Improving integration of human resources into quality management system standards
Bibliographic record
Abstract
Purpose – Little attention has been paid to how quality management systems (QMSs) are optimized by supportive employee behavior. The purpose of this paper is to provide a critical review of the literature on the inclusion of human factors in the ISO 9000 family of standards, identify deficiencies in the standard's current treatment of these issues, and offer recommendations on how human resources (HRs) can be better integrated into these business management standards. Design/methodology/approach – This concept paper presents a survey of both academic and practitioner literature on the topic of HR and its treatment in quality standards. The focus is restricted to consideration of human factors that are specifically identified in the ISO 9001:2008 and ISO 9004:2009 standards. Findings – ISO 9001 and 9004 include some HR topics, but their treatment is insufficient to meet the demands of today's business environment. The recent addition to the ISO 9000 family,ISO 10018 – Quality Management – Guidelines on People Involvement and Competence(ISO, 2012b) will help to address the deficiency if adopted by the marketplace. To improve the usefulness of ISO 9000 standards, the breadth of human factors should be enhanced both extensively to include components of workplace culture and work design and intensively to require more rigorous treatment of the HR considerations already included in the standards. Practical implications – There is a need for more comprehensive consideration of human contributions to quality if organizations are to optimize the value they receive from their HR and their investment on the ISO 9001 QMS. Originality/value – The limited references linking HR and the ISO 9000 series of standards have focussed upon how human factors contribute (or not) to the successful use of the ISO 9000 standards. In contrast, this paper offers a comprehensive and integrative examination of how the ISO 9000 QMS standards could more comprehensively and effectively incorporate HR into a firm's practices.
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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 | 0.112 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".