Team Social Network Structure and Resilience: A Complex System Approach

Abstract

Today organizations increasingly rely on working teams to compete in complex and turbulent environments since they exhibit high resilience, the ability to change, and secure desirable conditions after disruptions occurred. Improving team resilience is thus becoming a critical question and understanding its determinants a mandatory issue in literature. In this article, we contribute to this topic by investigating whether and how the pattern of social interactions among team members influences team resilience. To this aim, we conceptualize the team as a complex network solving a combinatorial decision-making task on complex and turbulent environments. We model the process of decision-making by means of a continuous-time Markov chain, whereby the decision flip is governed by the combination of two forces, consensus-seeking and search for high-performing solutions. Two alternative patterns of the social network are compared, respectively, the random and scale-free, and their effect on team resilience simulated. The results show that teams, where social interactions are randomly and evenly distributed outperform teams with a scale-free-like pattern, in any condition of turbulence and complexity. However, when complexity is high and environmental turbulence is low, the superiority of random pattern is more pronounced. Theoretical and managerial implications of these findings are finally discussed.

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