The moderating role of entrepreneurship education in shaping entrepreneurial intentions
Bibliographic record
Abstract
Abstract Few studies investigated the role of entrepreneurship education in students’ entrepreneurial intentions. These studies produced controversial results which invited the attention of researchers for further investigations. This paper examines the moderating role of entrepreneurship education on the predictive value of attitude, subjective norms and self-efficacy for entrepreneurial intentions. True Experimental Design (post-test-only control group design) is used to investigate the change in the nature and magnitude of the impact of independent variables (personal attitude, self-efficacy and subjective norms) on the dependent variable (intentions) using entrepreneurship education as a moderating variable. Data were collected from the treatment group (completed entrepreneurship course) and control group (not taken entrepreneurship course) from various higher education institutions in Oman. Total 500 questionnaires were distributed, out of which 204 filled questionnaires were received back in which 196 qualified as valid responses. Structural equation modeling was used to test hypotheses. The statistical relationship among the modeled variables was estimated using Partial Least Square method. The results revealed that attitude toward entrepreneurship, subjective norms and self-efficacy are the significant predictors of entrepreneurial intentions. However, entrepreneurship education moderates this relationship by strengthening the path coefficients of attitude toward entrepreneurship and self-efficacy. Same time it weakens the path coefficient of subjective norms.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".