Assessing relationship between quality management systems and business performance and its mediators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it