Using Machine Learning to Study Entrepreneurial Attitudes and Intentions Over Time : Evidence from before, during, and after the 2008 Financial Crisis
Bibliographic record
Abstract
Machine learning techniques have been applied in many domains, and offer analytical tools that can complement traditional methods. Although the advantages of machine learning have been recognized in the entrepreneurship literature, adoption has been limited. This study aims to investigate the importance of key antecedents of entrepreneurial intentions, and how external shocks, such as financial crisis, impact the importance of these determinants. Drawing on data from the Global Entrepreneurship Monitor (GEM), this study applies decision tree algorithms and SHAP to investigate the importance of the determinants of entrepreneurial intention and their changes over time. The results show that the importance of entrepreneurial attitudes and perceived behavior control change over time, and change in response to the financial crisis. We find that self-efficacy becomes more important during and after the financial crisis. Moreover, the findings of the individual decision tree highlight the heterogeneous nature of entrepreneurship, and the different combinations of factors that result in entrepreneurial intentions.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".