Functional Food and Nutraceutical Registration Processes in Japan and China: A Diffusion of Innovation Perspective
Bibliographic record
Abstract
Purpose - This paper looks into the functional food and nutraceutical registration processes in Japan and China. The Japanese have developed the Foods for Specified Health Use (FOSHU) registration process whereas the Chinese have put into place the Health Food (HF) registration process. The aim of this paper is to compare the regulation processes between the two countries in search for answers to three core empirical questions: (1) how have the registration processes developed and changed? (2) What are the similarities and differences between the processes of registration in the two countries investigated? (3) Why are the registration processes similar/different? Method - The study was conducted using secondary sources. The literature surveyed covered academic journals, trade journals, magazine and newspaper articles, market reports, proceedings, books and web pages of relevant regulatory authorities and regulatory consultants. Information from the more recently published sources was used preferentially over older sources. As well as using the most recent sources, information was selected on the basis of which source it was from. Official regulations and SFDA and MHLW websites would contain accurate and up to date information and information from here would be taken as true over other sources of information. Results - The two diagrams of the registration processes respectively in Japan and China clearly show that there are similarities and differences. There are six categories under which these can be found: (1) the scientific evidence required; (2) the application process; (3) the evaluation process; (4) the law and the categories of products; (5) the labels and the types of claims; and finally (6) the cost and the time involved. Conclusions -The data analysis suggests that the process of diffusion of innovation played a role in the development of the regulations. Further it was found that while Japan was at the outset a pioneer innovator in nutraceutical registration processes, there are indications that in more recent years it too imitated other countries. NOVELTY STATEMENT: The assortment of regulatory regimes creates much uncertainty for the firms and the lack of familiarity and poor knowledge of the regulatory situation increases the risk of failure. The research presented in this paper provides highly valuable information to any biotech/pharmaceutical/nutraceutical companies developing their market entry strategy in Japan and China. There are few national and international studies of drug registration application processes but even fewer comparative studies of functional food and neutraceutical registration application processes such as this one and none using a diffusion of innovation perspective.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".