Testing the Competing Models of Gender and Creativity on Entrepreneurial Self-Efficacy
Bibliographic record
Abstract
The following thesis adapts Schlaegel and Koenig’s integrated model of entrepreneurial intent (2014) to answer the question: “Why are some people and not others able to discover and exploit particular entrepreneurial opportunities?” (Mitchell et al, 2002). Based on past research utilizing the social role theory and the social cognitive theory, it is proposed that gender and self-perceptions of creativity have a role in determining entrepreneurial self-efficacy. The research aims to determine what happens to entrepreneurial self-efficacy when gender and creativity interact. Survey data is collected from a sample of 184 respondents across Canada and the United States through the Ted Rogers School of Management’s student research pool as well as Amazon Mechanical Turk. A hierarchical linear regression is used to analyze the data. Results from statistical analyses indicated that creativity is very strongly related to entrepreneurial self-efficacy, with gender having an unexpectedly low correlation and magnitude. The results indicate that the relationship between gender and entrepreneurial self-efficacy was not as strong as the relationship between self-perceptions of creativity and entrepreneurial self-efficacy. These findings suggest that there are other variables that influence entrepreneurial self-efficacy more so than gender, providing opportunities for future research.
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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 | 0.019 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".