Organizational Moral Disengagement: Suspending Morality at Work
Bibliographic record
Abstract
While individual’s wrongdoing through mechanisms of moral disengagement has grown in study, limited attention has been paid to collective moral disengagement. Although this has been mentioned in relation to well-known cases of corporate scandals and harmful industry practices and products, a distinct concept of organizational moral disengagement has not previously been theoretically developed or empirically operationalized. This is what we aim to do in this paper. Drawing on Bandura’s moral agency theory, we conceptually define organizational moral disengagement (OrgMD) as individual’s perception of the organizational suspension of morality which justifies and exonerates unethical activities. Using three studies, we develop and test the concept of OrgMD, devising a valid measure with demonstrable conceptual and empirical distinctiveness from personal moral disengagement, and showing a specific relationship with unethical pro-organizational conduct. Study 1 and Study 2 followed a two-wave design with the participation of UK employees (N=301 and N=297 respectively). Study 3 followed a cross-sectional design with the participation of 297 Italian employees. OrgMD is confirmed as unidimensional, with all the items loading onto one latent dimension (Hypothesis 1). OrgMD, although correlated, is different from personal moral disengagement (Hypothesis 2), and found to better predict unethical pro-organizational behavior (Hypothesis 3), compared to personal MD. We also show cross-cultural invariance of OrgMD based on data from two countries. Overall, in this paper we advance understanding of the role of perceptions of organizational disengagement mechanisms which justify malpractices and valorize unethical activities as permissible or even desirable.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".