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

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Abstract

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.

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Notes

  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.

  3. www.istk.re.kr, www.krcf.re.kr.

  4. www.alio.go.kr.

  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.

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Acknowledgments

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).

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Correspondence to Il Won Seo.

Appendices

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: \( C_{i} = \frac{1}{\lambda }\sum\nolimits_{j = 1}^{n} {a_{ij} } C_{j} \).

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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). https://doi.org/10.1007/s11192-011-0602-8

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