The mentor and the entrepreneur: a study of mentors and mentoring through the lens of entrepreneurs
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,004 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».