  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 &
Hanusch (2004) 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-classification 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.
