Leadership styles, emotion regulation, and burnout.
Bibliographic record
Abstract
This study investigated the potential impact of leadership style on leaders' emotional regulation strategies and burnout. Drawing on the full-range model of leadership and Conservation of Resources (COR) theory, we tested whether transformational, contingent reward, management by exception-active and -passive, or laissez-faire leadership exert direct effects on leaders' reported use of surface acting, deep acting, and genuine emotion. In turn, we hypothesized and tested the indirect effect of leadership on burnout through surface acting. Three waves of data from 205 leaders were analyzed using OLS regression. Transformational leadership predicted deep acting and genuine emotion. Contingent reward predicted both surface and deep acting. Management by exception-active and -passive predicted surface acting, and laissez faire predicted genuine emotion. The indirect effects of management by exception-active and -passive on burnout through surface acting were not significant. Indirect effects of transformational leadership and laissez-faire on burnout through genuine emotion, however, were significant. This study provides empirical evidence for the hypothesized relationships between leadership style, emotion regulation, and burnout, and provides the basis for future research and theory building on this topic.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".