Reducing Customer-Directed Deviant Behavior: The Roles of Psychological Detachment and Supervisory Unfairness
Bibliographic record
Abstract
Conservation of resources (COR) theory proposes that mistreatment by customers (termed “customer mistreatment”) can deplete employees’ resources, lessen their ability to regulate their behaviors, and result in them engaging in customer-directed deviant behavior. However, COR has been criticized for its lack of precision regarding how this process unfolds. Integrating the person-situation interactionist perspective with COR theory, the present paper aims to provide a deeper understanding of COR theory by explicating how individual characteristics and work context—namely, psychological detachment and supervisory unfairness—can combine to attenuate/exacerbate the relationship between customer mistreatment and employees’ customer-directed deviant behavior. Using a multilevel field study with 1,092 daily-based surveys among 157 Korean call-center representatives, our results show that frontline employees’ emotional exhaustion mediates the relationship between customer mistreatment and customer-directed deviant behavior that occurs on the next working day. When faced with customer mistreatment, employees with lower (vs. higher) psychological detachment were more likely to be emotionally exhausted and engage in customer-directed deviant behavior on the next working day. Moreover, their emotional exhaustion predicted customer-directed deviant behavior more so when their supervisors treated them unfairly (vs. fairly). Taken together, the results show that the mediating effect of emotional exhaustion was strongest among employees with low (vs. high) psychological detachment and who reported more (vs. less) supervisory unfairness. Theoretical, methodological, and practical implications as well as directions for future research are 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".