Can less be more? Mentoring functions, learning goal orientation, and novice entrepreneurs’ self-efficacy
Bibliographic record
Abstract
Purpose One of the main goals of entrepreneurial mentoring programs is to strengthen the mentees’ self-efficacy. However, the conditions in which entrepreneurial self-efficacy (ESE) is developed through mentoring are not yet fully explored. The purpose of this paper is to test the combined effects of mentee’s learning goal orientation (LGO) and perceived similarity with the mentor and demonstrates the role of these two variables in mentoring relationships. Design/methodology/approach The current study is based on a sample of 360 novice Canadian entrepreneurs who completed an online questionnaire. The authors used a cross-sectional analysis as research design. Findings Findings indicate that the development of ESE is optimal when mentees present low levels of LGO and perceive high similarities between their mentor and themselves. Mentees with high LGO decreased their level of ESE with more in-depth mentoring received. Research limitations/implications This study investigated a formal mentoring program with volunteer (unpaid) mentors. Generalization to informal mentoring relationships needs to be tested. Practical implications The study shows that, in order to effectively develop self-efficacy in a mentoring situation, LGO should be taken into account. Mentors can be trained to modify mentees’ LGO to increase their impact on this mindset and mentees’ ESE. Originality/value This is the first empirical study that demonstrates the effects of mentoring on ESE and reveals a triple moderating effect of LGO and perceived similarity in mentoring relationships.
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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 | 0.002 | 0.008 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".