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Member Self-Disclosure, Team Composition, and Team Performance in Crowdsourcing: The Case of Kaggle

Fri, May 26, 8:00 to 9:15, Hilton San Diego Bayfront, Floor: 2, Indigo Ballroom C

Abstract

The current study aims to contribute to crowdsourcing research by exploring how individuals self-organize themselves into successful teams to solve problems in crowdsourcing. Specifically, we examined the effects of member self-disclosure and team composition on team performance in crowdsourcing activities. We collected a sample of 5,082 self-organized teams in a crowdsourcing platform, Kaggle. By examining the relation between member self-disclosure, team composition, and team performance, our preliminary findings showed that 1) expertise self-disclosure and social media self-disclosure in teams positively predicted team performance in crowdsourcing activities; and 2) disparity of team member expertise negatively predicted team performance in crowdsourcing.

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