Extrinsic versus Intrinsic Rewards for Contributing Reviews in an Online Platform
Bibliographic record
Abstract
Firms have considered various forms of incentives for writing reviews, including the use of extrinsic rewards to attract reviewers. Building on this literature, we study the implications of monetary incentives on online reviews in the context of a natural experiment, where one review platform suddenly began offering monetary incentives for writing reviews. We refer to this as the treated platform. Along with data from Amazon.com and using the difference-in-differences approach, we compare the quantity and quality of reviews before and after rewards were introduced in the treated platform. We find that reviews are significantly more positive but that the quality decreases. Taking advantage of the panel data, we also evaluate the effect of rewards on existing reviewers. We find that their level of participation after monetary incentives decreases but not their quality of participation. Last, even though the platform enjoys an increase in the number of new reviewers, disproportionately more reviews appear to be written for highly rated products. The online appendix is available at https://doi.org/10.1287/isre.2017.0750 .
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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 | 0.017 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".