Mentoring for entrepreneurs: A boost or a crutch? Long-term effect of mentoring on self-efficacy
Bibliographic record
Abstract
This study focuses on whether a mentor can facilitate the development of entrepreneurial self-efficacy particularly with regard to opportunity recognition (ESE-OR) for novice entrepreneurs and whether their level of learning goal orientation (LGO) has a moderating effect. Based on a sample of 219 mentees and a longitudinal follow-up for 106 of these respondents, the results show that mentoring supports the development of ESE-OR, but only for low LGO mentees. Furthermore, the effect of mentoring on ESE-OR for low LGO mentees is ephemeral as it decreases once the relationship ends. This suggests the need for long-term support in order to maintain their ESE-OR high throughout the entrepreneurial endeavour. At the opposite end, high-LGO mentees see their ESE-OR slightly decline in an intense mentoring relationship suggesting that mentoring helps to adjust ESE-OR to a more appropriate level for novice entrepreneurs.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".