Extrinsic versus Intrinsic Rewards for Contributing Reviews in an Online Platform
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it