Can Entrepreneurs Who Experienced Business Closure Bring Their New Start-Up to a Successful M&A?
Bibliographic record
Abstract
Numerous technology start-ups end up shutting down their operations. The present study aims to answer the following research questions: can entrepreneurs who closed their previous ventures bring their new venture to a successful exit through M&A and to what extent does this positive outcome correspond to whether investors funded their start-up? We examine 9723 technology start-ups established by 19,458 entrepreneurs. About half of the start-ups were funded, and 3463 of them had entrepreneurs with closure or with M&A experience. We find that entrepreneurs with closure experience are negatively associated with the probability of M&A as a main effect, in line with the theory that indicates imprinting. Nevertheless, entrepreneurs with closure experience are positively associated with the probability of M&A when their co-founders have M&A experience. We suggest that entrepreneurs with closure experience can compensate for their lack of M&A experience by learning from their peers who possess this experience. We discuss implications for theory, investors, and 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 | 0.002 | 0.017 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".