University support and entrepreneurial intention: does a dedicated entrepreneurship course matter?
Bibliographic record
Abstract
Research on university support's effect on entrepreneurial intentions often overlooks cognitive and non-cognitive support distinctions. This paper compares students who receive cognitive support, particularly an entrepreneurship course, with those who do not. Analyzing data from 2,259 students through Structural Equation Modeling reveals that non-cognitive support significantly affects perceptions of cognitive support and selfefficacy in non-benefiting students, while cognitive support boosts self-efficacy among beneficiaries. The study shows that self-efficacy mediates the relationship between perceived university support and entrepreneurial intention, fully mediating non-cognitive support for non-beneficiaries and cognitive support for beneficiaries. Universities can tailor their support ecosystem to enhance entrepreneurial intentions beyond business disciplines. • The distinction between cognitive and non-cognitive supports remains largely overlooked. • The perceived non-cognitive support significantly affects students' perceptions of cognitive support. • A direct influence of non-cognitive support on students' entrepreneurial intentions was not found. • Universities could enhance both cognitive and non-cognitive support to promote entrepreneurial intention.
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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 | 0.002 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".