Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/123975 
Year of Publication: 
2013
Series/Report no.: 
53rd Congress of the European Regional Science Association: "Regional Integration: Europe, the Mediterranean and the World Economy", 27-31 August 2013, Palermo, Italy
Publisher: 
European Regional Science Association (ERSA), Louvain-la-Neuve
Abstract: 
Main tasks of academia are to create new knowledge and to transfer that knowledge into the economy. Regarding this work, four channels of knowledge transfer are considered (based on Hamm et al, 2012): (1) basic transfer e.g. publications, (2) human capital e.g. graduates, (3) formation of enterprises, in this case spin offs and (4) via research, e.g. especially cooperative research. The question to be answered is: How do the different channels through which the knowledge is meant to flow into the economy vary regarding different types of academia? Regarding the German higher education system especially two types of academia can be differentiated. These are the universities on the one hand and the universities of applied science on the other hand. These different types of higher education institutions (HEI) in Germany show distinctive characteristics, e.g. more application oriented versus research oriented teaching, personnel structure (more research assistance in universities) etc. Regarding the transfer of knowledge two questions (which are interwoven) arise: (1) Does the intensity of transfer vary between the different HEI types? (2) Is the spatial distribution of knowledge different, regarding the two diverging types of HEIs? While in this work only a few things will be said about the channels one to three, the focus is on the fourth channel: knowledge transfer via research. Using data from the ?FÖKAT? (the ?Förderkatalog? of the German Ministry of Education and Research) a research network between companies, institutions and academia is unveiled. This online catalog contains about 110000 data set entries of direct project funding of the German government. The analyzed data in this work is restricted to all currently ongoing projects. The resulting set of data contains 22722 data set entries which include information on cooperative and non-cooperative projects. 5966 cooperative projects are counted. These are the ones used to unveil the network which in the end includes institutional research facilities, companies and academia. Using centrality measures such as degree, closeness and betweenness the position of the different HEI types within the network can be measured and compared. Degree-centrality can be used to observe cooperative activity, as it measures the number of direct linkages. Closeness-centrality uses the information on the average path length to every other actor within the network. This way a high closeness-centrality can be used as an indicator for inheriting a position with good access to new knowledge and/or a position which can be used to give impulses to the network that reach all other actors fast. Lastly the betweenness-centrality can be used as an indicator for control options within the network, in terms of knowledge brokerage. It can be observed that the centrality measures are significantly higher for universities than for universities of applied science.
Subjects: 
University
Academia
Network
Knowledge
Document Type: 
Conference Paper

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