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Research in the domain of ‘Innovation Studies’ has been claimed to allow for the study of how technology will develop in the future. Some suggest that the National and Sectoral Innovation Systems literature has become bogged down, however, into case studies of how specific institutions affect innovation in a specific country. A useful notion for policy makers in particular, Balzat and Hanusch (2004 Recent trends in the research on national innovation systems Journal of Evolutionary Economics 14(2): 197–210) argued that there is a need for NIS studies to develop complementary and also quantitative methods in order to generate new insights that are comparable across national borders. We use data for patents granted by the World Intellectual Property Organization (WIPO) to map innovation systems. Groupings of patents into primary and secondary classes (co-classification) can be used as relational indicators. Knowledge from one class may be more easily used in another class when a co-classifi cation relation exists. Using social network analysis, we map the co-classification of patents among classes and thus indicate what characterizes an innovation system.A main contribution of this paper is methodological, adding to the repertoire of methods NIS studies use and using information from patents in a different way. Policy makers may also find benefits in the social network analysis of the complete set of patents granted by the WIPO to firms and individuals in a country. Social network analysis indicates what innovation activity occurs in a countries and which fields of technology are likely to give rise to innovative products in the near future. We offer such analysis for the Dutch and Indian Innovation Systems. This social network analysis could also be done for a sector innovation system, and we do so for nanotech to determine empirically the knowledge field relevant for this emerging scientific domain.
Dolfsma et al. (Thu,) studied this question.