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
Refer to Cooper et al. (2011) for further DEA models.
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.
See Appendix 2 for the centrality calculation formula.
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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Appendices
Appendix 1
See Table 4.
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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DOI: https://doi.org/10.1007/s11192-011-0602-8