Entrepreneurial logic and fit: a cross-level model of incubator performance
Bibliographic record
Abstract
Purpose Although business incubators are widely used support mechanisms for innovative entrepreneurship, the literature still lacks theoretically based explanations of how the incubation process creates value for stakeholders. This study aims to address this gap by developing a conceptual model, and related research propositions, that explains how the entrepreneurial logic in use by an incubator influences the incubation process (selection criteria and service offering) and performance. Design/methodology/approach Integrating the effectuation and entrepreneurial opportunities literature, which shares common conceptualizations about how the predictability of the future affects entrepreneurial action, the authors posit two archetypes of entrepreneurial logic that are associated with different incubation processes (causal or effectual) and two archetypes of opportunity attributes (discovery- or creation-based) that affect the incubation process needed to support their development. Findings Juxtaposing these archetypes, the proposed cross-level conceptual model specifies four levels of fit (ideal, surplus, deficit and mismatch) between the incubation process and the opportunity attributes of individual ventures, which directly influence venture performance (high, moderate and low). In turn, an incubator's performance is largely shaped by the overall performance of ventures in its portfolio. Originality/value This paper responds to the call for theory-building that links the antecedents and outcomes of the incubation process across levels of analysis. In addition to developing a conceptual model and research agenda at the intersection of entrepreneurship and business incubation, the proposed model also has implications for incubator directors deciding how to allocate limited resources, and for public/private sector administrators interested in leveraging investment in business incubators.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".