Salience Bias in Crowdsourcing Contests

Abstract

Crowdsourcing relies on online platforms to connect a community of users to perform specific tasks. However, without appropriate control, the behavior of the online community might not align with the platform’s designed objective, which can lead to an inferior platform performance. This paper investigates how the feedback information on a crowdsourcing platform and systematic bias of crowdsourcing workers can affect crowdsourcing outcomes. Specifically, using archival data from the online crowdsourcing platform Kaggle, combined with survey data from actual Kaggle contest participants, we examine the role of a systematic bias, namely the salience bias, in influencing the performance of the crowdsourcing workers and how the number of crowdsourcing workers moderates the impact of the salience bias as a result of the parallel path effect and competition effect. Our results suggest that the salience bias influences the performance of contestants, including the winners of the contests. Furthermore, the parallel path effect cannot completely eliminate the impact of the salience bias, but it can attenuate it to a certain extent. By contrast, the competition effect is likely to amplify the impact of the salience bias. Our results have critical implications for crowdsourcing firms and platform designers.