Exploring the relationship between emotional intelligence and workplace performance: a cross-cultural study
Bibliographic record
Abstract
A persistent problem in organizational psychology is understanding why some employees consistently outperform others who possess comparable technical skills and cognitive ability. Emotional intelligence the capacity to perceive, manage, and effectively use emotions in oneself and others has been proposed as a key differentiating factor, yet cross-cultural evidence remains fragmented. This research investigated the relationship between emotional intelligence and workplace performance across five culturally distinct professional populations drawn from Italy, Japan, Brazil, Nigeria, and Canada. A total of 412 mid-career professionals completed the Wong and Law Emotional Intelligence Scale alongside supervisor-rated performance evaluations at two Italian research institutions between September 2018 and March 2020. The sample included 214 males and 198 females aged 28 to 54 years. Results indicated a moderate positive correlation between total emotional intelligence scores and overall workplace performance (r = 0.43, p
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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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".