From burnout to behavior: the dark side of emotional intelligence on optimal functioning across three managerial levels
Bibliographic record
Abstract
Introduction: Burnout has been typically addressed as an outcome and indicator of employee malfunctioning due to its profound effects on the organization, its members, and its profitability. Our study assesses its potential as a predictor, delving into how different sources of motivation-autonomous and controlled-act as mediational mechanisms in the association between burnout and behavioral dimensions of functioning (namely, organizational citizenship behaviors and work misbehaviors). Furthermore, the buffering effects of emotional intelligence across three different managerial levels were also examined. Methods: To this end, a total non-targeted sample of 840 Romanian managers (513 first-, 220 mid-, and 107 top-level managers) was obtained. Results: Burnout predicted motivation, which predicted work behaviors in a moderated-mediation framework. Contrary to our initial prediction, emotional intelligence augmented the negative association between burnout and motivation, exhibiting a dark side to this intelligence type. These findings are nuanced by the three managerial positions and shed light on the subtle differences across supervisory levels. Discussion: The current article suggests a relationship between multiple dimensions of optimal (mal)functioning and discusses valuable theoretical and practical insights, supporting future researchers and practitioners in designing burnout, motivation, and emotional intelligence interventions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".