Coevolution of communication and knowledge networks in transactive memory systems: Using computational models for theoretical development

This study focuses on the initial conditions of work teams and the impacts of these conditions on the development of teams' transactive memory (TM) systems through computational modeling. TM theory describes the conditions under which team members retrieve and allocate information to accomplish collective tasks. Previous research has shown evidence for teams developing TM systems over time, but field research does not allow for the extensive manipulation of initial conditions a team might face when working together; conversely, this experimental research allowed for such manipulations without negatively impacting the ongoing productivity of organizations. Initial knowledge, initial accuracy of expertise recognition, and network size are explored as predictor variables on the development of a TM system as mediated through communication. System development is measured by the degree to which team members accurately perceive other members' expertise and the extent to which the system has differentiated its stored knowledge. This study includes theoretically derived propositions tested through a path analysis of computationally generated data. The analysis validates the five propositions and is consistent with the developmental mechanisms of TM theory. Three additional paths proved to be significant and directly connect the initial conditions with the developmental indicators at the end state model.