Difficulties and benefits of integrated management systems
Bibliographic record
Abstract
Purpose In recent years, the number of management systems (MSs) has sharply increased. These MSs can be certified with, for example, the quality standard ISO 9001 or the environmental standard ISO 14001 and they can subsequently be integrated into one single, jointly managed system. The main purpose of this research is to study the relationships between the level of system integration, on one hand, and the difficulties encountered in the integration process, as well as the related benefits, on the other. Design/methodology/approach Data for this study derive from a survey carried out in 76 organizations registered to, at a minimum, both ISO 14001:2004 and ISO 9001:2008 standards for quality and environmental MSs. A descriptive and an exploratory factor analysis (EFA) are provided. Additionally, structural equation modelling (SEM) is applied to the responses of these organizations to a mailed survey. Findings From the results, the paper proposes a model of the difficulties related to systems integration that have an effect on the level of integration of several specific items of the MSs involved. A model related to the effect of the integration level on the benefits is also provided. Originality/value The study provides an original contribution to the understanding of how difficulties and benefits of MSs integration relate to the level of integration achieved in the participating companies.
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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 | 0.016 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".