“Killing them with kindness”? A study of service employees' responses to uncivil customers
Bibliographic record
Abstract
Summary Experiencing uncivil customers is a frequent reality for many people working in the service industry. Past research has established that dealing with uncivil customers can be distressing for employees and can sometimes lead them to engage in reciprocal, discourteous behavior. The purpose of our research is to delve deeper into the experience of customer incivility from the perspective of service employees in order to better understand the various ways in which they respond to customer incivility. We conducted 64 interviews with service employees across an array of occupations and developed a typology of responses to customer incivility. These responses fell into four categories based on the extent to which service employees' actions were intended to promote social harmony (and therefore could broadly be considered civil or uncivil), as well as their perceived agency in the situation. We describe how each response was associated with different interpersonal and intrapersonal consequences and explain the implications of our typology for management theory and practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".