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Research leadership flow determinants and the role of proximity in research collaborations

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  • Chaocheng He
  • Jiang Wu
  • Qingpeng Zhang

Abstract

Characterizing the leadership in research is important to revealing the interaction pattern and organizational structure through research collaboration. This research defines the leadership role based on the corresponding author's affiliation, and presents the first quantitative research on the factors and evolution of 5 proximity dimensions (geographical, cognitive, institutional, social, and economic) of research leadership. The data to capture research leadership consist of a set of multi‐institution articles in the fields of “Life Sciences & Biomedicine,” “Technology,” “Physical Sciences,” “Social Sciences,” and “Humanities & Arts” during 2013–2017 from the Web of Science Core Citation Database. A Tobit regression‐based gravity model indicates that the mass of research leadership of both the leading and participating institutions and the geographical, cognitive, institutional, social, and economic proximities are important factors for the flow of research leadership among Chinese institutions. In general, the effect of these proximities for research leadership flow has been declining recently. The outcome of this research sheds light on the leadership evolution and flow among Chinese institutions, and thus can provide evidence and support for grant allocation policies to facilitate scientific research and collaborations.

Suggested Citation

  • Chaocheng He & Jiang Wu & Qingpeng Zhang, 2020. "Research leadership flow determinants and the role of proximity in research collaborations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(11), pages 1341-1356, November.
    • Handle: RePEc:bla:jinfst:v:71:y:2020:i:11:p:1341-1356
    DOI: 10.1002/asi.24331
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    2. He, Chaocheng & Liu, Fuzhen & Dong, Ke & Wu, Jiang & Zhang, Qingpeng, 2023. "Research on the formation mechanism of research leadership relations: An exponential random graph model analysis approach," Journal of Informetrics, Elsevier, vol. 17(2).
    3. Hong Liu & Zhihua Liu & Yongzeng Lai & Lin Li, 2021. "Factors Influencing Collaborative Innovation Project Performance: The Case of China," Sustainability, MDPI, vol. 13(13), pages 1-19, July.

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