Overcoming Free-Riding in User-Generated Content Platforms: Punishments and Rewards for Individuals and Groups

Abstract

User-generated content (UGC) platforms rely on user contributions to create value and frequently suffer from problematic free-riding. Although many platforms implement interventions, an open empirical question remains as to which interventions result in the best outcomes. As online hubs of collaboration, UGC platforms are well-suited to applying exogenous incentives for motivating participation á la a theoretical social planner. We propose that UGC platforms could use rewards or punishments at individual or group levels to mitigate problematic free-riding. The free-rider problem is typically studied using a public goods framework, thus we conduct a lab experiment using a public goods game to explore interventions applied to individuals or groups. Our results highlight interesting insights and non-obvious consequences. Punishing only the worst contributor results in a significant increase in contributions for the worst contributor and marginal increases in contributions for the group. Rewarding only the highest contributor results in a significant decrease in contributions for everyone in the group. Punishing the group results in an overall decrease in contributions, whereas rewarding the group results in an overall increase in contributions. We also apply our interventions to a context-based image tagging experiment. Our research offers insights for the design and implementation of incentive schemes in UGC platforms facing a free-rider problem.

Keywords: user-generated content platforms, free-riding, public good, economic experiment, economics of IS