Experience and Perceptions of ISO 9000 and HACCP by Hong Kong Food and Beverage Organizations
Bibliographic record
Abstract
The purpose of this research is to explore the views of individuals responsible for quality assurance in Hong Kong (HK) food and beverage companies with regards to their acceptance or rejection of the ISO 9000 quality management system or HACCP food safety system standards, along with the reasoning underlying such views. Thirty Hong Kong food or beverage manufacturing companies were approached and in‐depth interviews in the form of surveys were conducted with 11 companies. Participating companies included companies that had implemented both the ISO 9000 and HACCP standards, companies that had implemented only ISO 9000 or HACCP, and a company that had implemented neither. Half of the companies that participated in this study were large companies with 500 or more employees. The use of ISO 9000 was reported to improve the maturity of other quality systems. The use of HACCP was reported to improve the maturity of other food safety systems. While more companies used HACCP than the ISO 9000 standard to comply with customers’ requirements, the difficulties in the training of staff and added costs for documentation/data storage were reported as common to both standards.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".