Quality Management Principles: A Case in D’Kawi Chocolate Hut
Bibliographic record
Abstract
The purpose of the study was to explore the Quality management principles (QMPs) that applied at the D’Kawi Chocolate Hut (D’KAWI). The study was conducted to identify the level of awareness and implementation of QMPs in D’KAWI. The study use qualitative method. The researcher prepares questions for interview protocol. This interview conducted based on a set of questions (checklist) that were completed based on the quality management principles. The study demonstrates on several findings: data analysis reveals the Quality Management Principles (QMPs) that applied at D’KAWI. The findings also reveal the significant level of awareness and implementation of QMPs into organizational performances. The study is using perceptual data provided by production managers or quality managers which may not provide clear measures of performance. However, this can be overcome using multiple methods to collect data in future studies. D’KAWI should consider QMPs as an innovative tool for improving organizational performance in today’s dynamic food industry environment. The findings suggest the D’KAWI that the QMPs should be implemented holistically. The study integrates the principles of quality management with the level of awareness and implementation towards organizational performances as related drivers of the effectiveness and success of QMPs in the D’KAWI. Very few studies have been performed to investigate and understand this issue.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".