Does Managers’ Emotion Regulation in Leading Employees Help or Hurt?
Bibliographic record
Abstract
The more than two decades of scholarly research on emotional labor has been skewed towards service workers with the dominant view that engagement in emotional regulation impairs service workers’ well-being outcomes. Despite the potential knowledge gain of examining the role managerial emotion regulation plays in leadership of employees, the call for further study of its effect in managerial and leadership contexts has largely been unattended. Based on the findings of two studies, this research reports the interpersonal benefits of leader deep acting on employee outcomes without adversely affecting leaders’ within-person well-being. Using self-reported survey data (n = 175) collected from the employees of a mid-sized, reputable financial company, the findings from study 1 suggest that employee perception of leader deep acting was favorably related to employee job satisfaction and perceived leadership effectiveness; these relationships were mediated by employee perception of leader authenticity. In study 2, using managers of the same financial company as a sample (n = 81), diary data (experience sampling from 5 consecutive days) showed a significant positive relationship between leader surface acting and emotional exhaustion, with the relationship being mediated by daily emotional dissonance. However, as predicted, daily leader deep acting was not found to be related to leader’s daily well-being outcomes of emotional dissonance and emotional exhaustion. Combined, our two studies demonstrate that managers’ use of deep acting has the potential to favorably impact employee outcomes without impairing their (managers’) well-being.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".