Decomposing the Variance of Consumer Ratings and the Impact on Price and Demand
Bibliographic record
Abstract
Consumer ratings play a decisive role in purchases by online shoppers. Although the effects of the average and the number of consumer ratings on future product pricing and demand have been studied with some conclusive results, the effects of the variance of these ratings are less well understood. We develop a model where we decompose the variance of consumer ratings into two sources: taste differences about search and experience attributes of a durable good, and quality differences among instances of this good in the form of product failure. We find that (i) optimal price increases and demand decreases in variance caused by taste differences, (ii) optimal price and demand decrease in variance caused by quality differences, and (iii) when holding the average rating as well as the total variance constant, for products with low total variance, both price and demand increase in the relative share of variance caused by taste differences. Counter to intuition, we demonstrate that risk-averse consumers may prefer a higher-priced product with a higher variance in ratings when deciding between two similar products with the same average rating.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".