Unpacking the Relationship Between Customer (In)Justice and Employee Turnover Outcomes: Can Fair Supervisor Treatment Reduce Employees’ Emotional Turmoil?
Bibliographic record
Abstract
Service employees can experience considerable resource demands from customers and supervisors in their day-to-day work. Guided by the conservation of resources (COR) perspective and organizational justice research, we explored the relationship between interpersonal injustice (e.g., being treated with low dignity and respect) by customers and employee turnover (e.g., voluntary turnover, turnover intentions). Specifically, we proposed that customer interpersonal injustice relates positively to employee turnover outcomes through a process first involving employee experiences of negative emotions, and second, employee emotional exhaustion. We also examined whether supervisor interpersonal justice mitigates this process by providing emotional resources that buffer the demands of customer interpersonal injustice. We evaluated these predictions in a programmatic series of three complementary field studies involving retail employees (Study 1, N = 263), restaurant employees (Study 2, N = 206), and contact center employees (Study 3, N = 317). The results showed that (a) customer interpersonal injustice relates positively to employees’ negative emotions, (b) employee negative emotions are positively associated with emotional exhaustion, and (c) emotional exhaustion relates to higher employee turnover outcomes. Our results also show that the indirect effect of customer interpersonal injustice on employee turnover intentions (Study 2) and voluntary turnover (Study 3) is weaker when employees perceive more (vs. less) supervisor interpersonal justice. Theoretical and practical implications are discussed.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".