Assessing relationship between quality management systems and business performance and its mediators
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the relationship between implementation of quality management systems (QMS) and business performance, through mediating factors (operating performance, information quality, product quality, design performance, environmental performance and competitive priorities). Most of the published literature examines the direct impact of implementation of QMS on business performance, and on some of the above stated factors. However, the impact of implementation of QMS on business performance, through these mediating factors has not received much attention. Accordingly, the authors develop a theoretical framework depicting impact of implementation of QMS on business performance through the above stated factors. Design/methodology/approach The paper proposes several hypotheses linking implementation of QMS, mediating factors and business performance. The hypothesized model is empirically tested using data collected from 120 professionals working in quality engineering/management in India and North America. The collected data are analyzed with the aid of structural equation modeling (SEM) technique. Findings Information quality and design performance have emerged as the important factors in the research. Information quality directly effects design performance, operating performance and environmental performance. The model indicates that besides a well-designed product, managers need to focus on the operating performance to improve overall product quality. Empirical evidence is found regarding direct and indirect effect of implementation of QMS on above stated mediating factors and on business performance. Originality/value The research fills a gap in the literature by considering several mediating factors that aid in improving business performance with implementation of QMS.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".