Sticks and stones can break my bones but words can also hurt me: The relationship between customer verbal aggression and employee incivility.
Bibliographic record
Abstract
Customer service employees tend to react negatively to customer incivility by demonstrating incivility in return, thereby likely reducing customer service quality. Research, however, has yet to uncover precisely what customers do that results in employee incivility. Through transcript and computerized text analysis in a multilevel, multisource, mixed-method field study of customer service events (N = 434 events), we found that employee incivility can occur as a function of customer (a) aggressive words, (b) second-person pronoun use (e.g., you, your), (c) interruptions, and (d) positive emotion words. First, the positive association between customer aggressive words and employee incivility was more pronounced when the verbal aggression included second-person pronouns, which we label targeted aggression. Second, we observed a 2-way interaction between targeted aggression and customer interruptions such that employees demonstrated more incivility when targeted customer verbal aggression was accompanied by more (vs. fewer) interruptions. Third, this 2-way interaction predicting employee incivility was attenuated when customers used positive emotion words. Our results support a resource-based explanation, suggesting that customer verbal aggression consumes employee resources potentially leading to self-regulation failure, whereas positive emotion words from customers can help replenish employee resources that support self-regulation. The present study highlights the advantages of examining what occurs within customer-employee interactions to gain insight into employee reactions to customer incivility. (PsycINFO Database Record
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".