Investigating the structure of regional innovation system research through keyword co-occurrence and social network analysis
Pei-Chun Lee
Science and Technology Policy Research and Information Center, National Applied Research Laboratories, Taipei; and Graduate Institute of Technology and Innovation Management, National Chengchi University, Taipei, Taiwan
Hsin-Ning Su
Science and Technology Policy Research and Information Center, National Applied Research Laboratories, Taipei, Taiwan
PP: 26 - 40
Abstract
Research on Regional Innovation Systems 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 Regional Innovation System 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 Regional Innovation System. Network actors chosen in this study include country, research institute, first author, and keywords. These constitute four types of networks defined in this study: RFP-country Network, RFP-institute Network, RFP-author Network and KCO Network.
Keywords
regional innovation system; network theory; knowledge map; centrality; keyword co-occurrence network; research focus parallelship network
Article Text
1 Introduction
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, Nakakoji, Yamamoto, & Ostwald, 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 (Yoon & 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 mentod for concluding 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, de Moura, Lai, & Dasgupta, 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.
2 Research question
The scope of a research field has to be constantly evaluated and redefined due to societal and environmental changes over time. In order to examine the fundamental building blocks of research fields and explore directions toward future research, researchers need to review the literature on a regular basis, and if necessary, modify the scope of research fields in order to obtain state-of-the-art insights.
What are the boundaries and contexts of RIS research? The objective of this study is derived from this research question. and aims to analyze the academic literature of RIS research with bibliometric and network analysis to achieve the following purposes: 1) to present an overview of RIS research; and 2) to find the research contexts of RIS. To fulfill the aforementioned objectives, network visualization and analysis software is used to obtain a comprehensive overview of a large amount of literature. The results of this paper visually provide several networks as knowledge maps which define the scopes of RIS research, as well as network properties calculations for quantitatively mapping RIS research.
3 Theoretical background
Regional Innovation System ... continues ...
References
Asheim, B., Coenen, L., & Svensson-Henning, M. (2003). Nordic SMEs and regional innovation systems. Final Report, Nordic Industrial Fund.
Asheim, B., & Gertler, M. (2004). Understanding regional innovation systems. Handbook of Innovation. Oxford University Press, Oxford.
Brass, D. J., & Burkhardt, M. E. (1992). Centrality and power in organizations. Networks and organizations: Structure, form and action, 191, 215.
Callon, M., Courtial, J. P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemsitry. Scientometrics, 22(1), 155-205.
Cambrosio, A., Keating, P., & Mogoutov, A. (2004). Mapping collaborative work and innovation in biomedicine: A computer-assisted analysis of antibody reagent workshops. Social studies of science, 34(3), 325.
Carlsson, B., & Jacobsson, S. (1997). Diversity creation and technological systems: a technology policy perspective. Systems of Innovation: Technologies, Institutions and Organizations, 266-294.
Chiu, W. T., & Ho, Y. S. (2007). Bibliometric analysis of tsunami research. Scientometrics, 73(1), 3-17.
Cooke, P. (1992). Regional innovation systems: competitive regulation in the new Europe. Geoforum, 23, 365-365.
Cooke, P. (2001). Regional innovation systems, clusters, and the knowledge economy. Industrial and corporate change, 10(4), 945-974.
Cooke, P., Boekholt, P., & Tödtling, F. (2000). The governance of innovation in Europe. Pinter.
Cooke, P., & Morgan, K. (1994). The regional innovation system in Baden-Württemberg. International Journal of Technology Management, 9(3), 394-429.
Criscuolo, P., Salter, A., & Sheehan, T. (2007). Making knowledge visible: Using expert yellow pages to map capabilities in professional services firms. Research Policy, 36(10), 1603-1619.
Edquist, C. (1997). Systems of innovation: technologies, institutions and organizations. Routledge.
Edquist, C. (2004). Systems of innovation-a critical review of the state of the art. Handbook of Innovation, 181-208.
Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social networks, 1(3), 215-239.
Granovetter, M. S. (1973). The strength of weak ties. American journal of sociology, 78(6), 1360-1380.
Hori, K., Nakakoji, K., Yamamoto, Y., & Ostwald, J. (2004). Organic perspectives of knowledge management: Knowledge evolution through a cycle of knowledge liquidization and crystallization. Journal of Universal Computer Science, 10(3), 252-261.
Isaksen, A., & Hauge, E. (2002). Regional clusters in Europe-Observatory of European SMEs report No. 3. European Communities: Luxembourg.
Kaufmann, A., & Tödtling, F. (2002). How effective is innovation support for SMEs? An analysis of the region of Upper Austria. Technovation, 22(3), 147-159.
Kuhn, T. S., Dewey, J., & Neurath, O. (1970). The structure of scientific revolutions. University of Chicago Press Chicago.
Law, J., & Whittaker, J. (1992). Mapping acidification research: A test of the co-word method. Scientometrics, 23(3), 417-461.
Lee, P. C., Su, H. N., & Wu, F. S. (2009). Quantitative mapping of patented technology-The case of electrical conducting polymer nanocomposite. Technological Forecasting & Social Change.
Leydesdorff, L. (1998). Reply about using co-words. Journal of the American Society for Information Science, 49(1).
Motter, A. E., de Moura, A. P. S., Lai, Y. C., & Dasgupta, P. (2002). Topology of the conceptual network of language. Science Phys Rev E, 65, 065102.
Smith, A. E., & Humphreys, M. S. (2006). Evaluation of unsupervised semantic mapping of natural language with Leximancer concept mapping. Behavior Research Methods, 38(2), 262.
Tseng, Y. H., Lin, C. J., & Lin, Y. I. (2007). Text mining techniques for patent analysis. Information Processing and Management, 43(5), 1216-1247.
Van Eck, N. J., & Waltman, L. (2007). Bibliometric mapping of the computational intelligence field. International Journal of Uncertainty Fuzziness and Knowledge Based Systems, 15(5), 625.
Yoon, B. C., & Park, Y. T. (2004). Morphology analysis approach for technology forecasting. In 2004 IEEE International Engineering Management Conference, 2004. Proceedings (Vol. 2).
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