Coaching the Entrepreneur: Features and Success Factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose – Entrepreneurial coaching appears to be a sufficiently customized way to help novice owner-managers develop their managerial skills. However, its usefulness remains to be verified. The purpose of this research is thus to examine the effectiveness of coaching as a support measure for young entrepreneurs and to identify the factors likely to have an impact on the success of coaching initiatives. Design/methodology/approach – Given the exploratory nature of the study, a flexible and open approach was chosen in order to explore the concept of coaching in some depth. The strategy retained was the case study method, with inter-site comparisons of six coaching initiatives. Findings – The findings suggest that the success of a coaching relationship is explained by a set of factors or "winning conditions", some of which are more important than others. The most crucial one appears to be the entrepreneur's open attitude to change. Research limitations/implications – The main limitation of this study is the small number of cases observed. Practical implications – This research provides valuable information on coaching initiatives by means of real-life examples. It also highlights several factors likely to improve the delivery of coaching services to novice entrepreneurs. It will thus prove useful to those designing coaching programs for entrepreneurs. Originality/value – Given the lack of documentation on the subject of entrepreneurial coaching, this paper has the merit of identifying some of the elements likely to contribute to the success of coaching initiatives. In addition, its findings will fuel thinking on how to enhance the benefits of coaching for novice entrepreneurs
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it