Michael Fritsch (p), Mirko Titze, Matthias Piontek, Wilfried Ehrenfeld, Alexander Giebler

Stefan Lüthi

Intensive knowledge interactions are a crucial component for innovation processes. Policy provides numerous programs that aim at stimulating regional actors to connect and interact with each other. An efficient design of such programs requires detailed information on the nature of regional knowledge transfer. Based on different types of data sources―namely co-patents, co-publications and collaborative R&D projects―we construct a multi-layer framework for social network analysis that shows a comprehensive picture of regional knowledge transfer. Comparing the networks based the different data sources, we address problems of coverage and selection bias. We find, that the networks differ considerably depending on the data source used. Hence, the use of only one single data source leads to a severe underestimation of regional knowledge interactions, especially those of firms. We further find, that particularly large firms and firms in agglomerations tend to be involved in multi-transfer channels of knowledge interactions enabling intensive knowledge transfer.

Stefan Lüthi, Silke Zöllner (p)

Michael Fritsch

See extended abstract