Bibliographic record
Abstract
This study focuses on the important concepts of quality management, internal customer satisfaction, and business performance within the neglected purchasing unit of manufacturing firms on the basis of the European Foundation for Quality Management (EFQM) Excellence Model, thus, filling a void in the existing literature. In doing so, this study tests the viability of the EFQM model in a single functional unit. Three hypothesis were generated based on the EFQM model to identify the specific relationships between purchasing’s quality management practices (EFQM enabler), internal customer satisfaction, and business performance (EFQM results). The hypotheses were tested through structural equation modeling based on a sample of 306 purchasing agents within manufacturing. The results indicated that the EFQM seem to be a viable model that represents what impacts implementing QMP enablers will have on the resultants, ICS and OP. Additionally, the results identified that the extent of adoption of quality management purchasing has a direct positive impact on improving internal customer satisfaction and an indirect positive impact on business performance mediated by internal customer satisfaction, as predicted by the EFQM model. This study highlights the positive impact of adoption of EFQM in the purchasing area, thus, lends support to purchasing departments trying to justify the implementation of quality management practices to their administrations. Additionally, it gives upper management, looking for ways to improve the company’s bottom line, the specifics to do this through the implementation of quality management practices in purchasing. Management and purchasing departments are given a blueprint for improving their performance.
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.
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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".