Factors Affecting Green Entrepreneurship Intentions in Business University Students in COVID-19 Pandemic Times: Case of Ecuador
Bibliographic record
Abstract
This research assesses the influence of education development support, conceptual development support, and country support through entrepreneurial self-efficacy over green entrepreneurial intentions. A total of 532 business students in Ecuador participated in an online survey. Eight questions were focused on demographic information, and twenty-seven questions evaluated the green entrepreneurship intentions of students. An SEM-PLS technical analysis was used. The results showed that educational support for developing entrepreneurship (0.296), conceptual support for developing entrepreneurship (0.123), and country support for entrepreneurship (0.188) had a positive influence on entrepreneurial self-efficacy, and that entrepreneurial self-efficacy had a positive influence (0.855) on gren entrepreneurial intentions. The model explained 73.1% of the green entrepreneurial intentions. Outcomes of the bootstrapping test were used to evaluate if the path coefficients are significant. This study showed the impacts of education development support, conceptual development support, and country support on the entrepreneur’s ability to carry out green entrepreneurship were positive. This information can help universities develop strategic plans to achieve ecological ventures and ensure students have the necessary skills to do so on campus. The research findings also may be helpful for the governments in establishing new norms to promote entrepreneurship. The novelty is based on using the partial least square structural equation modeling (PLS-SEM) technique.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".