How do leaders foster morally courageous behavior in employees? Leader role modeling, moral ownership, and felt obligation
Bibliographic record
Abstract
Summary Recent trends in the academic literature indicate growing interest in bottom‐up strategies for addressing workplace misconduct. Scholars argue that leaders may empower employees to speak up and engage in morally courageous behaviors (MCB) when they witness transgressions. In this research, we integrate social cognitive theory and social exchange theory to explain how and when leaders—in their capacities as role models and organizational representatives—promote employee MCB. In Study 1, we find that leader ethical role modeling influences MCB by nurturing employee moral ownership and a sense of obligation to the organization. We show that the path from moral ownership to MCB is stronger for employees with high (versus low) moral efficacy. In Study 2, we find similar results with respect to the roles of safety‐specific moral ownership and felt obligation in explaining how leader safety role modeling influences safety‐related whistleblowing, a specific form of MCB. We also replicate the moderating effect of moral efficacy on the moral ownership—whistleblowing link. However, we find an unexpected negative moderating effect on the felt obligation—whistleblowing link. We discuss the implications of these findings for understanding and promoting morally courageous behaviors in the workplace.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".