Is being a leader a mixed blessing? A dual‐pathway model linking leadership role occupancy to well‐being
Bibliographic record
Abstract
Summary Recent leadership research has drawn greater attention to how the well‐being of leaders influences leadership behaviors, follower performance and well‐being, and overall leadership effectiveness. Yet little attention has been paid to the relationship between occupying leadership positions and job incumbents' well‐being. This research addresses this question by developing and testing a dual‐pathway model. Our model proposes that incumbency in leadership positions is positively related to high levels of both job demands and job control, whereas job demands and job control have offsetting effects on well‐being. Results based on a longitudinal sample revealed that employees who transitioned from nonleadership positions to leadership roles showed trajectories of increasing job demands and job control, whereas such trends were weaker among those who remained in nonleadership positions. Findings from three additional samples generally demonstrated that leadership role occupancy was indirectly related to various indices of psychological and physiological well‐being through job demands and job control. Because the signs of the indirect effects through job demands and job control differed in expected ways, the overall relationship between leadership role occupancy and the well‐being outcomes was generally small and nonsignificant. We discuss research and practical implications of our framework and findings for organizations, employees, and leaders.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".