The Compassion Advantage: Leaders Who Care Outperform Leaders Who Share Followers’ Emotions
Bibliographic record
Abstract
Effective management of negative follower emotions in the workplace is critical for organizational success, yet how leaders can do this effectively is unclear. In 9 studies (combined N = 4,434 leaders, N = 1,006 followers), we introduce here the Compassion-Emotion Sharing Leader Focus task, a situational-judgment task to index leaders' tendency to focus on sharing emotion (emotion sharing) or caring (compassion) when engaging with negative follower emotions. In Studies 1-5 we provide evidence for the construct, predictive, and incremental validity of our task, highlighting the practical implications of a compassion focus for leader well-being. In Study 6 we leverage a diverse global sample of over 2,000 leaders and more than a thousand followers to show that a leader's compassion focus benefits not only the leader but their followers as well. In Studies 7, 8, and 9, we revised the task to improve content validity, but this traded off predictive validity. Our research suggests that training programs for leaders should emphasize a compassion focus to engage wisely with negative emotions, and ensure this focus is communicated to followers. These findings have important implications for current issues in management, including the growing importance of employee well-being in the workplace.
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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 | 0.004 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".