The Role of Smart Contracts in Fresh Agricultural Product Supply Chain Finance: A Three-tier Supply Chain Game Theory Analysis
Bibliographic record
Abstract
Fresh agricultural product suppliers face severe financial constraints due to long production cycles, seasonality, and product perishability, which result in tight cash flows and limited financing options. This paper develops a three-tier supply chain game-theoretic model involving suppliers, retailers, and third-party logistics (3PL) providers. The model examines suppliers' optimal financing strategies under dual financing needs—pre-delivery financing (bank loans, retailer prepayments, and 3PL financing) and post-delivery financing (factoring)—and investigates how blockchain-based smart contracts affect supply chain efficiency and their applicability boundaries. The results reveal three key findings. First, under traditional financing modes, 3PL financing reduces the commitment friction zone, while buyer direct financing (BDF) eliminates it entirely. High-risk suppliers are better suited to bank financing, low-risk suppliers benefit more from 3PL financing, and BDF proves most advantageous for suppliers with medium liquidity risk. Second, smart contracts generate both commitment and credit gains under bank and 3PL financing, but only credit gains under BDF. Third, smart contract adoption is not universally beneficial; under low-risk conditions, they may erode 3PL profits and reduce overall supply chain performance. This study makes three contributions. It develops a novel three-tier supply chain framework incorporating both pre- and post-delivery financing interactions; it identifies the differentiated effects of smart contracts across financing structures; and it emphasizes that smart contracts are not a “one-size-fits-all” solution. These findings provide new theoretical insights and practical guidance for the design of financing strategies and blockchain-based smart contracts in fresh agricultural supply chains.
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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 | 0.002 | 0.004 |
| 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 | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".