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DEVELOPMENT OF COOPERATIVE NORMS IN THE BUYER‐SUPPLIER RELATIONSHIP: THE CHINESE EXPERIENCE

2008· article· en· W1979007531 on OpenAlexaff
Shaohan Cai, Zhilin Yang

Bibliographic record

VenueJournal of Supply Chain Management · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsLakehead University
FundersCity University of Hong Kong
KeywordsBusinessIndustrial organizationSupplier relationship managementMarketingSupply chain managementSupply chain

Abstract

fetched live from OpenAlex

Cooperative norms are a critical governance mechanism of the buyer‐supplier relationship. Prior studies generally assume that cooperative norms are pre‐existing and utilize norms as an antecedent to other constructs. Little attention has been paid to the antecedents of the decision to develop cooperative norms. In this paper, we draw upon transaction cost economics and resource dependence theory to develop a model that depicts the key factors that determine cooperative norms. Based on the results derived from an empirical study of 278 Chinese companies, we find that these factors significantly impact cooperative norms. The study also reveals that cooperative norms positively influence suppliers' performance, which subsequently affects buyers' satisfaction.

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)0.003
metaresearch head score (Gemma)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.005
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.253
Teacher spread0.228 · 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

Citations152
Published2008
Admission routes1
Has abstractyes

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