Exploring Sufficiency Conditions for Entrepreneurial Environment and Counseling Activities on Entrepreneurial Performance
Bibliographic record
Abstract
This empirical study aims to explore sufficiency conditions for Entrepreneurial Resources and Counseling Activities on Entrepreneurial Performance. The study collected 111 questionnaires from entrepreneurs in Taiwan and applied fuzzy set qualitative comparative analysis (fs/QCA) to explore the sufficiency conditions for entrepreneurial environment and counseling activities on entrepreneurial performance. In a regression analysis, there was no significant finding regarding the effects of entrepreneurial counseling on entrepreneurial performance. However, the fs/QCA analysis results show there is high causal relevance of entrepreneurial environment, entrepreneurial counseling and a combination of these two on entrepreneurial performance. Specifically, when entrepreneurial environment are available, the results (Y) of entrepreneurial performance are probably yielded. When entrepreneurial counseling is available, entrepreneurial performance can be created. Finally, when a combination of entrepreneurial environment and entrepreneurial counseling are available, entrepreneurial performance can be yielded. This study suggests that fs/QCA is a useful method to provide a calculus of compatibility and thus to contribute to an enhanced understanding of entrepreneurial environment and counseling activities on entrepreneurial performance.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".