Leaders’ emotional labour and abusive supervision: The moderating role of mindfulness
Bibliographic record
Abstract
In this study, we examine how leaders' emotional labour strategies (surface acting and deep acting) deplete leaders' self-control resources to predict abusive supervision, in addition to the moderating role of leader mindfulness. Integrating ego-depletion theory and emotion regulation theory, we hypothesise that deep acting and surface acting predict higher levels of abusive supervision, which is mediated by reduced self-control. Furthermore, we predict that leaders' trait mindfulness moderates the relationship between emotional labour and self-control on abusive supervision. Results from a three-wave study of leader-follower dyads supported mediation hypotheses; both deep and surface acting predicted abusive supervision, which is mediated by reduced self-control. Our moderated mediation hypotheses were supported for deep acting but not surface acting. This research contributes to the literature by demonstrating the depleting nature of emotional labour in leadership and the importance of leader mindfulness as a boundary condition that can make deep acting less harmful for leader behaviour.
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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 | 0.001 | 0.009 |
| 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 | 0.001 |
| 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".