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Understanding the relationship between team diversity and the innovative performance in research teams using decision tree algorithms: evidence from artificial intelligence

Author

Listed:
  • Junwan Liu

    (Beijing University of Technology)

  • Xiaoyun Gong

    (Beijing University of Technology)

  • Shuo Xu

    (Beijing University of Technology)

  • Chenchen Huang

    (Beijing University of Technology)

Abstract

Interdisciplinary research teams are crucial in solving complex problems by providing creative solutions that single-discipline teams cannot achieve. Despite considerable research has been conducted to enhance the efficacy of interdisciplinary teams, there is still a lack of understanding regarding the correlation between team diversity and innovative performance. Therefore, this study investigates this question thoroughly with the most influential scholars and their collaborators in artificial intelligence. Furthermore, decision tree algorithms were utilized to examine which interdisciplinary teams (according to diversity characteristics) are more likely to achieve high innovation performance, measured by novelty and impact. The results of the study show a U-shaped relationship between a combination of research interests diversity and member diversity and the “novelty” innovation performance. Specifically, teams exhibiting high diversity in research interests tend to demonstrate superior innovative performance, irrespective of member diversity. Conversely, teams with low research interest diversity can only attain higher novelty in their innovative performance if member diversity surpasses a certain threshold; otherwise, their novelty performance diminishes. Regarding “impact” innovation performance, teams characterized by higher member diversity, while maintaining research interest diversity within a reasonable range, are likely to achieve higher impact. Additionally, interdisciplinary teams that exhibit lower member diversity but higher institutional diversity also demonstrate enhanced performance. Moreover, the study found that research interest diversity served as the variable most strongly associated with team innovation performance. This study extends the research on the complex non-linear relationship between multi-factor combinations of team diversity and the innovative performance of interdisciplinary research teams.

Suggested Citation

  • Junwan Liu & Xiaoyun Gong & Shuo Xu & Chenchen Huang, 2024. "Understanding the relationship between team diversity and the innovative performance in research teams using decision tree algorithms: evidence from artificial intelligence," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(12), pages 7805-7831, December.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:12:d:10.1007_s11192-024-05183-0
    DOI: 10.1007/s11192-024-05183-0
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