What Makes a Leader? An Investigation into the Relationship between Leader Emergence and Effectiveness
Bibliographic record
Abstract
Are the traits that predict leadership emergence the same that predict leadership effectiveness? How do leader attributes play either a direct or dynamic role in predicting organisational outcomes? This paper presents an investigation into the personality-performance relationship to address these questions. 936 general population and 198 senior leadership participants took the High Potential Trait Indicator (HPTI). The first part examined how levels of personality traits differentiated leaders from a general population. The second examined the relationship between leader personality, competencies and organizational success to assess whether the traits of leader emergence are the same as effectiveness. Additionally, this section simultaneously examined the role of leader traits and attributes play in predicting organizational performance. The results indicated that all six HPTI traits were associated with leader emergence. However, only three—Adjustment, Risk Approach, and Ambiguity Acceptance—were also positively predictive of leadership effectiveness. Curiosity showed mixed benefits for leaders, whilst Conscientiousness and Competitiveness did not differentiative leader effectiveness. Additionally, leader attributes were found not to predict performance. This paper offers novel insight into the important role of personality in distinguishing leader emergence as well as leader effectiveness. Implications are discussed in relation to models of leadership and potential, as well as for practice include identifying high potential talent and diversity in psychometric testing.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".