Abstract

Studying team processes is critical to understanding how teams work to achieve team outcomes. To effectively study team processes, behavioral activities team members enact must be measured with sufficient granularity and intensity. Analyzing the detailed mechanics of team processes requires employing analytical methods sensitive to modeling the series of actions and interactions of team members as they execute taskwork and teamwork over time. Current empirical investigation of team processes lags with respect to intricately measuring and assessing team processes over time. Using dynamic network models, this dissertation sought to understand the behaviors responsible for interaction patterns amongst team members, how those interaction patterns and structures relate to team member behavior, and how interactive team processes relate to team outcomes. Specifically, this dissertation utilized interaction-level data from the National Basketball Association (NBA) and applied three dynamic network models to the data: Separable Temporal Exponential Random Graph Modeling (STERGM), Stochastic Actor-Oriented Modeling (SAOM), and Relational Event Modeling (REM). The purpose of this dissertation is to provide a descriptive foundation for future studies using theories of time to study team phenomena and to demonstrate the utility of dynamic network models. This dissertation details the theoretical foundations of team processes and network analysis, the temporal extensions of traditional network analyses, the utility and applicability of dynamic network models (STERGM, SAOM and REM) using NBA data, and shows insights these methods provide for studying team processes. Results of this dissertation showed reciprocity to be the strongest passing pattern amongst NBA teams, followed by transitive passing patterns.