CM
Burint Bevis
Imperial College Business School
Abdullah Almaatouq
Massachusetts Institute of Technology, United States
Mark Whiting
U. of Pennsylvania, United States
Duncan J. Watts
U. of Pennsylvania
Xinlan Hu
Wharton, United States
Gus Cooney
The Wharton School, U. of Pennsylvania
Burint Bevis
Imperial College Business School
Xinlan Hu
Wharton, United States
Mohammed Alsobay
MIT Sloan School of Management
Laurie Weingart
Carnegie Mellon U., United States
Randall Peterson
London Business School, United Kingdom
Juliana Schroeder
U. of California, Berkeley, United States
The goal of this symposium is to connect theorists, methodologists, and technologists at the interdisciplinary frontier of team collaboration and conflict management studies. Specifically, this symposium weaves together technology’s dual implications on both teams and the science of teamwork. Across four original research papers, we will demonstrate that studying teams in a digital setting is more than simply a recreation of in-person interactions, but rather a rich setting for methodological innovation. These innovations, in turn, push the boundaries of our knowledge about teamwork, particularly in a world in which collaboration increasingly occurs via technology. We call this bidirectional interplay between methods and theory a "computational science of collaboration." Paper 1 introduces a model designed to capture the dynamics of naturalistic turn-taking, showing how fine-grained data can help to bridge the gap between micro-level turn dynamics and broader macro-level outcomes. Paper 2 further expands on micro-level dynamics to explore how conflict dynamics differ across communication media, with implications for facilitating constructive disagreements in a digitized yet polarized world. Paper 3 presents the results of an online experiment that systematically varies five facets of teamwork (Team Composition, Team Size, Task Attributes, Task Complexity, and Communication Process). Its findings show that, all else equal, team outcomes are highly dependent on the task at hand. Lastly, Paper 4 demonstrates how researchers can take advantage of the latest advances in Large Language Models to build interactive agents for behavioral experiments. Following the four presentations, discussants Laurie Weingart and Randall Peterson will lead a conversation integrating the papers' theoretical and methodological contributions. In what ways do these novel tools extend prior theories of teamwork, perhaps with greater precision or resolution, and in what ways do they highlight novel forms of collaboration — whether across different modalities, different tasks, or different types of “teammates” (AI-powered versus human)? How might this "computational science" influence our field’s research agenda in the years to come?
Author: Gus Cooney – The Wharton School, U. of Pennsylvania
Author: Juliana Schroeder – U. of California, Berkeley
Author: Burint Bevis – Imperial College Business School
Author: Xinlan Emily Hu – Wharton
Author: Abdullah Almaatouq – Massachusetts Institute of Technology
Author: Mark Whiting – U. of Pennsylvania
Author: Duncan J. Watts – U. of Pennsylvania
Author: Mohammed Alsobay – MIT Sloan School of Management