Counseling assistance, entrepreneurship education, and new venture performance
Bibliographic record
Abstract
Purpose The purpose of this paper is to apply the theory of guided preparation to investigate the relative impact of outside counseling assistance and entrepreneurship courses on new venture creation and performance. Design/methodology/approach To attain a sample of nascent entrepreneurs who had been impacted by entrepreneurship education and entrepreneurial counseling, 256 individuals who received counseling from the Pennsylvania Small Business Development Center in 1996 or 1998 were surveyed. The authors ran a logistic regression model using venture start‐up as the categorical dependent variable to investigate whether entrepreneurial education and counseling had an influence on the creation of new ventures. To test whether entrepreneurial education or counseling had a long‐term impact on the growth of new ventures, hierarchical regression analyses were run using employment in 2003 as the dependent variable. Various control variables were used for both sets of analyses. Findings Findings indicate that counseling has a significant impact on venture performance but entrepreneurship courses do not. In contrast, entrepreneurship courses are related to venture creation while counseling is not. Research limitations/implications Consistent with theory, the results suggest that counseling programs allow entrepreneurs to develop context‐specific tacit knowledge about their ventures and are best delivered immediately prior to venture start‐up. Entrepreneurship courses appear to indirectly influence new venture performance by increasing the odds of start up. Originality/value This comparative test of the theory of guided preparation contributes to the understanding of the effects of education and counseling on the creation and long‐term performance of new ventures, informing how the delivery of such programs can be improved.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".