Predicting future conflict between team-members with parameter-free models of social networks

TL;DR

This study demonstrates that group-based models of social networks can effectively predict future conflicts in small teams, outperforming traditional micro-based models, with implications for team management and conflict prevention.

Contribution

It introduces the use of parameter-free, group-based social network models for predicting conflict in small teams, showing their superiority over traditional models.

Findings

Group-based models successfully predict team conflicts.

Micro-based models of structural balance are less effective.

Empirical analysis on real team data supports the models' predictive power.

Abstract

Despite the well-documented benefits of working in teams, teamwork also results in communication, coordination and management costs, and may lead to personal conflict between team members. In a context where teams play an increasingly important role, it is of major importance to understand conflict and to develop diagnostic tools to avert it. Here, we investigate empirically whether it is possible to quantitatively predict future conflict in small teams using parameter-free models of social network structure. We analyze data of conflict appearance and resolution between 86 team members in 16 small teams, all working in a real project for nine consecutive months. We find that group-based models of complex networks successfully anticipate conflict in small teams whereas micro-based models of structural balance, which have been traditionally used to model conflict, do not.