The Impact of Emotional Intelligence on Job Satisfaction: An Empirical Study on University Administrative Staff Members
Bibliographic record
Abstract
The main purpose of the study is to empirically assess the impact of Emotional Intelligence (EI) dimensions (self-awareness, social awareness, self-management, and relationship management) on each individual Job Satisfaction (JS) dimension (working conditions, supervision, co-workers, job security and pay and promotion) of administrative staff members at a public Egyptian university. The study considers EI as a multidimensional variable and investigates the impact of these dimensions on the JS dimensions. The study was conducted in a public university in Cairo, Egypt. The data was collected using questionnaires distributed to administrative staff members. The sample includes 361 responses. The results showed that there is a partial influence of the independent variable (EI dimensions) on the dependent variable (JS dimensions). The theoretical and practical implications of the study, the limitations and future research opportunities are also listed.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".