Skip to main content
Log in

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

  • Published:
Scientometrics Aims and scope Submit manuscript


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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others


  1. Refer to Cooper et al. (2011) for further DEA models.

  2. Scopus is the largest abstract and citation database containing both peer-reviewed research literature and quality web sources, offering 18,500 titles from more than 5,000 international publishers as of April 2011. All Korean PRIs are registered as affiliations.



  5. See Appendix 2 for the centrality calculation formula.

  6. A high level of appropriateness was found when the ALSCAL method of SPSS v.14 was applied, with S-stress standing at 0.18 and RSQ at 0.95.


  • Adler, N., & Raveh, A. (2008). Presenting DEA graphically. Omega, 36(5), 715–729. doi:10.1016/

    Article  Google Scholar 

  • Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: A longitudinal study. Administrative Science Quarterly, 45(3), 425–455.

    Article  MathSciNet  Google Scholar 

  • Allen, T. J. (1970). Communication networks in R & D laboratories. R&D Management, 1(1), 14–21.

    Article  Google Scholar 

  • 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.

    Article  MATH  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  MathSciNet  MATH  Google Scholar 

  • Bozeman, B., & Corley, E. (2004). Scientists’ collaboration strategies: Implications for scientific and technical human capital. Research Policy, 33(4), 599–616.

    Article  Google Scholar 

  • Brown, M., & Svenson, R. (1998). Measuring RD productivity. Research-Technology Management, 41(6), 30–35.

    Google Scholar 

  • Burt, R. S. (1992). Structural holes. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.

    Article  MathSciNet  MATH  Google Scholar 

  • Choi, Y. (2003). Evolution of science and technology policy in Korea (Vol. 3). Korea: Science and Technology Policy Institute.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Coccia, M. (2005). A scientometric model for the assessment of scientific research performance within public institutes. Scientometrics, 65(3), 307–321.

    Article  Google Scholar 

  • 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.

    MATH  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Geisler, E. (1995). An integrated cost-performance model of research and development evaluation. Omega, 23(3), 281–294.

    Article  Google Scholar 

  • Griliches, Z. (1990). Patent statistics as economic indicators: A survey. Journal of Economic Literature, 28(4), 1661–1707.

    Google Scholar 

  • Hashimoto, A., & Haneda, S. (2008). Measuring the change in R&D efficiency of the Japanese pharmaceutical industry. Research Policy, 37(10), 1829–1836.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kastelle, T., & Steen, J. (2010). Are small world networks always good for innovation? Innovation: Management, Policy & Practice, 12(1), 75–87.

    Article  Google Scholar 

  • Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1), 1–18.

    Article  Google Scholar 

  • Kerssens-van Drongelen, I., & Bilderbeek, J. (1999). R&D performance measurement: More than choosing a set of metrics. R&D Management, 29, 1.

    Article  Google Scholar 

  • 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 

  • Kogut, B. (2000). The network as knowledge: Generative rules and the emergence of structure. Strategic Management Journal, 21(3), 405–425.

    Article  MathSciNet  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  MATH  Google Scholar 

  • 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.

  • MOST (2009). Korean science and technology fact book 2009. Ministry of Science and Technology.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

  • Padula, G. (2008). Enhancing the innovation performance of firms by balancing cohesiveness and bridging ties. Long Range Planning, 41(4), 395–419.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Schilling, M., & Phelps, C. (2007). Interfirm collaboration networks: The impact of large-scale network structure on firm innovation. Management Science, 53(7), 1113–1126.

    Article  MATH  Google Scholar 

  • Sena, V. (2004). Total factor productivity and the spillover hypothesis: Some new evidence. International Journal of Production Economics, 92(1), 31–42.

    Article  Google Scholar 

  • 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 

  • Tangen, S. (2004). Performance measurement: From philosophy to practice. International Journal of Productivity and Performance Management, 53, 726–737.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

Download references


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).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Il Won Seo.


Appendix 1

See Table 4.

Table 4 Descriptive statistics of PRIs

Appendix 2

Closeness centrality

If the shortest distance of the path linking two nodes i and j is d ij , the closeness centrality of node i can be written as \( C_{i} = \left[ {\sum\nolimits_{j = 1}^{n} {d_{ij} } } \right]^{ - 1} \).

Node betweeness centrality

When g jk is the number of the shortest paths existing between two certain nodes (j, k) and g jk (i), the number of stops at i as a point existing between the points j and k, the node betweeness centrality of node i is: \( C_{i} = \sum\nolimits_{j < k} {g_{jk} (i)} /g_{jk} \).

Eigenvector centrality

When C j is the centrality of node j, a ij the intensity of the relation between i and j, and λ the biggest eigenvector value for the relation matrix between i and j, the eigenvector centrality of node i is expressed as follows: \( C_{i} = \frac{1}{\lambda }\sum\nolimits_{j = 1}^{n} {a_{ij} } C_{j} \).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lee, D.H., Seo, I.W., Choe, H.C. et al. Collaboration network patterns and research performance: the case of Korean public research institutions. Scientometrics 91, 925–942 (2012).

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI:


Mathematical Subject Classification (2000)

JEL Classification