Comparative Analysis on an International Survey of ISO 9000 and ISO 14000 Certification
Bibliographic record
Abstract
The fast evolution of these two management systems standards (ISO 9000 and ISO 14000) worldwide, from virtually unknown entities in the early 1990s’ to well-established and often required management practices, represents but another facet of the increasingly global marketplace many firms operate in. Over 400,000 firms in over 150 countries have adopted ISO 9000 since it was introduced in 1986. Its successor, ISO 14000, was introduced in 1996 and has already been adopted by over 30,000 firms in over 100 countries. This paper reports on the results of a global ISO 9000/14000 mail survey, administered in fifteen different countries including U.S., Canada, France, Sweden, Japan, South Korea, Hong Kong, Taiwan, Australia, New Zealand, Singapore, Philippines, Malaysia, Indonesia, and Thailand to explore and compare the similarities and differences of motivations, implementations and certification benefits among these countries. Survey data have been analyzed using the multivariate statistical methods and techniques such as factor analysis, cluster analysis, Kruskal-Wallis test, etc. Several conclusions and managerial implications are made based on the statistical analysis results.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".