An Exploratory Study of the Effects of Price Decreases on Online Product Reviews: Focusing on Amazon’s Kindle 2
Bibliographic record
Abstract
As online shopping proliferates, online product reviews (OPRs) play a crucial role in online consumers’ purchasing decisions. Although prior research on the effects of price changes on consumer reactions has provided insightful implications, little is known about the impact of price changes on the characteristics of OPRs. With the growing importance of OPRs as a key social recommendation system for potential consumers’ decision-making, it is important to understand the dynamics of OPRs around price changes. We select the Kindle 2 from Amazon.com as our focal product and conduct an exploratory case study. By analyzing 6,714 reviews on the Kindle 2, we examine how consumers respond to price decreases using OPRs. The results show that all four characteristics of OPRs (star-rating, review depth, positive emotion, and negative emotion) are significantly influenced by price decreases. Moreover, we found that the impacts of price decreases on OPRs’ characteristics are different between the first and the second attempts at price reduction. Interestingly, the number of reviews per day significantly soars immediately after the first price decrease, while there is no significant change in the number of reviews after the second price cut. We conclude the paper with a discussion of our findings.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".