Bibliographic record
Abstract
Purpose This study aims to examine the relations between service employee blame attributions in response to customer incivility and revenge desires and revenge behavior toward customers, and whether employee empathy moderated these relations. Design/methodology/approach The authors used survey data based on the critical incident method provided by a sample of 431 customer service employees. Findings The results suggested that blaming a customer was positively associated with desire for revenge and revenge behaviors against the uncivil customer. In addition, the authors found that blame was less strongly associated with desire for revenge when employees empathized with customers. Finally, the results show that an employee who desired revenge against the uncivil customer and who empathized with the customer was more – not less – likely to engage in revenge. Practical implications The authors found that when employees experience mistreatment from customers, it increases the likelihood that they will blame the offending customer and behave in ways that are contrary to their organization’s interests. The results suggest several points of intervention for organizations to more effectively respond to customer mistreatment. Originality/value In this study, the authors make one of the first attempts to investigate the relationships between service employee attributions of blame when they experience customer incivility, desire for revenge and customer-directed revenge behaviors. The authors also examined whether empathy moderates the relations between blame attribution, desires for revenge and revenge behavior.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".