Team Size and Its Negative Impact on the Disruption Index

2025-01-31

AI Summary

This study investigates how team size influences the disruptive impact of scientific innovation. Method: Leveraging a multimodal dataset comprising 65 million scholarly articles, patents, and open-source software projects, the authors apply temporal citation analysis and network-science-based disruption measurement (Disruption Index) to quantify long-term scientific influence. Contribution/Results: The analysis reveals a robust negative association between team size and disruption: large teams generate higher short-term output but contribute less to long-term scientific disruption; conversely, small teams often exhibit delayed “sleeping-beauty” effects—requiring over a decade to manifest significant impact—leading to systematic undervaluation under standard five-year evaluation windows. The study rigorously validates the robustness of the Disruption Index against methodological critiques and integrates heterogeneous data sources to model disruption dynamics. These findings provide critical empirical evidence for optimizing research organizational design and funding policy—particularly regarding grant duration and evaluation timelines—to better support high-impact, long-horizon science.

Technology Category

• Humans and AI: Teamwork, Team formation
• Multiagent Systems: Teamwork
• Reasoning under Uncertainty: Causality

Application Category

• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining
• Social Networks and Social Media: Computational social science
• Security and Privacy: Large-scale security measurements

Abstract

As science transitions from the age of lone geniuses to an era of collaborative teams, the question of whether large teams can sustain the creativity of individuals and continue driving innovation has become increasingly important. Our previous research first revealed a negative relationship between team size and the Disruption Index-a network-based metric of innovation-by analyzing 65 million projects across papers, patents, and software over half a century. This work has sparked lively debates within the scientific community about the robustness of the Disruption Index in capturing the impact of team size on innovation. Here, we present additional evidence that the negative link between team size and disruption holds, even when accounting for factors such as reference length, citation impact, and historical time. We further show how a narrow 5-year window for measuring disruption can misrepresent this relationship as positive, underestimating the long-term disruptive potential of small teams. Like"sleeping beauties,"small teams need a decade or more to see their transformative contributions to science.

Problem

• Team Size
• Innovation Capacity
• Long-term Scientific Progress

Innovation

• Team Size
• Innovative Output
• Sleeper Effect