Do moral disengagers experience guilt following workplace misconduct? Consequences for emotional exhaustion and task performance
Bibliographic record
Abstract
Summary According to Bandura, moral disengagement facilitates misconduct by minimizing feelings of guilt that normally arise when one contemplates wrongdoing. While trait moral disengagement has been negatively associated with anticipatory guilt, scholars have yet to fully consider its impact on guilt post ‐ misconduct. In this paper, we examine the indirect effects of trait moral disengagement on post‐misconduct guilt, and downstream effects on employees' mental health and performance. Lastly, we explore the moderating role of post‐misconduct state moral disengagement in shaping the effects of trait moral disengagement. Across three studies, we find that trait moral disengagement is positively linked to guilt following interpersonal deviance, unethical work behavior, and objective cheating behavior. Further, trait moral disengagement is indirectly, positively linked to emotional exhaustion and negatively related to executive function (specifically, the capacity to inhibit distraction during tasks). In a fourth study, we find that trait moral disengagement is positively associated with guilt and subsequent emotional exhaustion when individuals employ little to no state moral disengagement immediately post‐misconduct. In contrast, trait moral disengagement is negatively linked to guilt and emotional exhaustion when individuals employ state moral disengagement post‐misconduct. We discuss the implications of these findings for advancing moral disengagement theory and research.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".