The effect of quality management on mass customization capability
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the role of quality management (QM) in the development of mass customization (MC) capability. QM is modeled as a second‐order construct reflected by six QM practices (small group problem solving, top management leadership for quality, information and feedback, process management, customer focus, and supplier involvement). The paper proposes that these six practices reflect the core principles of QM, and in turn QM contributes to the development of MC capability. Design/methodology/approach Using the survey data collected from 167 manufacturing plants in three industries and eight countries, structural equation modeling was employed to test the hypotheses. Findings The results provide empirical evidence supporting the proposed relationships between QM and MC capability. Research limitations/implications The dataset for this paper is cross‐sectional. Future studies should consider a longitudinal setting that would provide a deeper understanding of causal relationships. Second, an existing database was used, thereby limiting the choices of variables analyzed. Practical implications The findings of empirical support for the positive impact of QM practices on MC capability provide guidance for managers in the allocation of resources for QM efforts in their pursuit of MC capability. Originality/value This is one of the first studies to shed light on the effects of QM on MC capability. The paper presents an explanation on how QM helps to develop MC capability and also finds empirical evidence supporting such a relationship.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".