The Impact of Entrepreneurship Education on the Development of Entrepreneurial Intention Among Engineering Students: The Mediating Role of Entrepreneurial Mindset and Self-Efficacy.
Bibliographic record
Abstract
This thesis explores how entrepreneurship education (EE) influences the development of entrepreneurial intention (EI) among engineering students, specifically examining the mediating roles of entrepreneurial mindset (EM) and entrepreneurial self-efficacy (ESE). Considering the growing global importance of innovation and entrepreneurial skills, the study investigates the degree to which EE enhances EI by fostering EM and strengthening ESE. Using a quantitative survey of 431 engineering students at York University’s Lassonde School of Engineering, the study applied structural equation modeling to examine the direct and indirect effects of EE on EI through EM and ESE. Our findings show that EE significantly enhances EI, with both EM and ESE positively mediating this relationship. In this research, we investigated the impact of entrepreneurship education on entrepreneurial mindset and on entrepreneurial intention. In reality this relationship is more complex, and causality might be in the opposite direction. Future research should investigate the interplay between these entrepreneurial components and the iterative nature of their evolving relationships. This highlights the value of integrating EE into engineering curricula to develop the EM needed in today’s technology-driven world. The research contributes to existing literature by quantifying EE's impact on EI and offers practical implications for educational policy and curriculum development, advocating for the continued inclusion of EE to effectively prepare engineering students for entrepreneurial careers and foster economic innovation and growth.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".