Ethical leadership and organizational commitment: the dual perspective of social exchange and empowerment
Bibliographic record
Abstract
Purpose Given recent prominent ethical scandals (e.g. Tesla, Uber) and the increasing demand for ethical management, the importance of business ethics has recently surged. One area that needs further research regards how ethical leaders can foster followers’ organizational commitment. Drawing upon social exchange theory, the current research proposes that ethical leadership relates to follower affective and normative commitment through perceived organizational support (POS). Moreover, based on self-determination theory, we expected follower psychological empowerment to positively moderate the relationship between ethical leadership and commitment components. Design/methodology/approach Data were collected using a three-wave study among employees from multiple organizations (N = 297) in Canada. Structural equations modeling and bootstrapping analyses were applied to test the hypotheses. Findings The results showed that ethical leadership was positively related to follower affective and normative commitment through POS. Furthermore, the relationship between ethical leadership and POS was stronger at high levels of empowerment. This moderating effect extended to the indirect relationship between ethical leadership and commitment components. Originality/value This study counts among the few investigations that have examined the mechanisms linking ethical leadership to followers’ organizational commitment and boundary conditions associated with this relationship. Moreover, our findings were obtained while controlling for transformational leadership, which highlights the incremental validity of ethical leadership.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".