Insights into the reasons behind the limited implementation of ISO 9001 certification: empirical evidence from Yemen
Bibliographic record
Abstract
Purpose While ISO 9001 standard certification is approaching saturation in developed countries, other nations are still struggling with the implementation of ISO 9001. This study provides insights into countries with a very low number of registered certifications to understand the reasons behind the limited implementations, more particularly using evidence from Yemen. The objective of the study is to assess: the awareness, understanding, motivations, barriers and the benefits of implementing ISO 9001. Design/methodology/approach A survey was conducted to measure awareness levels, understanding, driving factors, barriers and the benefits of ISO 9001. Data collection was carried out through the classification of targeted firms into two separate categories, certified and noncertified organizations. The classification of firms was conducted to enrich the understanding from two different perspectives. A total of 72 responses, from companies ranging in size from large to small, and in both the private and public sectors, were analyzed using the descriptive and multiple correspondence analysis (MCA). Findings The data analysis showed that the limited implementation of ISO 9001 certifications could be due to several internal and external factors such as the relatively low awareness level of the certification and guidelines, and the inconsistency of understanding the certification purpose. The benefits focused solely on improving the quality of systems efficiency and increasing quality awareness, and not on internationalization. Research limitations/implications The findings of this work provide the groundwork for decision-makers to understand the drivers and challenges of ISO 9001 to plan corrective actions and contribute to promote and increase the number of certified organizations in similar countries and economies. While the data in this study were collected in the context of one country, the methodology and framework used in this study can be utilized by other researchers to collect data in similar countries with a low number of ISO 9001 certifications. Originality/value This research is one of the very few that addresses the limited implementation of ISO 9001 in the Middle East and North Africa region and Yemen in particular.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".