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

Liangfei Qiu and Subodha Kumar ()
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Subodha Kumar: Fox School of Business, Temple University, Philadelphia, Pennsylvania 19122

Information Systems Research, 2017, vol. 28, issue 3, 529-546

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. 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.

Keywords: prediction market; social media; field experiment; treatment effects (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (45)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orisre:v:28:y:2017:i:3:p:529-546

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