The impact of customer ratings on consumer choice of fresh produce: A stated preference experiment approach
Bibliographic record
Abstract
Abstract The importance of customer ratings or reviews in online shopping has been recognized in the previous literature; however, few have studied how online customer rating scores affect consumers’ fresh produce purchases and its importance relative to other fresh produce attributes. The quality of fresh produce demonstrates high uncertainty and variation; therefore, the impact of user‐generated content such as customer rating scores on the choice of fresh produce may be more complex than on other product categories. Moreover, previous studies on customer ratings have not examined the price premium that retailers can obtain based on better ratings of fresh produce. Using a stated preference approach (i.e., choice experiment), this study measures the willingness to pay for a higher customer rating score and explores its relative importance to other popular fresh produce attributes (i.e., organic, place of origin, and shelf life). The results show that customer rating is the second most important attribute after the place of origin and is more important than production methods (e.g., organic and naturally grown) for fresh strawberry purchases. Also, rating scores demonstrate a diminishing marginal impact on consumer willingness to pay. Younger consumers and households with children are willing to pay more for fresh produce with high ratings than those with low ratings.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".