Managerial leadership behaviors: A longitudinal investigation of the role of job demands and resources, and implications for managers' own well‐being
Bibliographic record
Abstract
Abstract Although ample research has documented the implications, and organizational drivers, of leadership behaviors, very little research has considered these associations, and their consequences, from the perspective of managers. The present four‐wave longitudinal study addresses this limitation by focusing, using the Job Demands‐Resources model, on the work‐related drivers (job control, recognition, and workload) of transformational, transactional and laissez‐faire leadership behaviors, and the associations between these behaviors and manifestations of managers' psychological well‐being at work (job satisfaction, burnout, and turnover intentions). Analyzing data from 691 high‐level managers (i.e. school principals) using novel random intercept cross‐lagged panel model analyses, our results revealed that higher levels of job control and recognition, and lower levels of workload, predicted higher levels of transformational and transactional leadership behaviors. In contrast, laissez‐faire leadership behaviors were only negatively predicted by recognition. Transformational leadership was associated with the most desirable outcome levels (higher levels of job satisfaction, lower levels of turnover intentions and burnout), followed by transactional and laissez‐faire leadership. Most of these associations were limited to the between‐person‐level, reflecting stable mechanisms of influence, rather than at the within‐person level, suggesting the presence of homeostatic mechanism helping high levels managers to maintain a stable level of functioning over time.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".