Effects of Leader Conscientiousness and Ethical Leadership on Employee Turnover Intention: The Mediating Role of Individual Ethical Climate and Emotional Exhaustion
Bibliographic record
Abstract
Employees working under conscientious leadership perceive their leaders as ethical leaders. This study investigates the conscientiousness of leaders as an essential trait of ethical leadership and the relationship between ethical leadership and employee-turnover intention. Additionally, we study the potential mediating roles of the individual-level ethical climate (self-interest, friendship, and personal morality) as well as the level of employees' emotional exhaustion that contribute to the decision-making process of turnover intention. Building on social learning and social exchange theories, outcomes from nine industrial manufacturing organizations comprising 260 subordinates' responses show that leaders' conscientiousness is positively related to ethical leadership and negatively associated with employees' turnover intention. Consistent with this hypotheses, results found that, in an individual-level ethical climate, employees experience diminished emotional exhaustion. The relationships are found to mediate between ethical leadership and turnover intention in manufacturing organizations. Additionally, it was also found that individual-level ethical climates cause a relatively positive impact on employees' emotional exhaustion leading them to lower turnover intention.
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.
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 | 0.002 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".