Linking learning and effective process implementation to mass customization capability
Bibliographic record
Abstract
Abstract This study investigates the role of learning and effective process implementation in the development of mass customization capability. Building upon the knowledge‐based view of the firm, we argue that internal and external learning are two knowledge‐generation routines that contribute to effective process implementation. Effective process implementation, in turn, is a knowledge‐based manufacturing capability, which, as a function of internal and external learning, leads to mass customization capability. We employ structural equation modeling to empirically test the effects of learning on mass customization capability, mediated by effective process implementation, using survey data collected from 100 manufacturing plants in 3 industries and 6 countries. Our results provide empirical evidence supporting the proposed model of the effect of internal and external learning on mass customization capability, fully mediated by effective process implementation. This research is one of the first studies to integrate insights from the knowledge‐based view of the firm and mass customization. It complements the OM view of mass customization, which to date has largely focused on the technical side, by demonstrating the role of managerial practices and learning in cultivating mass customization capability in a manufacturing environment.
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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 | 0.006 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".