How Does Rating Specific Features of an Experience Alter Consumers' Overall Evaluations of That Experience?
Bibliographic record
Abstract
Abstract How does the way companies elicit ratings from consumers affect the ratings that they receive? In 10 pre-registered experiments, we find that consumers rate subpar experiences more positively overall when they are also asked to rate specific aspects of those experiences (e.g., a restaurant's food, service, and ambiance). Studies 1–4 established the basic effect across different scenarios and experiences. Study 5 found that the effect is limited to being asked to rate specific features of an experience, rather than providing open-ended comments about those features. Studies 6–9 provided evidence that the effect does not emerge because rating positive aspects of a subpar experience reminds consumers that their experiences had some good features. Rather, it emerges because consumers want to avoid incorporating negative information into both the overall and the attribute ratings. Lastly, study 10 found that asking consumers to rate attributes of a subpar experience reduces the predictive validity of their overall rating. We discuss implications of this work and reconcile it with conflicting findings in the literature.
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How this classification was reachedexpand
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".