The influence of role models and entrepreneurial support on the business start-up intentions of women and men
Bibliographic record
Abstract
This research examines how role models and entrepreneurial support distinctly affect the entrepreneurial intention of female vs male students in five universities located in South and Central America. Moreover, it examines how gender is related to variables such as entrepreneurial exposure, capability, intention, and subjective norms. We surveyed 1,213 undergraduate students in business-related fields using a correlational, non-experimental, cross-sectional design. We conducted an univariate and bivariate descriptive statistical analysis, followed by structural equation modeling (SEM) to assess various correlations and causal relationships. Our findings indicate that factors influencing entrepreneurial intention may differ between female and male students. Specifically, exposure to support services and role models may affect the entrepreneurial intention of female students differently than male students. Additionally, perceived behavioral control and subjective norms may not be positively correlated with entrepreneurial intention for either gender. This study provides insights into how entrepreneurial knowledge, capabilities, subjective norms, and intentions vary by gender in Latin American countries, contributing to the existing literature. It also offers gender-sensitive managerial recommendations to promote entrepreneurship in the region.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".