New Venture Teams and Acquisition: Team Composition Matters
Bibliographic record
Abstract
Acquisition is often one of the most coveted exit paths by entrepreneurs. As the acquisition market continues to entice both sellers (entrepreneurs and investors) and buyers, identifying the determinants of these acquisitions is an important research area for entrepreneurship scholars. Combining upper echelons theory with insights from similarity-attraction theory and information processing theory, this study examines the relationship between the composition of new venture founder teams (NVTs) and acquisition likelihood; a relationship that has received little attention in prior literature. The sample analyzed is from the Kauffman Firm Survey of US new ventures started in 2004 and tracked annually through 2011. We find that gender diversity and the average education level of team members are positively related to acquisition likelihood. In addition, our results indicate a negative effect from the presence of founders born outside of the US and the diversity of industry experience on acquisition likelihood. We find no relationship between race diversity and education level diversity on acquisition likelihood. This study provides insight into the significant role of the founder characteristics in team-founded ventures as it relates to acquisition likelihood and the results may be of interest to both founders and external investors.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".