Team Power Dynamics and Team Impact: New Perspectives on Scientific Collaboration using Career Age as a Proxy for Team Power
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Abstract (arXiv)
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 co-authors 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 co-authors 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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 4 of its 85 references that are in the map. First posted on arXiv 9 Aug 2021.
Builds on
4 of 85 references.
References · 4
- 2019Historical comparison of gender inequality in scientific careers across countries and disciplinescited by 14
- 2014Team size matters: Collaboration and scientific impact since 1900cited by 8
- 2018International Research Collaboration: Novelty, Conventionality, and Atypicality in Knowledge Recombinationcited by 8
- 2016Growth of International Cooperation in Science: Revisiting Six Case Studiescited by 3
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 3.
5 papers.
- 2023Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Predictioncited by 8
- 2025The Effect of Gender Diversity on Scientific Team Impact: A Team Roles Perspectivecited by 1
- 2026Quantifying the Knowledge Proximity Between Academic and Industry Research: An Entity and Semantic Perspectivecited by 1
- 2026Exploring Novelty Differences between Industry and Academia: A Knowledge Entity-centric Perspectivecited by 0
- 2026Exploring the relationship between team institutional composition and novelty in academic papers based on fine-grained knowledge entitiescited by 0
- 2026Exploring the relationship between team institutional composition and novelty in academic papers based on fine-grained knowledge entities30 Jun 2026
- 2026Exploring Novelty Differences between Industry and Academia: A Knowledge Entity-centric Perspective16 Mar 2026
- 2026Quantifying the Knowledge Proximity Between Academic and Industry Research: An Entity and Semantic Perspective5 Feb 2026
- 2025The Effect of Gender Diversity on Scientific Team Impact: A Team Roles Perspective29 Dec 2025
- 2023Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction29 Sep 2023