Do Moral Disengagers Feel Guilt and Shame? Moral Self-Condemnation Following Immoral Work Behaviors
Bibliographic record
Abstract
In the past decade, a number of studies have found a consistent, positive link between moral disengagement (Bandura, 1986) and immoral behaviors in the workplace. According to Bandura, this occurs because moral disengagement minimizes one’s anticipation of moral self- condemnation - feelings of guilt and shame that normally arise when one contemplates wrongdoing. Although research shows that moral disengagement is negatively associated with anticipatory guilt, scholars rarely consider its impact on self-condemning moral emotions after wrongdoing. In this paper, we examine the relationship between moral disengagement and experiences of guilt and shame following three types of immoral work behaviors - interpersonal deviance, (low) interpersonal citizenship behaviors (OCBI), and (low) cooperation. Across two studies using mixed methods, we find that moral disengagers experience guilt and shame following interpersonal deviance, (low) cooperation, and objectively measured cheating, but not (low) OCBI. In a third study, we show that moral disengagers exhibit greater psychological distress, turnover intentions, social loafing, and lower task performance as a result of their guilt and shame. We discuss the implications of these findings with respect to advancing moral disengagement and moral emotions theory in organizational scholarship.
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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 | 0.001 | 0.011 |
| 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 | 0.001 |
| 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".