Emotional Intelligence and Career Outcomes: Evidence from Lebanese Banks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this research study is to investigate the relationship among emotional intelligence components with career commitment and turnover intention. Several hypotheses were tested based on a sample set of 273 senior managers working in Lebanese banks. The findings show that there is a positive and significant relationship between self‐emotion appraisal, others' emotion appraisal, regulation of emotions, use of emotion and career commitment. There is negative and significant relationship between self‐emotion appraisal, others' emotion appraisal, regulation of emotions, use of emotion and turnover intention. The main implication of the study highlights that self‐emotion appraisal is the most important predictor of career commitment and turnover intention followed by other's emotional appraisal, regulation of emotions, and use of emotions, respectively. Ultimately, all emotional intelligence components are important in determining career commitment and turnover intention within this research setting. Implications for practitioners and researchers are also offered. Copyright © 2017 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it