The effect of emotional intelligence on organizational commitment: Understanding the mediating role of job satisfaction
Bibliographic record
Abstract
This study aims to analyze how emotional intelligence (EI) influences organizational commitment along with the correlation between job satisfaction and these two aspects. To collect the data for this study, a polite and pre-validated, self-structured questionnaire was used. Additionally, ethical issues were considered with the assurance of anonymity. The study also took the convenience sampling approach and collected samples from customer service employees working in all main branches of Saudi banks located in Riyadh. It further employs the structural equation modeling method for analyzing the data with AMOS 22.0 software. Before examining the structural model framework and hypotheses, a confirmatory factor analysis was used to estimate the measurement model and support the research. Results showed that emotional intelligence affects both job satisfaction and organizational commitment significantly and positively. Moreover, results showed that job satisfaction, as a mediator, has a significant indirect impact on EI and organizational commitment. Emotionally intelligent customer service employees of Saudi commercial banks demonstrated high psychological empowerment visible through their perception of work as meaningful, increased feeling of competence, guaranteed freedom of choice, and significant impact on 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 | 0.001 | 0.004 |
| 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 | 0.002 | 0.001 |
| 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".