Step by step: Capturing the dynamics of work team process through relational event sequences

Abstract

The emergence of group constructs is an unfolding process, whereby actions and interactions coalesce into collective psychological states. Implicitly, there is a connection between these states and the underlying procession of events. The manner in which interactions follow one another over time describe a group's behavior, with different temporal patterns being indicative of different team characteristics. In this study, we explicitly connect event sequences to the process of emergence. We argue that the temporal relationship between events in a sequence will vary depending on the team's psychological outcome. Further, certain patterns of behavior will be repeated at different rates in teams with varying emergent states. To support this approach, we apply a statistical methodology—relational event modeling—for analyzing sequences of interactions that builds on the foundation of social network analysis. Using a dataset comprised of 55 work teams of military personnel engaged in a tactical scenario, we found that individuals who perceived team process (regarding coordination and information sharing) as having different qualities engaged in significantly different patterns of behavior. Our findings indicate that individuals who had a positive perception of process quality were more likely to initiate communication events in a reciprocal, transitive, and decentralized fashion.

Document Details

Document Type
Pub Defense Publication
Publication Date
Nov 27, 2017
Source ID
10.1002/job.2247

Entities

People

  • Aaron Schecter
  • Alice Leung
  • Andrew Pilny
  • Marshall Scott Poole
  • Noshir Contractor

Organizations

  • BBN Technologies
  • Northwestern University
  • United States Army Research Laboratory
  • University of Georgia
  • University of Illinois Urbana–Champaign
  • University of Kentucky

Tags

Readers

  • Mathematical Modeling and Probability Theory.
  • Neural Network Machine Learning.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.