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Acknowledgement network and citation count: the moderating role of collaboration network

Author

Listed:
  • Shanwu Tian

    (Renmin University of China)

  • Xiurui Xu

    (Beijing Institute of Technology)

  • Ping Li

    (Renmin University of China)

Abstract

The scholars mentioned in a paper’s acknowledgement (acknowledgees) play an important role in the paper’s development and citation count. However, there are exiguous studies have been conducted on the acknowledgees from a network perspective. Based on the social network theory, we propose that the citation count of a paper is greatly affected by the acknowledgement network. The collaboration network is expected to negatively moderate the relationship between acknowledgement network and citation count. We establish the acknowledgement network with nodes as acknowledgees and authors, and ties as acknowledgement relationship between them. Using the bibliographic data of scientific articles from 2008 to 2010 in the field of wind energy as sample, which is provided by Web of Science Core Collection database, we build both acknowledgement network and collaboration network. Negative binomial regression model and several robustness tests are used as our research methods. The results show that acknowledgees’ centrality in the acknowledgement network has a positive effect on citation count. The authors’ centrality in the collaboration network negatively moderates the relationship between acknowledgees’ centrality and citation count. Specifically, the higher the author’s centrality in the collaboration network, the more likely the effect of acknowledgees’ centrality on the paper’s citation count will be inhibited. Finally, the theoretical and methodological contributions as well as practical implications are discussed.

Suggested Citation

  • Shanwu Tian & Xiurui Xu & Ping Li, 2021. "Acknowledgement network and citation count: the moderating role of collaboration network," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7837-7857, September.
  • Handle: RePEc:spr:scient:v:126:y:2021:i:9:d:10.1007_s11192-021-04090-y
    DOI: 10.1007/s11192-021-04090-y
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    4. Nina Smirnova & Philipp Mayr, 2023. "A comprehensive analysis of acknowledgement texts in Web of Science: a case study on four scientific domains," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 709-734, January.

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