Embedded Necessity Theorizing to Understand the Influence of the Precursors of Entrepreneurial Intention of University Students
Bibliographic record
Abstract
This study examines the entrepreneurial intention of university students by analysing the necessity and sufficiency of its precursors through a combination of partial least squares structural equation modelling and necessary condition analysis. Grounded in the Theory of Planned Behaviour, the research identifies essential factors (“must-have” elements) that are critical for forming an entrepreneurial intention, distinguishing them from beneficial but non-essential (“should-have” elements). This approach offers a fresh perspective in entrepreneurship research by highlighting the necessary conditions that contribute to entrepreneurial intention. The findings provide valuable insights for universities seeking to enhance their entrepreneurial ecosystems and for policymakers aiming to foster entrepreneurial talent. By clarifying how various factors interact to shape entrepreneurial intention, this study makes significant contributions to both theoretical advancements and practical applications in promoting entrepreneurship among students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".