Exploring the gendered entrepreneurial identity gap: implications for entrepreneurship education
Bibliographic record
Abstract
Purpose Women are underrepresented in most university entrepreneurship education (EE) programmes and less likely than men to pursue business venturing as a career. One reason may be the “entrepreneurial identity gap”, whereby female students do not see themselves as successful entrepreneurs. This paper aims to explore the nature of this identity gap and its relationship to entrepreneurial intent and entrepreneurship education. Design/methodology/approach A set of contemporary, gender-inclusive entrepreneurial attributes was developed using entrepreneurial subject matter experts and tested with 591 university students to explore the nature of the gendered entrepreneurial identity gap. Findings While masculine stereotypes persist and the entrepreneurial identity gap is larger for female students, results suggest that a more gender-inclusive vocabulary of entrepreneurship is emerging among the student population and an androgynous perception of the idealized entrepreneur. Entrepreneurship education had a positive influence on entrepreneurial intent. Research limitations/implications Study findings advance the conversation about entrepreneurial identity, the nature of the gendered identity gap and the role of education in closing that gap. The questionnaire and set of gender-inclusive attributes should continue to be tested beyond student samples. Practical implications Based on this study, entrepreneurship education could benefit from more gender-inclusive instructional practices and vocabulary and a broadened definition of what it means to be entrepreneurial. More students – both men and women – will see themselves as entrepreneurs and be inspired to participate in the innovation economy. Originality/value This study takes a novel approach to the study of entrepreneurial identity, developing a new set of attributes and contemporary vocabulary around business venturing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".