The Impact of Entrepreneurial Education on Technology-Based Enterprises Development: The Mediating Role of Motivation
Bibliographic record
Abstract
Technology-based enterprises play a paramount role in blooming a country economically. Nevertheless, according to a society’s capacity to launch such enterprises in various eras, their volume is less than expected in many economies. Therefore, establishing such enterprises is necessary for developing any country, although its innovation system contributes to establishing them. This paper considers the impact of entrepreneurial education on technology-based enterprise development, including motivation as a mediator variable, in Esfahan Scientific and Industrial Town. Despite much research investigating the correlation between entrepreneurial education and technology-based enterprises’ progress, it seems that no study has already considered this correlation with remarking the motivation as a mediator variable. This applied research follows a quantitative research design. The statistical population includes 500 enterprises in the Esfahan Scientific and Industrial Town, and for sampling, Cochran’s formula was applied (n = 217). Additionally, the researcher-made questionnaire and PLS3 software were used for data gathering and analysis. The results demonstrated that entrepreneurial education elements (including entrepreneurial skill, entrepreneurial learning, and entrepreneurial intention) positively affect technology-based enterprises’ development, considering motivation as a mediator variable. However, the impact of entrepreneurial intention on technology-based enterprises was not supported. It reveals that the entrepreneurial intention of motivated individuals could have a meaningful effect on the development of technology-based enterprises. Therefore, motivation is a critical issue to be considered by managers and policymakers while considering entrepreneurial education-related policies and initiatives.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".