From business incubator to crucible: a new perspective on entrepreneurial support
Bibliographic record
Abstract
Purpose Although business incubators are a widely recognized form of entrepreneurial support, this paper aims to challenge the assumption that incubation is necessarily beneficial for early-stage entrepreneurs, and considers cases where, due to variability in the motives and behaviours of entrepreneurs, incubation may be unwarranted or even undesireable. Design/methodology/approach This study presents a theoretically derived typology of incubated entrepreneurs, based on their entrepreneurial competence and capacity for learning, which asserts that incubation may be unwarranted or even undesireable for three of the four proposed entrepreneur types. Qualitative data from interviews with entrepreneurs and managing directors from 10 business incubators is used to illustrate the existence of these types. Findings The data provides evidence of entrepreneurial types whose incubation may be counterproductive to the goals and objectives of their host incubators. Practical implications Implications for incubator management (intake screening and ongoing monitoring of portfolio) are developed and aimed at improving the outcomes of business incubation for stakeholders. Originality/value The paper contributes to the incubation typology literature by challenging a widely held assumption that entrepreneurs have the potential to benefit from incubation and by reconceptualizing incubators as “crucibles” that perform a critical function in distinguishing high-potential entrepreneurs.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".