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

Lipeng Fan, Yuefen Wang (), Shengchun Ding and Binbin Qi
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Lipeng Fan: Nanjing University of Science and Technology
Yuefen Wang: Nanjing University of Science and Technology
Shengchun Ding: Nanjing University of Science and Technology
Binbin Qi: Nanjing University

Scientometrics, 2020, vol. 125, issue 2, No 21, 1179-1196

Abstract: 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.

Keywords: Institutional collaboration pattern; Productivity trends; Citation impact; Negative binomial; Artificial intelligence (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11192-020-03609-z

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