Negative Reviews, Positive Impact: Consumer Empathetic Responding to Unfair Word of Mouth
Bibliographic record
Abstract
This research documents how negative reviews, when perceived as unfair, can activate feelings of empathy toward firms that have been wronged. Six studies and four supplemental experiments provide converging evidence that this experienced empathy for the firm motivates supportive consumer responses such as paying higher purchase prices and reporting increased patronage intentions. Importantly, this research highlights factors that can increase or decrease empathy toward a firm. For instance, adopting the reviewer’s perspective when evaluating an unfair negative review can reduce positive consumer responses to a firm, whereas conditions that enhance the ability to experience empathy—such as when reviews are highly unfair, when the identity of the employee is made salient, or when the firm responds in an empathetic manner—can result in positive consumer responses toward the firm. Overall, this work extends the understanding of consumers’ responses to word of mouth in the marketplace by highlighting the role of perceived (un)fairness. The authors discuss the theoretical and practical implications of the findings for better management of consumer reviews.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".