Do They Have your Back or Hang you out to Dry? Exploring Supervisors’ Role in Customer Incivility
Bibliographic record
Abstract
Workers in the customer service industry must frequently interact with customers who treat them in a dismissive, discourteous, or demeaning manner. These “uncivil service encounters” can provoke stress and work withdrawal. Through interviews with 84 frontline employees (FLEs) across a variety of service organizations, we develop new theory about the role that supervisors can play within these interactions. We identify five supervisor intervention behaviors that differ based on whose interests they prioritize (the worker’s and/or the customer’s), as well as whether they are primarily active or passive. These interventions in turn yield distinct emotional and cognitive outcomes for FLEs, as well as for the quality of their relationship with their supervisor. We additionally develop the concept of uncivil customer self-efficacy, which captures workers’ comfort and willingness to positively take charge of an interaction with an uncivil customer. By explaining how each supervisor intervention can affect the development of uncivil customer self-efficacy, we provide insight into how to protect FLEs from the damaging effects of customer incivility. Our findings contribute to the nascent literatures on the role of supervisors in helping FLEs cope with customer incivility, and on the constructive outcomes of workplace mistreatment. We also outline actionable recommendations for service organizations.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".