When fellow customers behave badly: Witness reactions to employee mistreatment by customers.
Bibliographic record
Abstract
In 3 experiments, we examined how customers react after witnessing a fellow customer mistreat an employee. Drawing on the deontic model of justice, we argue that customer mistreatment of employees leads witnesses (i.e., other customers) to leave larger tips, engage in supportive employee-directed behaviors, and evaluate employees more positively (Studies 1 and 2). We also theorize that witnesses develop less positive treatment intentions and more negative retaliatory intentions toward perpetrators, with anger and empathy acting as parallel mediators of our perpetrator- and target-directed outcomes, respectively. In Study 1, we conducted a field experiment that examined real customers' target-directed reactions to witnessed mistreatment in the context of a fast-food restaurant. In Study 2, we replicated Study 1 findings in an online vignette experiment, and extended it by examining more severe mistreatment and perpetrator-directed responses. In Study 3, we demonstrated that employees who respond to mistreatment uncivilly are significantly less likely to receive the positive outcomes found in Studies 1 and 2 than those who respond neutrally. We discuss the implications of our findings for theory and practice. (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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".