Methodological framework

The complex, diverse and multi-dimensional character of knowledge relations in general and business-to-science knowledge relations in particular entails certain challenges for analytical studies and requires a fitting model of analysis and assessment (Vedovello, 1997). On the one hand, multiple empirical studies (e.g. Fukugawa, 2006; Löfsten & Lin-delöf, 2002) that have focused on match-pair analyses of knowledge flows between on-park and off-on-park firms have utilized quantitative methods such as microdata analyses and surveys. In these cases, the quantitative research approach has allowed for an accurate

143 However, Schartinger et al. (2002) have pointed to similarly strong linkages to industry for university de-partments in natural sciences, as well as in technical sciences and economics.

comparative analysis of different types of companies and for additional multivariate statisti-cal analyses. The same applies to selected studies (e.g. Fu et al., 2011; Hoekman et al., 2010) that have used survey data and data on co-publications, respectively, to examine the role of proximity in knowledge relations and interactive innovation processes in specific TIS.

On the other hand, the larger share of empirical studies on knowledge relations and knowledge interaction in distinct localities of learning such as STPs and TIS in general have utilized qualitative methods or a mix of different methodologies, most notably, surveys and expert interviews.¹⁴⁴ Such methodology mix has been applied in order to create an in-depth understanding of the underlying mechanisms, for example, in terms of spatial and non-spatial proximity in interactive relations established between firms and co-located sci-entific actors (e.g. Huber, 2012; Mian et al., 2012). Moreover, qualitative interview data in particular with actors representing different perspectives on the same process may increase the reliability of the data (Ibert & Hautala, 2015).

Accordingly, a methodological mix of complementary empirical approaches is increasingly regarded as essential in academic research, in particular in geography. The implementation of various methodologies enables the control of biased results potentially derived from the use of specific research techniques and also enhances the reliability of the research find-ings’ interpretation and of the evaluation of research hypotheses (Kromrey, 1998; Schätzl, 1994; Wessel, 1996).¹⁴⁵ Overall, the value of the mixed-method approach lies in the ability to provide a more comprehensive and detailed understanding of individual cases (Ibert &

Hautala, 2015).

Based on the distinct and complementary strengths of quantitative and qualitative research methods, I used multiple methods and sources for the collection of data (triangulation ap-proach) in my empirical analysis (Mian et al., 2012; Brammer, 2013). In each of the two STPs, starting from secondary data and published material, I collected primary data through standardized interviews and semi-structured expert interviews, which were con-ducted in-person with numerous STP resident firms in each case. Additional expert inter-views included individual STP management staff and researchers.

144 Additional prominent qualitative research methods applied in the analysis of knowledge relations and knowledge creation include, for example, innovation biographies (Stein, 2014; Ibert & Müller 2015).

145 A standardized survey allows the identification of trends and specific patterns in large samples. In contrast, qualitative interviews enable the further examination and the collection of more detailed descriptive evi-dence of these identified trends and patterns. Also, personal interviews are very valuable to ensure the in-terviewees’ comprehension and to obtain a greater accuracy in responses (Smith, 1998).

Chapter 3.1, which has outlined the structure and development process of the two STPs Berlin-Adlershof and Seville-Cartuja, is predominantly based on secondary data and pub-lished material. In addition, expert interviews contributed to the description of the STPs’

structure and development processes in particular.

Chapter 4 is primarily based on the empirical results derived from the standardized and semi-structured interviews with multiple resident companies in the two science parks.¹⁴⁶ The quantitative research technique, the standardized interview, is based on a standardized questionnaire and a standardized interview procedure (e.g. phrasing and order of questions) (Gläser & Laudel, 2010; Schätzl, 1994). The standardized interviews aimed to generate comparative data of the STP resident companies’ interactive relations to scientific institutions. In addition, comparative data were collected regarding the resident firms’ assessment of specific internal and external channels, including a variety of KNM instruments, as influencing factors for the successful creation of knowledge relations to scientific institutions and related knowledge sharing processes. In addition, distinct types of firms’ egocentric knowledge networks to academia were identified using the multivariate statistical method of a cluster analysis.¹⁴⁷

However, questions regarding the detailed character and the influencing factors of the firms’ interactive linkages to scientific instiutions, as well as the role of multi-dimensional proximity and proximity-organizing KNM instruments in link creation and knowledge sharing cannot be depicted by quantitative research methods. This is the case especially as there is no universal proximity configuration in knowledge relations (Boschma & Frenken, 2010).

Thereafter, in the different contexts of the two STPs and the diverse resident companies, qualitative interviews aimed to explore in detail how the firms’ knowledge relations to academia are established and structured, as well as what factors and external framework conditions draw them to form such interactive ties and enable successful knowledge sharing processes. Consequently, the qualitative research method of semi-structured inter-views aimed to complement to the previously derived quantitative findings. Also, the quali-tative analysis intended to further amplify the identified types of firms’ knowledge exchange

146 Academia-industry knowledge relations and interaction can be measured on two sides: on the side of the firm and on the side of the scientific institution (Schartinger et al., 2002). In this empirical analysis, resident high-technology companies of the Adlershof and Cartuja science parks were selected as the reporting units.

147 In this thesis, I focus on the analysis of STP resident firms’ knowledge exchange behaviour on the egocen-tric network level, i.e. the specific firm (ego) and its direct linkages. Similar studies have been conducted, for example, by Chan et al. (2010). Generally, there is no general agreement in the literature whether or not the analysis of egocentric networks should also include the linkages among the ego’s contacts (Granovet-ter, 1973). In regard to the whole network, it would require data on the entire set of existing and absent links among the ego’s direct ties.

behaviour with academia, which are conveyed from the statistical analysis (cluster analy-sis).