The Impact of Institutional Dimensions on Entrepreneurial Intentions of Students—International Evidence
Bibliographic record
Abstract
Acknowledging the role of different forms of entrepreneurship to continued economic prosper-ity and the role of institutional dimensions on entrepreneurship, this paper investigates if and to what extent a selected number of institutional dimensions influence students’ intentions to ei-ther start a company or take over an existing one. Based on a Global University Entrepreneurial Spirit Students’ Survey (GUESS) dataset and international country-level databases, evidence shows that both entrepreneurship options are hampered by corruption and limited business freedom while promoted through favourable labour regulations and trade freedom. Property rights, fiscal freedom, government spending, monetary freedom, and investment freedom only affect start-ups, while financial freedom adversely affects both options. The study provides new insight into the impact of institutional dimensions on different types of entrepreneurship. Thus, in contrast to extant research in this area, it goes beyond the typical focus on start-ups. Evidence also suggests that male students prefer starting a new company, while female students seem to prefer a takeover. This improved understanding could help in not only designing more targeted entrepreneurship and entrepreneurial financing policies but also in improving entrepreneurship education.
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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 | 0.003 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".