Full transparency or restricted visibility? Mechanisms of blockchain enabled data sharing in supply chain.
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| Title: | Full transparency or restricted visibility? Mechanisms of blockchain enabled data sharing in supply chain. |
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| Authors: | He, Yu1 (AUTHOR), Jiang, Cuiqing1 (AUTHOR), Dong, Junfeng1 (AUTHOR) jfdong4@hfut.edu.cn, Ding, Yong1 (AUTHOR), Chen, Bo1 (AUTHOR) |
| Source: | International Journal of Production Research. Feb2026, Vol. 64 Issue 4, p1227-1248. 22p. |
| Subjects: | Blockchains, Supply chain management, Data encryption, Disclosure, Demand forecasting, Incentive (Psychology), Privacy, Information sharing |
| Abstract: | This study examines values and adoption conditions of Blockchain Technology (BCT) in horizontal demand forecast sharing among retailer, focusing on the influence mechanism of transparency-restriction approaches and BCT's endogenous effects on firms' sharing incentives. We model a supply chain with one manufacturer and multiple retailers, comparing four BCT-enabled data-sharing regimes: open access (permissionless) versus no-open access (permissioned), with or without encryption. Results show that restricted transparency, combined with selective accessibility, aligns individual and collective incentives by curbing wholesale price inflation and improving forecast accuracy. Contrary to intuition, higher transparency does not universally benefit retailers; supplementary encryption can balance data utility and privacy, enabling Pareto-superior outcomes. We further demonstrate BCT can reduces moral hazards in horizontal sharing (e.g. sharing biased forecast), allowing retailers to leverage aggregated demand signals without inefficiently verification. However, excessive transparency in BCT can accelerates retailers' profit erosion, akin to perfect competition. These findings offer micro-foundations for adopting visibility-restriction technologies (e.g. Zero-Knowledge Proofs) and guide the design of context-specific BCT systems. By reconciling transparency-privacy tensions and demonstrating BCT's endogenous role in forecasting, this study advances strategies for enhancing supply chain resilience through BCT innovation. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | This study examines values and adoption conditions of Blockchain Technology (BCT) in horizontal demand forecast sharing among retailer, focusing on the influence mechanism of transparency-restriction approaches and BCT's endogenous effects on firms' sharing incentives. We model a supply chain with one manufacturer and multiple retailers, comparing four BCT-enabled data-sharing regimes: open access (permissionless) versus no-open access (permissioned), with or without encryption. Results show that restricted transparency, combined with selective accessibility, aligns individual and collective incentives by curbing wholesale price inflation and improving forecast accuracy. Contrary to intuition, higher transparency does not universally benefit retailers; supplementary encryption can balance data utility and privacy, enabling Pareto-superior outcomes. We further demonstrate BCT can reduces moral hazards in horizontal sharing (e.g. sharing biased forecast), allowing retailers to leverage aggregated demand signals without inefficiently verification. However, excessive transparency in BCT can accelerates retailers' profit erosion, akin to perfect competition. These findings offer micro-foundations for adopting visibility-restriction technologies (e.g. Zero-Knowledge Proofs) and guide the design of context-specific BCT systems. By reconciling transparency-privacy tensions and demonstrating BCT's endogenous role in forecasting, this study advances strategies for enhancing supply chain resilience through BCT innovation. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00207543 |
| DOI: | 10.1080/00207543.2025.2567503 |
