Impact of Big Five personality traits on authentic leadership
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
Purpose The bulk of the current research on authentic leadership focuses on the examination of its consequences. Little attention has been paid to the predictors of authentic leadership. We examined how the Big Five personality traits can predict an authentic leadership style. Design/methodology/approach Using multisource time-lagged data from 305 leader–subordinate dyads, we examined how the Big Five traits (extraversion, agreeableness, consciousness, openness to experience and neuroticism) are related to authentic leadership. While leader personality was measured through self-reports, we measured authentic leadership style through subordinate reported data. Findings We found good support for the proposed hypotheses. While extraversion, agreeableness, conscientiousness and openness to experience were positively related to authentic leadership style, neuroticism was negatively related to it. Practical implications The findings support the trait view of leadership, suggesting that the personality traits of a leader can predict his/her authentic leadership style. These findings hold promise for managers in that they can use personality inventories and tests in the selection and evaluation process to select and train potential authentic leaders. Originality/value We proposed a unique idea and tested it using leader–subordinate dyadic data that are time-lagged to test our hypotheses.
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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 | 0.001 | 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) | 0.014 | 0.001 |
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