Transformational leadership and autonomy support management behaviors
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the relationships between transformational leadership (TL), autonomy support management behaviors and employees’ psychological health. Design/methodology/approach A total of 512 Canadian workers assessed their immediate supervisor’s level of TL and autonomy support management behaviors. Participants also assessed their own psychological health through measures of psychological well-being and burnout at work. Findings Results from structural equation modeling indicate that TL is related to employee psychological well-being and burnout. This effect is fully mediated by more specific autonomy support and psychological control management behaviors. These results suggest that autonomy support and psychological control management behaviors may have a more proximal effect on employees’ psychological health than TL does. Also, managers’ leadership and behaviors appear to better predict employees’ psychological well-being at work than employee burnout. Practical implications Managers with a TL style employ more autonomy support and fewer psychological control behaviors, which makes employees happier and less burned out. Based on these results, leadership training programs would gain to focus on the development of more specific management behaviors among leaders, such as autonomy support, to enhance employees’ psychological health, especially their well-being. Originality/value This research expands understanding of the relationship between TL and the psychological health of employees by shedding light on the mediating role of autonomy support management behaviors in this relationship.
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".