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Record W2096946941 · doi:10.1002/smj.348

Organizational structure, context, customer orientation, and performance: lessons from Chinese state‐owned enterprises

2003· article· en· W2096946941 on OpenAlexaff
Xiaohua Lin, Richard Germain

Bibliographic record

VenueStrategic Management Journal · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsDecentralizationContingency theoryContext (archaeology)BusinessIndustrial organizationContingencyOrganizational structureControl (management)Sample (material)Management control systemMarketingProduct (mathematics)EconomicsManagementMarket economyMathematics

Abstract

fetched live from OpenAlex

Abstract This study develops a model grounded in the contingency theory (i.e., context–structure–performance) applicable to Chinese state‐owned enterprises (SOEs). Using data from a sample of 205 industrial SOEs, the study shows that SOE growth performance relative to the industry is positively predicted by formal control, inversely predicted by decentralization, and positively predicted by the interaction of the two. Customer product knowledge utilization, unrelated to growth performance relative to the industry, is positively predicted by formal control and the interaction of formal control with decentralization. Foreign induced industry competitiveness, technological turbulence, size, and production technology routineness are treated as context variables and modeled accordingly. Copyright © 2003 John Wiley & Sons, Ltd.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.238
Teacher spread0.225 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations257
Published2003
Admission routes1
Has abstractyes

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