Revealed Preference in Online Reviews: Purchase Verification in the Tablet Market
Bibliographic record
Abstract
The review systems of online platforms create a stream of online word-of-mouth that allows consumers to learn from others’ purchasing experience. However, it is difficult for consumers to discern the authenticity of a review or the reviewer’s level of experience with the product. Platforms can aid the authentication process by incorporating a verified purchase (VP) indication, or “badge” as is done on Amazon, in reviews where the consumer writing the review has verifiably purchased the focal product. A VP is a revealed preference for a product implying a utility-maximizing choice where the consumer writing the review has experience with the product. Combining an Amazon data set on tablet computers with the theory of revealed preference in online reviews, we uncover a surprising new result: the proportion of VP reviews (a revealed preference) is associated with higher future sales, and the effect of the proportion of VP reviews on sales dominates the effect of the mean rating. This novel use of VP with revealed preference theory has implications for new research in the design of recommendation systems, detecting fraudulent reviews, and online profiling/privacy. Moreover, the use of a VP badge is immediately applicable to firms and platforms.
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.
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 | 0.009 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".