Featuring Mistakes: The Persuasive Impact of Purchase Mistakes in Online Reviews
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
Companies often feature positive consumer reviews on their websites and in their promotional materials in an attempt to increase sales. However, little is known about which particular positive reviews companies should leverage to optimize sales. Across four lab studies involving both hypothetical and real choices as well as field data from a retailer’s website (Sephora), the authors find that consumers are more likely to purchase a product if it is recommended by a reviewer who has (vs. has not) made a prior purchase mistake. The authors define a purchase mistake as a self-identified suboptimal decision whereby people purchase a product that subsequently fails to meet a threshold level of expected performance. This persuasive advantage emerges because consumers perceive reviewers who admit a purchase mistake as having more expertise than even reviewers whose purchase experience has not been marred by mistakes. As a result, in marketers’ attempts to increase the persuasive influence of reviews featured in their promotional materials, they may inadvertently decrease it by omitting the very information that would lead consumers to be more likely to purchase recommended products.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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