, Volume 91, Issue 3, pp 925–942 | Cite as

Collaboration network patterns and research performance: the case of Korean public research institutions

  • Duk Hee Lee
  • Il Won Seo
  • Ho Chull Choe
  • Hee Dae Kim


This study examines the impact of collaborating patterns on the R&D performance of public research institutions (PRIs) in Korea’s science and engineering fields. For the construction of R&D collaborating networks based on the co-authorship data of 127 institutions in Scopus, this paper proposes four types of collaborations by categorizing network analyses into two dimensions: structural positions (density, efficiency, and betweeness centrality) and the relational characteristics of individual nodes (eigenvector and closeness centralities). To explore the research performance by collaboration type, we employ a data envelopment analysis window analysis of a panel of 23 PRIs over a 10-year period. Comparing the R&D productivities of each group, we find that the PRIs of higher productivity adhere to a cohesive networking strategy, retaining intensive relations with their existing partners. The empirical results suggest that excessively cohesive alliances might end up in ‘lock-in’ relations, hindering the exploitation of new opportunities for innovation. These findings are implicit in relation to the Korean Government’s R&D policies on collaborating strategies to produce sustained research results with the advent of the convergence research era.


Collaboration pattern R&D performance Social network analysis Science policy DEA window 

Mathematical Subject Classification (2000)


JEL Classification

O32 O33 



The authors acknowledge that this work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2011-330-B00046).


  1. Adler, N., & Raveh, A. (2008). Presenting DEA graphically. Omega, 36(5), 715–729. doi: 10.1016/ Scholar
  2. Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: A longitudinal study. Administrative Science Quarterly, 45(3), 425–455.MathSciNetCrossRefGoogle Scholar
  3. Allen, T. J. (1970). Communication networks in R & D laboratories. R&D Management, 1(1), 14–21.CrossRefGoogle Scholar
  4. Banker, R., Charnes, A., & Cooper, W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092.zbMATHCrossRefGoogle Scholar
  5. Banwet, D., & Deshmukh, S. (2008). Evaluating performance of national R&D organizations using integrated DEA-AHP technique. International Journal of Productivity and Performance Management, 57(5), 370–388.CrossRefGoogle Scholar
  6. Barabasi, A. L., Jeong, H., Neda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications, 311(3–4), 590–614.MathSciNetzbMATHCrossRefGoogle Scholar
  7. Bozeman, B., & Corley, E. (2004). Scientists’ collaboration strategies: Implications for scientific and technical human capital. Research Policy, 33(4), 599–616.CrossRefGoogle Scholar
  8. Brown, M., & Svenson, R. (1998). Measuring RD productivity. Research-Technology Management, 41(6), 30–35.Google Scholar
  9. Burt, R. S. (1992). Structural holes. Cambridge, MA: Harvard University Press.Google Scholar
  10. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.MathSciNetzbMATHCrossRefGoogle Scholar
  11. Choi, Y. (2003). Evolution of science and technology policy in Korea (Vol. 3). Korea: Science and Technology Policy Institute.Google Scholar
  12. Coccia, M. (2004). New models for measuring the R&D performance and identifying the productivity of public research institutes. R&D Management, 34, 267–280.CrossRefGoogle Scholar
  13. Coccia, M. (2005). A scientometric model for the assessment of scientific research performance within public institutes. Scientometrics, 65(3), 307–321.CrossRefGoogle Scholar
  14. Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-solver software. New York: Springer.zbMATHGoogle Scholar
  15. Cooper, W. W., Seiford, L. M., & Zhu, J. (2011). Data envelopment analysis: History, models, and interpretations. In Handbook on data envelopment analysis (Vol. 164, pp. 1–39). International series in operations research & management science. US: Springer.Google Scholar
  16. Defazio, D., Lockett, A., & Wright, M. (2009). Funding incentives, collaborative dynamics and scientific productivity: Evidence from the EU framework program. Research Policy, 38(2), 293–305. doi: 10.1016/j.respol.2008.11.008.CrossRefGoogle Scholar
  17. Esposti, R., & Pierani, P. (2003). Building the knowledge stock: Lags, depreciation, and uncertainty in R&D investment and link with productivity growth. Journal of Productivity Analysis, 19(1), 33–58.CrossRefGoogle Scholar
  18. Etzkowitz, H., & Leydesdorff, L. (2000). The dynamics of innovation: From national systems and “Mode 2” to a Triple Helix of university–industry–government relations. Research Policy, 29(2), 109–123.CrossRefGoogle Scholar
  19. Geisler, E. (1995). An integrated cost-performance model of research and development evaluation. Omega, 23(3), 281–294.CrossRefGoogle Scholar
  20. Griliches, Z. (1990). Patent statistics as economic indicators: A survey. Journal of Economic Literature, 28(4), 1661–1707.Google Scholar
  21. Hashimoto, A., & Haneda, S. (2008). Measuring the change in R&D efficiency of the Japanese pharmaceutical industry. Research Policy, 37(10), 1829–1836.CrossRefGoogle Scholar
  22. He, Z.-L., Geng, X.-S., & Campbell-Hunt, C. (2009). Research collaboration and research output: A longitudinal study of 65 biomedical scientists in a New Zealand university. Research Policy, 38(2), 306–317. doi: 10.1016/j.respol.2008.11.011.CrossRefGoogle Scholar
  23. Jiménez-Sáez, F., Zabala-Iturriagagoitia, J. M., Zofío, J. L., & Castro-Martínez, E. (2011). Evaluating research efficiency within national R&D programmes. Research Policy, 40(2), 230–241. doi: 10.1016/j.respol.2010.10.005.CrossRefGoogle Scholar
  24. Kastelle, T., & Steen, J. (2010). Are small world networks always good for innovation? Innovation: Management, Policy & Practice, 12(1), 75–87.CrossRefGoogle Scholar
  25. Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1), 1–18.CrossRefGoogle Scholar
  26. Kerssens-van Drongelen, I., & Bilderbeek, J. (1999). R&D performance measurement: More than choosing a set of metrics. R&D Management, 29, 1.CrossRefGoogle Scholar
  27. Kerssens-van Drongelen, I., & de Weerd-Nederhof, P. (2010). The use of performance measurement tools for balancing short- and long-term NPD performances. International Journal of Innovation Management, 2, 54.Google Scholar
  28. Kogut, B. (2000). The network as knowledge: Generative rules and the emergence of structure. Strategic Management Journal, 21(3), 405–425.MathSciNetCrossRefGoogle Scholar
  29. Koka, B. R., & Prescott, J. E. (2008). Designing alliance networks: The influence of network position, environmental change, and strategy on firm performance. Strategic Management Journal, 29(6), 639–661.CrossRefGoogle Scholar
  30. Lee, H., Park, Y., & Choi, H. (2009). Comparative evaluation of performance of national R&D programs with heterogeneous objectives: A DEA approach. European Journal of Operational Research, 196(3), 847–855.zbMATHCrossRefGoogle Scholar
  31. Liberman, S., & Olmedo, R. L. (2008). Scientist’s semantic meaning of the concept of coauthorship. In Fourth international conference on webometrics, informetrics and scientometrics and ninth COLLNET meeting, Berlin. Google Scholar
  32. MOST (2009). Korean science and technology fact book 2009. Ministry of Science and Technology.Google Scholar
  33. Mote, J. (2005). R&D ecology: Using 2-mode network analysis to explore complexity in R&D environments. Journal of Engineering and Technology Management, 22(1–2), 93–111. doi: 10.1016/j.jengtecman.2004.11.004.CrossRefGoogle Scholar
  34. Mote, J. E., Jordan, G., Hage, J., & Whitestone, Y. (2007). New directions in the use of network analysis in research and product development evaluation. Research Evaluation, 16(3), 191–203.CrossRefGoogle Scholar
  35. Newman, M. E. (2001). The structure of scientific collaboration networks. Research support, non-U.S. Government research support, U.S. Government, non-P.H.S. Proceedings of the National Academic of Science USA, 98(2), 404–409. doi: 10.1073/pnas.021544898.
  36. Newman, M. E. (2004). Coauthorship networks and patterns of scientific collaboration. Research support, non-U.S. Government research support, U.S. Government, non-P.H.S. Proceedings of the National Academic of Science USA, 101(Suppl 1), 5200–5205. doi: 10.1073/pnas.0307545100.
  37. Padula, G. (2008). Enhancing the innovation performance of firms by balancing cohesiveness and bridging ties. Long Range Planning, 41(4), 395–419.CrossRefGoogle Scholar
  38. Park, H. W., Hong, H. D., & Leydesdorff, L. (2005). A comparison of the knowledge-based innovation systems in the economies of South Korea and the Netherlands using triple helix indicators. Scientometrics, 65(1), 3–27.CrossRefGoogle Scholar
  39. Porac, J. F., Wade, J. B., Fischer, H. M., Brown, J., Kanfer, A., & Bowker, G. (2004). Human capital heterogeneity, collaborative relationships, and publication patterns in a multidisciplinary scientific alliance: A comparative case study of two scientific teams. Research Policy, 33(4), 661–678. doi: 10.1016/j.respol.2004.01.007.CrossRefGoogle Scholar
  40. Reagans, R., & Zuckerman, E. W. (2001). Networks, diversity, and productivity: The social capital of corporate R&D teams. Organization Science, 12(4), 502–517.Google Scholar
  41. Rigby, J., & Edler, J. (2005). Peering inside research networks: Some observations on the effect of the intensity of collaboration on the variability of research quality. Research Policy, 34(6), 784–794. doi: 10.1016/j.respol.2005.02.004.CrossRefGoogle Scholar
  42. Schilling, M., & Phelps, C. (2007). Interfirm collaboration networks: The impact of large-scale network structure on firm innovation. Management Science, 53(7), 1113–1126.zbMATHCrossRefGoogle Scholar
  43. Sena, V. (2004). Total factor productivity and the spillover hypothesis: Some new evidence. International Journal of Production Economics, 92(1), 31–42.CrossRefGoogle Scholar
  44. Sueyoshi, T. (1992). Measuring technical, allocative and overall efficiencies using a DEA algorithm. Journal of the Operational Research Society, 43(2), 141–155.Google Scholar
  45. Tangen, S. (2004). Performance measurement: From philosophy to practice. International Journal of Productivity and Performance Management, 53, 726–737.CrossRefGoogle Scholar
  46. Tsekouras, K., Pantzios, C., & Karagiannis, G. (2004). Malmquist productivity index estimation with zero-value variables: The case of Greek prefectural training councils. International Journal of Production Economics, 89(1), 95–106.CrossRefGoogle Scholar
  47. Van der Valk, T., & Gijsbers, G. (2010). The use of social network analysis in innovation studies: Mapping actors and technologies. Innovation: Management, Policy & Practice, 12(1), 5–17.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2012

Authors and Affiliations

  • Duk Hee Lee
    • 1
  • Il Won Seo
    • 1
  • Ho Chull Choe
    • 2
  • Hee Dae Kim
    • 3
  1. 1.Department of Management ScienceKorea Advanced Institute of Science and TechnologyTaejeonRepublic of Korea
  2. 2.Management Strategy Team, Korea Research Institute of Chemical TechnologyTaejeonRepublic of Korea
  3. 3.Future Strategy Team, Daegu Digital Industry Promotion AgencyDaeguRepublic of Korea

Personalised recommendations