The effect of quality management on mass customization capability
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 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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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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