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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".