Initial situation and objectives of the dissertation thesis

It is widely recognized in the academic literature that knowledge is the central element for a firm’s competitiveness and ability to innovate and grow in today’s globalizing learning economy.¹ In addition to the internal production of new knowledge, especially a company’s ability to collaborate, and to find, access, absorb and exploit external knowledge has be-come the central determinant for its commercial success. The dynamic, non-linear model of innovation emphasizes interactive learning, i.e. the interactive process of knowledge pro-duction, appropriation and distribution, as the basis of innovation.² Subsequently, learning is understood as a predominantly interactive and socially embedded process, which in-volves a wide range of actors and sub-systems (Lundvall, 2010). The large variety of actors involved in processes of interactive learning is also expressed in the concepts of triple helix (Leydesdorff & Etzkowitz, 1996), quadruple helix (Etzkowitz & Leydesdorff, 2003), and quintuple helix (Carayannis & Campbell, 2010).

Important for economic geographers, the exchange of tacit, experience-based knowledge (Polanyi, 1966), which is assumed to be fundamental for learning and innovation, requires face-to-face interaction favouring the local and regional scale over others. As a conse-quence, and also due to the observed economic success of innovative regions, such as, Third Italy (Bagnasco, 1977), Silicon Valley (Saxenian, 1994) and Hollywood (Storper &

1 The terms learning economy (Lundvall, 1992) and knowledge-based economy (OECD, 1996) are generally used synonymously as they commonly stress knowledge as the most important resource and learning as the most fundamental activity for a competitive advantage in the globalizing economy. However, they slight-ly differ as the term knowledge-based economy puts a distinct emphasis on the differentiation between dif-ferent degrees of high-, medium- and low-tech industries (Asheim & Coenen, 2005). Also, the term knowledge economy is often used synonymously to the two other terms. However, they are not defined alike. The term knowledge economy was established earlier and underlines the composition of the labour force as an input factor in the production process (Cooke & Leydesdorff, 2006).

2 The dynamic process of innovation underlines the fundamental importance of various actors (e.g. suppliers, customers and academia), as well as multiple loops of feedback and reproduction of knowledge. In con-trast, the traditional linear process of innovation stressed technical change and innovations as a result of scientific and research efforts being directly transferred to the firm and, then, introduced to the market (Lundvall, 2010).

Christopherson, 1987), various theoretical concepts have been developed that underline the facilitative role of geographical proximity for knowledge spillovers and interactive learn-ing – the most prominent examples belearn-ing the innovative milieu (Aydalot, 1986; Camagni, 1991), Porter’s cluster (Porter, 1990), learning regions (Florida, 1995, Morgan, 1997), new industrial districts (Scott, 1988; Markusen, 1996) and territorial innovation systems (Lundvall, 1992; Braczyk et al., 1998).

Also, policy makers have been paying increasing attention to specific places and regions as designated sites of innovation and competitiveness. For the triple helix (industry-academia-government) in particular, a wide spectrum of technology and innovation policies have aimed at fostering and even planning interaction between science and high-technology industries in order to increase regional economic growth, competitiveness and innovativeness (Sternberg, 1995).³

In this respect, science and technology parks (STPs) have become a prominent instrument as planned seedbeds of innovation (Felsenstein, 1994) in regional economic development policy. Similar to the theoretical concepts of the spatial innovation systems literature, STPs are typically linked to the geographical co-location of the triple helix, i.e. firms and scientific institutions operating in similar or related sectors and technology areas, respectively, and a certain socio-institutional thickness. Consequently, this setting of geographical proximity, related variety (cognitive proximity) and “institutionalized high-trust environments (institu-tional and social proximity)“ (Fitjar & Rodíguez-Pose, 2011: 1248) seeks to thrive personal interaction and, in turn, the diffusion of tacit knowledge among co-located knowledge or-ganizations (Boschma, 2005; Fitjar & Rodríguez-Pose, 2011). However, many STPs reveal shortcomings in the anticipated effects of localized interaction and knowledge spillovers promoting interactive learning and, in turn, also in their expected role as important organizational links driving regions’ innovativeness (e.g. Quintas et al., 1992; Vedovello 1997; Fukugawa, 2010).

In addition, increasing theories and empirical evidence for the equivalent importance of local and non-local connections as roots of knowledge diffusion and innovation, which is, for example, represented in the local buzz and global pipelines dichotomy (e.g. Bathelt et al., 2004; Wolfe & Gertler, 2004; Trippl et al., 2009), have led to the critical assessment of

3 While industry refers to companies, academia refers to higher education and public sector research institu-tions (Polt et al., 2001). In this dissertation thesis, the terms R&D institution and non-university research in-stitution are used synonymously.

the role of ‘proximity’ in knowledge interaction and learning in the more recent academic discussion.⁴

Especially the French school of proximity dynamics (e.g. Torre & Gilly, 2000; Gallaud &

Torre, 2005; Torre & Rallet, 2005) and Boschma (2005), among others, has challenged the traditional perception that spatial proximity is a necessary and sufficient criterion for knowledge interaction and interactive learning to take place. Instead, the significance of the multi-dimensional character of proximity, integrating non-spatial proximity to the strict geo-graphical interpretation of proximity, is strongly advocated for the multi-scalar geography of knowledge sourcing and knowledge interaction.

In addition to knowledge interaction based on direct relations, knowledge diffusion between actors also results from indirect links facilitated by governance and intermediation (Noote-boom, 2003). Thus, a firm’s capability to manage both internal knowledge generation and the access to external knowledge (knowledge management) finally determines their inno-vativeness (Ibert & Kujath, 2011). Yet, few studies have adapted the concept of knowledge management from the organizational learning literature to spatially defined innovation sys-tems (e.g. Harmaakorpi & Melkas, 2005).

Thus, the innovative approach of this doctoral thesis is the combination of theoretical con-cepts drawn from economic geography, namely STPs and the proximity framework, and from the organizational learning literature, namely knowledge management. In combina-tion, I aim to determine the applicability of knowledge network management systems for the organization of multi-dimensional proximity in order to foster cross-institutional knowledge relations in STPs and external to STPs.

In response to the recent developments of the specific interrelated strands of research, I aim to determine (a) the quality, structure and geography of STP resident firms’ linkages to scientific knowledge sources in their pursuit of learning and innovation, as well as (b) the driving factors and criteria in terms of specific types of proximity behind successful link cre-ation and knowledge interaction with scientific institutions on different geographical scales.

Furthermore, I examine (c) to what extent firm-specific as well as external channels and platforms including STP-related knowledge network management systems affect resident firms’ knowledge interaction with academia on the local and extra-local scale. I address

4 The location paradox reflects the global and local dialectic of, on the one hand, the global exchange of information and knowledge based on ICT technologies and global mobility, and, on the other hand, of the consistent trend of geographical agglomeration of especially knowledge and technology-intensive industries due to the proximity to markets, availability of skilled labour, anticipated knowledge spillovers and need for personal interaction in interactive relations, among others (Malecki, 2000; Anttiroiko, 2004).

these topics by exploring the geographical sources of innovation and related influencing factors of technology-oriented resident firms in two science and technology parks in Berlin (Germany) and Seville (Spain).

The following research questions are at the centre of this dissertation thesis:

1. What knowledge relations to academia are evident for the STP resident firms in the two science parks?

2. What types of firm-centred knowledge networks to academia can be identified?

3. What are the influencing factors enabling and driving knowledge interaction with aca-demia in the STP and external to the STP? Which firm-specific and external channels and platforms enable and promote the formation and realization of STP resident firms’

knowledge relations to academia on the local and extra-local scale?

4. Which dimensions of proximity matter in a firm’s knowledge relations with academia?

5. To which extent do knowledge network management systems in STPs create and organ-ize proximity to stimulate industry-academia knowledge relations?

As a result of this work, I aim to add new aspects to the “soft architecture of learning”

(Thune, 2009: 9) by developing specific policy recommendations for the design and or-chestration of effective knowledge network management systems in STPs. The policy im-plications aim to address STP developers and managers in particular, as well as additional stakeholders and policy-makers involved in the development and implementation of re-gional innovation policies.

The practice-oriented approach of this dissertation thesis is heavily influenced and motivat-ed by the research project’s integration in the EU INTERREG IVC project Knowlmotivat-edge Net-work Management in Technology Parks (Know-Man), as well as my professional experi-ences as a consultant in regional economic development and STP management.