Opening the “Black Box” of University Entrepreneurial Intention in the Era of the COVID-19 Pandemic
Bibliographic record
Abstract
This research aims to study the determinants of entrepreneurial intention in academia and compare the outcomes from two different moments, before and during the COVID-19 pandemic. For this purpose, a quantitative methodology was used, whereby a questionnaire was given to higher education students in these two chronological moments. From the obtained results, it was possible to ascertain that, given the motivational dimension, the attitude towards behavior and perceived behavioral control are having a positive impact on entrepreneurial intentions during the pandemic and that subjective norms have a negative impact on entrepreneurial intentions. This relationship of influences is unchanged, either before or during the pandemic. Regarding the environmental dimension, both of the variables under analysis are having a negative impact on entrepreneurial intention during the pandemic period, which corresponds to an aggravation or loss of positive influences when compared to the context before the pandemic, and the next assessment had a positive impact on entrepreneurial intentions. On the theoretical contributions, the findings are very important, as they strengthen the literature on entrepreneurial intentions, and in specific contexts of social and economic instability. As for practical contributions, this research suggests actions to agents with an important intervention role in the community, one of these agents is Higher Education Institutions, which play a determining role by creating a positive environment to support their students’ entrepreneurial intent. This research is original, as far as we are informed, and it is the first to study entrepreneurial intention in academia during the COVID-19 pandemic in the Portuguese context. Moreover, we suggest that the obtained results should be succeeded by further studies to confirm the evolutionary trends now identified on the subject under analysis.
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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 | 0.004 | 0.013 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".