Understanding Voluntary Knowledge Provision and Content Contribution through a Social Media-Based Prediction Market: A Field Experiment

Abstract

The performance of prediction markets depends crucially on the quality of user contribution. A social media-based prediction market can utilize aspects of social effects to improve users’ contribution quality. Drawing upon literature from diverse areas such as prediction markets, knowledge contribution, public goods provision, and user generated content, we examine the causal effect of social audience size and online endorsement on prediction market participants’ prediction accuracy through a randomized field experiment. By conducting a comprehensive treatment effect analysis, we estimate both the average treatment effect (ATE) and quantile treatment effect (QTE) using the difference-in-differences method. Our empirical results on ATE show that an increase in audience size leads to an increase in prediction accuracy, and that an increase in online endorsement also leads to prediction improvements. Interestingly, we find that quantile treatment effects are heterogeneous: users of intermediate prediction ability respond most positively to an increase in social audience size and online endorsement. These findings suggest that corporate prediction markets can target people of intermediate abilities to obtain the most significant prediction improvement.

Keywords: Prediction Market, Social Media, Field Experiment, Treatment Effects