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Record W3197317004 · doi:10.5267/j.uscm.2021.8.004

The role of relationship quality and loyalty between rice farmers and food companies in supply chain

2021· article· en· W3197317004 on OpenAlexvenueno aff
Quoc Nghi Nguyen

Bibliographic record

VenueUncertain Supply Chain Management · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain Resilience and Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessSupply chainStructural equation modelingLoyaltyStratified samplingQuality (philosophy)MarketingPaymentLoyalty business modelSample (material)Industrial organizationService qualityStatisticsMathematics

Abstract

fetched live from OpenAlex

Relationship quality plays a key role in maintaining the connection among parties in a supply chain. Relationship quality promotes loyalty of the supply chain’s factors. This study applies structural equation modeling (SEM) to point out factors affecting the relationship quality and loyalty between farmers and food companies in the rice supply chain. The research data were collected by stratified sampling with a sample size of 232 farmers associating with food companies in the rice supply chain in the Mekong Delta. The four impacting factors that the study has found out include trust, perceived benefit, support policy, and payment terms. Most importantly, the study has demonstrated that relationship quality has a positive correlation with loyalty among parties in the regional rice supply chain.

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 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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
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
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.027
GPT teacher head0.265
Teacher spread0.238 · 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 teacher head, not a consensus.

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

Citations4
Published2021
Admission routes1
Has abstractyes

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