Discouraging the Non-Expert, Motivating the Expert: The Differential Effects of Feedback Inequality in Crowdsourcing Contests

Sustained participation is crucial for the success of crowdsourcing contests. Shifting from micro-level feedback attributes, this study investigates macro-structural feedback inequality and examines whether feedback inequality discourages participation through social comparison or motivates participation as a competitive signal. Integrating feedback intervention theory with signaling theory, we argue that feedback inequality influences participation through attentional allocation, while seeker activeness shapes whether this structural cue is interpreted as credible or noisy. Analyzing 41,095 Crowdspring contests and over 4.5 million submissions, we find that feedback inequality is negatively associated with subsequent participation among non-experts, consistent with resource-depleting meta-task concerns. For experts, feedback inequality is associated with higher sustained participation only when seeker activeness is high, suggesting that visible seeker engagement helps make unequal feedback distributions interpretable as diagnostic signals. This study reconciles competing perspectives by identifying boundary conditions for structural feedback signals.