Quality management practices and their impact on performance
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 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.
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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