When Paying for Reviews Pays Off: The Case of Performance-Contingent Monetary Rewards
Bibliographic record
Abstract
Several online review platforms offer monetary incentives to motivate individuals to write reviews and keep them engaged with the platforms. While existing studies have examined the effects of completion-contingent monetary incentives (which uniformly reward users as long as they write reviews on the platform), we know little about the effectiveness of performance-contingent monetary incentives (which reward platform users based on the quality of their reviews). In this paper, we examine the effects of receiving performance-contingent rewards on users’ continued contribution in terms of the quantity, quality, and valence of the reviews they generate. We leverage a quasi-experiment research design to analyze a large dataset that we obtain through a collaboration with a large restaurant review platform in Asia. Our evidence shows that after receiving performance-contingent rewards, individuals write more reviews and write reviews that are of better quality. Interestingly, receiving rewards does not significantly affect the valence of subsequent reviews. Our study extends past research by being one of the first to examine how receiving performance-contingent rewards affects subsequent behaviors of reviewers. Our results can also guide platform managers to design efficient and effective incentive policies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".