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Retailer's willingness to adopt blockchain technology based on private demand information

This research is supported by the National Natural Science of China under Grants (No:71771082, 71850012, 72171077), Hunan Provincial Key Laboratory (No. 2020TP1013), National Social Science Foundation of China (No.19AZD014)

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  • This paper considers a supply chain that includes one supplier and one retailer, in which the retailer has a more accurate demand forecast. The blockchain technology can verify the authenticity of the information, then the retailer can choose to truly share the demand information with the supplier by adopting such a costly technology. We discuss three scenarios based on signaling game: the retailer bears all the cost (no subsidy), the supplier bears part of the cost by providing direct subsidy or wholesale discount, respectively. Specifically, in a demand information asymmetric setting, we mainly focus on exploring the conditions of retailer adopting blockchain technology and the supplier's subsidy strategy choice, and further verify the robustness of the model by considering the retailer's risk aversion or multiple suppliers. In all scenarios, we find that the retailer will apply threshold strategy to adopt blockchain technology. The retailer's willingness to adopt blockchain technology is negatively correlated with the corresponding adoption cost, the supplier's profit level, and positively correlated with the number of suppliers. Additionally, we find the supplier can profit from providing subsidies when the cost of adopting blockchain technology is around a medium level, and the direct subsidy is superior to wholesale discount. More surprisingly, we find subsidies may work to the disadvantage of the subsidized party. Specifically, compared to no subsidy, we find the direct subsidy and the wholesale discount can increase the retailer's willingness to adopt blockchain technology, but hurt the retailer's as well as the supply chain's profits.

    Mathematics Subject Classification: Primary: 91A27, 90B06; Secondary: 91A28.


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  • Figure 1.  Equilibrium point $ \xi_i $ (where $ i = 0, 1, 2 $) with different values of $ CR $ and $ M $

    Figure 2.  The retailer's expected profit $ \Pi_r $ under different scenarios

    Figure 3.  Direct subsidy $ H $ and wholesale discount $ \delta $

    Figure 4.  Sketch of the active region for the direct subsidy and wholesale discount

    Figure 5.  Rough sketch structure of different threshold when $ M $ is in an intermediate range

    Figure 6.  $ \Pi_s $ difference between three scenarios with different values of $ CR $ and $ M $

    Figure 7.  $ \Pi_r $ difference between three scenarios with different values of $ CR $ and $ M $

    Figure 8.  The profit ratio $ \rho \left({\Pi^{ds}}/{\Pi^{cs}}, {\Pi^{A}}/{\Pi^{cs}}, {\Pi^{B}}/{\Pi^{cs}}, {\Pi^{C}}/{\Pi^{cs}}\right) $

    Figure 9.  Profits as a function of risk-adjusted profit margin

    Figure 10.  Profits as a function of degree of forecast information asymmetry

    Figure 11.  The decision-making process

    Table 1.  Number of suppliers

    The retailer Number of suppliers from 'qcc.com' Number of suppliers from 'tianyancha.com' Average number of suppliers Whether to use blockchain technology (using 1, not 0)
    Wal-Mart 154 134 144 1
    YONGHUI 47 47 47 1
    Amazon 15 14 14.5 1
    China Resources Vanguard Shop 18 12 15 0
    Minnings 14 24 19 0
    Miniso 12 9 10.5 0
    Lianhua Supermarket 7 5 6 0
    Watsons 5 6 5.5 0
    Metro AG 33 31 32 0
    RT-MART 54 54 54 0
    Hyper-mart 75 57 66 0
    Auchan 45 33 39 0
    Wumart 7 6 6.5 0
    JIAJIAYUE 43 29 36 0
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