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Blockchain-Based Design of a Government Incentive Mechanism for Manufacturing Supply Chain Data Governance

Author

Listed:
  • Jinyu Wei

    (School of Management, Tianjin University of Technology, No. 391, Binshui West Road, Tianjin 300384, China)

  • Xiuping Yi

    (School of Management, Tianjin University of Technology, No. 391, Binshui West Road, Tianjin 300384, China)

  • Xin Yang

    (School of Economics and Management, Tianjin Agricultural College, Tianjin 300384, China)

  • Yaoxi Liu

    (School of Management, Tianjin University of Technology, No. 391, Binshui West Road, Tianjin 300384, China)

Abstract

In the context of “double carbon”, constructing green supply chains is the only way to implement sustainable development strategies in the manufacturing industry. This paper, therefore, examines the manufacturing supply chain for low-carbon products. More recently, the lack of technical information flow due to data barriers up and down the supply chain has led to high energy consumption, the serious waste of raw materials, and the substandard production of green products. Therefore, the level of supply chain data governance must be improved to enhance the sustainability of the supply chain. By studying blockchain-based data governance and government policy incentives for manufacturing supply chains, this study constructed an evolutionary game model based on prospect theory for the tripartite relation of government, manufacturers, and retailers. The difference between the perceived and actual value was introduced into a three-way evolutionary game model based on prospect theory to optimize the practical implications of the model. The model was then simulated using system dynamics. Through the simulation, it could be concluded that the ability of the three-way evolutionary game to reach the optimal stability point is only related to the sensitivity of the retailer’s perceived value. Additionally, the outcome of the three-way evolutionary game can be unstable, with changes in perceived value sensitivity. Finally, relevant policy recommendations are made. The innovation of this study is establishing a data governance platform that uses data governance to build green supply chains. Additionally, the government was added to the subjects of the game to explore the role of government policy in data governance and sustainable development. In addition, the evolutionary game model was incorporated with prospect theory and traditional expected utility theory, and the rational deficits and preferences of decision makers were taken into account, which brings the results closer to the reality of the situation.

Suggested Citation

  • Jinyu Wei & Xiuping Yi & Xin Yang & Yaoxi Liu, 2023. "Blockchain-Based Design of a Government Incentive Mechanism for Manufacturing Supply Chain Data Governance," Sustainability, MDPI, vol. 15(8), pages 1-21, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6968-:d:1128862
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    References listed on IDEAS

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    1. Muhammad Umar Farooq & Amjad Hussain & Tariq Masood & Muhammad Salman Habib, 2021. "Supply Chain Operations Management in Pandemics: A State-of-the-Art Review Inspired by COVID-19," Sustainability, MDPI, vol. 13(5), pages 1-33, February.
    2. Shengjun Yuan & Jie Li & Xin Su, 2022. "Impact of Government Subsidy Strategies on Supply Chains Considering Carbon Emission Reduction and Marketing Efforts," Sustainability, MDPI, vol. 14(5), pages 1-19, March.
    3. Jiayi Joey Yu & Christopher S. Tang & Musen Kingsley Li & Zuo‐Jun Max Shen, 2022. "Coordinating Installation of Electric Vehicle Charging Stations between Governments and Automakers," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 681-696, February.
    4. Li, Kun & Dong, Feng, 2022. "Government strategy for banning gasoline vehicles: Evidence from tripartite evolutionary game," Energy, Elsevier, vol. 254(PA).
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Badi, Sulafa & Naidoo, Livashnee, 2025. "A process model of governance adaptation and performance outcomes in e-commerce permissioned blockchain networks," International Journal of Production Economics, Elsevier, vol. 288(C).
    2. Wid Alrashidi & Safiah Alhazmi & Fotoon Sayegh & Sherif Edris, 2025. "Microalga-Based Electricity Production: A Comprehensive Review," Energies, MDPI, vol. 18(3), pages 1-23, January.
    3. Joudi Dibsi & Jaeyoung Cho, 2023. "Data Management for Environmentally Sustainable and Profitable Business: Evidence from the Food Retail Industry," Sustainability, MDPI, vol. 15(23), pages 1-28, November.
    4. Muen Uddin & Shitharth Selvarajan & Muath Obaidat & Shams Ul Arfeen & Alaa O. Khadidos & Adil O. Khadidos & Maha Abdelhaq, 2023. "From Hype to Reality: Unveiling the Promises, Challenges and Opportunities of Blockchain in Supply Chain Systems," Sustainability, MDPI, vol. 15(16), pages 1-24, August.
    5. Zhi Li & Fuhe Liang & Henan Hu, 2023. "Blockchain-Based and Value-Driven Enterprise Data Governance: A Collaborative Framework," Sustainability, MDPI, vol. 15(11), pages 1-15, May.

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