Are Team-founded Ventures More Likely to be Acquired?
Bibliographic record
Abstract
Given the vibrant market for the acquisitions of new ventures, identifying the determinants of these acquisitions is an important question for practitioners and academics alike. We investigate whether team ventures are more likely to be acquired than single-founder ventures, and if so, attempt to determine what number of founders, or team size, is associated with the highest acquisition likelihood. Using the Kauffman Firm Survey (KFS) panel data, our results indicate that team-founded new ventures are 42% more likely to be acquired, and that there is an inverted U-shaped relationship between team size and acquisition likelihood. However, a more rigorous look at the data reveals that the inverted U-shaped relationship suggested by the regression results is driven by data points associated with very large teams, the incidence of which is too rare to provide reliable estimates. If we focus only on single digit team sizes for which the data is more abundant (especially up to 4-5 team members), a positive relationship between team size and acquisition likelihood is observed. The hypothesized inverted U-shaped relationship cannot be supported, most likely because new ventures self- select out of cumbersome team sizes.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".