What Can Universities Do to Promote Entrepreneurial Intent? An Empirical Investigation
Bibliographic record
Abstract
Our university has promoted entrepreneurship extensively through networking events, business plan competitions, funding sources, degree programs, and on-campus incubators. But do these efforts work? We integrate and extend intentions-based models and the psychosocial cognitive model to develop a model of entrepreneurial intent and behaviour. We test our theory using partial least squares structural equation modelling on survey data collected from 334 undergraduate business students in Canada. We find that the belief constructs, namely: subjective norms toward entrepreneurship, prevalence of entrepreneurship on social milieu, and goal-orientation are found to positively influence the attitude constructs of the: 1) desirability of an entrepreneurial career; 2) perceived feasibility of an entrepreneurial career. These attitudes, in turn, positively impact entrepreneurial intent, which subsequently positively influences entrepreneurial behaviour. We, thus, found support for the hypotheses that university support for these psychosocial influences has a positive effect on student entrepreneurial intent and behaviour. Practical implications are discussed and future directions are suggested.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".