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Record W2591656124 · doi:10.1177/030630700903400401

How do Chinese Firms Sustain their Cost Advantage in Labour-Intensive Industries?

2009· article· en· W2591656124 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

VenueJournal of General Management · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsYork University
Fundersnot available
KeywordsChinaBusinessIndustrial organizationChinese marketSustainabilityProduction (economics)Economics

Abstract

fetched live from OpenAlex

In the past decade, Chinese firms have been competing aggressively in the world labour-intensive industries and have substantially increased their market share. This study explores the mechanisms Chinese firms employ to develop and sustain their cost advantage in labour-intensive industries. The evidence shows that Chinese firms mainly rely on country-specific factors in the initial stage and cluster-specific factors in the growth stage. They integrate these country- and cluster-specific factors to develop firm-specific resources and capabilities in the mature stage. This integration and the dynamics of these resources and capabilities result in the sustainability of their cost advantage. The findings of this study have important managerial implications for those Western firms that wish to duplicate Chinese cost advantages by setting up manufacturing facilities in China and those that are competing against Chinese firms in the world market.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.238
Teacher spread0.227 · 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