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

The essentials

• Organizations rely on group formation to solve complex tasks
• Groups adapt to the demands of the task by changing their composition periodically
• Previous research on the effects of group adaptation on task performance has produced ambiguous results
• Researchers conducted an agent-based model study using the NK-framework to better understand this relationship
• Reorganizing well-performing groups may be beneficial, but only if individual learning is restricted
• Centralizing decision-making led to better performance than random or decentralized decision-making for low-complexity tasks (K=3)
• Decentralized decision-making was more effective than centralization or randomness for moderate complexity tasks (K=5)
• Group adaptation had a positive effect on performance for both low and moderate complexity tasks when individual learning was restricted
• Group adaptation had a negative impact on performance for moderate complexity tasks when individual learning was not restricted
• The study highlights the importance of considering both individual learning and task complexity when examining the impact of group adaptation on performance in complex environments.
• Organizations should carefully consider whether to change their winning teams depending on these factors.

Key points written by AI: they may contain errors, check them against the paper.