Authentic leadership and turnover intention: does organizational size matter?
Bibliographic record
Abstract
Purpose The purpose of this paper is to address two of the ongoing theoretical and empirical questions in the literature on authentic leadership: does authentic leadership negatively influence employees’ turnover intentions by enhancing their levels of affective commitment? and does organizational size act as a boundary condition for the mediating effects of affective commitment in the link between authentic leadership and turnover intentions? Design/methodology/approach A moderated mediation model of the affective process linking authentic leadership and turnover intentions was developed and tested on a sample of 375 employees working in South Korean firms. Findings The negative indirect effects of authentic leadership on employees’ turnover intentions through changes in the levels of affective commitment were significant. Furthermore, the mediated relationship between authentic leadership and turnover intentions via employees’ levels of affective commitment was stronger in smaller organizations. Practical implications The findings of the present study may be useful to practitioners who are interested in employee retention. They can mitigate against turnover intentions by enhancing the authentic leadership qualities of the managers. More specifically, this approach is especially ideal for small organizations. Originality/value This study corroborates the results of previous studies in highlighting the pervasive effects of authentic leadership on turnover intentions via enhancing employees’ levels of affective commitment. Moreover, organizational size as a boundary condition for the aforementioned relationship was empirically examined.
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 | 0.003 | 0.011 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".