The Influence of Personality Domains and Working Experience in Peruvian Managers’ Leadership Styles: An Initial Study
Bibliographic record
Abstract
The purpose of this quantitative investigation is to examine the relationships that may exist among the five personality domains, working experience, and the three leadership styles in a sample of 500 managerial Master of Business Administration (MBA) students of a leading business school in Peru. Similar studies have previously been performed in developed countries; well-known examples come from the United States of America, Norway, Germany, Australia, Canada, and Singapore, but no such studies are found in a developing country. The Neuroticism Extraversion Openness to Experience Personality Inventory Revised (NEO-PI-R) and Multifactor Leadership Questionnaire (MLQ) were the instruments used for personality and leadership, respectively. In this sample, conscientiousness demonstrates the strongest and most consistent correlation with transformational (.426 ), transactional (.43 0), and passive-avoidant (-.354 ) leadership styles. Extraversion has the next highest correlation with transformational (.400) leadership styles and a weak correlation with transactional (.152) and passive-avoidant (-.166) leadership styles. Agreeableness has no significant correlation with any of the leadership styles, and openness to experience shows a significant correlation only to transformational (.201) leadership styles. Neuroticism shows weak correlations with transformational (-.214 ) and passive-avoidant (.26 7) leadership styles. Conscientiousness and extraversion may encourage individuals to emerge as leaders. Transformational and transactional leadership behaviors are demonstrated more frequently with increasing working experience.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".