The mentor and the entrepreneur: a study of mentors and mentoring through the lens of entrepreneurs
Bibliographic record
Abstract
It has been estimated that in excess of 1,000 publications on the topic of mentoring have been produced in the last 25years (Baugh & Fagenson-Eland, 2007) with the concentration of that work being in the three primary areas of youth, student-faculty, and workplace mentoring, and with the greatest proportion of that literature having its origins in the United States of America (Allen & Eby, 2007). However, despite the popularity of the topic and the use of the terms ‘mentor’ and ‘mentoring’ being increasingly transposed to the world of the entrepreneur, particularly with regard to support provided to entrepreneurs through programs aimed at business development, little research has examined the concept from the entrepreneurs’ standpoint. This thesis reports an exploratory study into the nature of mentors and mentoring, viewed through the lens of the entrepreneur. The research approach involved in-depth interviews supported by an embedded survey, with 32 founder owner-managers of small or medium sized enterprises based in the US, Canada, UK, or Australia. The interviews, being the stories of the entrepreneurs, were fully transcribed and NVivo7 software utilised to organise and interrogate the data. Analysis of the stories identified the sources of assistance for the entrepreneurs and, from those sources, who or what was designated a mentor and the nature of the mentoring provided. The findings revealed that the term ‘mentor’ was not freely used or lightly applied by the entrepreneurs in this study. Of particular note was the finding that only three of the eight Australian entrepreneurs elected to designate a mentor, suggesting the need for further research into cultural meaning of the term. Five clusters of mentoring contributions made by the mentors were identified and named confirming, corporeal, experiential, attributional, and affinial. Whilst there were similarities with descriptions of mentoring in the literature, there were also subtle differences; in particular, the attributional and affinial clusters emerged to be points of difference as was the transactional nature of the activity. Two particular characteristics of the entrepreneurs also emerged, namely reciprocity and the ability to communicate and ask for help. When these findings were combined with the survey data, the nature of mentoring received from mentors and other sources of assistance could be identified for each entrepreneur.
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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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.023 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.008 |
| 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".