The effects of interpersonal customer mistreatment on employee retaliation
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the relationship between customer mistreatment and employee retaliation. The moderating effect of employee psychological strain on the relationship between customer mistreatment and employee retaliation is also examined. Design/methodology/approach A sample of 107 contact centre customer service representatives completed a survey. Moderated multiple regression analysis was conducted to examine the relationship between customer mistreatment and psychological strain on employee retaliation. Findings Customer mistreatment emerged as a significant predictor of employee retaliation against the customer (customer mistreatment: β=0.252, p<0.01), providing support for hypothesis 1. Psychological strain was found to significantly moderate the effects of customer mistreatment on employee retaliation against the customer, (β=0.197, p<0.01) supporting hypothesis 2. Originality/value The results provide a greater understanding of individuals’ responses to customer incivility. Previous research has demonstrated that uncivil customer behavior leads to emotional exhaustion and absences from work within the call centre industry. Our results suggest that call centre customer service employees may also engage in retaliatory behavior when they perceive that they have been treated unjustly by customers. The positive relationship between customer mistreatment and employee retaliation against customers was stronger when employees reported high (versus low) psychological strain.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".