Paper Number
1616
Paper Type
Short
Abstract
Many digital platforms offer monetary incentives to encourage user-generated content. While the effectiveness of piece-rate schemes (e.g., pay-per-review) and linear pay structures (e.g., pay-per-view) has been extensively studied, research on relative performance schemes is limited. In this paper, we leverage proprietary data from deal-sharing platforms that introduced a tournament-like relative performance scheme by rewarding contributors who posted the highest-voted contribution per day in each main category. This scheme was not publicly announced; contributors learned about it only after winning for the first time. Our results show that after receiving the reward, contributors become more active in posting deals and voting on others’ deals. However, they strategically exploit their knowledge by withholding upvotes and increasing downvotes on other deals posted in the same category as their incentivized deal.
Recommended Citation
Pethig, Florian; Hoehle, Hartmut; Hui, Kai-Lung; and Lanz, Andreas, "Unexpected Monetary Incentives and User-Generated Content on Digital Platforms" (2024). ICIS 2024 Proceedings. 15.
https://aisel.aisnet.org/icis2024/user_behav/user_behav/15
Unexpected Monetary Incentives and User-Generated Content on Digital Platforms
Many digital platforms offer monetary incentives to encourage user-generated content. While the effectiveness of piece-rate schemes (e.g., pay-per-review) and linear pay structures (e.g., pay-per-view) has been extensively studied, research on relative performance schemes is limited. In this paper, we leverage proprietary data from deal-sharing platforms that introduced a tournament-like relative performance scheme by rewarding contributors who posted the highest-voted contribution per day in each main category. This scheme was not publicly announced; contributors learned about it only after winning for the first time. Our results show that after receiving the reward, contributors become more active in posting deals and voting on others’ deals. However, they strategically exploit their knowledge by withholding upvotes and increasing downvotes on other deals posted in the same category as their incentivized deal.
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21-UserBehavior