Relative Importance of Emotional Intelligence’s Dimensions in Contributing to Dimensions of Job Performance
Bibliographic record
Abstract
Career in service industry is emotional labor intensive, which turns performance of the employees into undesired status who are not emotionally intelligent. To put light on this issue the present study scrutinizes the significant contributor from four dimensions of emotional intelligence to three dimensions of job performance individually as well jointly. Data gathered from 292 bankers through instrument adopted from literature, regression results revealed that self emotional appraisal, others emotional appraisal, regulation of emotions and use of emotions significantly contribute task performance, counterproductive work behaviors and organizational citizenship behaviors individually as well as jointly. The use of emotions remained significant when included with other dimensions of emotional intelligence in the hierarchical regressions model after controlling for age and gender, whereas regulation of emotions lost its significance. In organizational citizenship behaviors maximum variation was observed due to emotional intelligence’s dimensions as compare to the other dimensions of job performance. Banks’ management could use these findings for recruitment, training and promotions of employees. Limitations and future suggestions are presented in later part.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".