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Record W2913658313 · doi:10.5430/bmr.v8n1p11

Game Analysis between Banks and B2B Platforms in Agricultural Electronic Order Financing

2019· article· en· W2913658313 on OpenAlexaffvenue
Xiaoxu Chen, Peng Xu

Bibliographic record

VenueBusiness and Management Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsConcordia University
FundersNational Social Science Fund of China
KeywordsProfit (economics)Order (exchange)BusinessCompensation (psychology)Key (lock)AgricultureProcess (computing)Distribution (mathematics)MicroeconomicsIndustrial organizationEconomicsFinanceComputer scienceComputer security

Abstract

fetched live from OpenAlex

In recent years, agricultural electronic order financing has developed rapidly, and cooperation between banks and B2B platforms has become the main mode of operation. In the process of cooperation, there are moral hazards caused by information concealment. On the basis of analyzing the business characteristics and the behavior strategies of both sides, this paper discusses the cooperation mechanism between the two sides by using the game analysis method. The results show that the strategy choice of the banks and the B2B platforms is not only affected by the credit of financing customers, but also by the concealment cost and the concealment income. When the concealment cost is less than the concealment income, the profit distribution ratio and the default compensation ratio can be the key factors of affecting the strategy choice of the B2B platforms and the banks. When the concealment cost is greater than the concealment benefit, the change value of the income distribution caused by the different strategies has an important impact on the strategy choice of the banks.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.023
GPT teacher head0.269
Teacher spread0.245 · 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.

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

Citations0
Published2019
Admission routes2
Has abstractyes

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