A performance realization framework for implementing ISO 9000
Bibliographic record
Abstract
Purpose The purpose of this study is to propose a performance realization framework based on key factors of ISO 9000 implementation. Design/methodology/approach A three‐stage approach of a systematic review is employed to examine the literature and develop the framework. The review is concentrated on three research topics: motivations; critical success factors; and impacts of ISO 9000 implementation. Findings This study identifies five motivation factors (quality‐related; operations‐related; competitiveness‐related; external pressure‐related; organizational image‐related factors) and ten critical success factors (leadership; training; involvement of everyone; organizational resource; quality‐oriented culture; customer‐based approach; process‐centered approach; communication and teamwork; customizing the ISO requirements; quality audit). This study also develops a performance realization framework composed of three parts: conversion; enhancement; and competitive priority stages. Originality/value This study contributes to the development of the literature by providing a set of motivation factors and of critical success factors that can assist practitioners to effectively implement the standard. Further, the proposed framework helps to explain causal relationships among ISO 9000 impacts and provide guidelines about critical considerations.
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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 | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".