Absorptive capacity and 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 effects of a manufacturer’s absorptive capacity (AC) on its mass customization capability (MCC). Design/methodology/approach – The authors conceptualize AC within the supply chain context as four processes: knowledge acquisition from customers, knowledge acquisition from suppliers, knowledge assimilation, and knowledge application. The authors then propose and empirically test a model on the relationships among AC processes and MCC using structural equation modeling and data collected from 276 manufacturing firms in China. Findings – The results show that AC significantly improves MCC. In particular, knowledge sourced from customers and suppliers enhances MCC in three ways: directly, indirectly through knowledge application, and indirectly through knowledge assimilation and application. The study also finds that knowledge acquisition significantly enhances knowledge assimilation and knowledge application, and that knowledge assimilation leads to knowledge application. Originality/value – This study provides empirical evidence of the effects of AC processes on MCC. It also indicates the relationships among AC processes. Moreover, it reveals the mechanisms through which knowledge sourced from customers and suppliers contributes to MCC development, and demonstrates the importance of internal knowledge management practices in exploiting knowledge from supply chain partners. Furthermore, it provides guidelines for executives to decide how to manage supply chain knowledge and devote their efforts and resources in absorbing new knowledge for MCC development.
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 | 0.001 | 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 | 0.003 |
| 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