Bibliographic record
Abstract
A general assumption is that early entry into themarket with a new venture is advantageous, increasing the profit potential.However, empirical research suggests that pioneers have a greater risk offailure than later entrants. A model is proposed of the entry timing decision that depends both onenvironmental volatility and competitive rivalry. The approach isdecision-theoretic; it identifies when a decision-maker will benefit most fromstarting an activity rather than delaying it for one more period. The modelconsiders the trade-off between profit potential and mortality risk, as well asother factors encouraging the new venture to enter the market. It offers asimple optimal entry rule based on combinations of values for environmentalvolatility and competitive rivalry, which defines an entry decision threshold:the likelihood of earlier entry for the new venture should increase when thereis an increase in the environmental volatility initially faced by thepioneer. The performance-maximizing time to enter the market is described, and it isdemonstrated how the entry time is affected by changes in the businessenvironment. The time-based criteria are translated into environmental terms,and it is suggested that management set a target environmental level to be metbefore entering the market. The target should be adjusted over time in order tomeet the changes in the business context. The model's theoretical insights aretranslated into testable propositions, and recommendations are made for futureresearch.(LMH)
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".