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

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. In this study, 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 the quantile treatment effect using the difference-in-differences method. Our empirical results on ATE show that an increase in audience size leads to an improvement in prediction accuracy, and that a higher level of online endorsement also leads to prediction improvements. Interestingly, we find that the 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 prediction markets can target people of intermediate abilities to obtain the most significant prediction improvement.

The online appendix is available at https://doi.org/10.1287/isre.2016.0679.