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Dynamic groups in complex task environments: To change or not to change a winning team?

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Listed:
  • Dar'io Blanco-Fern'andez
  • Stephan Leitner
  • Alexandra Rausch

Abstract

Organisations rely upon group formation to solve complex tasks, and groups often adapt to the demands of the task they face by changing their composition periodically. Previous research comes to ambiguous results regarding the effects of group adaptation on task performance. This paper aims to understand the impact of group adaptation, defined as a process of periodically changing a group's composition, on complex task performance and considers the moderating role of individual learning and task complexity in this relationship. We base our analyses on an agent-based model of adaptive groups in a complex task environment based on the NK-framework. The results indicate that reorganising well-performing groups might be beneficial, but only if individual learning is restricted. However, there are also cases in which group adaptation might unfold adverse effects. We provide extensive analyses that shed additional light on and, thereby, help explain the ambiguous results of previous research.

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  • Dar'io Blanco-Fern'andez & Stephan Leitner & Alexandra Rausch, 2022. "Dynamic groups in complex task environments: To change or not to change a winning team?," Papers 2203.09157, arXiv.org.
    • Handle: RePEc:arx:papers:2203.09157
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    References listed on IDEAS

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    Cited by:

    1. Stephan Leitner, 2022. "Collaborative search and autonomous task allocation in organizations of learning agents," Papers 2206.02142, arXiv.org, revised Sep 2022.
    2. Dar'io Blanco-Fern'andez & Stephan Leitner & Alexandra Rausch, 2022. "The benefits of coordination in (over)adaptive virtual teams," Papers 2206.14508, arXiv.org.

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