New venture start‐ups and technological innovation
Bibliographic record
Abstract
Purpose The purpose of this paper is to compare investment in innovation (e.g. R&D) between new venture start‐ups before commercialization and operating businesses after commercialization. Design/methodology/approach Real options methods were used to model a new venture start‐up as a perpetual call option on an operating business that grows with R&D. The operating business uses R&D to improve actual earnings while the start‐up uses R&D to improve prospective earnings. When the start‐up entrepreneur commercializes his/her new product, device, or service with conventional investment (e.g. plant, property, and equipment to begin production), prospective earnings convert into actual earnings. Findings The ability of the start‐up entrepreneur to avoid commercialization costs upon failed R&D makes R&D more valuable to the start‐up entrepreneur than to the manager of the already operating business (for whom commercialization costs are sunk) and despite R&D costs that the start‐up incurs without the revenues that only commercialization generates. The value of R&D to the start‐up can be so great that the entrepreneur invests in R&D before the manager of an otherwise similar operating business in similar business conditions. Originality/value Without favoring either a priori , the authors show that under broad circumstances, a new venture start‐up undertakes R&D before an already operating business. The authors also discuss the empirical implications of the results.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".