*Modeling teams: A general systems theory approach
How do teams achieve their best performance? Why do some teams develop into top performers while others, seemingly the same, do not? The answers to these questions will provide a practical and theoretical team behavior understanding. Traditional models do not explain how teams work (Lembke and Wilson, 1998). A static model offers a snapshot on team behavior (Kelly and McGrath, 1984), but no real understanding of its dynamics. A dynamic team behavior model, on the other hand, captures the development processes. A dynamic model is required if insights about why and how team performance is created are to be obtained.
In this work, I approach the problem of creating a complex dynamic model of team behavior. I offer a framework for developing such models by combining systems approaches and empirical team research. This framework allows a team to be formally represented as a system of mathematical relationships and parameters in order for its behavior, complexity and dynamics to be quantitatively explored.
The purpose of this doctoral dissertation is to demonstrate how models exhibiting complex behavior can be developed and to validate a sample model. To do this, first, a framework for complex model development is derived and presented. Second, using the framework guidelines and based on team behavior research, a team model is constructed. Third, the model relationships are quantified and a behavioral simulation is performed. Fourth, the relationships in the model are estimated from the data from the laboratory simulation, thereby allowing the theoretical model to be verified. Fifth, systems specific analytical tools are used to analyze the model.
To provide data for the model, 72 three-member student teams conducted a behavioral simulation. Statistical analysis showed that all hypothesized relationships are significant, except Team Quality Performance. Based on the statistical results, simulation models were constructed and simulations performed. The simulations show the dynamics of the systems studied. Further, the simulated systems were analyzed to assess stability, equilibrium, equifinality and controllability. These analyses showed that the systems studied are stable, non-equifinal, not completely controllable and do not achieve equilibrium. Exploratory findings and future research opportunities are also discussed.