The role of leadership in shared mental model convergence and team performance improvement: An agent-based computational model

Research in shared mental models has immeasurably aided our understanding of effective teamwork and taskwork. However, little research has focused on the role that leaders play, if any, in influencing, developing and/or fostering shared mental models and thereby improving team performance. We developed an agent-based computational model based on McComb's theory of three-phase mental model development, where agents repeatedly share individual opinions (orientation phase), evaluate and respond to the opinions expressed by others (differentiation phase), and modify their understanding of the team based on the responses (integration phase). Leadership and team properties are represented in three experimental parameters: social network structure, heterogeneity of agents' domains of expertise, and level of their mutual interest. Participative leadership is represented by a fully connected network, while Leader–Member eXchange (LMX) is represented by a fully connected network of ingroup members and several out-group members connected only to the leader. Our simulation results show that, in general, participative leadership promotes mental model convergence better than LMX. In the meantime, the team performance improvement is achieved by participative leadership only when members have both heterogeneous domains of expertise and strong mutual interest. In all other conditions, participative leadership causes the worst degradation of team performance through team development processes, while LMX is the best to minimize such team degradation. Implications and suggestions for future research are