Ethical Leadership and Team Ethical Voice and Citizenship Behavior in the Military: The Roles of Team Moral Efficacy and Ethical Climate
Bibliographic record
Abstract
In recent years, unethical conduct (e.g., Enron, Lehman Brothers, Oxfam, Volkswagen) has become an important issue in management; relatedly, there is growing interest regarding the nature and implications of ethical leadership. Drawing from social learning theory, we posited that ethical leadership would positively relate to team ethical voice and organizational citizenship behavior (OCB) through team moral efficacy. Furthermore, building on social information processing theory and the social intuitionist model, we expected these effects to be accentuated in teams with a strong ethical climate. Using survey data from subordinates and leaders pertaining to 150 teams from the Republic of Korea Army, ethical leadership was found to indirectly relate to increased team ethical voice and OCB directed at individuals and the organization through team moral efficacy. These relationships tended to be amplified among teams with a strong ethical climate. In addition, these findings persisted while controlling for transformational leadership, thereby highlighting the incremental value of ethical leadership for team outcomes. Theoretical and practical implications are discussed.
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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 | 0.003 | 0.009 |
| 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 | 0.000 | 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".