Linking learning and effective process implementation to 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
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.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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