Changing Appropriability Conditions and Technological Opportunities of Innovation in Japan: 1994–2020
Bibliographic record
Abstract
It has been thought that the main determinants of innovation are the appropriabilities of profits for the company that carried out the innovation and the acquisition of technological opportunities that link the research and development of the company to the innovation. NISTEP conducted a survey in 1994 to clarify the actual conditions of those factors in the Japanese manufacturing industry. They also conducted a “Survey on Research and Development Activities of Firms in the Private Sector 2020” with questions that are comparable to those in the past survey. In this paper, using the data obtained from these two surveys, we analyzed the changes that occurred in appropriabilities and technological opportunities during the past quarter century and obtained the following results. First, the effectiveness of the various methods for profiting from the innovations implemented by the company has diminished, and the appropriability of profits has declined significantly. Second, the time taken by competitors to introduce a competing alternative (imitation lag) has become considerably longer. Third, while universities and public research institutions have become significantly more important as sources of information to provide technological opportunities, competitors have become less important as sources of information. These findings suggest a decline in competition among companies for innovation and new challenges for science and technology innovation policy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".