The rationalizing animal: moral disengagement and ethical decision making
Bibliographic record
Abstract
Purpose Research in behavioral ethics has focused on factors allowing individuals to act unethically without feeling distress. Moral disengagement refers to the process by which we persuade ourselves that ethical standards do not apply in a particular situation. It consists of eight interrelated self-serving biases. This paper aims to examine the implications of moral disengagement for ethical decision-making in organizational contexts. Design/methodology/approach Two experimental studies evaluated whether the influence of moral disengagement is amenable to change. In Study 1 (N = 55), a direct approach was used, in which individuals were trained to recognize moral disengagement openings in everyday ethical scenarios. Study 2 (N = 501) explored an indirect, low-pressure approach that primed an individualist or collectivist mindset. Study 3 (N = 462) involved a survey to test a theoretical framework examining the influence of situational factors and individual differences on ethical decision-making. Findings In Study 1, despite practicing identifying moral disengagement “windows” in real-life scenarios, self-assessed vulnerability to moral disengagement remained unaffected. In Study 2, inducing an individualist mindset fostered a greater reduction in moral disengagement than a collectivist mindset (particularly amongst female participants), but the change in moral disengagement was insignificant. The results of Study 3 highlight the potential to foster ethical culture change in organizations by focusing on the environmental factors that shape our behavior in different contexts. Practical implications Interventions to mitigate self-serving biases that impede accurate evaluations of our actions in an organizational context are considered. Originality/value We are not the rational animals that we would like to think we are. The human brain appears to be prewired for self-justification and for reducing dissonance. Moreover, changing our beliefs about our behavior is difficult, and changing those beliefs for the worse may be impossible. However, by fostering a culture of consideration and respect, organizations can improve the ethicality of decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".