Is turnover contagious? The impact of transformational leadership and collective turnover on employee turnover decisions
Bibliographic record
Abstract
Purpose This study addresses three research questions related to employee turnover: (1) does transformational leadership act as a pull-to-stay factor for employees? (2) How well does turnover intention predict actual turnover behavior? (3) Does collective turnover moderate the link between turnover intentions and turnover behaviors? Design/methodology/approach Latent moderated structural equation modeling was employed with longitudinal and multi-source data from car dealerships located in the Seoul Capital Area, South Korea. Findings The results indicate a negative relationship between transformational leadership and turnover intentions and a positive relationship between turnover intentions and turnover behavior. Furthermore, the results provide empirical support for turnover contagion as a mechanism triggering turnover intentions into turnover behavior in the workplace. Originality/value This study provides a timely and novel contribution to the areas of leadership and employee turnover due to the underexplored research area of transformational leadership, the growing body of literature that questions the fixed assumption in employee turnover studies and the increasing interest in collective turnover. Importantly, existing research has examined the concept of collective turnover from a quantity perspective, aggregating individual turnover to group levels. This study provides a more nuanced, comprehensive evaluation of the quality of turnover, by considering the impact of performance contribution aspects of turnover at the business unit level.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".