Influences of Customer Mistreatment: Moderating Roles of Employees' Emotion Regulation Strategies
Bibliographic record
Abstract
Drawing on affect- and resource-based mechanisms, this study specified two forms of customer mistreatment: aggressive and demanding mistreatment and tested their proximal and lagged effects in predicting within-person fluctuation of employees’ emotional well-being, as well as the moderating roles of employees’ emotion regulation after work (i.e., rumination and social sharing). One thousand one hundred and eighty-five daily surveys were collected from 149 Chinese customer service representatives from a call center for 8 weekdays. Results from multilevel analyses largely supported the role of emotional exhaustion in mediating the lagged association between customer mistreatment and employees’ negative mood in the next morning. Positive treatment by customers buffered the detrimental effect of demanding mistreatment. In addition, the current findings supported the moderating roles of rumination and social sharing in strengthening the impacts of customer mistreatment, but were not consistent across aggressive mistreatment and demanding mistreatment. Implications and limitations were discussed.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".