Inter-organisational knowledge generation in European R&D networks. Results from a latent regression - index function model

Florian Reinold

Publikation: AbschlussarbeitMasterarbeit


The objective of this diploma thesis is to explore the determinants of inter-organisational knowledge generation within European networks of R&D collaboration. It is argued that social capital is a key determinant for successful knowledge generation. Thus, factors that influence the development of social capital like geographical separation, or collaboration duration and intensity are expected to have an impact on inter-organisational knowledge generation. Determinants of inter-organisational knowledge generation are investigated by casting a binary response model in the form of a latent regression - index function model. Units of analysis are dyads of organisations that jointly participated in projects of the Fifth EU Framework Programme [FP5]. The data used in this study derives from a survey among FP5 participants and the EUPRO database. Findings suggest that crossing national border has a significantly positive rather than negative effect on scientific knowledge generation [measured in terms of co-publications]. This can be attributed to the participation rules and proposal selection procedures of the Framework Programmes. Another important result is that university dyads have the highest probability not only to jointly generate scientific knowledge, but also to jointly generate knowledge that resulted in commercial outcome. In contrast, industry dyads show a low probability for both types of knowledge generation. This result is probably due to the fact that inter-organisational knowledge generation entails disclosure of knowledge, which is actually a task of universities, but problematic for industry organisations.
Gradverleihende Hochschule
  • Vienna University of Economics and Business
Betreuer/-in / Berater/-in
  • Paier, Manfred, Betreuer:in
  • Fischer, Manfred, Betreuer:in, Externe Person
PublikationsstatusVeröffentlicht - 2012

Research Field

  • Ehemaliges Research Field - Innovation Systems and Policy


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