Author
Listed:
- Guo, Penghui
- Feng, Gengzhong
- Wang, Kai
- Wei, Liqun
Abstract
In hierarchical deep-tier supply chains, private quality information in the headstream’s raw materials and financial constraints in the midstream’s product procurement significantly restrict the downstream’s sales and market supply. To address these issues, downstream retailers can implement blockchain technology to trace and transparentize the headstream’s quality information across the chain and finance the midstream by digitizing accounts payable, i.e., invoice tokenization. To investigate how quality information and financing strategies interact, we formulate a multilevel Stackelberg game to analyze a deep-tier supply chain involving a retailer who may adopt blockchain, a tier-1 capital-constrained supplier, and a tier-2 supplier who privately owns quality information and can provide trade credit financing to the tier-1 supplier. Intuitively, blockchain benefits the retailer since transparentizing quality information can attract more purchases. However, we find that this may not be true, especially when the expected quality is relatively low. Interestingly, we find that merely using blockchain-enabled transparency decreases suppliers’ profits, but further incorporating invoice tokenization can benefit them, potentially achieving a triple-win result, although two suppliers’ interests may not always align. Finally, as the expected quality rises, equilibrium results move from trade credit under no blockchain (NT) to trade credit under blockchain-enabled transparency (BT) and then to blockchain-enabled transparency and invoice tokenization (BI) if the price of raw materials is high; otherwise, BI is more likely to be the equilibrium result. Our findings uncover how to utilize blockchain-driven traceability and invoice tokenization strategically.
Suggested Citation
Guo, Penghui & Feng, Gengzhong & Wang, Kai & Wei, Liqun, 2026.
"Blockchain-enabled quality transparency and invoice tokenization in deep-tier supply chains,"
European Journal of Operational Research, Elsevier, vol. 331(1), pages 260-277.
Handle:
RePEc:eee:ejores:v:331:y:2026:i:1:p:260-277
DOI: 10.1016/j.ejor.2025.09.010
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