Читать книгу Social Capital in the University-Based Innovation Ecosystems in the Leading Life-Science Clusters: Implications for Poland - Małgorzata Runiewicz-Wardyn - Страница 6

The Triple Helix (TH) (university-industry-government interlinkages) approach to ‘innovation systems’ has been widely accepted, especially in the public sector. However, there has recently been an attempt to enrich this approach with a new concept of the Quadruple Helix (QH), which is grounded on the idea that innovation is the outcome of an interactive and trans-disciplinary process involving “all stakeholders as active players in jointly creating and experimenting in the new ways of doing things and creating new services and products” (European Commission 2015). Notably, the QH approach builds on the emerging concept of an ‘innovation ecosystem’ and widens the TH concept with one more helix – society and societal perspective (McAdam and Debackere 2018; Carayannis and Campbell 2012). Consequently, in the QH interactions, knowledge transfer among innovation actors is additionally strengthened by social, trust-based relations among the actors or so-called “social proximity”. The concept of an ‘innovation ecosystem’ refers to a network of interconnected organizations, connected to a focal firm or a platform that incorporates both production and uses side participants and creates and appropriates new value through innovation (Autio and Thomas 2014).

The life sciences industry, including biotechnology, is advancing at an unprecedented rate. As for 2018, the global life sciences sector accounted for approximately $1.6 trillion and was expected to reach over $2 trillion in gross value by 2023 (www.bisnow.com). Most of biotechnology research and industry innovation activities were concentrated in just few locations in the world. For example, San Francisco Bay Area is the largest recipient of the venture capital investments, along with the Boston-Cambridge area, and employs the highest share of biotechnology work force in the US (U.S. Life Sciences Clusters, 2019). In Europe, Cambridge (United Kingdom) life sciences is home to around 25% of Europe’s biotechnology companies and employs 57,000 people. It also accounts for 20% of the world’s Nobel Prize winners in medicine and chemistry (Cambridge Cluster 2019).

This high level of geographic concentration persists despite the subsequent rise in funding programs in the European Union to spur the development of the life sciences industry (Innovation Union Scoreboard 2018). In the last decade, another cluster in the north end of the United States – Seattle (Washington state) showed its incredible dynamics by becoming one of the fastest-growing life sciences market in the United States, with the rate of 16% growth on average in 2014–2017 (CBRE Research 2019). In Europe, a cluster on a cross-border region between Denmark and Sweden – the Medicon Valley – revealed its incredible scientific potential, which is reflected in the sharp increase of the volume of scientific publications in the life sciences – 23% between 2013–2016, and, to a lesser extent, in patent applications – 15% and 6% increase in Denmark and Sweden, respectively) (State of Medicon Valley 2018).

The success of these life sciences clusters poses questions as to which factors drove their success? There is a substantial amount of the high-tech-cluster-related literature considering the following success factors of the life sciences clusters: strong science and industry base, strong networks between industry and science, that facilitate the growth of both academic and industrial spin-offs, finance availability for new biotech companies (including venture capital and government funds), as well as traditions of local entrepreneurship (Maskell and Malmberg 2002; Su and Hung 2009). Relatively fewer sources mention the role of networks between faculty, investors, students, intermediary agents, and local authorities in sharing knowledge, information and thus stimulating inventions and innovations (Broekel and Boschma 2016; Ponds, Oort, and Frenken 2009; Audretsch and Feldman 2004; Audretsch and Stephan 1996; Adams 2002; Anselin et al. 1997; Golejewska 2018). The following study focuses on a relatively less discussed factor – social capital and social networks or larger social structures as a key determinant of the success of the life sciences ecosystem.

The core mission of the following study is to enable the reader to better understand the mechanisms and the significance of the networks and social capital in the selected sample of life sciences university-based ecosystems, as well as draw implications for the new emerging life sciences ecosystems in Poland. Thus, the study analyses the Triple (Quadruple) Helix networks within the life sciences ecosystems from a bottom-up perspective, by studying peoples` behaviour at the grassroots level. The study focuses on three major research problems: 1) the mission, structure and types of social networks; 2) the methods and the intensity of social networking/interactions as well as different dimensions of social capital; 3) the impact of social networks on R&D collaboration, innovative performance and future development plans.

In terms of methodology, most social science researchers acknowledge that the “social capital” and “social networks” are complex issues and therefore, they would benefit most from the integration of qualitative and quantitative approaches. In practice, however, effective quantitative research requires a larger sample size, which was not possible in the case of the following research study, due to the limited time and resources. Therefore, applying qualitative case-study research and direct observations were the best suited method to explore all sides of the social capital within the selected sample of life sciences clusters. The qualitative sample includes five case studies – life sciences ecosystems in San Francisco Bay Area (United States), Cambridge (United Kingdom), Copenhagen-Lund (Denmark/Sweden), Seattle (Washington State, United States) and Poland. The personal ‘interview’ technique was applied in order to collect in-depth content from the above ecosystems. The concept of a ‘university-based ecosystem’ was defined as a complex set of relationships among actors from universities and research institutes, enterprises, and other institutions, that lead to an inter-exchange of technology and information, and stimulate innovations. The broad goal of the interviews was to gain knowledge of and insights into how social interaction/networking fosters research collaboration and innovations. The questionnaire contained mixed questions (open and closed ones) and was composed of four parts: (1) the mission, structure and types of social networks; (2) the methods of networking and the intensity of interactions; (3) the role of different types of proximities in social networking; (4) the impact of social networks on R&D collaboration and innovative performance. The authors conducted interviews with the heads and deans of departments, the technology transfer offices (TTO), related educational institutions and companies in the following life sciences cluster ecosystems in the United Kingdom, the European Union and the United States. The list of all interviewed organizations is enclosed at the end of the paper. In order to analyze the evidence gathered, a multi-step thematic content approach was applied. The researchers transcribed the interviews to gain preliminary results, then looked for common and different patterns for all the analyzed ecosystems.

The present monograph is divided into seven chapters. The introduction is followed by a presentation of the theoretical and conceptual framework of social networks, social capital formation and university-based innovation ecosystems. The second chapter discusses major trends, developments and the role of technological convergence in the life sciences sector. The next four chapters discuss the life sciences clusters in Cambridge, Medicon Valley, the Bay Area and the metropolitan region of Seattle. The last chapter presents the life sciences cluster in Poland: its structure, important drivers and challenges. The monograph ends with important conclusions and implications for further studies and public policies.