Exploring the effects of individual customer incivility encounters on employee incivility: The moderating roles of entity (in)civility and negative affectivity.
Bibliographic record
Abstract
Incivility between customers and employees is common in many service organizations. These encounters can have negative outcomes for employees, customers, and the organization. To date, researchers have tended to study incivility as an aggregated and accumulated phenomenon (entity perspective). In the present study, we examined incivility as it occurs during a specific service encounter (event perspective) alongside the entity perspective. Using a mixed-method multilevel field study of customer service interactions, we show that individual customer incivility encounters (i.e., events) trigger employee incivility as a function of the employee's overall accumulated impression of the (in)civility in his or her customer interactions, such that the effects are more pronounced among employees who generally perceive their customer interactions to be more versus less civil. We also find that these interactive effects occur only among employees who are lower (vs. higher) in negative affectivity. Our results show that, in order to expand the understanding of customer incivility, it is important to study the incivility encounter, the social context in which negative customer interactions occur, and individual differences.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".