Power motives, personality correlates, and leadership outcomes: A person‐centered approach
Bibliographic record
Abstract
OBJECTIVE: We investigated how these motivations combined within individuals to form unique profiles, and how these different profiles relate to personality traits and team behaviors. BACKGROUND: Dominance, prestige, and leadership motives each play a key role in shaping social success or failure in gaining social rank and influence. METHOD: = 466) to identify profile configurations and how such profiles related to important outcomes. RESULTS: We identified qualitatively distinct profiles: ultra-dominance profile (prominent dominance motive with high prestige and leadership motives); prestigious leadership profile (moderately high prestige and leadership motives, low dominance motive); and weak social power motive profile (low on all three motives). Individuals with the prestigious leadership profile were more likely to emerge as leaders, compared to those with a weak social power motive profile. People with an ultra-dominance profile scored higher on narcissism and tended to perceive themselves as leaders, despite not being deemed more leader-like by teammates. CONCLUSION: Using a person-centered approach allowed us to identify three power motive profiles across independent samples and generate insights into how these profiles manifest different social behaviors and outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".