Effect of gender role identity on the entrepreneurial intention of university students
Bibliographic record
Abstract
This study investigates the influence of gender role identity (GRI) on the precursors of entrepreneurial intention (EI) of university students. Understanding the EI of university students is essential since this is the stage in life when they need to make career choices, including that of becoming self-employed. Most studies in the past have focused on examining the gender gap in entrepreneurial behavior by analyzing EI as influenced by a person’s biological sex, i.e. a binary difference between men and women (e.g. man = 0, woman = 1). That approach for studying the gender gap in entrepreneurial behavior has produced inconsistent results. We argue that the discrepancies in those results are due to the over-simplistic approach involving biological sex instead of their self-perception of their gender. This study devised a mathematical model based on the theory of planned behavior (TPB) and social role theory to examine the influence of GRI in shaping the EI of university students and investigate the mediating role of the TPB’s constructs in the relationship. Research on EI as influenced by GRI leads to a more in-depth understanding of entrepreneurial behavior since both men and women may incorporate higher or lower levels of masculine and feminine characteristics into their self-identities. By analyzing the influence of GRI on EI, this study can explain the gender-related factors shaped by cultural and social dimensions that are absent in previous studies. One of the most important findings of this study is an understanding of the pathways by which GRI affects the EI of university students through the more proximal antecedents of EI. This new understanding can inform universities trying to increase gender equality in entrepreneurship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".