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Productivity trends and citation impact of different institutional collaboration patterns at the research units’ level

Author

Listed:
  • Lipeng Fan

    (Nanjing University of Science and Technology)

  • Yuefen Wang

    (Nanjing University of Science and Technology
    Jiangsu Collaborative Innovation Center of Social Safety Science and Technology)

  • Shengchun Ding

    (Nanjing University of Science and Technology)

  • Binbin Qi

    (Nanjing University)

Abstract

In order to gain a deeper understanding of how research performance and collaboration patterns of institutions affect productivity trends and citations, this paper classifies institutions into two types: main and normal institutions, and then divides the dataset into six types: M and N as intra-institution collaboration types, and M&M, M&N, N&M, N&N as inter-institution types (M: main institutions, N: normal institutions). After analysing the productivity trends and citation impact at the research units’ level, the main results are shown as following: through a large-scale and long-span data, M papers account for the highest percentage, and play an important leading role in the beginning, and the average citation value of M&M papers is significantly higher than other types; although the number of papers with multi-authors is increasing over time, the impact of the number of authors on citations may vary from discipline to discipline, and there is a slightly negative relationship between them in artificial intelligence field in our data; despite the number of institutions and countries has a positive impact on citations in whole dataset, it differs when considering different institutional collaboration patterns and the first author’s country; no matter what institutional collaboration pattern is, the papers with USA as first author’s country always have a significant greater impact than China as first author’s country. After analysing two negative binomial regression models, some results support the above conclusions. Moreover, we find that the number of M institutions has a significant greatest impact on citations, while M institution as first author’s affiliation only has a slightly influence; China as first author’s country has a negative impact, while USA as first author’s country has a moderately positive impact, and slightly lower than that of the number of countries, moderately higher than that of the number of institutions.

Suggested Citation

  • Lipeng Fan & Yuefen Wang & Shengchun Ding & Binbin Qi, 2020. "Productivity trends and citation impact of different institutional collaboration patterns at the research units’ level," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1179-1196, November.
    • Handle: RePEc:spr:scient:v:125:y:2020:i:2:d:10.1007_s11192-020-03609-z
    DOI: 10.1007/s11192-020-03609-z
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