The Impact on Management Experience on the Performance of Start-Ups within Accelerators
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Bibliographic record
Abstract
We investigate the effects that the experience level of accelerator management teams has on the performance of the accelerators they manage. In particular, we examine how the collective business experience of the accelerator managers influences the survival and growth of tenant firms within the accelerator. The experience of accelerator managers is assessed from two perspectives: their own direct knowledge from operating entrepreneurial startups, and their ability to access the knowledge of others from their professional networks. The survival and growth of tenant firms is assessed as the hazard rates for successful exits (acquisitions) and unsuccessful exits (firm failures). We find evidence to suggest that increased knowledge of accelerator managers reduces the risk of firm failures and that this reduction can be attributed more to differences in the amount of direct experience the accelerator management team has as founders in startups, than to differences in connectedness to the ecosystem. <b>TOPICS:</b>Private equity, equity portfolio management, manager selection, statistical methods
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 it