Impacts of sellers’ responses to online negative consumer reviews: Evidence from an agricultural product
Bibliographic record
Abstract
Abstract Sellers’ responses to online negative consumer reviews (NCRs) have a marked effect on consumer purchasing intentions. In this study, we divide seller's responses to NCRs into two categories: rational responses and emotional responses. Through two separate studies, we examine the impact of sellers’ responses to online NCRs on consumer purchasing intention. Results reveal that product‐related NCRs reduce consumer purchasing intentions more than service‐related NCRs and having no reaction to NCRs from the sellers decreases consumer purchasing intentions. In addition, consumer trust mediates the relationship between seller's response to online NCRs and consumer purchasing intentions. The results also show that the impact on consumer purchasing intentions can be modified by the type of NCRs and sellers’ responses. In particular, rational responses will be more effective for product‐related NCRs, and for service‐related NCRs, there does not appear to exist a significant difference between the effects of rational and emotional responses.
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How this classification was reachedexpand
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".