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Record W2120252821 · doi:10.1016/j.jom.2009.11.005

Implementing supply chain information integration in China: The role of institutional forces and trust⋆

2009· article· en· W2120252821 on OpenAlexaff
Shaohan Cai, Minjoon Jun, Zhilin Yang

Bibliographic record

VenueJournal of Operations Management · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsCarleton University
Fundersnot available
KeywordsGuanxiInformation sharingBusinessGovernment (linguistics)ChinaStructural equation modelingSalientAffect (linguistics)Supply chainKnowledge sharingIndustrial organizationInterpersonal communicationKnowledge managementMarketingComputer sciencePsychology

Abstract

fetched live from OpenAlex

Abstract This study investigates the effects of Chinese companies’ institutional environment on the development of trust and information integration between buyers and suppliers. Three aspects of China's institutional environment are salient: legal protection, government support, and the importance of guanxi (interpersonal relationships). This study uses structural equation modeling to analyze data collected from 398 Chinese manufacturing companies. Government support and importance of guanxi significantly affect trust, which subsequently influences two elements of information integration, namely, information sharing and collaborative planning. Furthermore, the importance of guanxi has a direct, positive impact on information sharing, and government support has a direct, positive effect on both information sharing and collaborative planning.

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)0.006
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0000.002
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 head0.008
GPT teacher head0.274
Teacher spread0.265 · 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 designQualitative
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

Citations549
Published2009
Admission routes1
Has abstractyes

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