Digital Explosion and Entrepreneurship Education: Impact on Promoting Entrepreneurial Intention for Business Students
Bibliographic record
Abstract
This study aims to examine the effect of entrepreneurship education and artificial intelligence (AI) development on entrepreneurial intentions while investigating the mediating role of perceived behavioral control. The proposed model also accounts for individual and contextual socioeconomic factors. This study tries to fill the gap in the entrepreneurship literature, which is still lacking with respect to the impact of new technologies on entrepreneurship intentions and shows conflicting results regarding the influence of entrepreneurship education. Our study surveyed 223 business students in Lebanon. The context of this study is of high importance, particularly since the country is currently facing a deep, multifaced political, economic, and financial crisis, and entrepreneurship might be considered an important channel for generating basic sources of income, steering the recovery process, and increasing Lebanese resilience against this highly unstable economy. The structural equation modeling technique (SEM) was conducted to validate the hypotheses. The results show that perceived behavioral control fully mediates the relations between performance expectancy of AI solutions, entrepreneurship education, and entrepreneurial intention. Risk aversion and social support exert a direct impact on entrepreneurial intentions. The findings highlight the need to account for entrepreneurship education and AI development when analyzing entrepreneurial intentions.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".