Transferencia de tecnología y conocimiento universidad-empresas. Análisis de las spinoffs académicas desde una perspectiva internacional

Laburpena

The State of the Art Since the enactment of the Bayh-dole Act in the USA in 1980, there has been a substantial rise in commercialisation of science created by universities in the USA (Grimaldi et al., 2011) in Europe (Maia & Claro, 2013; McAdam et al., 2016) and Asia (Zhang et al., 2013). These marketable actions are known as academic entrepreneurship and comprise the Knowledge and Technology Transfer activities between universities and industries. They constitute the third mission of universities apart from teaching and researching (Clark, 1998; Etzkowitz, 1998). They are defined as the interchange of new knowledge, products, and processes from one organization to another for the economic benefit of both parts (Decter et al., 2007). They include generation of new ideas, creation of USOs, intellectual property, and technology licences. In the last twenty years, due to the recent world financial crisis and an increasingly competitive global marketplace, legislators have been adopting policies to stimulate innovation and entrepreneurship in the hope of producing economic growth (Autio et al., 2014). Universities have been the target of these policies (Morgan, 2007; Nicolaou & Birley, 2003), given their ability to stimulate the production and diffusion of new knowledge and act as catalysts of innovation across their regions (Nicolaou & Birley, 2003; Wright, 2014). As a consequence, universities are increasingly adopting a stronger entrepreneurship and innovation profile and reputation in order to provide a wider social and economic benefit to their territories (Siegel & Wright, 2015). It has given birth to the entrepreneurial university (Guerrero et al., 2014, 2016). This new model is characterized by providing a supportive ecosystem to the university community and its surroundings, in order to produce, diffuse, absorb, and use new knowledge that could become entrepreneurial initiatives (Carree et al., 2014; Guerrero et al., 2014). These entrepreneurial initiatives are mostly University Spin-off companies (USOs) which involves the creation of for-profit firms based on university research (Philpott et al., 2011). We have perceived that little research is known concerning this entrepreneurial innovation ecosystem created by universities. The most recent literature stream on the issue points at it as a promising and emerging research area (Autio et al., 2014; Graham, 2014; Hayter, 2016a; Siegel & Wright, 2015). Aims of the Thesis Dissertation The purpose of this work is to extend our knowledge on innovation ecosystems in academic entrepreneurship literature in three directions. First, to review the existing literature about this phenomenon to get familiar with the state of the art. Second, to evaluate the effect of USOs as the main mechanism used for Knowledge and Technology Transfer between universities and industries. Finally, to give light about the dynamism of entrepreneurial innovation ecosystems. In particular, the general aim of this thesis dissertation is divided into five objectives, explained below. Firstly, related to the first mentioned direction, to identify the key research themes to date and the challenges for future investigation. Secondly, related to the second direction, two specific objectives are set up. First, to analyse if the investment and creation of innovative ventures around universities leads to the emergence of new business ecosystems. Second, to study the importance of the context where the entrepreneurial university is embedded in promoting academic entrepreneurship activities to achieve the development of an ecosystem. Finally, regarding the third direction, other two specific objectives are established. First, to examine the role of the agents involved in the dynamics of the entrepreneurial university ecosystems, with special attention paid to the intermediaries. Second, to carry out an internationally cross comparison to achieve a better understanding of the role of the context in the dynamic of the entrepreneurial university ecosystem. In addition, a basic assumption behind this study is that not all entrepreneurial innovation ecosystems are the same. Therefore, the findings supported by this study can be tested in other regions or nations in order to extend the generalization of its results. Theoretical approach When studying a complex and emerging research area, a combination of theoretical perspectives offers a more comprehensive viewpoint and stronger explanations than a singer view (Van de Ven & Poole, 1995). In this sense, to analyse the innovation ecosystems in the academic entrepreneurship, we combine three theoretical perspectives: the emergent ecosystem approach, the social network theory to entrepreneurship, and the Knowledge Spillover Theory of Entrepreneurship (KSTE) (Acs et al., 2009). The ecosystem approach, inspired on biological theories, has grown recently in the economic literature (Moore, 1993). Defenders of this emergent approach use it to define business environments of innovation (Durst & Poutanen, 2013; Jackson, 2011; Mercan & Göktaş, 2011; Moore, 1993; 1996; Oh et al., 2016). Two main general perspectives of this emergent approach have been identified in the literature. In the first perspective, mostly adopted in the entrepreneurship literature, ecosystems are understood as communities of associated actors defined by their networks (Autio & Thomas, 2014; Graham, 2014). This perspective emphasis on the breakdown of the traditional industry boundaries and offers a new economic thinking where different agents, markets, organizations and governments interact to generate innovation (Autio et al., 2014). It focuses on questions related to access and openness, network density, or actors’ centrality in a network (Clarysse et al., 2014). In the second perspective, mostly embraced in the strategy literature, ecosystem is understood as configurations of activity defined by a value proposition (Adner & Kapoor, 2010; Zahra & Nambisan, 2011). It starts with a value proposition and seeks to identify the set of actors that need to interact to come it up. According to the research gap identified and the declared aims of this work, this study contributes to the first perspective. In particular, it defines innovation ecosystem as a loosely interconnected network (of companies and other entities) that coevolves capacities around a shared set of technologies, knowledge, or skills; and works cooperatively and competitively to develop the next round of innovation (Moore, 1996). In this vein, different theories have been used to conceptualise innovation ecosystem in academic entrepreneurship to test and extend this burgeoning approach (see Chapter 2 section 4.1. Nature of the field for a review). As mentioned, we built this research upon the social network theory to entrepreneurship, as part of the resource-based theory (Sirmon et al., 2011), to explain how networks enable entrepreneurs to acquire information and resources important to their firm (Brüderl & Preisendorfer, 1998). It allows us to explore how social networks arise within an entrepreneurial innovation ecosystem according to the different roles and position of the participants in the value creation of a USO (Hoang & Antoncic, 2003). In addition, following the recommendation of Hayter (2013a), we combine this network approach with the Knowledge Spillover Theory of Entrepreneurship (KSTE) (Acs et al., 2009), in order to link the micro-level, the entrepreneurial behaviour of the ecosystem participants, with the macro-level, the social-economic impact of the ecosystem. KSTE focuses on individual “agent of knowledge” and their role in the knowledge spillover (Acs et al., 2009). It embraces the assumption that new knowledge is the source of innovation, productivity and economic growth (Grant, 1996; Romer, 1990). In addition, it takes issue with traditionally theoretical assumptions that all knowledge is economically useful and spills over “automatically”. It suggests that entrepreneurship is an important vehicle for the spillover of new knowledge and therefore critical to economic growth (Acs et al., 2009; Hayter, 2013a). Consequently, we base on KSTE to explain how faculty entrepreneurs produce, diffuse, absorb, and use new knowledge that become entrepreneurial initiatives (Carree et al., 2014; Guerrero et al., 2014; 2016) and understand networks as mechanisms for the knowledge spillover to occur, giving rise to an entrepreneurial university ecosystem with socio-economic impact. Data and information sources Multiple data and information sources were used in this work. For the literature review, different strategies to find the most relevant published research were followed. As will be explained in detailed later, in Chapter 2, we conducted Boolean searches with keywords in repositories such as ISI Web of Knowledge, Proquest (ABI/Inform), ScienceDirect, and Wiley Online Library. As a result, we found that the cutting edge scientific knowledge on the matter comes mainly from papers indexed in journals such as Research Policy, Journal of Technology Transfer, Technology Forecasting and Social Change, R&D Management, and Small Business Economics. In addition, we used direct citations (papers in the reference lists of the articles analysed) and backward citation search (papers referring to the article analysed) to complete the literature review. These strategies led also to a relevant book (Graham, 2014) in the research field. Other information sources, such as institutional web pages (INE, 2016; UPA, 2016), were also used. Finally, feedback and comments from presentations at different conferences were particularly important. For the empirical research, we collected qualitative data from two different regions, Andalusia in Spain and England in the UK. A total of 70 in-depth interviews (48 in Andalusia and 22 in England) were carried out over two years’ period (2012 and 2013), and all of them took place in their settings. Different techniques were used to collect them. In Andalusia, we contacted the TTOs to obtain a complete list of USOs that fitted with the criteria established for the investigation. In the UK, a snowball method was used. In this regard, both my staying at University of Leeds and the collaboration of Nigel Lockett as expert in the field were fundamental for it. Research methodologies used This section is used to explain why we chose mix-methods to conduct this thesis dissertation and how it is embedded in a social constructionist epistemology with a subtle realist ontology (Twining et al., 2016). The ontological question deals with the form and nature of reality, and what can be known about it (Guba & Lincoln, 1994). Basically, there is a dicothomy in ontology between two stances, the existence of one objective realility and the the existence or multiple realities (Twining et al., 2016). Our ontological answer is related to the second stance, and it is based on the belief that reality can be described as subtle realism (Hammersley, 2013). Subtle realism is defined as the belief in an external world, independent from the mind, but it can only be understood through the human mind and socially constructed meanings. Therefore, the goal of subtle realism is to describe and understand social life in terms of social actors’ motives and understandings (Blaikie, 2007). Once we answered the ontological question, we turned to the epistemological question. It gives answer to what is the nature of the relationship between the knower and what can be known (Guba & Lincoln, 1994), having also a basic dicothomy between the existence of one reality that can be known, so there is one true explanation, and the existence of different meanings wich are defined, among others, culturally (Twining et al., 2016). Then, we understand the knowledge as socially constructed between individuals (Berger & Luckmann, 1966). We cannot separate ourselves from what we know (Hammersley, 2013). As Burr (2003) states, the understanding of the world is historically and culturally specific, and all knowledge derives from looking at the world from one vantage point or another. This in turn paves the way for the triangulation of perceptions (also termed ‘critical multiplism’ (Guba & Lincoln, 1994), to uncover the underlying reality. In addition, following Blaikie (2007), the combination of subtle realist ontology and social constructionist epistemology lead the development of theory that can be elaborated iteratively by individuals. Finally, the methodological question needs to be answered. It explains how to go about what we believe can be known (Guba & Lincoln, 1994). Based on a subtle realist ontology and a social constructionist epistemology, we see individuals as the unit of analysis and their experience as an interpretive activity mediated and sustained by signs (Baškarada, 2014). This gets into the heart of mix-methods debate regarding how data is viewed. In this sense, we see data as a symbolic representation, which need to be interpreted and thus is subjective and context dependent (Twining et al., 2016). Therefore, the strategy chosen to gather the relevant data follows a case study design (Yin, 2011). We grouped the individual accounts into cases, and dedicated one case for each country studied. Case study design is the best strategy for data collection that aims to understand a process that is embedded in a specific context (Yin, 2011) as the emergent ecosystem approach requires. Then, based on previous findings found in the literature review, a method following a deductive approach is needed to compare the facts to existing theory and research within the analytical research framework developed in the literature review. In addition, due to the novelty of the analysed field, it is also needed a method that follow an inductive approach to conduct more exploratory research and extend this emergent ecosystem approach within the academic entrepreneurship. Therefore, we chose a mix-method approach based on a quantitative Social Network Analysis (SNA) and qualitative analysis of in-depth interviews to key participants. Firstly, the quantitative methodology was useful to test the ecosystem approach in two different regions, Andalusia in Spain (in Chapter 3), and England UK (in Chapter 4). Using Ucinet software bundled with NetDraw (Borgatti et al., 2002) we built the visual representation of the social network of both ecosystems. In addition, we calculated a series of relationship indexes to give a deeper explanation of the network structure (Borgatti & Everett, 2000). Then, we captured network dynamics which allowed us to be more predictive of subsequent entrepreneurial outcomes (Hoang & Antoncic, 2003). Secondly, the qualitative methodology based on in-depth interviews was used to extend the emergent ecosystem approach in understanding how these interactions occur and the specific contributions of the networks, as well as why the context in which the ecosystem is embedded matters. Finally, we used the concurrent triangulation strategy to cross-validate the two databases (Creswell, 2002; Jick, 1979). Structure of the thesis This thesis dissertation follows the structure of compendium of three future papers. It contains an introductory chapter, three chapters outlined below and a conclusion chapter, as well as references and appendices. Additionally, to meet the requirements of the Doctoral Office of the University of Malaga, an executive summary and a concluding chapter in Spanish are also included at the end. The chapters mentioned below belong to the three future papers, which are not published yet. All of them have been submitted to high quality journals and, if we have received comments, they have been used to improve the quality of the research. Chapter 2 is a systematic review of the literature on innovation ecosystems and academic entrepreneurship. It identifies the key research points to date and the challenges for future researchers interested in the field. This chapter has been continuously updated during the present research and has been decisive in guiding us in the subsequent chapters of this dissertation. Finally, chapter 2 and 3 have its own theoretical background, which are directly connected with the results of this chapter and the research questions of each investigation. Chapter 3 shows a case of study performed in the region of Andalusia (south of Spain) to find if the investment and creation of innovative ventures around universities leads automatically to the emergence of new business ecosystems. In addition, it analyses the context in which Andalusian entrepreneurial universities are embedded and their effectiveness in promoting academic entrepreneurship and achieving vibrant entrepreneurial university ecosystems. Chapter 4 provides an international comparison regarding the role of the knowledge intermediaries, specifically University-focused Venture Capital firms (UVCs), in the creation of University Spin-off companies (USOs) and the dynamics of entrepreneurial university ecosystems in Andalusia and England. It also explores if policies applied in Anglo-Saxon contexts can be used in other European countries, highlighting the importance of the context. The concluding chapter summarises the main findings and contributions to the state of the art, discusses some limitations, and establishes future challenges for entrepreneurship researchers. Finally, it invites university managers and regional policymakers to think about the findings of this study to guide their decisions.