How coaching services help early stage entrepreneurs: an exploration of gender differences
Bibliographic record
Abstract
Purpose Entrepreneurial support organizations, such as business incubators and accelerators (BIAs), provide coaching as a core element of their service offering for startups. Yet little is known about how coaching creates value from the entrepreneur's perspective. This is an important issue given that entrepreneurship is recognized as a gendered phenomenon. The purpose of this article is to explore how the coaching services provided during incubation create value for men and women entrepreneurs. Design/methodology/approach Focusing on university business incubators, our comparative qualitative study of 18 men and women entrepreneurs takes a grounded theorizing approach, and draws abductively on entrepreneurial learning theory, to explore the dimensions of coaching services that support venture development and explain gender differences. Findings The emergent explanatory model suggests that venture development is supported by coaching service design (at the incubator level) and by coaching content and rapport (at the entrepreneur-coach dyad level). Gender differences were observed in the emphasis placed on accessibility of coaching services provided by the incubator and the guidance provided by the coaches. We theorize that these findings reflect differences in entrepreneurial learning. Practical implications To better support entrepreneurial learning, gender differences should be considered in both the design and delivery of coaching services. Originality/value Our findings provide deeper insight about how coaching services create value for entrepreneurs by revealing explanatory dimensions at two levels of analysis and theorizing the interrelationship between entrepreneurial learning, gender and venture development.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".