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

TL;DR

This study uses an agent-based model to analyze how periodic group reorganization affects performance in complex tasks, revealing that benefits depend on individual learning and task complexity.

Contribution

It provides a nuanced understanding of when group adaptation improves or hampers performance, clarifying ambiguous prior findings.

Findings

Reorganizing well-performing groups can be beneficial if individual learning is limited.

Group adaptation may have adverse effects depending on conditions.

The impact of group changes depends on task complexity and learning constraints.

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.