Negative Reviews, Positive Impact: Consumer Empathetic Responding to Unfair Word of Mouth
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it