Identifying Cooperation for Innovation―a Comparison of Data Sources
Industry and Innovation,
The value of social network analysis is critically dependent on the comprehensive and reliable identification of actors and their relationships. We compare regional knowledge networks based on different types of data sources, namely, co-patents, co-publications, and publicly subsidized collaborative R&D projects. Moreover, by combining these three data sources, we construct a multilayer network that provides a comprehensive picture of intraregional interactions. By comparing the networks based on the data sources, we address the problems of coverage and selection bias. We observe that using only one data source leads to a severe underestimation of regional knowledge interactions, especially those of private sector firms and independent researchers.
What Drives Innovation Output from Subsidized R&D Cooperation? — Project-level Evidence from Germany
Using a large dataset of 406 subsidized R&D cooperation projects, we provide detailed insights into the relationship between project characteristics and innovation output. Patent applications and publications are used as measures for the innovation output of an R&D project. We find that large-firm involvement is strongly positively related with the number of patent applications, but not with the number of publications. Conversely, university involvement has positive effects on projects’ innovation output in terms of the number of publications but not in terms of patent applications. In general, projects’ funding as measure of projects’ size is an important predictor of the innovation output of R&D cooperation projects. No significant effects are found for the number of partners as (an alternative) measure of projects’ size, for spatial proximity between cooperation partners, for the involvement of a public institute for applied research, and for prior cooperation experiences. We derive conclusions for the design of R&D cooperation support schemes.
Determinants of the Efficiency of Regional Innovation Systems
Determinants of the efficiency of regional innovation systems, Regional Studies. This paper analyses differences in the efficiency of regional innovation systems. Alternative measures for the efficiency of regional innovation systems based on the concept of a knowledge production function are discussed. The empirical findings suggest that spillovers from within the private sector as well as from universities and other public research institutions have a positive effect on the efficiency of private sector research and development. It is particularly the intensity of interactions between private and public sector research and development that increases the efficiency. It is found that regions dominated by large establishments tend to be less efficient than regions with a lower average establishment size.
How Does Industry Specialization Affect the Efficiency of Regional Innovation Systems?
The Annals of Regional Science,
This study analyzes the relationship between the specialization of a region in certain industries and the efficiency of the region in generating new knowledge. The efficiency measure is constructed by relating regional R&D input and output. An inversely u-shaped relationship is found between regional specialization and R&D efficiency, indicating the presence of externalities of both Marshall and Jacobs’ type. Further factors influencing efficiency are externalities resulting from high R&D intensity of the local private sector as well as knowledge from local public research institutions. The impact of both the specialization and the additional factors is, however, different for regions at different efficiency levels.
Universities and Innovation in Space
Industry and Innovation,
We investigate the role of universities as a knowledge source for regional innovation processes. The contribution of universities is tested on the level of German NUTS‐3 regions (Kreise) by using a variety of indicators. We find that the intensity and quality of the research conducted by the universities have a significant effect on regional innovative output while pure size is unimportant. Therefore, a policy that wants to promote regional innovation processes by building up universities should place substantial emphasis on the intensity and quality of the research conducted there. We also find the effects of universities to be concentrated in space. Obviously, the geographical proximity to particular knowledge sources is important for regional innovative activities.