Entrepreneurs’ Decisions on Timing of Entry: Learning from Participation and from the Experiences of Others
Bibliographic record
Abstract
Depending on the type of industry, an entrepreneur's decision of when to enter an industry may be a crucial one. The longer entrepreneurs wait, the more they learn from others. However, by waiting, they reduce their ability to learn directly and the possibility of locking in competitive advantages. We suggest that the optimal time of entry depends on the hostility of the learning environment since the latter has an impact on dimensions of performance, such as profit potential and mortality risk. The environment for entrepreneurial learning is less hostile when the information to be learned is abundant and when learning from others is relatively more effective at increasing performance than learning from participation. Our results suggest that delaying entry is desirable when the environment is less hostile. Entrepreneurs, however, cannot wait forever. We show that, under some general conditions, an optimal time of entry can be determined, and discuss the specific situations in which, instead, multiple equilibria may arise.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".