Quality management practices and their impact on performance
Bibliographic record
Abstract
Purpose This paper aims to explore the relationship between quality management practices and their impact on performance. Design/methodology/approach First, critical quality management practices are identified and classified in three main categories: management, infrastructure, and core practices. Then, a model linking these practices and performance is proposed and empirically tested. The empirical data were obtained from a survey of 133 Tunisian companies from the plastic transforming sector. Findings The results reveal a positive relationship between quality management practices and organizational performance. Moreover, the findings show a significant relationship between management and infrastructure practices. In addition, the results illustrate a direct effect of infrastructure practices on operational performance and of core practices on product quality. Research limitations/implications The conceptual model proposed and tested in this study can be used by researchers for developing quality management theory. In addition, this model may offer a flow chart to practitioners for effective quality management implementation. Originality/value The proposed model is the first one to distinguish the direct effects of infrastructure practices on performance from the indirect effects of these practices through the core practices. Besides, the use of path analysis method to study the direct and indirect relationships between quality management practices and their effect on performance dimensions.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".