different results.

References Ankrah, S., & Al-Tabbaa, O. (2015). Universities-industry collaboration: A systematic review. Scandinavian Journal of Management, 31(3), 387–408. Arnkil, R., Järvensivu, A., Koski, P., & Piirainen, T. (2010). Exploring quadruple helix outlining user-oriented innovation models. Audretsch, D. B., & Keilbach, M. (2008). Resolving the knowledge paradox: Knowledge-spillover entrepreneurship and economic growth. Research Policy, 37(10), 1697–1705. Bjerregaard, T. (2010). Industry and academia in convergence: Micro-institutional dimensions of R&D collaboration. Technovation, 30(2), 100–108. Campbell, D. F. J., Carayannis, E. G., & Rehman, S. S. (2015). Quadruple helix structures of quality of democracy in innovation systems: The USA, OECD countries, and EU member countries in global comparison. Journal of the Knowledge Economy, 6(3), 467–493. Carayannis, E.  G., Barth, T.  D., & Campbell, D.  F. (2012). The Quintuple Helix innovation model: Global warming as a challenge and driver for innovation. Journal of Innovation and Entrepreneurship, 1(1), 2. Carayannis, E. G., & Campbell, D. F. J. (2009). ‘Mode 3’ and ‘Quadruple Helix’: Toward a 21st century fractal innovation ecosystem. International Journal of Technology Management, 46(3–4), 201–234.

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Regional Industrial Restructuring Jan Ole Rypestøl

Abstract  This chapter studies regional industrial restructuring and focuses on the role of the ecosystem for restructuring. The chapter examines more precisely how various types of ecosystems hold different preconditions for regional industrial change. The question is how various stages of the restructuring process are affected by the ecosystem. The chapter moves beyond traditional evolutionary economic geography by examining a wide range of actors, mechanisms, and outcomes. A particular focus is placed on the importance of asset modification for regional industrial restructuring. The chapter concludes by suggesting that thin regional innovation systems (RISs) mainly support the reuse of existing assets which mainly leads to regional industrial path extension. Thick and diversified RISs, on the other hand, are better conditioned to promote more radical changes like asset creation and asset destruction which can support regional industrial path creations. Keywords  Regional industrial restructuring · Ecosystem · Asset · Asset modification · Path development

1  Introduction Today, increased competition from digitalisation and increased globalisation challenge regional economies as never before (Randall et  al. 2018). To meet this increased competition, regional firms and industries need to restructure to maintain their competitiveness. How such processes of regional industrial restructuring unfold is at the core of this chapter. In this regard, a specific focus is placed on the role of the ecosystem in industrial restructurings and the research questions addressed are: How do regional industries restructure? How does the ecosystem influence such processes?

J. O. Rypestøl (*) NORCE Norwegian Research Centre, Kristiansand, Norway University of Agder, Grimstad, Norway e-mail: [email protected] © Springer Nature Switzerland AG 2020 L. Farinha et al. (eds.), Regional Helix Ecosystems and Sustainable Growth, Studies on Entrepreneurship, Structural Change and Industrial Dynamics, https://doi.org/10.1007/978-3-030-47697-7_8

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Industrial and spatial development is a fundamental topic within the field of evolutionary economic geography (EEG). The traditional EEG approach maintains that regional economies develop from (mainly) endogenous, technology-driven and firm-led processes that follow a path-dependent procedure (Trippl et al. 2018). The essence of this path-dependent assumption is that regional actors, networks and institutions are formed by history in an accumulative manner. This means, inherently, that regional progression in the future will be heavily influenced by a region’s current situation (Martin 2010; Martin and Sunley 2006). Within EEG, an essential development is branching processes. Such processes can be defined as industrial emerge following from incremental innovations formed by the recombination of knowledge and skills in related or unrelated industries (Boschma and Frenken 2011). More recently, critique has been raised towards the traditional EEG approach. Amongst more, the approach has been criticised for paying too little attention to the influence provided by actors and agencies outside the firm-level (Isaksen et  al. 2018a, c). Further, the approach has been critiqued for being overly focused on the importance of technology, knowledge and skills for regional industrial change while neglecting the significance of other types of regional assets (MacKinnon et al. 2018, 2019; Trippl et al. 2020). Finally, this criticism has also addressed the overemphasis on related and unrelated diversification, while less focus is placed on alternative developments and outcomes (Isaksen et al. 2018b). In this chapter, we add to the existing literature as we move beyond conventional EEG and complement the approach with insights from the regional innovation system (RIS) approach, from innovation studies and entrepreneurship literature. This triangulation of perspectives enables us to extend existing research, as it allows for an analysis of regional industrial change as a process of opportunity exploitation from a multi-actor and multilevel perspective. Through the RIS approach, we gain insight into how contextual factors can affect processes of restructurings; from the innovation literature, we explore innovation potential and how this potential links to various processes and outcomes; from the entrepreneurship literature, we advance knowledge on entrepreneurial ecosystems as well as from the actor and agency perspective to regional industrial restructurings. A central focus of this chapter is to explore the role that regional settings play in regional industrial restructurings. A key assumption here is that since regions vary in their organisational and institutional density and diversity, they will also provide varying conditions for future industrial development and growth. This assumption is supported by an extensive body of research within the EEG domain (Martin 2010; Martin and Sunley 2006), RIS literature (Asheim and Gertler 2005; Asheim and Isaksen 2002; Cooke 1992, 2001), entrepreneurship literature (Feld 2012; Mack and Mayer 2016; Stam and Spigel 2016) and innovation studies (Fagerberg et al. 2005). Common to these perspectives is an understanding that regional territorial development follows on from cooperation between regional actors. From a Triple Helix perspective (Leydesdorff and Etzkowitz 1996), cooperation is analysed between university, industry and governmental actors, while the Quadruple Helix model (Leydesdorff 2012) adds civil society as a fourth helix. In later years, the Quintuple Helix model was introduced (Carayannis et  al. 2018) and includes the natural

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environment as a fifth helix. In this paper, the RIS approach (Asheim and Gertler 2005; Cooke 1992, 2001) constitutes our academic framing of the regional helix ecosystem. As the approach incorporates all regional economic, social and institutional factors as relevant to influence innovation and entrepreneurial activity (Asheim et al. 2011), no factor is excluded. The chapter begins with an examination of how various types of ecosystems constitute different preconditions for regional industrial change. Then, it identifies important stages within the process of regional industrial restructuring and explores how each of these stages is affected by the ecosystem in which it unfolds. The chapter concludes with a short demonstration of the findings and a presentation of two figures that illustrates how regional industries restructure and how the ecosystem influences such processes.

2  Regional Preconditions for Industrial Restructurings Within innovation and entrepreneurship studies, two central findings are: (1) innovation and entrepreneurial activity are influenced by the organisational and institutional settings in which they are performed (See, e.g. Asheim and Gertler 2005; Cooke 1992, 2001; Florida 1995; Marshall 1890; Porter 2000), and (2) the region is the overall1 preferred geography for collaborations and knowledge sourcing activities (Boschma 2005; Martin and Moodysson 2011; Martin and Rypestøl 2018). This common understanding of the importance of the local is grounded in the multidimensional concept of proximity (Boschma 2005), which, in short, argues that physical co-location lowers transaction costs (Coase 1937; Williamson 1975) due to the short geographical, institutional, organisational and social distance. In this chapter, we understand regions to be eco-systemic agglomerations of organisational and institutional entities. Further, we understand RISs to be multi-­ helix ecosystems that encompass ‘all regional economic, social and institutional factors that affect the innovativeness of firms’ (Asheim et al. 2011, p. 48). Thus, with regard to the contextual dimension, we lean towards the RIS approach in this chapter. The RIS approach follows on from a long history of research that focuses on the contextual preconditions for innovation and entrepreneurship. Introduced in the 1990s, the RIS approach argues that innovation is an interactive process that includes a wide range of local actors, including firms, industries, universities, policy support organisations and policy actors. Further, it includes an institutional dimension that includes formal rules and regulations as well as informal routines, norms and values (North 1990). Based on the definition that said RIS encompasses all the regional economic, social and institutional factors that affect the innovativeness of firms, it 1  The knowledge base literature (See, e.g. Asheim and Gertler 2005) demonstrates, however, that geographical proximity is more important for experience-based knowledge while it is less important for analytical knowledge.

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follows that each RIS represents a unique regional ecosystem that either enables or constrains environments for further development (Asheim and Gertler 2005; Asheim and Isaksen 2002; Asheim and Coenen 2006; Cooke 2001, 2002). The literature distinguishes between three main categories of RIS, namely, organisationally thick and diversified RISs, organisationally thick and specialised RISs and organisationally thin RISs (Isaksen and Trippl 2016). An organisationally thick and diversified RIS is typically understood as an urbanised space that includes a variety of organisational actors. Examples of such actors include a rich portfolio of firms and industries, higher-level education organisations and several research and development (R&D) milieus such as universities, private research organisations and research-­ based firms. Further, thick and diversified RISs often host several clusters and various types of support organisations that facilitate innovation in different economic and technological fields. Such metropolitan environments are also diverse in population and are often understood as being institutionally diverse and exhibiting low cultural barriers to alternative solutions. The second type of RIS, the organisationally thick and specialised type, shares many features with the first type. However, unlike its counterpart, thick and specialised RISs are ecosystems that are tailored to support one or only a few industries. This tailoring permeates both the organisational and institutional setup that discriminates in favour of the dominant industry. This discrimination includes formal institutions and informal societal norms and values, as well as organisational elements such as cluster organisations, R&D organisations and policy support instruments. Thus, in such RISs, most of society is tailored towards supporting the dominant industry that provides jobs and security to its citizens. Finally, the literature identifies organisationally thin RISs as peripherally located ecosystems that host a lower number of firms, only a few industries, very few R&D representatives and only one (or no) cluster organisations. However, organisationally thin ecosystems do tend to be rich in bonding social capital (Granovetter 1973), which encourages the sharing of knowledge and skills through close interactions. As argued above, the three ecosystems represent different preconditions for further industrial development. In the coming paragraphs, we will discuss in more depth how these types of RISs are expected to influence the various stages of regional industrial restructurings.

3  Opportunities According to entrepreneurship literature, the exploitation of favourable opportunities is a trigger of start-ups and the emergence of new industries (S.  Shane and Venkataraman 2000). The literature finds that such opportunities are subjectively evaluated and ‘consist of ideas, beliefs and actions that enable the creation of future goods and services in the absence of current markets for them’ (Sarasvathy et al. 2003, p. 142).

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In entrepreneurship literature, two contrasting theories exist in relation to how opportunities arise. The first theory refers to as the opportunity discovery view, which argues that opportunities exist independent of entrepreneurs. Thus, according to the opportunity discovery view, the objective of entrepreneurs is to identify opportunities and exploit their potential. This discovery approach to entrepreneurship argues that opportunities exist due to an imperfection between supply and demand, and S. A. Shane (2003) finds that such competitive imperfections can arise from technological, political, regulatory, social or demographic changes that disrupt a market’s competitive equilibrium. Thus, an entrepreneur fills gaps in the market and constitutes ‘the equilibrating force whose activity responds to the existing tension and provides those corrections for which the unexploited opportunities have been crying out’ (Kirzner 1973, p. 127). The opportunity discovery view of entrepreneurship is contrasted with the opportunity creation view. According to this perspective, entrepreneurs do not passively monitor the market for profit opportunities. Instead, entrepreneurs ‘form opportunities that could not have been known without the action taken by these entrepreneurs’ (Alvarez and Barney 2007, p. 15). Thus, in this view, entrepreneurs actively create new opportunities that, to various degrees, disrupt the market and create disharmony and progress (Schumpeter 1934). No matter its origin, however, entrepreneurship literature seems to agree that entrepreneurial action originates in an identified opportunity. While some of the literature posits that such ideas often involve the prospect of gaining profit (Kirzner 1973; Schumpeter 1934), more recent contributions expand this narrow understanding of entrepreneurship to embrace other motives as initiators of entrepreneurial action. Examples of these alternative motivations include an urge to reduce social needs (Petrella and Richez-Battesti 2014), improve environmental issues (Ndubisi and Nair 2009), break free of hampering institutional arrangements (Sotarauta and Suvinen 2018) and improve ineffective structures (Kyllingstad and Rypestøl 2018). According to innovation literature, the potential embedded in entrepreneurial opportunities varies significantly. The literature separates innovations from imitations by emphasising that innovations include an element of newness (Fagerberg 2004). The literature argues that this element of newness can be classified along a continuum ranging from incremental innovation to radical innovation. Incremental innovation refers to commercialised inventions that distance themselves from existing solutions in an evolutionary manner. Radical innovations, on the other hand, represent outcomes (e.g. products, services, marketing methods or organisational methods) that did not exist previously (Fagerberg et al. 2005). Further, the literature distinguishes between various spatial scales when referring to the element of newness. OECD/Eurostat (2005, p. 57) categorises innovation novelty in terms of an innovation between ‘new to the firm’, ‘new to the market’ or ‘new to the world’. The distinction between incremental and radical innovations also refers to its potential to cause change. While the step-by-step nature of incremental innovations supports evolutionary progressions, radical innovations favour disruptive change (Schumpeter 1934). A final distinction between incremental and radical innovations is found in the knowledge base (KB) literature (See, e.g. Asheim 2007; Asheim and Gertler

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2005). The KB literature finds that experience-based knowledge and skills dominate processes that support incremental innovations, while radical innovations form mostly from formal, R&D-based, analytical knowledge. As various types of opportunities have different potential in terms of causing regional industrial change, an important question must be asked, namely, how do various regional settings influence the innovative potential of exploited opportunities? This question has been explored by Rypestøl and Aarstad (2018), who found that the exploited opportunities in thick RISs were more radical than the opportunities exploited in thin RISs. This finding aligns with that of Acs et al. (2009, p. 16) who asserted that ‘start-ups with access to entrepreneurial talent and intra-­temporal spillovers from the stock of knowledge are more likely to engage in radical innovation leading to new industries or replacing existing products’. One possible explanation for this uneven distribution of innovative exploitations is, according to Rypestøl and Aarstad (2018), that entrepreneurs in thick RISs are more exposed to analytical knowledge due to their geographical proximity to R&D milieus and analytical, knowledge-based firms and industries. The authors argue that this geographical proximity lowers the barriers to the sourcing of analytical knowledge through direct interaction, labour mobility and the monitoring of regional firms, industries and R&D milieus. These findings coincide with other studies, where it is found that diversity breeds innovative performance, as diversified environments are more generous providers of resources (see, e.g. Aarstad et  al. 2016; Castaldi et  al. 2015; Tavassoli and Carbonara 2014). Another finding related to the issue of contextual influence on opportunities highlights that new ideas tend to relate to existing solutions (Boschma 2017). This tendency occurs, according to Boschma (2017), due to the fact that entrepreneurs draw on existing regional assets and firm capabilities. This argument of conservation of existing solutions is supported also by the RIS literature that argues that RIS development is a cumulative process that strengthens existing strong industries while less support is provided to path breaking opportunity explorations (Björn T Asheim et al. 2019). The RIS literature finds, however, that this process of conserving existing solutions is less evident in thicker regions due to increased organisational and institutional diversity (Björn T Asheim et al. 2019). Even if opportunities are crucial to further development, the literature highlights that they are worthless as initiators of change if not exploited (Fagerberg 2004). Thus, understanding who the exploiters of opportunities are and what their motivation is comprises an important task if one wishes to understand regional industrial restructurings.

4  Exploiters of Opportunities More recently, the RIS approach has been criticised for its overly heavy focus on systemic factors with less attention being placed on the actors and agencies that unfold within these systems (Isaksen and Jakobsen 2017; Isaksen et al. 2018c; Qian

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et al. 2012; Rypestøl 2018; Sternberg and Müller 2005; Uyarra 2010). This skewed distribution of attention has been nuanced by research that has placed an increasing focus on the role of human agents in regional industrial change (see, e.g. Isaksen et al. 2018a, c; Rypestøl 2018). This more recent human agency literature distinguishes between two types of actors that unfold within regional industrial change processes, namely, firm-level entrepreneurs and system-level entrepreneurs (Asheim et al. 2019). The firm-level entrepreneur is covered extensively in entrepreneurship literature and is defined as a human agent who is motivated by opportunities to gain profit and exploit entrepreneurial opportunities through venture creation or innovative activity in existing firms Asheim et al. 2019; Isaksen et al. 2018a). More recent contributions have presented a supplementary entrepreneurial agent—the system-level entrepreneur. Unlike the firm-level entrepreneur, system-level entrepreneurs are not motivated by opportunities for profit (Asheim et  al. 2019). Instead, system-level entrepreneurs are motivated by system failures and aim to increase the collective performance of a specific RIS by altering its conditions (Isaksen et al. 2018a). Such altering can affect the RIS’s structural elements (e.g. the introduction of an incubator), relational elements (e.g. the introduction of a new knowledge-sharing arena) and cognitive elements (e.g. launching a PR campaign to change public opinion about a specific theme). According to Isaksen et al. (2018c), both entrepreneurial types can explore opportunities that cause regional industrial change. However, the authors argue that their motivation and the process that follows its initiation differ. Isaksen et al. (2018a); Kyllingstad and Rypestøl (2018), classify processes of change initiated by systemlevel entrepreneurs as planned processes, while processes initiated by firm-level entrepreneurs are understood to be organic processes. The literature argues that a planned process is created by system-level agencies that generate new opportunities for firm-level entrepreneurs to exploit. An example of a planned version of regional industrial change is presented and described by the Research and Innovation Smart Specialisation Strategy (RIS3), which encourages regions to initiate a forced process to identify what the region does best in terms of R&D and innovation. In this identification process, the RIS3 programme argues that one should include ‘whoever is best placed to discover the domains of R&D and innovation in which a region is likely to excel given its existing capabilities and productive assets’ (Foray et al. 2012, p. 12). In contrast, the organic version towards regional restructuring is initiated by profit-seeking firm-level entrepreneurs who are ‘triggered by an entrepreneurial vision, the discovery of a new domain and the integration of different types of knowledge to turn this discovery into reality’ (Foray 2015, p. 20). Examples of organic change processes caused by firm-level entrepreneurs include the restructuring of business models that have followed from new solutions introduced by firms like Facebook, Snapchat and Uber. As previously argued in this chapter, regional ecosystems such as RISs constitute evolutionary entities. This evolutionary notion implies that ecosystems grow by strengthening already strong elements. In this regard, well-functioning ecosystems are ‘prone to lock-in and path dependency and largely geared to generate

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incremental innovations and gradual change’ (Boschma et  al. 2017, p.  36). According to Isaksen et  al. (2018a), system-level entrepreneurs are important change-makers, as they can bring about three types of possible RIS change. First, they can create new institutions, organisations and policy instruments. Second, they can adapt existing institutions, organisations and policy instruments to fit emerging industries. Third, they can use existing institutions, organisations and policy instruments in new ways. These three types of RIS change are described by Miörner and Trippl (2017) as layering, adaption and novel application. As regional industrial restructurings are processes that can be both planned and organic, it becomes important to include contextual factors to identify which type of entrepreneurial activity is most evident in various types of RISs. This is important if one wishes to understand more about how regional industrial restructuring can be supported and fostered in various regional settings. As previously described, thick ecosystems are most often urban areas which imply that local enterprises can access large markets in their neighbourhood (Krugman 1991). Further, the urban nature of the RIS implies that regional firms can benefit from diverse and proximate regional resources (Maskell et al. 1998) and spillovers from a wide range of local actors (Acs et al. 2009). Further, thick regional settings provide most often well-developed physical infrastructure that constitutes good accessibility to markets outside the region (Rypestøl and Aarstad 2018). Finally, because of its great local market potential, its organisational and institutional diversity and its well-developed infrastructural element, thick and diversified RISs are dynamic entities that often experience a high rate of new firm formations (Duranton and Puga 2001; Fritsch 2011). As thick and diversified RISs host conditions favourable to firm-level entrepreneurs, organic restructurings caused by firm initiatives are most evident in this type of geographies (Asheim et al. 2019). Moving away from the diverse versions of RISs, the research finds that specialisation is another driver of firm revenues and productivity gains (Aarstad et al. 2016). Again, this brings the focus to firm-level entrepreneurs. Possible explanations for these revenue and productivity gains in specialised regions include the observation that such ecosystems provide economies of scale in the specialised industries (Glaeser et al. 1992). Thus, in thick and specialised RISs, new entrepreneurial ventures can potentially benefit from a wide range of specialised assets in terms of infrastructure, potential customers and knowledge spillover. Further, in thick and specialised regions, new entrepreneurial activity can benefit from well-developed proximity dimensions that increase the firms’ capability to coordinate and enhance communication in collaboration and innovation. Most important in this regard is cognitive proximity, which refers to the proximity of knowledge used for innovation purposes (Boschma 2005). However, even if a high degree of various types of proximity can contribute positively to knowledge-sharing and collaboration, too much proximity can hamper innovation and future progress (Fitjar and Rodríguez-Pose 2011; Hassink 2010). This follows because firms that are too like have limited new knowledge to exchange. Finally, thin regions host systems of innovation that are not as developed and have more systemic failures and less dynamism (Isaksen 2015). In thin RISs, local

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markets are more restricted, and the cost of transportation to alternative markets is high due to this lack of geographical proximity. Further, thin RISs constitutes environments with relatively little R&D, weakly developed clusters (if any), and a firm portfolio dominated by small- and medium-sized enterprises holding limited resources (Isaksen 2015). Such contextual settings are not optimal for firm-entrepreneurial activity. Instead, this lack of regional dynamism and diversity in thin RISs spurs system-level entrepreneurship, as there exists the need for systemic failures to be addressed and improved. Thus, we agree with Asheim et al. (2019, p. 62) that ‘...expect system-level agency and planned initiatives to be relatively more important for the further evolution of thin RISs compared to thick and diversified ones’.

5  Assets and Their Modifications In the 1990s, the resource-based view of the firm dominated the research agenda of regional industrial change (Barney 1986, 1991; Dierickx and Cool 1989; Lippman and Rumelt 1982; Prahalad and Hamel 1990). In short, this approach argues that regions develop from processes ‘…in which firms acquire or rent tangible or intangible resources and combine them in building firm-specific competencies’ (Maskell et al. 1998, p. 4). The approach holds, however, that the ability of firms to build firm-specific competencies is highly influenced by localised capabilities, identified as a unique set of regional resources. Within traditional EEG, firm-specific competencies typically comprise a firm’s technology, knowledge and skills (Boschma 2017, 2018; Boschma and Frenken 2011, 2012; Neffke et al. 2011). The prevailing theory in this traditional EEG literature is that regional industries evolve in new directions based on intense knowledge sourcing and localised learning of related firms (Boschma 2005) embedded in locations that have unique and localised capabilities (Neffke et al. 2011). In this section, we move beyond the limitations of firm-specific competencies as primarily encompassing knowledge, skills and technology. We also go beyond the narrow focus on localised capabilities as embracing only structural and institutional elements. Instead, we refer to firm-specific competencies and localised capabilities as firm-­level assets and system-level assets, respectively. Further, we maintain that the value of all asset types is dynamic, as it will change over time compared with the value of existing alternatives. Finally, and in line with MacKinnon et al. (2019), we distinguish between five types of assets. These are presented and exemplified in Table 1. In this chapter, our point of departure is the notion that the world is constantly changing and that regional industries must restructure to maintain their competitiveness. However, as has been stated in the previous section, the RIS is primarily tailored to strengthening what already exists, while less support is allocated to renewal

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Table 1  Asset types and scales Asset scale Examples of firm-level asset Land, water reservoirs, mineral mines, oil wells owned by a firm or an organisation Infrastructural Firm-specific buildings, and material machines, vehicles logistics and networks assets

Type of assets Natural assets

Industrial assets Human assets

Institutional assets

Examples of system-level asset Climate, waterfalls, coastlines, unrestricted commodity sources

Buildings, machines, etc. that are not restricted by the ownership of one (or a group of) firm(s) or organisation(s), knowledge infrastructure and physical infrastructure Generic technology that is accessible in the area, Firm-specific technology, available risk capital, availability of great financial leverage and leaders, organisational methods management In-house knowledge and skills Knowledge spillover, knowledge and skills that are available in the workforce, access to R&D knowledge through local universities and research organisations In-house formal and informal Institutional settings, laws and regulations rules and regulations, organisational culture and history

and modernisation. Central to this cementing process of RIS is a tailored base of tangible and intangible assets that have been developed over time to support dominant industries. In short, such tailored assets increases accessibility to related resources, lower risk and increase predictability, which, again, lowers transaction costs and increases profitability (Coase 1937; Williamson 1975). In this vein, we conclude that firms and regions tend to develop a foundation of assets that favours more of the same. Following the above argument, and in line with Trippl et al. 2020, we claim that processes of regional restructuring require processes of asset modification. An example of this need for asset modification  is illustrated by  the empirically rich Eyde study that explored green restructuring within the metallurgical and chemical industry in southern Norway (Kyllingstad and Rypestøl 2018). In 2012, the urge to implement more sustainable processes of production resulted in a collective cluster strategy to put sustainability at the forefront of decisions. The following process unfolded as a collaborative process between the member firms and the  cluster organisation, and today many of the cluster firms are world leaders in sustainable production and circular economic processes. During this industrial restructuring, Kyllingstad and Rypestøl (2018) found that some of the existing firm-level assets, such as finance, laboratories, technology, knowledge and competencies, were put to alternative use. Rather than serving their initial purpose, some assets were instead aimed at investigating and exploring new green alternatives. However, the case study also showed that regional firms needed to go beyond the reallocation routine to accomplish their goal of becoming more sustainable. In this case, the scope and

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complexity of the task required a significant asset upgrade. This upgrade materialised in renewed firm-level human assets like knowledge and skills, the expansion and revitalisation of the material and industrial asset base in terms of technology, machinery and equipment upgrades. As well as this, a significant effort was made to develop a unified understanding of the nature of the challenge and harmonise various institutional elements, such as the world view of leaders and workers. The example of the greening process of the metallurgical and chemical industry in Norway also illustrates two other processes of asset modification, namely, the need to asses and create new assets and the need to destruct existing ones. The need for asset creation is, in the Eyde study, illustrated by the broad range of R&D projects that were launched during the process. These processes were vital for the creation of new human assets, such as knowledge and skills, and an important driver for the introduction of new material assets, such as machines and equipment for progression. A final important modification element in this ongoing greening process was the need for asset destruction. This was, perhaps, most evident in the domain of institutions, where the firms experienced conflicting understandings of basic concepts and definitions within the domain of sustainable production. Since such conflicting understandings would most certainly delay the process of becoming more sustainable, the top leaders of the core firms realised that some mental images and attitudes had to be modified and replaced with more updated and harmonised understandings. Thus, the local industry launched, together with the local university, a course on sustainability for leaders within the regional metallurgical and chemical process industry. Regional leaders were encouraged to participate, and through discussions and joint case studies, the goal was to establish a more unified world view amongst leaders within the industry in question. Drawing from this example, and also inspired by the recent contribution provided by Trippl et al. (2020), we distinguish between four modes of asset modification. The four alternatives and their mechanisms are presented and described in Table 2. In Table 3, we suggest that an association exists between various regional settings and their expected dominant asset modification process. Following the research conducted by Aarstad et al. (2016) and Castaldi et al. (2015), we suggest that thick Table 2  Modes of asset modification Modes of asset modification Reuse of existing assets Upgrade of existing assets Creation of new assets Destruction of old assets

Mechanisms The recombination of existing assets or use of existing assets for new purposes The significant improvement of existing assets The emergence of entirely new assets from R&D and radical innovation processes or the importation of an asset that is not already present in the organisation (firm-level) or geography (system-level) The destruction of assets that are hampering future development

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Table 3  Asset modifications in various ecosystems Regional setting Thin RIS Thick and specialised RIS Thick and diversified RIS

Asset reuse X (x)

Asset upgrade (x) X X

Asset creation

Asset destruction

(x) X

(x) X

and diversified RISs are best situated to foster processes of both firm-level and system-­level asset creation. This suggestion is based on the finding that diversity in the regional economy increases the possibility of novel resource combinations. Further, we suggest that thick and specialised RISs breed processes of asset upgrade in both scales, as specialisation implies being better at what one already does (Tödtling and Trippl 2005). Finally, we suggest that thin RISs favour processes of asset reuse. This suggestion is based on the finding that thin RISs host a relatively limited actor base and because thin RISs are weak on regionally generated R&D knowledge. Further, at a firm-level, thin RISs host a majority of small- and medium-­ sized enterprises that innovate mostly by experience-based knowledge and skills (Isaksen 2015). This implies that thin RISs provide limited resources and constrained possibilities for novel asset combinations at both the firm-level and the system-level. In Table 3, a large (X) suggests a strong relation, while a small (x) suggests a less strong relation.

6  The Process of Mass Adoption and Mass Utilisation In the previous section, we argued that regional industrial restructuring requires processes of asset modification. However, one instance of an event does not necessarily indicate a trend. Thus, in this section, we move beyond the request for asset modification in regional restructurings and posit that, for a regional restructuring to occur, the modified asset(s) must be adopted and utilised by a critical mass of firms (Asheim et al. 2019; Foray 2015). In his paper, ‘Regional industrial path development: The role of new entrepreneurial firms,’ Rypestøl (2017) researches the role of new entrepreneurial firms to regional industrial restructuring. In this work, Rypestøl identifies two variables that are key to regional industrial restructuring, namely, entrepreneurial growth intention and innovation novelty. The author leans towards the work of Ajzen (1991) when he argues that entrepreneurial growth intentions are important to regional restructuring. This is because, according to Ajzen, entrepreneurial growth intentions is a good indicator of the nature of the result as it determines ‘how much of an effort they are planning to exert, in order to perform the behaviour’ (Ajzen 1991, p. 181). Innovation novelty, on the other hand, is important, Rypestøl argues, since the degree of novelty reviles the potential that is embedded in the business idea explored by the new firm. The more innovative idea, the more destructing effect is supposed to follow from its

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introduction. After identifying the two critical variables, Rypestøl continues by distinguishing between four types of entrepreneurial exploitations that are expected to affect future industrial development differently. He argues that a combination of high growth intentions and high innovation novelty is the best alternative condition to promote significant industrial change within regional industries, while the opposite combination (i.e. low growth intentions and low innovation novelty) supports mostly an extension along the existing line of development. However, as argued in Sect. 4, firm-level entrepreneurs are not the only change-­ makers that arise in processes of regional industrial restructurings. An alternative change-maker, as described previously, is the system-level entrepreneur (Asheim et al. 2019). Here, we argue that the same logic applies to system-level entrepreneurs as to firm-level entrepreneurs, namely, that the ambition of the entrepreneur and the degree of newness found in their introduction will determine its future restructuring potential. One example of a system-level entrepreneur that has proven its potential to promote significant regional industrial change is the Norwegian innovation cluster programme.2 This programme aims to support regional development by fuelling regional clusters with human and financial assets critical for innovation and further development (Jakobsen and Røtnes 2012). The support from the Norwegian innovation cluster programme has played a crucial role in supporting significant cluster development in several Norwegian geographies. One example is the GCE Node cluster of oil- and gas-related firms in the Agder region who developed into being a world leading milieu for oil rig equipment in 10 years.3,4

7  A  lternative Outcomes of Regional Industrial Restructurings Regional industrial development is typically researched from the perspective of path dependency (see, e.g. Boschma and Frenken 2006; Isaksen and Trippl 2014; Martin 2010; Martin and Sunley 2006). Originating in the work of Paul David and Bryan Arthur (see, e.g. Arthur 1988; David 1985), the basis of the concept that future development is heavily influenced by the past. Thus, the theory highlights that history matters for future development and that regional economies tend to develop along certain trajectories that are anchored in the past. Within EEG, the notion of path dependency is most often used to analyse and describe the alternative trajectories of regional industrial development (Martin 2010; Martin and Sunley 2006). According to contemporary EEG research, a regional process of industrial development  can result in several outcomes.  http://www.innovationclusters.no/english/  Website, https://gcenode.no/about-node/ 4  Numbers according to https://sysla.no/offshore/dette_er_de_beste_klyngene 2 3

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Contemporary research separates several types of outcome, like extension of existing pathway, upgrading, modernisation, branching, importation and new path creation (see, e.g. Grillitsch and Trippl 2016). Even if the variation of outcomes is significant, this paper distinguishes three different main categories of path developments: path extension, path renewal and path creation. In Table 4, we display various routes towards the three main categories of path development. In Sects. 3 and 4, we referred to the entrepreneurship literature and the innovation literature when arguing that, when exploited, the most radical opportunities support the most radical changes to regional industrial development (see, e.g. Rypestøl 2018). In Sect. 5, we argued that assets need to be modified to support regional industrial restructuring, and we identified four types of asset modification processes, namely, reusing, upgrading, creating and destructing processes. In Sect. 6, we claimed that restructuring required some form as mass distribution of new assets, while we, in this Sect. 7, have presented diverse outcomes of regional processes of restructuring. In Table 5, we suggest a merge of the previous findings and conclusions as we introduce a suggested link between asset modification and path outcome. Table 5 suggests that the least radical asset modification process support the least radical outcome, while the most radical asset modification mode supports the most radical outcome. According to Isaksen (2015, p. 587), regional path extension consists of ‘incremental product and process innovation in existing industry and along prevailing technological paths’. Thus, industrial path extension involves innovation that will extend the existing life cycle. However, processes of industrial path extension follow a ‘more of the same’ strategy that lacks inflow of new assets, like knowledge and skills (Isaksen et  al. 2018a). This lack of inflow of new assets can, ultimately, result in industrial stagnation and decline (Tödtling and Trippl 2013). Unlike path extension, the path renewal type of development includes two alternative routes towards industrial revitalisation. The two processes illustrate a major shift in existing development from either a shift of position within the value chain, or from the implementation of significantly new technology or organisational innovation. Such shifts and implementations, we argue, require an upgrade of existing assets. This upgrade is needed because new positions, new technology and organisational innovations require new knowledge, skills, tools, infrastructure and more. Finally, path creation is the most comprehensive alternative to further path development. Table 4 suggests that this type of future path development can follow from radical innovations, from path importations, or branching processes. Branching processes describes a process of development where existing industries evolve into new once based on knowledge sourcing and innovation collaboration between related or unrelated firms. Path importation, on the other hand, describes regional industrial development that follows from a process where an industry is planted at a new location. Finally, new path creation represents the implementation of new business models, user-driven innovation or social innovation (Rypestøl 2018). In essence, these types of development distance themselves most from existing solutions. Thus, existing assets need to be upgraded and new assets need to be created in the region and the firm, if existing development is to be disrupted. Finally, as illustrated in Table 5,

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Table 4  Regional industrial restructuring: outcomes and mechanisms Main category of path development Path creation

Path renewal

Path extension

Sub category of path development Mechanisms New path This involves the creation emergence and growth of entirely new industries based on radically new technologies and scientific discoveries, or as an outcome of search processes for new business models, user-driven innovation and social innovation The creation of This is comprised of the new industries setting up of an established industry that is from path new to the region (e.g. importation through foreign firms) The creation of This concerns the new industries development of a new industry based on from path competencies and branching knowledge of existing related or unrelated industries This relates to the major Renewal of departure of an industrial existing industries from path in a new direction modernisation based on new technologies or organisational innovation This concerns the major Renewal of departure of a regional existing industries from industrial path related to the enhancement of firms upgrading shifting position within global production networks or moving up the value chain based on upgrading skills and production capabilities This relates to the Extension of extension of an existing existing path through incremental industries firm innovation along prevailing technological paths

Source: Modified from Grillitsch and Trippl (2016, p. 10)

Example from the literature A typical example is provided by Joseph Schumpeter, who describes the effect that the ‘railroadization’ process had on other types of transportation in the 1800s (Schumpeter 1939)

An example here is Philip Cooke’s work, where the growth of the automotive and electronic industries in Wales is described and analysed (Cooke 2003) An example of this process is provided by Tödtling and Trippl (2013). Their study showed how a new industry of environmental protection firms branched out from an old cluster of mining and steel firms in the Ruhr area Kyllingstad and Rypestøl (2018) offer an analysis of a modernisation process by exploring how the process firms in the Eyde network transformed to become more sustainable A process of path upgrading is researched by Tödtling and Trippl (2004) in their analysis of how the Styrian metal cluster restructured through a shift from mass production to specialised production

An example of regional industrial path extension is identified by Isaksen et al. (2018a) when researching the development of the Heidner cluster

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Table 5  A suggested association between asset modification and path outcome Future path development Path creation Path renewal Path extension

Asset reuse (x) X

Asset upgrade X X (x)

Asset creation X

Asset destruction X (x)

we assert also that new path development often requires some form of asset destruction as existing assets are tailored to mostly support existing solutions. Thus, existing routines, knowledge, skills and hardware might hamper disruptive ideas and therefore needs to be destructed to open new alternatives. In Table 5, a large (X) suggests a strong relation, while a small (x) suggests a less strong relation.

8  Conclusion To conclude, this chapter seeks to answer two research questions, namely, how do regional industries restructure and what role do ecosystems play in such processes? Regarding the first question, we have argued that regional industrial restructurings should be understood as a process initiated by the exploitation of an identified opportunity. We have argued that such opportunities can be either incremental or radical in nature. We also posited that the most potent opportunities are the radical ones. Further, we have found that not all identified opportunities focus on profit; instead, some focus on how to alter RISs by improving systemic factors and create increase collective value. As well as this, we have found that opportunities are exploited by two possible types of entrepreneurs, namely, firm-level entrepreneurs and system-level entrepreneurs. The chapter has argued that firm-level entrepreneurs initiate organic change processes, while system-level entrepreneurs initiate planned processes. Further, we have seen that processes of regional industrial change benefit from a tight collaboration between the two types of entrepreneurs, as system-level entrepreneurs facilitate change by opening new possibilities that can then be exploited by firm-level entrepreneurs. Moreover, we have highlighted the process of asset modification as a key mechanism within processes of regional industrial change. We argue for a broad definition of assets and posit that restructurings require the modification of existing assets. We then suggested four types of asset modification: reuse, upgrade, creation and destruction before we argued that modifications need to be adopted and utilised by a critical mass of firms to cause a restructuring. Finally, we argued that the outcomes of regional industrial change are varied and can be roughly categorised as the extension or renewal of existing industries, as well as the creation of new ones. In Fig. 1, we visualise our answer to the first research question.

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Fig. 1  The process of regional industrial restructuring

Regarding research question two, we find that the regional context influences all stages of the suggested process. Following the RIS literature, the chapter has categorised regional ecosystems as being either organisationally thick and diversified, organisationally thick and specialised or organisationally thin. Further, we have emphasised that regional institutional arrangements are crucially affecting both the initiation and the following process of regional industrial change. Regarding the initiating phase of discovery, we have found that organisational thick RISs are best conditioned to foster and promote radical innovations, while thin ecosystems are prone to incremental change. This follows because thick and diversified ecosystems include a variety of different firms, industries, R&D organisations, clusters, policy instruments and an inclusive and open civil society that promote institutional diversity. Further, we argued that organisational thin ecosystems mostly foster incremental innovations because they are low on R&D organisations and scientific innovating firms. Further, we maintained that thick and diversified RISs promote the best environments for firm-level entrepreneurs, while thin regions are more dependent on system-­level initiatives. This dominance of firm-level entrepreneurs in thick regions follows because the local market is huge and because transaction costs are low. Further, we found that thick regions are generous in local asset spillover and, finally, that diversity breads novel asset combinations. Moreover, we assessed that thick and diversified ecosystems are best conditioned to promote asset creation and radical asset upgrades. This claim of thick regions being better conditioned to promote asset creation was raised since diversity breads novel asset combinations. Further, since new routines and new ways of doing things requires relearning and renewed institutions, we argued that thick and diversified ecosystems are more prone to asset destruction than the other alternatives. Finally, we concluded the line of argument by claiming that thin ecosystems were most prone to industrial extension, while the thick and diversified ecosystem was best situated to promote the creation of new industries and the renewal of existing once. In sum, Fig. 2 illustrates our answer to the second research question.

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Fig. 2  How context affects various steps of regional industrial change

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The Role of Clusters in the Smart Specialisation Process: The Case of Inovcluster in Portugal Teresa Paiva, Cláudia Domingues, Luis Farinha, and Marina Ranga

Abstract  Clusters are a key driver of the competitiveness and economic growth of a region, and they become even more important when the region has a smart specialisation strategy that involves clusters as dynamic innovation stakeholder. This chapter illustrates how Inovcluster, an agri-food cluster oriented on SMEs and microbusiness, operates within the Research and Innovation Strategies for Smart Specialisation (RIS3) of the centre region of Portugal, promoting regional business competitiveness. The chapter also examines the ways in which Inovcluster acts in order to effectively improve the market position and behaviour of its members. Keywords  Cluster · Inovcluster · Smart specialisation · Competitiveness · SMEs

1  Introduction Smart specialisation strategies were required for every European region as an ex ante condition for accessing European Union Structural Funds in the programming period 2014–2020 and represented a new vision on combining regional development and research, technological development and innovation goals (Bellini 2015). The smart specialisation strategy concept was originally proposed by a group of T. Paiva (*) NECE – Research Center in Business Sciences and Guarda Polytechnic Institute, Guarda, Portugal e-mail: [email protected] C. Domingues Inovcluster – Associação do Cluster Agroindustrial do Centro, Castelo Branco, Portugal L. Farinha Polytechnic Institute of Castelo Branco and NECE—Research Center in Business Sciences, Castelo Branco, Portugal M. Ranga European Commission—Joint Research Centre in Seville and University of Warsaw (Poland), Seville, Spain © Springer Nature Switzerland AG 2020 L. Farinha et al. (eds.), Regional Helix Ecosystems and Sustainable Growth, Studies on Entrepreneurship, Structural Change and Industrial Dynamics, https://doi.org/10.1007/978-3-030-47697-7_9

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innovation and growth economists and was further explored and defined by D. Foray, focusing on regional development (Foray 2015). It consists of an integrated and place-based economic transformation agenda that is characterised by five elements (Foray 2012): (1) focus on specific priorities; (2) reliance on strengths, competitive advantages and potential of the reference region; (3) use of a broad concept of innovation, involving the private sector; (4) full involvement of stakeholders through open, participatory processes of ‘entrepreneurial discovery’; and (5) being evidence-­ based and using central monitoring systems and evaluation as a learning tool. Smart specialisation strategies do not intend to specialise a region in a set of industries but to ensure a better connection between policies, objectives and funding instruments and to promote a well-targeted diversification based on related variety (Bellini 2015). To facilitate the implementation of the concept, the European Commission’s Joint Research Centre in Seville, Spain, launched the Smart Specialisation Strategies Platform (S3 Platform), which provides a variety of learning tools to support the different phases of the process, from strategy design to monitoring, evaluation and feedback to the next cycle of policymaking. The design and the subsequent implementation of smart specialisation strategies imply a continuous involvement of different regional innovation stakeholders from the Quadruple Helix: universities and public research organisations, private companies and entrepreneurs, clusters, national and regional government agencies, etc. In this chapter we illustrate how a particular cluster contributed to these goals. First, a description of Research and Innovation Strategies for Smart Specialisation (RIS3) is provided, followed by a discussion on the role of clusters in this context. Next, the case of Inovcluster, an agri-food cluster of the centre region of Portugal, and the way it contributed to RIS3 design and implementation in the region are discussed.

2  Literature Review 2.1  R  esearch and Innovation Strategies for Smart Specialisation and the Role of Clusters The regional application of the RIS3 concept aims to create unique assets and capabilities based on a region’s distinctive industrial structures and knowledge base (European Commission 2012b). Therefore, RIS3 focuses on regional priorities, challenges and needs for knowledge-based development and designs policies that are centred on each region’s strengths, competitive advantages and potential for excellence. Such policies are supported by technology, ICT and practice-based innovation and aim to stimulate private sector investment, with stakeholders’ involvement, monitoring and evaluating the process and its development of policy implementation (European Commission 2012a). RIS3 has a place-based approach that implies a deep understanding of regional specificities, in terms of characteristics, skills, value chains and connections, recognising the path dependencies and the competitiveness of entities in each sector (European Commission 2013). These strategies also address missing or weak

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relations between R&D and innovation resources and activities on the one hand, and the sectoral structure of the economy, on the other (Foray et al. 2011). The interaction between different local innovation stakeholders in the RIS3 design is very important, and therefore it is necessary to understand and support intrasectoral and intersectoral connections and the socio-economic contexts within which innovation occurs. Consequently, the learning process involving entrepreneurial actors, in a bidirectional, iterative and dynamic way, is fundamental for the creation of new products, markets, technologies and processes (Foray et al. 2011). Another condition for RIS3 to succeed is a systematic interaction between top-­ down and bottom-up approaches in order to channel resources, monitor and assess outcomes, address potential coordination failures and disseminate and guide the formation of shared strategic vision. The development of a critical mass of certain actors is crucial for policy implementation (Foray et al. 2011). RIS3 can be summarised in four Cs (European Commission 2012a): • Choices and critical mass (limited number of priorities defined on the basis of own strengths and international specialisation) • Competitive advantage (mobilise talent by matching research, technology development and innovation capacities with business needs through an entrepreneurial discovery process) • Connectivity and clusters (develop world class clusters and provide ground for related variety/cross-sector links internally and externally in the region) • Collaborative leadership (efficient innovation systems as a collective effort based on public-private partnership) A cluster can be defined as ‘a geographically proximate group of interconnected companies, suppliers, service providers and associated institutions in a particular field linked by externalities of various types’ (Porter 2003, p. 562). This concept has been described extensively in the literature (Cruz and Teixeira 2010), focusing particularly on the impact of the location and special proximity on industrial performance and competitiveness. This is an advantage, according to several authors (e.g. Porter 1990, 1998; Ketels and Huggins 2011) because regional external economies of scale and agglomeration effects are supported and benefitted from the association with social networks, regional innovation systems and local knowledge exchange. To Foray et al. (2011), RIS3 is not the same as cluster policies, since some of these policies tend to focus on a knowledge base standardisation, narrowing the sectors range. Nevertheless, clusters have relevance in what they may contribute to RIS3 since these two concepts share two critical elements: (1) a focus on productivity and innovation as key drivers of competitiveness and (2) an accent on fostering regional embeddedness with a view to capitalise on the advantages of proximity (European Commission 2013). Although there are differences between the main goal of RIS3 (i.e. transformation of regional economies around unique, knowledge-based activity domains) and the main goal of many clusters (i.e. to enhance the performance of companies that are cluster members) (European Commission 2013), clusters and cluster policies, in many regions, are the building blocks in developing and implementing RIS3 due to

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their complementarity with other cross-cutting and technology/knowledge domain-­ specific activities and due to their adaptation to the regional environment. As pointed out by European Commission (2013), clusters and cluster policies have the potential to contribute to RIS3, being aware that there is often a gap between the potential and the reality of the cluster and that RIS3 efforts need to complement cluster policies with other policy action to reach their goal (see Table 1). The most obvious potential of cluster policies is in supporting prioritisation and stakeholders’ engagement, but they also have a role in the other dimensions of the RIS3 process.

2.2  RIS3 in the Centre Region of Portugal The RIS3 of the centre region resulted from a strategic reflection exercise, organised by the regional government agency, on the future of the region, as an initial stage of the ‘entrepreneurial discovery process’. The reflection exercise (CR-RIS3) envisioned the improvement of the region’s living standards and was guided by specific territorial needs. In the context of CR-RIS3, the regional stakeholders involved in the reflection exercise (more than 7171 participants from different groups) defined a set of distinct thematic domains of the region, which consist of areas where there Table 1  The potential of clusters and cluster policies in Smart Specialisation Strategies

Prioritisation

Integrated policy

Smart policymaking

Multilevel governance Cross-border collaboration Stakeholders engagement

Potential of cluster policy Clusters are a natural dimension for selection

Beyond cluster policy Knowledge domains can be different from clusters Policies often fragmented Cross-cutting policies Clusters are naturally for business and focused on single suited to organise the environment upgrading issues design and delivery of needed as well integrated policies Cross-cutting regional Limitations in existing A range of cluster-­ data is needed as well cluster data; use of data specific data and and is only partially often ad hoc analytical tools are available available Important multilevel Clusters draw on Limited actual multiple levels of policy collaboration across levels issues are cross-cluster of government Important cross-border The geographic footprint Cluster boundaries are of cluster organisations are issues are cross-cluster defined by their often administratively set economic reach Regional engagement Clusters combine critical Cluster initiatives have a stakeholders in relevant key role as bridge builders structures are needed as well groups

Source: European Commission (2013)

Reality of cluster policy Lack of tools to identify emerging clusters

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is an established production capacity and/or a capacity of scientific knowledge and technological interest. Thus, taking into account the physical characteristics of the region and its endogenous resources, eight distinct thematic domains were identified in the CR-RIS3, which are presented in Fig. 1. In addition to the eight thematic domains, four horizontal priority areas that ensure interconnectivity between the thematic domains have also been defined, as ‘innovation platforms’, which stimulate the emergence of new, innovative research activities and projects, as follows: 1 . Sustainable industrial solutions 2. Valorisation and efficient use of natural endogenous resources 3. Technologies at the service of quality of life 4. Territorial innovation Clusters are of great importance in the implementation of the RIS3 of the centre region, since they are considered central instruments for the dynamic functioning of the innovation platforms, through the mobilisation of different actors, in order to promote cross-fertilisation and collaboration. As relevant tools for the promotion of research, development and innovation (RDI) activities, clusters play an essential role in the growth of employment, productivity and exports, recognising that the

Fig. 1  Thematic domains of the RIS3 of centre region of Portugal. Source: Comissão de Coordenação da Região Centro (CCDRC) (2013)

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existence of these infrastructures provides policymakers with an excellent opportunity to promote economic growth.

3  Methodology The case study method has been chosen since our study is based on empirical research that engages in an in-depth examination of a contemporary phenomenon and real-life context (Marques et al. 2015). Moreover, it is a single case study, representative and unique (Yin 2010). The case study method allows the understanding of the dynamic present within a single setting (Eisenhardt 1989) and permits to comprehend what is being done and what the dynamics mean since it’s not a collection method (Martins 2008). It relies on multiple sources of evidence and data which converge (Yin 2010). This method is useful not only for its exploratory, descriptive and explanatory character, in relation to the type of question defined (Yin 2010) but is also useful for the purpose of description, testing a theory or generating a theory (Eisenhardt 1989). Indeed, our study case is descriptive, illustrating how Inovcluster contributed to RIS3 implementation in the centre region of Portugal and consequently, to the promotion of the region’s competitiveness and development. Our general research goal is to understand how Inovcluster contributed to the RIS3 strategy, specifically, how the cluster helps to achieve and enhance business competitiveness in the centre region of Portugal. Therefore, our research hypotheses are: H1: Inovcluster contributes to the RIS3 dimensions H2: Inovcluster contributes to business competitiveness

4  Case Study: The Inovcluster Inovcluster—The Association of the Agro-Industrial Cluster of the Centre region— was created in May 2009 and was recognised as a Collective Efficiency Strategy by the Portuguese government. It presents itself as a cluster association based on strategic cooperation between entities that pursue values, principles and methodologies that complement each other and carry out work based on an action programme that includes a set of projects (anchor and complementary). The strategic vision of the cluster is based on five pillars: (1) the territory, (2) the ranks, (3) the infrastructure and support services, (4) human capital and training and (5) research, innovation and internationalisation. In 2017 the cluster gained the ESCA Cluster Management Excellence Label GOLD—Proven for Cluster Excellence1—that demonstrates a  ESCA—European Secretariat for Cluster Analysis

1

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highly sophisticated cluster management and commitment to further improve the organisational structures and routines for the benefit of an even higher performance. The cluster aims to be acknowledged, in 2020, nationally and internationally, as a Cluster of Excellence that is able to decisively help the centre region of Portugal to assert itself at national, Iberian and European level as a leading territory in agro-­ industry, and to support the uniqueness and quality of their agricultural resources and products. To that end, Inovcluster needs to act as an active platform of the agro-­ industry sector of the centre region, supporting initiatives and the provision of services in the areas of research, development and innovation, internationalisation and training of the business companies, working in close liaison with and benefiting from the competences and resources available in the other reference actors in the region. Recognising the centre region as a territory with a diversified economic base, and one that provides ideal conditions for the development and affirmation of the agro-­ industry sector at national and international level, Inovcluster defines the centre region2 as its scope of activity, at geographic level. At sectoral level, Inovcluster has developed its activity in the agro-industry sector, focusing on eight main domains: olive oil, meat, cereals, horticultural fruits, milk and dairy products, honey, fish, and wine and vines. In addition, a set of seven complementary domains has been identified, in order to create added value and help the differentiation of the main domains, namely, (1) cold, (2) packaging, (3) logistics and distribution, (4) equipment supply, (5) quality monitoring and control, (6) territorial and sectoral marketing and (7) design, labelling site and management. Since its establishment, Inovcluster has been able to mobilise the most diverse regional partners, being currently constituted by 179 associates, of which 144 are business companies. In addition, given the presence of companies with very different characteristics, one can observe a segmentation of the cluster members through the creation of working groups that can facilitate the development of specific strategies and activities for the entities with common interests (i.e. tailor-made solutions). It is also important that Inovcluster acknowledges an orientation of its operations around a set of priority thematic areas, aligned with the national and international priorities of the agro-industry sector, namely, safe and healthy food; high-value-­ added gourmet products; innovative packaging; and sustainability. Thus, considering national and international technological priorities of the sector, Inovcluster should act, in the period 2014–2020, around the following priority thematic areas, without prejudice to others that may be identified: • Thematic Area 1: Safe and healthy foods • Thematic Area 2: Gourmet products with high added value

2  The centre region of Portugal, according to the recognition made by the Operational Program Competitiveness Factors – COMPETE, corresponds to six NUTS III regions: Centre-Beira Interior North, Beira Interior South, Cova da Beira, Pinhal Interior Norte, Pinhal Interior Sul and Serra da Estrela.

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• Thematic Area 3: Innovative packaging • Thematic Area 4: Sustainability (efficiency of production processes and valuation of by-products) Notwithstanding a structure of the associative network appropriate to European standards of excellence, namely, those established by the ESCA (in terms of representativeness of companies and diversity of actors), it is considered that Inovcluster should align the structure of the cluster members with the priority thematic areas, seeking to integrate companies and entities with competencies and know-how in key areas (e.g. in the area of health), while seeking to attract more reference companies (in terms of dimension) of the region. Inovcluster has played an important role in the development of activities in the themes of research, development and innovation, internationalisation and empowerment. Always attributing a very important role to cooperation, Inovcluster has been promoting the establishment of partnership agreements of different scopes, either with other clusters (thus enhancing inter-­ clustering) or with other entities of reference at regional and national level. It is also noteworthy the relevant role of Inovcluster in establishing partnerships amongst its members. Knowing the strategic lines of action of CR-RIS3, Inovcluster sought to develop different projects so that it could contribute to its implementation. In this sense, it involved its members in projects that, coordinated with the domains and innovation platforms, contributed to their appreciation and interaction (Fig. 2). As can be seen in Fig. 2, Inovcluster has presented ten projects that are aligned to the CR-RIS3 action lines, contributing to its implementation, in addition to having actively participated in the working groups and in the strategy dissemination actions. These projects have an important impact on companies in the region and sector, which achieve better competitiveness and innovation due to their reduced size and consequent operational capacity, in conjunction with the cluster and other project partners, business or not, improving their positioning in the market. This impact has been assessed in accordance with the European Cluster Excellence Initiative (ECEI) principles that can assess the relationship with and impact of the cluster on its members through different indicators, namely, business. ECEI focuses on quality indicators that show if the cluster has a harmonised and transparent organisation management, and they cover the following dimensions: (1) structure of the cluster; (2) typology, governance and cooperation; (3) financing cluster organisation management; (4) strategy, objectives and services; and (5) achievements and recognition. Following ECEI principles, the direct impact achieved is based on success stories and media visibility. Furthermore, tools for assessing customer satisfaction must be in place to indicate if the expectations of the cluster’s stakeholders and participants are fulfilled. The business impact evaluation is strongly linked to the business activities and may not have a direct link to the cluster behaviour. Therefore, success stories and cluster members’ satisfaction assessment are the main tools of the quality indicators.

Fig. 2  Contribution of Inovcluster to the line of action of agri-food industry of RIS3 of centre region of Portugal. Source: Inovcluster

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Inovcluster has already participated in some success stories of its members, e.g. by taking them to international fairs, integrating them in business visits from abroad to promote and help them find suppliers or buyers, in helping them to innovate their products, access markets and improve the production quality, as shown in Table 2 of the last cluster evaluation. Also, in every training, workshop or other activity linked or not to a project, an assessment survey is filled in. These principles are good procedures and enable Inovcluster to change and design its activities accordingly with the expectations of its members and, at the same time, assist them as needed.

5  Discussion Considering Inovcluster dimension and partnership, in a region scoring 8.2 on business density (INE 2014) in the agri-food sector, we may say that the cluster role in the CR-RIS3 is important through the potential involvement of stakeholders. It’s obvious that the engagement t of clusters in the CR-RIS3 participatory process is very important for its members, and the dissemination process is much broader. The Table 2  Inovcluster performance achievements according to ECEI indicators that allowed the award of the Gold Label to the cluster by ESCA (2015) Indicator Events organised by the managing body for the promotion of internationalisation Household economic target companies involved in workshops for the promotion of entrepreneurship, internationalisation, innovation, qualified RTD and competitiveness organised by the managing body Specialist claims support on the part of network entities Number of companies present in the actions of awareness and dissemination of project Number of contacts arising from internationalisation actions Number of bilateral meetings (one-to-one) with potential customers in several markets promoted by the project actions: promotional activities, international events and business missions Number of pre-agreements of formalised cooperation between the partners (as a result of the project actions) Matchmaking, made by Inovcluster, of meetings between companies and potential customers, in Portugal or in the target market Number of companies present in new countries (contacts and/or effective presence) Knowledge acquisition/technical and scientific update Product development support New products development New business creation support Number of business participants in the international actions Operational meetings Bilateral meetings with associates Bilateral meeting with potential associates Source: Inovcluster

Results 30 348

84 88 203 64

47 33 49 63 26 7 2 139 47 84 52

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number of projects developed by the cluster not only contributed to the region performance within its smart strategy but also helped SMEs to achieve results that they could not have achieved on their own. So, we may say that Inovcluster combines critical stakeholders in relevant groups and provides an important prioritisation tool, as defined by the European Commission (2013). The cross-border collaboration can also be observed in the cluster presentation above, by engaging a considerable number of SMEs in bilateral meetings and international events. As the cluster, by nature and definition, includes not only business companies but also research and knowledge centres that seek innovation and development and also focuses on several business areas that together create synergies, not directly competing, but enhancing its capacities and competitiveness, it’s possible to understand why the cluster is naturally suited to organise the design and delivery of integrated policies, as envisaged by the European Commission (2013). This confirms hypothesis 1 of this research that Inovcluster contributes to RIS3 dimensions and also confirms hypothesis 2 as the cluster promotes and develops as many projects as possible to finance and promote innovation and business development in the agri-food sector, oriented by the thematic domains and lines of action of CR-RIS3. These activities, as the results presented, demonstrate how the cluster helps business competitiveness.

6  Conclusion This research presented a case study of an agri-food cluster in the centre region of Portugal to illustrate how a cluster can play an important role in the definition and implementation of RIS3. It is just one example since the region has other clusters operating in different sectors. The participation in RIS3 design was one of the most relevant. The written contributions that were asked by the regional government agency that coordinated the process and the meetings held as part of the ‘entrepreneurial discovery process’ and the work done demonstrated that it is possible for every type of regional stakeholder to be heard and to contribute to the process. The role of the cluster was visible through the participation of its different stakeholders, serving as a communication and engaging tool for the centre regional government agency. This contribution occurred before and during the RIS3 design and in the dissemination of the strategy. The research was able to confirm the research hypotheses that we proposed using the single study case methodology, accepting its limitations and contributions. An interesting avenue for further research would be to make an international comparison of various agri-food clusters indifferent regions of Europe in regard to the role they play in RIS3 and to understand the performance and contribution of each. The RIS3 evaluation is yet to come, but the process is likely to continue, since there is evidence of positive results.

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References Bellini, N. (2015). Smart specialisation in Europe: Looking beyond regional borders. Symphonya. Emerging Issues in Management, 1, 22–29. CCDRC. (2013). RIS3 do Centro de Portugal-Estratégia de Investigação e Inovação para uma Especialização Inteligente. Retrieved from http://ris3.ccdrc.pt/ Cruz, S. C. S., & Teixeira, A. A. C. (2010). The evolution of the cluster literature: Shedding light on the regional studies–regional science debate. Regional Studies, 44(9), 1263–1288. https:// doi.org/10.1080/00343400903234670 Eisenhardt, K. M. (1989). Building theories from case study research. The Academy of Management Review, 14(4), 532–550. ESCA (2015). European secretariat for cluster analysis. www.cluster-analysis.org. Accessed December 2015. European Commission. (2012a). Guide to research and innovation strategies for smart specialisations (RIS3). Luxembourg: Publications Office of the European Union. ISBN: 978-92-79-25094-1. https://doi.org/10.2776/65746. European Commission. (2012b). Connecting smart and sustainable growth through smart specialisation. Luxembourg: Publications Office of the European Union. ISBN: 978-92-27345-2. https://doi.org/10.2776/70221. European Commission. (2013). The role of clusters in smart specialization strategies. Directorate General for Research and Innovation, European Commission, Brussels. ISBN: 978-92-79-33233-3. https://doi.org/10.2777/43211 Foray, D. (2012, May). Types of strategies for smart specialization, 2nd TIP workshop on smart specialization, OECD. (pp. 10–11). Foray, D. (2015). Smart specialisation – opportunities and challenges for regional innovation policy (1st ed.). New York USA: Routledge. Foray, D., David, P. A., & Hall, B. H. (2011). Smart specialisation from academic idea to political instrument, the surprising career of a concept and the difficulties involved in its implementation. (No. REP_WORK). EPFL. INE. (2014). Anuário Estatístico da Região Centro 2013. Portugal: Instituto Nacional de Estatística. ISSN 0872-5055; ISBN 978-989-25-0281-6. Ketels, C., & Huggins, R. (2011). Clusters and competitiveness: Porter’s contribution. In Competition, competitive advantage and clusters: The ideas of Michael Porter (pp. 173–191). Marques, K. C. M., Camacho, R. R., & Alcantara, C. C. V. (2015). Assessment of the methodological rigor of case studies in the field of management accounting. Revista Contabilidade e Finanças, 26(67), 27–42. Martins, G. A. (2008). Estudo de caso: uma reflexão sobre a aplicabilidade em pesquisas no Brasil. Revista de Contabilidade e Organizações, 2(2), 8–18. Porter, M. E. (1990). The competitive advantage of nations. USA: Harvard Business Review. Porter, M. E. (1998). Clusters and the new economics of competition. Harvard Business Review, 76(6), 77–90. Porter, M. (2003). The economic performance of regions. Regional Studies, 37(6–7), 549–578. Yin, R. K. (2010). Estudo de caso: planejamento e métodos (4th ed.). Porto Alegre: Bookman.

Incubation: Does It Make a Difference After Graduation? Analysis from Portugal Daniel Ferreira Polónia, Jorge Cunha, and Tiago Leite

Abstract  In this chapter, we analyse the context in which two regional and university incubators were created and from which 32 companies graduated. The 2016 results for these companies are compared against the same set of results for 32 nonincubated companies. All 64 companies are of the same age (started between 2007 and 2011), work in technology and are located in the District of Aveiro. We conclude from the results that there is no significant difference as regards general business results. However, a closer examination of specific indicators shows that incubated companies behave differently from non-incubated ones in terms of the productivity of their intangible assets, grant dependency and external markets openness. Keywords  Incubators · Regional business incubation · University business incubation · Graduation · Entrepreneurship · Performance indicators

1  Introduction Ever since the first business incubator was set up in the 1950s, in an egg farm in Batavia, NY, cities and institutions have been using decommissioned factory spaces to attract innovative entrepreneurs. More recently, in an international context of globalisation, in which intangible assets (e.g. intellectual capital, research and D. F. Polónia (*) GovCOPP (Governance, Competitiveness and Public Policy) Research Group, Economics, Management, Industrial Engineering and Tourism Department, University of Aveiro, Aveiro, Portugal e-mail: [email protected] J. Cunha ALGORITMI Research Centre, University of Minho, Guimarães, Portugal e-mail: [email protected] T. Leite Department of Production and Systems, University of Minho, Guimarães, Portugal © Springer Nature Switzerland AG 2020 L. Farinha et al. (eds.), Regional Helix Ecosystems and Sustainable Growth, Studies on Entrepreneurship, Structural Change and Industrial Dynamics, https://doi.org/10.1007/978-3-030-47697-7_10

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development activities and services) are more highly valued than tangible assets (e.g. facilities and equipment, capital spending and manufacturing) (Madhani 2012); business incubators have been used as the place where entrepreneurs and venture capitalists come together to quickly create innovative companies, grow them and then sell them on to larger companies or to investors willing to pay a premium for an innovative business concept with adequate traction in the marketplace. Efforts to replicate this concept in Europe have led to interesting results, with the bibliography reporting several case studies (Nogueira et  al. 2018; Sammut et  al. 2017) of successful incubators located in Europe. Furthermore, public policies, particularly European Commission initiatives designed to drive cohesion and regional development (Mathernova and Bail 2010), have encouraged local decision-­ makers to adopt clear policies on supporting the business incubation concept. This has led to a renewal of the local entrepreneurial profile and triggered the emergence of innovative companies focused on monetising the benefits of the knowledge-based economy. Universities have also developed incubation spaces alongside these local ­political incentives, to satisfy their need to generate alternative revenue sources beyond traditional governmental funding. This is notably the case where, for a variety of reasons, universities have become owners of decommissioned industrial spaces. These two factors underpin the rationale for creating university-based incubators (Benneworth and Pinheiro 2017). Given these contextual drivers, universities and municipalities located in regions with accelerated deindustrialisation processes have promoted the creation of business incubators and accelerators. These are either located in abandoned industrial spaces or in new buildings that have been built or refurbished using European cohesion funding. In this chapter, we analyse the context in which two regional and university incubators were created and from which 32 companies graduated. We then compare the results achieved by these companies against the same set of results from 32 non-­ incubated companies. All 64 companies are of the same age (started between 2007 and 2011), work in technology and are located in the District of Aveiro, one of the most industrialised districts in Portugal. The remainder of this chapter is organised as follows. Section 2 provides an extensive theoretical background around the concept of business incubation and graduation from incubators, thus framing the research questions to be addressed. Section 3 describes the regional context and the business incubators included in this study. Section 4 covers the research design applied to our research question. Section 5 presents the main results, and Section 6 contains the discussion of the results, in the light of our research questions. Finally, Section 7 addresses our main conclusions and suggests avenues for further research.

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2  Theoretical Approach We carried out a search of management journals using the two keywords incubation and graduation, as a first step in constructing our theoretical background on these concepts. The results were then framed according to the life cycle that organisations have in incubators, starting with the selection process and ending with an analysis of their performance after graduating, by way of the events that typically occur during the incubation phase. Before presenting the systematised findings of this search, we should address incubator taxonomy. According to Grimaldi and Grandi (2005), incubators can be classified in a binary manner: on the one hand there are not-for-profit public (governmental) business incubators, created with the objective of promoting economic development, and, on the other, for-profit private incubators, set up by individuals with the objective of generating a profit. Zedtwitz (2003) identified five different types of incubator: regional, university, independent commercial, internal business and virtual. Regional incubators are established by governments or local organisations with regional political and economic interests. They provide office space and support for regional start-ups, and their main objectives are to generate employment and enhance the quality of the local business sector. Typically, the main investors in these incubators come from the public sector and include regional governments and local authorities. University incubators result from a growth in demand and a political interest in having universities develop entrepreneurial activities. Moreover, they are a way for universities to profit from the knowledge assets they own, by generating additional revenue at a time when they face severe budgetary constraints on their teaching and research activities (Hearn 2004). Thus, some universities offer researchers and students with an entrepreneurial spirit the facilities required to start-up their own business. They provide such support as the transfer of knowledge to start-up firms, help with the preparation and execution of the business plan, advice on management and business practices and access to venture capital. In recent years, new forms of incubation, known as accelerators, have begun to appear. Pauwels et al. (2016) analysed and classified these into three different types: the ‘welfare stimulators’, which have governmental agencies as their main stakeholders and aim to stimulate start-up activity and foster economic growth; the ‘deal-flow maker’ accelerators, which are privately run and aim to identify promising investment opportunities for the investors; and, finally, ‘ecosystem builders’, set up by corporate companies wishing to develop a company-centred ecosystem of customers and stakeholders. Bergek and Norrman (2008) identified three main components for incubation models: (1) selection; (2) infrastructure, business support services and mediation; and (3) graduation. Having analysed the post-graduation survival rates of incubated companies, Schwartz (2009) noted that the mechanisms of the selection process in the public (governmental) business incubators are often biased and are no substitute for market

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selection. There is a significant discrepancy between the intentions of policymakers and incubator promoters and the results achieved, mainly due to the fact that follow-up mechanisms for companies that have already left the incubator are seldom established. As regards the second component (infrastructure, business support services and mediation), Tötterman and Sten (2005), as well as Vanderstraeten and Matthyssens (2012), maintain that business incubators can enhance the credibility of new companies in their development process and can help them build promising business networks. The mentioned research shows that entrepreneurs who have received substantial help in creating such networks are more satisfied than those who have only been given space and support facilities. After analysing the behaviour of 114 start-up incubated firms, Peña (2004) ­concluded, with respect to this same component, that policymakers and directors of incubation centres should focus on enhancing entrepreneurs’ human capital. They would do this by encouraging a more effective use of networking opportunities inside and outside the incubator, in order to improve the likelihood of venture ­success (i.e. access to venture capitalists, financial institutions, potential partners to form alliances, public services to support entry into foreign markets, etc.). In addressing the strategic positioning of the incubator in the business value chain, Hughes et al. (2007) pointed out that, in order to add value to the portfolio of incubated firms, incubator management teams must act in a tertius iungens (third party that links) capacity (Obstfeld 2005). They would be relational brokers, linking incubated firms through resource pooling activities and strategic networking. Patton (2014) noted that, as far as the incubator’s role as a hinge is concerned, when founders, advisers, mentors and incubator directors engage collaboratively to create an iterative dialogue that informs the development of a viable business model, the process by which potential absorptive capacity can be fully realised is substantially strengthened. This leads to the third component of incubation that Bergek and Norrman (2008) define as graduation. Schwartz (2009) carried out extensive analysis of this area, focusing on post-graduation survival rates in general and exit dynamics after graduation. One major finding was that there is a relevant time window (between 1 and 3 years after graduation) in which one in every five graduates does not survive. The discontinuation of support (relatively low rents, collectively shared facilities, management support and assistance, access to business networks, etc.) has an immediate negative effect on subsequent survivability, meaning that a substantial proportion of emerging firm closures/failures is potentially deferred to the post-­ graduation period when market forces are at work. The same author (Schwartz 2011) later found, from an analysis of 324 (successful and unsuccessful) graduate firms’ employment and sales figures, that, after initial strong firm growth during the initial incubation period, the empirical results refute the argument that graduation (automatically) marks a starting point for sustainable and high firm growth beyond incubation. This finding was confirmed in Schwartz (2012), in which an analysis of 149 graduate firms revealed a statistically

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significant negative impact of incubation time on post-graduation firm survival, who consequently recommended a strict limit on incubation times. Theodorakopoulos et al. (2014), who studied incubator performance, which is assumed to be heterogeneous, noted it is difficult to establish the extent to which business incubators add value. The study showed that there are still significant shortcomings in terms of support for entrepreneurial development during and immediately after graduation. A number of studies have analysed firm performance during the post-graduation period. Schwartz (2013) analysed the long-term survival of a sample of 371 start-up firms supported by publicly funded incubation initiatives and compared the results with a group of 371 comparable start-ups not receiving incubator support. This study found that incubated firms do not have higher survival rates than comparable non-incubated firms, which raises certain doubts regarding the impact of incubation on long-term firm survival. In another study, this time based on the sales and employment growth data for incubated and non-incubated firms during the initial maturation period, Şehitoğlu and Özdemir (2013) compared firm performance and found evidence that incubated firms outperform non-incubated firms on both indicators. More recently, Dvouletý et al. (2018) studied the relationship between business incubation and the firm-level performance of 205 incubated Czech enterprises founded after 2003, comparing them with non-incubated firms on a group of indicators that included sales, price-cost margin, asset turnover, value added, size of total assets and personnel costs. The results indicated that incubators have not been successful in their mission of promoting the growth of incubated firms, as they are not able to support their competitiveness or job growth. The findings of these studies make it clear that incubation is no guarantee of future success for the graduate companies vis-à-vis non-incubated companies of a similar nature. Ferreira et al. (2019) proposed a set of nine indicators to measure Portuguese start-ups performance in what concerns the use of R&D tax incentives. Of the diverse indicators, one is related to the management skills of the company and the uniqueness of the value proposition (Lumpkin and Ireland 1988). Another item to be considered is the financial performance, as measured by the productivity and profitability of the organisation (Guo et al. 2011; Jain and Kini 1994; Kaplan 1989). It is considered that the commercial performance, as described by Wiggins and Gibson (2003), supports the creation of valuable jobs (Sauermann 2016) where competitive salaries are offered to employees (Botti 2013). The analysis of these indicators leads to the evaluation of the company performance and its comparison against others. The analysis is complemented, in the present case, with the analysis of the role of national and European grants in what concerns the development of exportable intangible assets (Ferreira et al. 2019; Howell 2017; Rose and Shoham 2002). The information contained in these indicators supports the response to the following research questions: The main and more generic research question is related to the fact that, once the incubation process is concluded, firms must be self-sustainable. This engenders the

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following question: ‘Once they graduate, do incubated companies have a different general performance than non-incubated companies?’. The second research question is more specific and is related to the fact that incubated companies have been exposed to networking opportunities that were fostered by incubator management, thus leading to a different behaviour in what regards the use of grants to develop innovative, intangible offers that can be easily exported. This gives us our second question: ‘Once they graduate, do incubated companies present different behaviours for certain indicators, such as the productivity of intangible assets, grant dependency or external markets openness, when compared to non-incubated companies?’.

3  Regional Context Around 670,000 people live in the Aveiro District. Here, businesses have developed a unique dynamism in recent decades, when compared with the rest of the country. There is a high business density and a thriving productive apparatus, in which the industrial sector predominates. Numerous leading companies, many of which are strong exporters, are based in the district. These include the world’s largest cork manufacturer and several mould companies with significant market shares in the auto, shoes and hats sectors in the northern part of the district. In the south, telecommunications and high-tech/IT companies are predominant, along with ceramics and metalworking businesses (AIDA 2018). SMEs have always played a significant role in this region and account for a vast majority of jobs and exports, shielding the region during recessions and leading the country in times of prosperity (Varum and Rocha 2013). However, companies in the region, especially SMEs, clearly still lack the capabilities and competencies needed to promote sustained growth and their market structures (Erixon 2009). There are a small number of recently created incubators in the district. The two oldest (and largest) incubators are located in the north (Sanjotec) and the south (IEUA). IEUA (Incubadora de Empresas da Universidade de Aveiro  – University of Aveiro Business Incubator) is a non-profit university incubator founded in 1996, in the city of Aveiro. In 2015, the IEUA management team developed a business concept aimed at earning international recognition for the quality of its incubation of science and technology-based projects. This resulted in the IEUA earning second place in the World Best Science-Based Incubator rankings, making it indisputably one of the world’s best incubators.1 1  In the meantime, the head of the incubator was appointed CEO of Portugal Ventures (the largest, state-owned, venture capital organisation in Portugal). However, the new management team has continued in the same strategic direction that led to the abovementioned international recognition.

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Potential incubatees must present scientific and/or technological projects with a well-structured business plan and should preferably, but not exclusively, have their roots in the University community. The incubation programme is divided into two stages: The Start Incubation Programme and the Graduate Incubation Programme. In both programmes, basic incubation services are matched to firms’ needs and more sophisticated services are only provided when the company has moved into a more advanced stage. These services include the structuring of the internationalisation process, support in establishing contacts with investors and financial entities, organisation of exhibition events and staff selection and recruitment. These services are adapted to the type of company and most of them also include help with funding opportunities at the regional, national and European level. The IEUA has also led the effort to create a network of small business incubators in municipalities across the District of Aveiro. There are now 12 such centres across 11 cities. In 2016, IEUA hosted a total of 10 business ideas (resulting in the creation of 4 new firms) and 35 companies, with a total turnover, for both incubating and accelerating firms, of 4.6 million euros (University of Aveiro 2019). Sanjotec is a regional non-profit incubator, founded in 2008 as part of a strategic initiative implemented by the municipality of São João da Madeira (in the north of the District of Aveiro). Its objective is to support the local and regional business community by providing a favourable environment for the creation and development of firms. It encourages entrepreneurs to build new business projects with a technological basis and plays a facilitating role in bringing the business community closer to the scientific community and promoting local entrepreneurship. Having initially started with only one building, it currently operates in two buildings that were constructed with the help of European Union regional development funds. This expansion was underpinned by the growing demand for the services provided to both incubated and graduate companies. Sanjotec provides a range of incubation services to selected firms. These include basic incubation services, legal and tax counselling, investment support and advice (e.g. access to venture capital, microcredit and business angels). It also renders business advice and support in the preparation of business and marketing plans, scientific and technical consulting, market research, public relations, advertising and the search for qualified human resources for the incubated firms. Firm selection is based on a logic of complementarity with the other firms they host, which is assessed by analysing the technological or creative business value proposition. Support is mainly provided in the areas of automation, robotics, industrial control, electronics and information and communication technologies. There is no set incubation period. Nevertheless, after 5 years, companies are normally judged to be mature enough to be classified as graduates and to move on to the other spaces and services provided by the incubator. In 2014, the local authority took the step of going beyond technological ­companies and started working with local creative businesses. This involved o­ pening a new space in the former headquarters of Oliva, once one of Portugal’s largest ­metalworking companies. The Oliva Creative Factory hosts projects from such

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creative industries as product design, fashion design, footwear, the performing arts and handicrafts, among others. In 2016, the factory hosted 26 business ideas and 71 incorporated firms. Although it makes no distinction between incubating and graduate firms, a company is considered to have graduated after having been in the incubator for 5 years. By this measure, 41 of the 71 incorporated firms are graduates and 30 are still in the incubation phase (Sanjotec 2019).

4  Methods and Materials In terms of research design, we chose to analyse 32 companies that had been incubated in IEUA or in Sanjotec and that have, in the meantime, graduated from these incubators (experimental group). Since these incubators have been active for at least for 10 years, we selected a cohort of companies that were set up between 1 January 2007 and 31 December 2011. For comparison purposes, we selected a set of 32 companies that were founded in the same period in the District of Aveiro but those (as far as we know) were not incubated. These companies formed the control group. We then determined whether the experimental group (incubated companies) delivered a better overall performance than the control group (non-incubated companies) in 2016 (by which time, all the companies were at least 5 years old and had become fully fledged companies not in incubation). We were, thus, able to measure the impact of the incubation process on the graduate companies, by comparing their performance to that of non-incubated companies of about the same age. This allowed us to set up the general research hypothesis (null hypothesis): H0: Once they graduate, incubated companies present the same performance as non-incubated companies. In order to test this hypothesis quantitatively, we need to establish the variables that will allow us to evaluate the incubation process. Even though there are no established indicators set to evaluate the incubation process, several studies provide a number of pointers. The literature indicates that the focus of incubators has been changing from the tangible support (provision of space with basic services) that evolved during the late 1990s and early 2000s to a focus on the speed and resistance that incubated companies acquire during the process (Hansen et  al. 2000). Emphasis was later placed on the intangible process of business preparation and networking, through support for developing business plans and sourcing financing for the venture as well as the building of management skills, particularly in the area of team management (Von Zedtwitz and Grimaldi 2006). The resource-based perspective underpinning the methodology used in this study to compare incubated and non-incubated companies performance assumes that resources allocated to incubated firms result in a competitive advantage and allow them to perform better than non-incubated firms (Eveleens et al. 2017).

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Based on the literature review previously presented, and especially on the group of indicators that Ferreira et al. (2019) developed for the evaluation of Portuguese start-ups, and also taking into consideration the available data, we propose the following indicators to evaluate both incubated companies and non-incubated companies performance in the same region. Thus, the following key performance indicators are evaluated: 01COMPER is the turnover against the employees’ number ratio that leads to the commercial performance of the company. 02PROFIT is the EBITDA against the turnover ratio that leads to the profitability of the company. 03PRODASSE is the EBITDA against the total assets’ ratio revealing the productivity of the usage of total assets. 04PRODINTA is the EBITDA against the intangible assets’ ratio leading to the productivity of the usage of intangible assets. 05GRANTS is the grant against the turnover ratio, identifying the dependency on public or private grants. 06EXPEREMP is the total yearly expenditure on salaries against the employees’ number ratio, pointing to the quality of the created jobs. 07VAPEREMP is the total added value against the number of employee’s ratio revealing the value added per employee. 08EXPORTURN is the exports against the turnover ratio that indicates the openness to external markets by the company As we intended to compare the performance of graduate incubated companies and non-incubated ones set up in the same period of time in a specific region of Portugal, we allowed a maximum time of 5 years for companies to ‘graduate’ from the incubators. We also assumed that all companies tend to mature and stabilise once the 2-year mark is reached, so a company can be deemed incubated after 24 months. On the basis of this assumption, we created a database with all the pertinent companies listed on the incubator websites, identifying these by their tax number. We also searched the SABI database (Bureau Van Dijk 2019) for companies that have one of the four postcodes covering the areas in which the incubators are installed. This resulted in a pool of 157 tax numbers: 79 pertaining to companies incubated in Sanjotec and 78 to companies incubated in IEUA. Comprehensive cross-checking of the company information for the 157 tax numbers revealed that 36 companies that were no longer active. Two of them had been taken over (one in each incubator), 1 was registered in both incubators but its main activity was connected to IEUA, 16 others were active but were less than a year old, so they were not yet registered on the SABI database, and the remaining 17 had, in the meanwhile, closed down or were temporarily inactive. Of the 121 active companies with records on the SABI database, 71 had been attached to Sanjotec and 50 to IEUA. Filtering the companies by headquarters (legal registered office location) showed that 22 had moved from the Aveiro District,

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mainly to Lisbon (5) or Porto (8). Stripping out companies not created between 1 January 2007 and 31 December 2011, we were left with a total of 32 incubated companies, 13 from IEUA and 19 from Sanjotec. SABI records show that there are 20,914 active non-incubated companies with a fiscal address in the Aveiro District. Those founded between 1 January 2007 and 31 December 2011 were selected and a further selection from this subgroup was made of those businesses that have the same two first CAE2 rev. 3 digits as the incubated companies, which resulted in 640 possible subjects. We then eliminated all the very small companies (i.e. those that had one or no employees or whose share capital was less than 1000 euros in 2016), which left 310 companies in the non-incubated pool. A random number generator was used to select 32 companies from this pool to form the non-incubated company control group. Our panel of eight indicators was used to measure company performance in 2016, the year in which all 64 companies were deemed to have ‘graduated’ from their early-stage status (where they were not incubated) or from their incubation status (where they started in one of the two incubators). On the basis of this panel, and assuming that incubated/non-incubated is a dichotomous qualitative variable (INCNIN) that defines two independent groups and that all the eight dependent variables are continuous and do not follow a normal distribution, a non-parametric independent samples Mann-Whitney U (MWU) test was applied, to compare the means of the variables in both. The descriptive statistics for the eight variables for the two groups are presented in Table 1:

Table 1  Descriptive statistics 01COMPER 02PROFIT 03PRODASSE 04PRODINTA 05GRANTS 06EXPEREMP 07VAPEREMP 08EXPORTURN Valid N (listwise)

N 64 64 64 64 64 64 64 64 64

Minimum 12,209.0300 −1.4448614 −.56644100 −185.94590 .000000000 7000.00000 .000000000 .000000000

Maximum 766,177.365 .696709471 .670666526 656.927178 .938451790 52,592.6775 97,323.5795 1.00000000

Mean 75,477.4646 .072917831 .107067618 14.0382640 .086451295 16,663.1810 23,057.5997 .161960373

Std. deviation 116,269.753 .278113836 .217508854 88.9422241 .199216155 8161.86725 18,032.8091 .291870970

2  CAE (Código de Atividade Económica) is the Portuguese equivalent of the Eurostat NACE (Nomenclature générale des Activités économiques dans les Communautés Européennes or statistical classification of economic activities in the European Community) codes.

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5  Results As mentioned previously, the differences were evaluated using a Mann-Whitney U test, where all the eight variables were measured for non-incubated and incubated companies. As Table 2 shows, the null hypothesis cannot be rejected for the four of them, since p > 0.05. Thus, there is no statistically significant difference between the two kinds of companies in terms of: commercial performance (01COMPER); profitability (02PROFIT); productivity of the usage of total assets (03PRODASSE); and value added per employee (07 VAPEREMP). A detailed analysis of the results of the MWU test for each of the four variables is presented in Table 3: This analysis shows that there is statistically significant difference regarding: Productivity of the usage of intangible assets (04PRODINTA) with scores for incubated companies (mean rank = 37.52) significantly higher (from the statistical point of view) than for non-incubated companies (mean rank = 27.48), U = 351.500, z = −2.377 and p = .017. Dependency on public or private grants (05 GRANTS) with scores for incubated companies (mean rank = 37.88) significantly higher (from the statistical point of view) than for non-incubated companies (mean rank =  27.13), U  =  340.000, z = −2.426 and p = .015. Quality of the created jobs (06 EXPEREMP) with scores for incubated companies (mean rank =  37.06) significantly higher (from the statistical point of view) than for non-incubated companies (mean rank = 27.94), U = 366.000, z = −1.960 and p = .050. Openness to external markets of the companies (08 EXPORTURN) with scores for incubated companies (mean rank =  41.72) significantly higher (from the statistical point of view) than for non-incubated companies (mean rank = 23.28), U = 217.000, z = −4.410 and p = .000.

6  Discussion Given the above results and despite the fact that it is not possible to categorically reject the null hypothesis (H0: Once they graduate, incubated companies present the same performance as non-incubated companies), it can be seen that, 3–5 years after incubation, incubated companies have a different behaviour profile from their counterparts in several aspects. This leads into a discussion of how incubated companies behave in the market, when compared with their non-incubated counterparts and the role of incubators in forming such behaviour. Of the four variables that relate to the quality of created jobs, the ‘expenditure per employee (06EXPEREMP)’ is of key relevance, given that there is always an underlying political and social objective of retaining talent in the region and of

01COMPER 02PROFIT 03PRODASSE 04PRODINTA 05GRANTS 06EXPEREMP 07VAPEREMP 08EXPORTURN Mann-Whitney U 433.000 495.000 465.000 351.500 340.000 366.000 400.500 217.000 Wilcoxon W 961.000 1023.000 993.000 879.500 868.000 894.000 928.500 745.000 Z −1.061 −.228 −.631 −2.377 −2.425 −1.960 −1.497 −4.410 Asymp. Sig. (2-tailed) .289 .819 .528 .017 .015 .050 .134 .000

Table 2  Mann-Whitney U test results

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Table 3  Detailed Mann-Whitney U test results Ranks 04PRODINTA

05GRANTS

06EXPEREMP

08EXPORTURN

INCNIN 0 1 Total 0 1 Total 0 1 Total 0 1 Total

N 32 32 64 32 32 64 32 32 64 32 32 64

Mean rank 27.48 37.52

Sum of ranks 879.50 1200.50

27.13 37.88

868.00 1212.00

27.94 37.06

894.00 1186.00

23.28 41.72

745.00 1335.00

creating better paying jobs. Since this variable represents the ratio between salaries and the number of employees, in the case of incubated companies, the variable has an average value of nearly 19,000 euros, whereas the average figure for non-­ incubated companies is about 13,400 euros per year. This suggests that better paying jobs are being created and that the incubators are achieving their goal of retaining talented people while, at the same time, creating better-paid jobs than those offered by non-incubated companies in the region. This result is underpinned by the fact that, although both types of company operate in the technology sector, many of the graduate companies work in highly innovative businesses with a strong demand for well-qualified employees that can perform non-routine jobs, particularly in such areas as the development of software and materials. It should be pointed out that 19,000 euros per year is 2.34 times the national minimum wage and 1.23 times the average national wage, which, in turn, is about half of the average EU-28 wage (Instituto Nacional de Estatística-National Statistics Institute 2017). Of course, the incubator does not play a direct role in generating such salaries. However, by creating the conditions for the companies to stay in the region and through the support given to entrepreneurs for the development of their human capital, both incubators promoted the retention of qualified human resources. These employees were able to create more value for the companies, which, in turn, were able to pay better salaries. Another relevant indicator, and one closely related to the objectives underlying the creation of regional and university incubators, relates to export volumes. The openness to external markets of companies’ (08 EXPORTURN) variable is measured as the ratio between exports and company turnover. Graduate companies (some of which trade exclusively with external markets) earned an average of 26.4% of their turnover from exports, whereas non-incubated companies (many of which only trade domestically) earned an average of about 6% from exports.

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This result can also be explained by the highly innovative nature of the companies involved, as they sell intangible products and services to external markets, compete with other suppliers in foreign markets and overcome the difficulties of entering new markets. In this case, the effect of the incubators can be more clearly linked to subsequent company behaviour. In fact, both incubators promote the structuring of the internationalisation process by incubated companies and run specific initiatives in this regard. This work is heavily reliant on funding from European Union regional development funds, which are used to promote the internationalization of start-ups. The result is a virtuous circle that benefits graduate companies in both the medium and long term. Given the vast majority of grants come from public funds, at the regional, national and/or European level, the interpretation of the variable related to the ‘dependency on public of private grants (05 GRANTS)’, which is calculated as the ratio between grants and annual turnover, can be twofold. There is a clear difference between the average grant dependency of graduate company turnover, which stands at about 12.9% of yearly turnover, and that of non-­ incubated companies (around 4.4%). This highlights the fact that most graduate companies take advantage of EU research, technology and development funding and regional development funds in order to develop innovative new products and promote these externally, thus contributing to the abovementioned virtuous circle. However, these results must be interpreted with caution, since there are several outlier companies that are heavily dependent on such funding (between 75% and 95% of their turnover is dependent on grants). This denotes a worrisome behaviour of grant dependence that, if repeated over the years, could transform into a vicious cycle, making graduate companies dependent on grants and, thus, corrupting their initial intentions. Once again, the results can be traced to the early work of the incubators, which promote and expose the incubated companies to a series of initiatives presenting opportunities for development funding, at the regional and national level, as well as RTD funding, especially at the European level. The last indicator for which there is a clear statistical difference between graduate companies and non-incubated ones relates to the productivity of the usage of intangible assets, measures as the ratio between EBITDA and intangible assets (04PRODINTA). Once again, this difference can be traced to the fact that most of the incubated companies are not just technology oriented but are also developing intangible products and services, thus creating a set of intangible assets that are sold in the marketplace and generate revenues and profits for the company. However, when looking beyond the absolute value of the ratio and comparing the data between both groups, we found that, of the 64 companies analysed, only 28 had intangible assets on their balance sheets. Nineteen of these were graduate companies, whereas only nine of the non-incubated companies had intangible assets on their books.

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In this case, there is no clear relationship between the data and the role of the incubators in the early life of the companies. However, since graduate companies mostly work in knowledge-based areas with a significant service component, it is to be expected that they would properly value their workforce and its technological know-how. Also, and since the graduate companies are dependent on grants, it would be reasonable to expect that research and development investments (and the patents these generate) would, one way or another, be represented on the companies’ balance sheets. However, this was not found and one of the likely explanations for this is the fact that, although companies invest heavily in supporting services, it is rare to find accounting firms with a modern approach to intangibles, for two main reasons: firstly because most of the companies in the region are industry based and most of their assets are tangible and, secondly, Portuguese companies do not make much effort to individually identify and disclose intangibles acquired in business combinations, as found Carvalho et al. (2016) when analysing the companies listed on the Portuguese Stock Index. In considering our second research question, our analysis of involved variables (04PRODINTA, 05 GRANTS and 08 EXPORTURN) shows that incubated companies do behave differently to non-incubated companies with respect to the productivity of intangible assets, grant dependency and external markets openness. This different behaviour stems from the fact that, during the incubation period and among other experiences, the incubated firms were exposed to networking opportunities that, together with the innovative nature of their business, led to further differentiation from non-incubated firms and, thus, significant differences on these specific indicators.

7  Conclusions and Further Work In this chapter we analysed the behaviour of two incubators located in an industry intensive district and compared the business performance indicators for incubator graduates with those of companies of about the same age and located in the same district, but which were not incubated in the early stages of their development. It is possible to conclude from the data that there is a statistically significant ­difference between incubated and non-incubated companies when analysing s­ pecific indicators. The main differences lie in the fact that incubated companies offer better paying jobs and are export focused. They also make more of the grants that are provided by public entities to develop the business offer. It is also possible to ­differentiate these companies based on the usage that they make of their intangible assets, with the graduate companies reporting more intangible assets than the non-­ incubated ones. Although the indicators we used do not allow us to ascertain whether or not the incubator selection process is optimised or whether the graduate companies are able to survive longer in the market, it is possible to evaluate the role of the incubators in

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the second component of the incubation model proposed by Bergek and Norrman (2008), particularly as regards mediation. The mediation role proposed by Hughes et al. (2007) involves the incubators acting as a tertius iungens (third party that links), linking incubated firms through resource pooling activities and strategic networking. Although this behaviour does not translate into significant differentiation for the incubated companies with respect to traditional performance indicators (such as commercial performance or profitability), it does enable them to build promising business networks that, as suggested by Tötterman and Sten (2005), improve the success of the venture. This is particularly true of the creation of networking opportunities in the marketplace, in the sense in which Peña (2004) associated this with the use of public services to support entry into foreign markets, and the use of public grants to support an innovative offer. The networking effect can then be extended to other fields, engendering collective stakeholder engagement in the development of viable business models that, according to Patton (2014), enable the full realisation of the potential absorptive capacity of the knowledge and the intangible assets associated with the organisation. Although we identified a positive virtuous circle in the development of the companies in question, we would introduce a few caveats regarding the current practices of the cohort of graduate companies: Firstly, the dependence of this cohort on (mostly public) grants is worrisome. Although it indicates that these companies are taking due advantage of public grants to develop their businesses and products, this dependence may impede their business development practices in the near future. Secondly, even though graduate companies have a remarkable growth potential, it is worrisome that this potential is still not reflected in the performance indicators, namely, turnover and EBITDA.  Growth in recent years has been modest, even though the companies are operating in competitive and expanding sectors. Although it is still too early to know how this cohort of companies will evolve, it is possible, at this stage, to say that they have clearly benefited from the incubation process, reflecting the support and market connections provided, particularly in terms of internationalisation, and, thus, validating the approach taken by the incubators. However, this support could be improved in some respects. The most worrying aspect is the growth rate of the companies that is not quite as good as might be expected from companies in the technology sector. When nurturing incubated companies, incubators should point out the need for a ‘high-tech, high growth’ approach, based on organic growth stimulated by equity, provided by external business angels and venture capitalists. This contrasts with the current approach, which is based on limited equity and heavy dependence of public grants. This gap has already been assessed by Ratinho and Henriques (2010) and Lobosco et  al. (2019) for the Portuguese case and is still considered, by policymakers and practitioners, as one of the Achilles’ heels of the Portuguese knowledge-based entrepreneurial ecosystem (Moutinho et al. 2016).

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Another problem, closely related to the previous one, lies in the fact that graduate companies that have chosen a growth strategy based on public grants will, in the near future, face worse ‘growing pains’ as the cohesion funds for regional development become scarcer and access to research, technology and development funds becomes harder (Rego et al. 2018). We believe that future research should track graduate companies’ performance from these two incubators and compare this with graduate companies from other incubators in Portugal and across Europe. Furthermore, there is a case to be made for a cohort-based study of graduate companies from these two incubators, analysing their behaviour on a periodic basis and linking their growth strategies to the early-­ stage incubation services that they have benefited from.

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