How Transactive Memory System Becomes Ineffective Over Time and When it is Revitalized

Abstract

Although research on transactive memory system (TMS) generally shows that a well-developed TMS has a positive impact on team performance, less attention has been paid to the possibility that TMS could become ineffective over time. Using an agent-based modeling (ABM) approach, we directly examine how two effects—the information concentration on experts and the transaction cost incurred in TMS information processing—interplay across levels over time. We further explore the aspects of the communication networks and the membership change on TMS dynamics. Our ABM simulations reveal that along the nonlinear development of TMS, the coevolved two effects emerge and magnify over time; and ultimately override the benefits of TMS. The results also show that a TMS teamwork with a moderate-density communication network helps sustain the efficacy of TMS. Moreover, to overcome the rigidity of the team performance, we find that a delicate change of the membership in an appropriate timing weakens the team inertia through invigorating team member learning to revitalize the TMS team functioning. Based on the findings, we propose a three-stage model of TMS effectiveness, suggesting that a TMS teamwork can continue to be effective in a long run if the appropriate interventions are made.