A Theory, Measure, and Empirical Test of Subgroups in Work Teams

Thumbnail Image



Journal Title

Journal ISSN

Volume Title

Repository Usage Stats



Although subgroups are a central component of work teams, they have remained largely unexamined by organizational scholars. In three chapters, a theory and measure of subgroups are developed and then tested. The theory introduces a typology of subgroups and a depiction of the antecedents and consequences of subgroups. The measure, called the subgroup algorithm, determines the most dominant configurations of subgroups in real work teams--those that are most likely to influence team processes and outcomes. It contrasts the characteristics within a subgroup or set of subgroups versus the characteristics between subgroups or a set of subgroups for every potential configuration of subgroups on every work team in a given sample. The algorithm is tested with a simulation, with results suggesting that it adds value to the methodological literature on subgroups. The empirical test uses the subgroup algorithm to test key propositions put forth in the theory of subgroups. First, it is predicted that teams will perform better when identity-based subgroups are unequal in size and knowledge-based subgroups are equal in size. Second, it is predicted that, although teams will perform better with an increasing number of both identity-based and knowledge-based subgroups, there will be a discontinuity in this linear function for identity-based subgroups: teams with two identity-based subgroups will perform more poorly than teams with any other number of identity-based subgroups. The subgroup algorithm is used to test these predictions in a sample of 326 work teams. Results generally support the predictions.





Carton, Andrew Mascia (2011). A Theory, Measure, and Empirical Test of Subgroups in Work Teams. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/3853.


Dukes student scholarship is made available to the public using a Creative Commons Attribution / Non-commercial / No derivative (CC-BY-NC-ND) license.