Incentive Contract between Banks and B2B Platform in Online Agricultural Product Supply Chain Finance
Bibliographic record
Abstract
Online agricultural supply chain finance, as an effective way to solve the financing difficulties of small and medium-sized agricultural enterprises in the chain, has made rapid progress in recent years, and the cooperation between banks and e-commerce has become the mainstream mode. Taking the electronic order pledge of agricultural products as an example, this paper discusses the incentive contract design between banks and B2B platform from the perspective of moral hazard prevention and the use of principal-agent theory. In this paper, the principal-agent model is constructed by considering the bank's effort and no effort, and then give the optimal incentive coefficient and fixed return. The results show that banks’ effort will increase the level of efforts of B2B platform, but also increase their own variable payment; under the given conditions, the bank’s effort will increase its income, and at a certain level of effort, the largest increase in revenue, In addition, improving the application level of B2B data processing technology and the degree of data pledge development will contribute to the increase of incentive coefficient and revenue.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".