Attributes of Business Incubators: A Conjoint Analysis of Venture Capitalist’s Decision Making
Bibliographic record
Abstract
Startups contribute significantly to the economic development of a country. Despite their importance and promising future, they are extremely fragile, mainly for their lack of tangible and intangible resources. Since this can be obtained through an incubation process, business incubators (BIs) could have a significant impact on the survival rate of startups. Once defined their core structure and value proposition, there are other players, such as venture capitalists who could guarantee the funds necessary to make the startup’s business grow over time. Drawing on the resource-based view theory, this research explores whether some BIs could represent a certification of startup quality for venture capitalists (VCs). Specifically, we investigate whether some specific attributes of BIs increase the probability that a VC funds startups after being incubated; to this purpose, we carry out an experiment on a European sample of VCs. Results demonstrate that some characteristics of the BI can produce a sort of certification effect to the incubated startups, increasing the probability of being funded by VCs.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".