Unraveling the mentor-mentee knot in entrepreneurship: narcissism, emotional intelligence, and dyadic configurations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines how narcissism, a common personality trait among entrepreneurs, interacts with emotional intelligence in mentoring relationships to facilitate learning outcomes for mentees. Focusing on three emotional intelligence components (self-emotion appraisal, others’ emotion appraisal, and emotion regulation), data from 127 entrepreneurial mentoring dyads were analyzed. Necessary Condition Analysis revealed that for medium learning, only others’ emotion appraisal, trust, and mentor functions were necessary conditions, while self-emotion appraisal and emotion regulation were necessary for the highest learning levels. Fuzzy-set Qualitative Comparative Analysis identified three configurations for high mentee learning: two highlighting the importance of mentors’ emotional intelligence when highly narcissistic, and one emphasizing some mentor emotional intelligence components when low in narcissism but tolerating the absence of others’ emotional appraisal. Findings underscore beneficial personality configurations in entrepreneurial mentoring, guiding strategically pairing mentors and mentees based on their characteristics.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it