Contextual, interpersonal, and personal predictors of young adults' affective-identity motivation to lead
Bibliographic record
Abstract
Purpose Why some people are motivated to become leaders is important both conceptually and practically. Motivation to lead compels people to seek out leadership roles and is a distinct predictor of leader role occupancy. The goal of our research is to determine contextual (socioeconomic status and parenting quality), interpersonal (sociometric status), and personal (self-esteem and gender) antecedents of the motivation to lead among young adults. Design/methodology/approach The authors tested the model using two samples of Canadian undergraduate students (Sample 1: N = 174, M age = 20.02 years, 83% female; Sample 2: N = 217, M age = 18.8 years, 54% female). The authors tested the proposed measurement model using the first sample, and tested the hypothesized structural model using the second sample. Findings The proposed 5-factor measurement model provided an excellent fit to the data. The hypothesized model also provided a good fit to the data after controlling for potential threats from endogeneity. In addition, gender moderated the relationship between sociometric status and affective-identity motivation to lead, such that this interaction was significant for females but not males. Practical implications The findings make a practical contribution in understanding how parents, teachers, and organizations can encourage greater motivation to lead, especially among young adults who have faced poverty and marginalization and tend to be excluded from leadership positions in organizations. Originality/value The authors conceptualize and test the contextual, interpersonal, and personal predictors of affective-identity motivation to lead among young adults.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".