Research on regional innovation systems (RIS) has evolved into a widely used analytical framework generating the empirical foundation for innovation policy making. The purpose of this research is to shed light on network-based author keyword analysis by integrating social network analysis and bibliometric analysis on the development of RIS research. A total of 432 papers belonging to 36 countries, 276 research institutes, and comprising 1165 keywords, are retrieved from the Web of Science databases for network construction and analysis. The obtained network in this study is capable of providing visual and quantitative insights into the publication trends or knowledge evolution of RIS. Network actors chosen in this study include country, research institute, first author, and keywords. These constitute four types of networks defined in this study: three research focus parallelship (RFP) networks (RFP-country network, RFP-institute network, RFP-author network) and one keyword co-occurrence (KCO) network.
Keywords: Regional innovation system; network theory; knowledge map; centrality; keyword co-occurrence network; research focus parallelship network
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The concepts of regional innovation system (RIS) have been developed into an important framework for evaluating regional performance in the knowledge-based economy from the early 1990s (Cooke, 1992; Cooke, 2001; Cooke & Morgan, 1994). The important elements and mechanisms of a innovation process have been investigated in view of regional-scaled development. Since the early 1990s, the concept of RIS has drawn much attention from policy makers. and it emerged at a time when policy focused toward systemic promotion of localized learning processes in order to establish the competitive advantage of regions (Asheim & Gertler, 2004). RIS approach has received considerable attention as a promising analytical framework for advancing the understanding of the innovation process in the regional economy (Asheim, Coenen, & Svensson-Henning, 2003; Cooke, Boekholt, & Tödtling, 2000; Leydesdorff, 1998).
A lot of attempts have been made to explore ways of mapping knowledge evolution. Author keyword (keywords specified by author), based analysis as a type of co-word analysis has started to play an important role in understanding the dynamics of knowledge development (Hori et al., 2004; Law & Whittaker, 1992; Edquist, 1997). Author keyword analysis is also used to supplement other analytical methods. For example, morphology analysis is a conventional method of forecasting future technology and identifying technology opportunities. Yoon and Park (2004) argued that morphology analysis is subject to limitations because there is no scientific or systematic way of establishing the morphology of technology. Therefore, keyword-based morphology analysis, which is supported by systematic procedures and quantitative data is thereby proposed as a method for conducting the morphology of technology.
Social network analysis based on keywords has been explored as well. Motter et al. (2002) constructed a conceptual network from the entries in a thesaurus dictionary and consider two words connected if they express similar concepts (Motter et al., 2002). He argued that language networks exhibit the small-world property as a result of natural optimization. Hence, these findings are important not only for linguistics, but also for cognitive science. Author keywords, by presenting the most important core concept of the articles' subject, could provide the information about which research trends are of most concern to researchers. The bibliometric method concerning author keywords analysis was developed recently, which uses the author keywords to analyze which trends of research are infrequent (Chiu & Ho, 2007). The technique of author keywords analysis might be a potential method for monitoring development trends or for the evolution of science, as well as for projecting future research directions. …