Determinants of the Entrepreneurial Influence on Academic Entrepreneurship—Lessons Learned from Higher Education Students in Portugal
Bibliographic record
Abstract
Academic entrepreneurship is becoming increasingly important to the field of research as well as to policy makers due to its ability to contribute to the economic, technological, and social development of regions and countries. This research aims to evaluate the determinants that influence the interest of Portuguese higher education students (HEI’s) to become entrepreneurs. The methodology used is quantitative and uses structural model equations. The results obtained demonstrate that the student’s perception of business skills, business growth skills, strategy, and successful business are key factors that students take into account in their entrepreneurial orientation. The research contributes to this theory by adding new knowledge to the literature on the perception of the HEI’s students to become entrepreneurs, specifically the students of Portuguese universities. In practical terms, the contributions offered within this research are based on suggestions for the third mission of universities, explicitly knowledge transfer to the community, business groups, and policy makers, as well as the creation of the essentials within university boundaries to promote entrepreneurship amongst its students. The research is original and innovative, as no research on this field with all the aggregated elements under study has been previously performed in Portugal. Furthermore, the obtained results can translate into ideas that potentially create jobs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".