Online consumers’ reactions to price decreases: Amazon’s Kindle 2 case
Bibliographic record
Abstract
Purpose – The purpose of this paper is to investigate how consumers respond to price changes by analyzing online product reviews (OPRs) posted on a product (Amazon’s Kindle 2), and to suggest several future research topics on online consumers’ reactions embedded in OPRs. Design/methodology/approach – An exploratory case study is conducted using OPRs added to the Kindle 2. By analyzing 6,714 OPRs, the authors examine how online consumers respond to two continual price decreases embedded in the observable (star rating and review depth) and implicit (positive and negative emotions) features of OPRs as well as how the number of OPRs per day has changed after two price drops. Findings – The authors found that all four features of OPRs (star rating, review depth, positive emotion, and negative emotion) and the number of OPRs per day had significantly changed after two price decreases for both long-term and short-term periods. In addition, online consumers’ reactions to price decreases in terms of these four features and the change in the number of OPRs per day were different between the first and the second price drops. Research limitations/implications – This study investigates online consumers’ reactions to price decreases only. Future research should investigate other cases where price changes under the dynamic pricing strategy in order to find the relationship between price increases/decreases and consumers’ reactions. Practical implications – This study implies that online merchants should consider consumer groups’ innovation adoption stages and make strategic decisions for price decreases to improve the sales of their products. Originality/value – While prior research involving the effects of price changes on consumers’ reactions has focussed on offline consumers, this is among the first attempts to address the long- and short-term reactions to price changes in terms of both the observable and implicit features of OPRs, and suggests that consumers’ reactions to price changes in OPRs are more complex.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".