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Embracing Complexity While Communicating Practicality
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AI
DOI：10.1177/10464964261430724.png)
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Abstract
En 中文
It is my hope that in the next decade our field fully embraces complexity while clearly communicating practicality. Teams are inherently dynamic systems, and progress will require moving beyond static models that regress performance on aggregated individual differences. I highlight three interrelated sources of complexity: temporal dynamics (e.g., episodic, event-based, and longitudinal change), inter-relational dynamics (evolving and multiplex relationships), and dynamic inputs (e.g., dynamic team composition). Advances in unobtrusive data collection and analytic approaches—from accessible word counts to sophisticated natural language processing—now enable rigorous tests of long-standing temporal and relational theories. Yet complexity must be paired with application. By partnering with organizations and conducting field experiments, scholars can translate nuanced models into actionable interventions, where even small effects meaningfully shift outcomes. Looking ahead, the field’s promise lies in integrating new phenomena, including AI, without losing theoretical continuity or practical relevance.
Keywords:
Complexity
Team dynamics
Temporal dynamics
Relational dynamics
Practicality