Quantifying the dynamics of research teams' academic diversity

Abstract The growing complexity of modern scientific challenges demands research teams that integrate diverse perspectives, yet the role of academic status diversity—variation in team members' scholarly achievements—remains insufficiently understood. This study aims to bridge this gap by examining the dynamics of academic diversity within research teams and its association with innovation, analyzing more than 17 million articles across 292 fields. We introduce new metrics—academic entropy, academic standard deviation, and academic disparity—to capture the heterogeneity of team members' academic backgrounds. Using a network null model to account for temporal and disciplinary differences, we uncover significant increases in academic diversity, particularly within STEM fields and developed regions, where diversity levels are notably overrepresented. While we find a positive correlation between academic diversity and interdisciplinarity, higher diversity is associated with lower levels of scientific disruption. Teams with greater academic diversity tend to be associated with consolidating existing knowledge rather than producing disruptive innovations that challenge prevailing frameworks. This trend is especially evident in larger teams, where diversity is linked to incremental progress rather than transformative breakthroughs. These findings underscore the need for a balanced approach to promoting diversity in relation to scientific advancement.

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Publication Details

Journal
Journal of the Association for Information Science and Technology
Published
2025-08-29
DOI
https://doi.org/10.1002/asi.70023
Citations
3
Primary Topic
scientometrics and bibliometrics research
Type
article
Field-Weighted Citation Impact
4.20

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article

Quantifying the dynamics of research teams' academic diversity

Yi Bu, Star X. Zhao, Ying Ding, Alex J. Yang et al.
3 citations
Journal of the Association for Information Science and Technology
scientometrics and bibliometrics research
4.20
article

Quantifying the dynamics of research teams' academic diversity

Yi Bu, Star X. Zhao, Ying Ding, Alex J. Yang, Sanhong Deng, Meijun Liu
article en
3 citations

Abstract

Abstract The growing complexity of modern scientific challenges demands research teams that integrate diverse perspectives, yet the role of academic status diversity—variation in team members' scholarly achievements—remains insufficiently understood. This study aims to bridge this gap by examining the dynamics of academic diversity within research teams and its association with innovation, analyzing more than 17 million articles across 292 fields. We introduce new metrics—academic entropy, academic standard deviation, and academic disparity—to capture the heterogeneity of team members' academic backgrounds. Using a network null model to account for temporal and disciplinary differences, we uncover significant increases in academic diversity, particularly within STEM fields and developed regions, where diversity levels are notably overrepresented. While we find a positive correlation between academic diversity and interdisciplinarity, higher diversity is associated with lower levels of scientific disruption. Teams with greater academic diversity tend to be associated with consolidating existing knowledge rather than producing disruptive innovations that challenge prevailing frameworks. This trend is especially evident in larger teams, where diversity is linked to incremental progress rather than transformative breakthroughs. These findings underscore the need for a balanced approach to promoting diversity in relation to scientific advancement.

Journal of the Association for Information Science and TechnologyVol. 76(12)
Peking University (CN), Fudan University (CN), Harvard University Press (US), Beijing Institute of Big Data Research (CN), Chinese Academy of Governance (CN), The University of Texas at Austin (US), Nanjing University (CN)
National Social Science Fund of China
Openalex Percentile: Top 5%
scientometrics and bibliometrics research
4.20
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