Year: 2019
Type: article
Abstract: Team Mental Models (TMM) are one of the strongest predictors of team behavior and performance. TMM direct team behaviors through the series of tasks they perform over time. Research in the area, although crucial in demonstrating the effect of TMM, has been largely static, failing to articulate specifically how TMM emerge or function in teams over time. This dissertation develops a computational model to explicate the process of TMM emergence and demonstrate necessary factors. First, I explain the core concepts of TMM emergence, including team composition, dyadic interactions, and contextual variables. Second, I develop a process-oriented theory of TMM development in narrative format. Third, I translate the narrative theory into a computational model proposed to explore how the core processes interact to influence TMM emergence. Results of the model suggest that teams may simultaneously increase TMM similarity and decrease overall accuracy as a team. Additionally, team intelligence may be viewed as a liability in some respects. While intelligence in team members could facilitate more efficient, faster sharing of information, incorrect information spreads more quickly. As team members are more agreeable starting from the beginning of the simulation, members could be more susceptible to believing more incorrect information.
Author: Neil Benoit Outland
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2964741600