Abstract
This study examines network topologies of interdisciplinary research relationships in science and technology (S&T) and investigates the relational linkages between the interdisciplinary relations and the quality of research performance. A network analysis was performed to evaluate the General Research Grant (GRG) program, an interdisciplinary research funding program of the Korea Science and Engineering Foundation (KOSEF); the dataset covered the 2002–2004 period. The analytical results reveal the hidden network structure of interdisciplinary research relationships and demonstrate that the quality of research performance might be enhanced not only by interdependent pressures placed on various research fields but also by accumulated research capabilities that are relatively difficult to access and reproduce by other research fields.
Similar content being viewed by others
Notes
With regard to the pressure for interdisciplinarity by funding agencies, Bohme et al. (1976) ignited a debate by proposing the concept of “finalization in science,” which refers to the actual influence of societal (social, political, and institutional) interventions on scientific progress. However, we do not specifically investigate the following issues in this paper: how much funding policies actually influence and can influence science and to what extent science is still an autonomous system. Such a review of the scholarly debate around “finalization in science” is well documented in Schroyer (1984).
This study employs Freeman’s (1979) concept of centrality to analyze the interdisciplinary network of interdisciplinary relations.
References
Altmann, K. G., & Gorman, G. E. (1998). The usefulness of impact factors in serial selection: A rank and mean analysis using ecology journals. Library Acquisitions: Practice and Theory, 22(2), 147–159.
Archibugi, D., & Pianta, M. (1992). Specialization and size of technological activities in industrial countries: The analysis of patent data. Research Policy, 21, 79–93.
Barabasi, A. L., Jeong, H., Neda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A, 311(3–4), 590–614.
Bohme, G., van den Daele, W., & Krohn, W. (1976). Finalization in science. Social Science Information, 15(2/3), 307–330.
Bonitz, M., Bruckner, E., & Scharnhorst, A. (1993). The Science Strategy Index. Scientometrics, 26(1), 37–50.
Bordons, M., Fernandez, M. T., & Gomez, I. (2002). Advantages and limitations in the use of impact factor measures for the assessment of research performance in a peripheral country. Scientometrics, 53(2), 195–206.
Borgatti, S. P. (2002). Netdraw network visualization. Harvard, MA: Analytic Technologies.
Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). UCINET for Windows: software for social network analysis. Harvard, MA: Analytic Technologies.
Braun, T., Gomez, I., Mendez, A., & Schubert, A. (1992). International co-authorship patterns in physics and its subfields, 1981–1985. Scientometrics, 24(2), 181–200.
Coleman, M. S. (2007). Partner or perish? Universities as agents of change. AAAS-CSPO S&T policy review: highlights of the 2007 forum on S&T policy. http://www.umich.edu/pres/speeches/070503aaas.html.
Corley, E., Melkers, J., & Johns, K. (2006). Layered and evolving networks: Innovative evaluation methods for interdisciplinary research in university-based research centers. Paper presented at The Atlanta Conference on S&T Policy, Atlanta, GA.
Cummings, J. N., & Kiesler, S. (2005). Collaborative research across disciplinary and organizational boundaries. Social Studies of Science, 35(5), 703–722.
Durland, M. M., & Fredericks, K. A. (Eds.). (2006). Social network analysis in program evaluation: New directions for evaluation, 107, Fall 2005. San Francisco: Jossey-Bass and the American Evaluation Association.
Egghe, L., & Rousseau, R. (2003). A general framework for relative impact indicators. Canadian Journal of Information and Library Science, 27(1), 29–48.
Freeman, L. C. (1979). Centrality in social networks: Conceptual clarifications. Social Network, 1, 215–239.
Glanzel, W., & Schubert, A. (2003). A new classification scheme of science fields and subfields designed for scientometric evaluation purposes. Scientometrics, 56(3), 357–367.
Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360–1380.
Ha, T. C., Tan, S. B., & Soo, K. C. (2006). The journal impact factor: Too much of an impact? Annals of the Academy of Medicine, Singapore, 35(12), 911–916.
Havemann, F., Heinz, M., & Kretschmer, H. (2006). Collaboration and distances between German immunological institutes—a tend analysis. Journal of Biomedical Discovery and Collaboration, 1(1), 6.
Hinds, P., & Kiesler, S. (Eds.). (2002). Distributed work. Cambridge, MA: MIT Press.
Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26, 1–18.
Landry, R., Traore, N., & Godin, B. (1996). An econometric analysis of the effect of collaboration on academic research productivity. Higher Education, 32(3), 283–301.
Leydesdorff, L. (2007). “Betweenness centrality” as an indicator of the “interdisciplinarity” of scientific journals. Journal of the American Society for Information Science and Technology, 58(9), 1303–1309.
Leydesdorff, L. (2008). Patent classifications as indicators of intellectual organization. Journal of the American Society for Information Science and Technology, 59(10), 1582–1597.
Luukkonen, T., Persson, O., & Sivertsen, G. (1992). Understanding patterns of international scientific collaboration. Science, Technology & Human Values, 17(1), 101–126.
Moed, H. F. (2005). Citation analysis of scientific journals and journal impact measures. Current Science, 89(12), 1990–1996.
Moody, J. (2004). The structure of a social science collaboration network: Disciplinary cohesion from 1963 to 1999. American Sociological Review, 69(26), 213–238.
Nowell, B. L. (2005). Evaluating social capital in interorganizational alliances: An application of social network analysis. Paper presented at the Joint Conference of the Canadian Evaluation Society and the American Evaluation Association, Toronto, Ontario, Canada.
Park, H. W., & Jankowski, N. (2008). A hyperlink network analysis of citizen blogs in South Korean politics. Javnost–The Public, 15(2), 57–74. A special issue on methodological issues in conducting online political communication research. http://www.javnost-thepublic.org/media/datoteke/park-jankowski.pdf.
Park, H. W., & Lee, Y. (2008). The Korean presidential election of 2007: Five years on from the “internet election”. Journal of Contemporary Eastern Asia, 7(1), 1–4. http://www.eastasia.at/download/park.pdf.
Park, H. W., So, M.-H., & Leydesdorff, L. (2009). Longitudinal trends of networked research systemness in South Korea using the triple-helix indicator. In Proceedings from the Triple Helix 2008 conference. http://www.triple-helix-7.org.
Presser, S. (1980). Collaboration and the quality of research. Social Studies of Science, 10(1), 95–101.
Schoonbaert, D., & Roelants, G. (1996). Citation analysis for measuring the value of scientific publications: Quality assessment tool or comedy of errors? Tropical Medicine and International Health, 1(6), 739–752.
Schroyer, T. (1984). On finalization in science. Theory and Society, 13(5), 715–723.
Schubert, A., & Braun, T. (1986). Relative indicators and relational charts for comparative assessment of publication output and citation impact. Scientometrics, 9(5–6), 281–291.
Stark, D., & Vedres, B. (2005). Social times of network spaces: Network sequences and foreign investment in Hungary. Santa Fe Institute Working Paper (No. 05-06-023). Santa Fe, NM: Santa Fe Institute.
Tarnow, E. (2002). Coauthorship in physics. Science and Engineering Ethics, 8(2), 175–190.
Traweek, S. (1992). Beamtimes and lifetimes: The world of high energy physicists. Cambridge, MA: Harvard University Press.
Vonortas, N. S., & Malerba, F. (2005). Using social networks methodology to evaluate research and development programs. Paper presented at the Joint Conference of the Canadian Evaluation Society and the American Evaluation Association, Toronto, Ontario, Canada.
Walsh, J. P., & Maloney, N. G. (2002). Computer network use, collaboration structures and productivity. In P. Hinds & S. Kiesler (Eds.), Distributed work (pp. 433–458). Cambridge, MA: MIT Press. Retrieved June 10, 2002 from the World Wide Web: http://tigger.uic.edu/~jwalsh/Collab.html.
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. New York: Cambridge University Press.
Author information
Authors and Affiliations
Corresponding author
Additional information
Han Woo Park is considered as co-first author.
Rights and permissions
About this article
Cite this article
Yang, C.H., Park, H.W. & Heo, J. A network analysis of interdisciplinary research relationships: the Korean government’s R&D grant program. Scientometrics 83, 77–92 (2010). https://doi.org/10.1007/s11192-010-0157-0
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-010-0157-0