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Does country-level R&D efficiency benefit from the collaboration network structure?

机译:国家一级的研发效率是否从协作网络结构中受益?

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摘要

In this study, we attempt to fill an important gap that literature has yet to investigate, that is, the influence of collaboration network structure on national research and development (R&D) efficiency. We not only provide country-level evidence that the collaboration network structure influences the R&D result performance measured by output quantity. We also prove that the collaboration network structure influences the R&D process performance measured by input-output efficiency score. The latter exploration presents the underlying explanations for the former conclusion. We construct a unique dataset that enables us to build seven scientific collaboration networks at the country level. Based on the collections of R&D data for each country in our networks, we have measured R&D efficiency scores by using the Malmquist productivity index associated with data envelopment analysis. The clustering coefficient (CC), structural holes (SH), degree centrality (DC), closeness centrality (CNC), and betweenness centrality (BC) of each country are jointly used to comprehensively measure the structural properties of collaboration networks. Panel data models are employed to explore the effect of the network properties on R&D efficiency. Our results not only reconfirm that collaboration network structure influences scientific publications at the country level, but also show that the higher SH, DC, CNC, and BC correlate positively with the better future efficiency.
机译:在本研究中,我们试图弥补文献尚未调查的重要空白,即协作网络结构对国家研发(R&D)效率的影响。我们不仅提供国家级的证据,证明协作网络结构会影响以产出量衡量的R&D结果绩效。我们还证明,协作网络结构会影响通过投入产出效率得分衡量的R&D过程性能。后面的探索为前一个结论提供了潜在的解释。我们构建了一个独特的数据集,使我们能够在国家/地区一级建立七个科学合作网络。基于我们网络中每个国家的R&D数据收集,我们通过使用与数据包络分析相关的Malmquist生产率指数来测量R&D效率得分。各个国家的聚类系数(CC),结构孔(SH),度中心性(DC),亲密性中心(CNC)和中间性中心(BC)共同用于全面衡量协作网络的结构特性。使用面板数据模型来探索网络属性对研发效率的影响。我们的结果不仅证实了协作网络的结构会影响国家层面的科学出版物,而且还表明,较高的SH,DC,CNC和BC与更高的未来效率成正比。

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