Leveraging Corporate Assets and Talent to Attract Investors in Japan: A Country with an Innovation System Centered on Large Companies
Bibliographic record
Abstract
Drug discovery and development require significant costs and time, making investment acquisition crucial. However, there are few biopharmaceutical startups with high valuations in Japan. Unlike other countries, entrepreneurship in Japan is relatively inactive, and startups have a minimal presence in the drug-discovery field. Instead, in Japan’s innovation system, research and development (R&D) has been led by large incumbent companies, which are believed to have a wealth of promising assets and talent. This study tested the hypothesis that biopharmaceutical startups leveraging these assets and talent might be more attractive to investors by regression analysis using a dataset of Japanese unlisted biopharmaceutical startups. The results demonstrated that Japanese biopharmaceutical startups showed significantly higher valuations and total funding amounts if they were corporate spin-offs (CSOs). Additionally, they achieved significantly higher valuations and total funding amounts if their R&D lead persons had corporate backgrounds. These findings suggest that in Japan’s innovation system, which is centered on large companies, CSOs and startups leveraging R&D talent with corporate experience may be more appealing to investors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".