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Record W2409494868

Absorptive capacity and mass customization capability

2015· book-chapter· en· W2409494868 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueResearch Portal (Queen's University Belfast) · 2015
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicProduct Development and Customization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAbsorptive capacityMass customizationKnowledge acquisitionKnowledge managementSupply chainPersonalizationKnowledge value chainContext (archaeology)BusinessComputer scienceProcess managementOrganizational learningMarketingGeography
DOInot available

Abstract

fetched live from OpenAlex

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 kn...

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.718
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.036
GPT teacher head0.240
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it