The Roles of Transformational Leadership and Collective Turnover on Employee Turnover Decisions
Bibliographic record
Abstract
The present study attempted to address three questions that are part of the ongoing theoretical and empirical research on employee turnover: 1) Does transformational leadership act as a pull-to-stay factor? 2) Can turnover intention predict actual turnover behaviour? and 3) Does collective turnover act as a boundary condition for the link between turnover intentions and turnover behaviours? The data were generated from a survey questionnaire administered to salespeople working at dealerships of a Korean car brand in the Seoul Capital Area. To test our hypotheses, we used the Latent Moderated Structural equation approach. The results show the negative relationship between transformational leadership and turnover intentions and the positive relationship between turnover intentions and turnover behaviour. In addition, the results demonstrate empirical support for turnover contagion as a mechanism for translating turnover intentions into turnover behaviour in the workplace. The successful investigation of these three questions has made timely and novel contributions to the areas of leadership and employee turnover.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".