Ethical Leadership and Team Ethical Voice and Citizenship Behavior in the Military: The Roles of Team Moral Efficacy and Ethical Climate
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it