Matching or Crashing? Personality-based Team Formation in Crowdsourcing Environments
This addresses the problem of optimizing team performance in crowdsourcing environments for platforms and workers, and it is incremental as it applies existing personality theories to a new context.
The study investigated whether forming crowdsourced teams based on personality compatibility improves performance compared to non-personality-based methods, finding that personality compatibility significantly affects team outcome quality, interactions, and emotions.
"Does placing workers together based on their personality give better performance results in cooperative crowdsourcing settings, compared to non-personality based crowd team formation?" In this work we examine the impact of personality compatibility on the effectiveness of crowdsourced team work. Using a personality-based group dynamics approach, we examine two main types of personality combinations (matching and crashing) on two main types of tasks (collaborative and competitive). Our experimental results show that personality compatibility significantly affects the quality of the team's final outcome, the quality of interactions and the emotions experienced by the team members. The present study is the first to examine the effect of personality over team result in crowdsourcing settings, and it has practical implications for the better design of crowdsourced team work.