Emotional intelligence of leaders: a profile of top executives
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the emotional intelligence (EI) scores of two high profile executive groups in comparison with the general population. Also the study aims to investigate the executive group's EI scores in relation to various organizational outcomes such as net profit, growth management, and employee management and retention. Design/methodology/approach The Emotional Quotient Inventory (EQ‐i) was administered to a sample of 186 executives (159 males and 27 females) belonging to one of two executive mentoring associations, the Young Presidents' Organization (YPO) and the Innovators' Alliance (IA). A series of questions relating to pre‐tax operating profits over the past three years, previous year's net profit, and various business challenges were asked of each executive. Findings The results showed that top executives differed significantly from the normative population on the EQ‐i in eight of the 15 EQ‐i subscales. Executives who possessed higher levels of empathy, self‐regard, reality testing, and problem solving were more likely to yield high profit‐earning companies, while Total EQ‐i was related to the degree to which a challenge was perceived as being easy with respect to managing growth, managing others, and training and retaining employees. Practical implications The findings enable researchers and practitioners to better understand what leadership differences and similarities exist at various organizational levels. These profiles further aid in human resource initiatives such as leadership development and personnel selection. Originality/value Despite empirical evidence supporting the relationship between EI and leadership, research with high‐level leadership samples is relatively sparse. The study examines EI in relation to two unique, yet high functioning executive groups, which will enable further exploration into the emotional and psychological structure of these high‐performing groups.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".