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Team power dynamics and team impact: New perspectives on scientific collaboration using career age as a proxy for team power

Author

Listed:
  • Huimin Xu
  • Yi Bu
  • Meijun Liu
  • Chenwei Zhang
  • Mengyi Sun
  • Yi Zhang
  • Eric Meyer
  • Eduardo Salas
  • Ying Ding

Abstract

Power dynamics influence every aspect of scientific collaboration. Team power dynamics can be measured by team power level and team power hierarchy. Team power level is conceptualized as the average level of the possession of resources, expertise, or decision‐making authorities of a team. Team power hierarchy represents the vertical differences of the possessions of resources in a team. In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics. This research examines how team power dynamics affect team impact to fill the research gap. In this research, all coauthors of one publication are treated as one team. Team power level and team power hierarchy of one team are measured by the mean and Gini index of career age of coauthors in this team. Team impact is quantified by citations of a paper authored by this team. By analyzing over 7.7 million teams from Science (e.g., Computer Science, Physics), Social Sciences (e.g., Sociology, Library & Information Science), and Arts & Humanities (e.g., Art), we find that flat team structure is associated with higher team impact, especially when teams have high team power level. These findings have been repeated in all five disciplines except Art, and are consistent in various types of teams from Computer Science including teams from industry or academia, teams with different gender groups, teams with geographical contrast, and teams with distinct size.

Suggested Citation

  • Huimin Xu & Yi Bu & Meijun Liu & Chenwei Zhang & Mengyi Sun & Yi Zhang & Eric Meyer & Eduardo Salas & Ying Ding, 2022. "Team power dynamics and team impact: New perspectives on scientific collaboration using career age as a proxy for team power," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(10), pages 1489-1505, October.
    • Handle: RePEc:bla:jinfst:v:73:y:2022:i:10:p:1489-1505
    DOI: 10.1002/asi.24653
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    2. Raghuvanshi, Adarsh & Vinayak,, 2024. "Profiling team exploration strategies of collaborating authors from artificial intelligence in computer science," Journal of Informetrics, Elsevier, vol. 18(4).
    3. Liu, Meijun & Hu, Xiao, 2022. "Movers’ advantages: The effect of mobility on scientists’ productivity and collaboration," Journal of Informetrics, Elsevier, vol. 16(3).
    4. Liu, Meijun & Jaiswal, Ajay & Bu, Yi & Min, Chao & Yang, Sijie & Liu, Zhibo & Acuña, Daniel & Ding, Ying, 2022. "Team formation and team impact: The balance between team freshness and repeat collaboration," Journal of Informetrics, Elsevier, vol. 16(4).

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