Abstract
This paper explores the optimal joint decision of product information disclosure and ordering in a blockchain-enabled luxury supply chain. Using analytical models, we investigate the optimal joint decision of information disclosure and ordering under three scenarios (i.e., wholesale contracts only, revenue-sharing (RS) contracts only, and a hybrid of these two types of contracts). Furthermore, we extend our study to examine the impacts of the number of competing retailers and the retailers’ fairness concerns on supply chain members’ optimal decisions. Lastly, the theoretical results are checked and illustrated by numerical examples with sensitivity analysis. The main findings are as follows: (1) As the proportion of information-sensitive consumers in the market increases, the level of product information disclosure of supply chain members increases in varying degrees, while supply chain members’ order quantities and profits first decrease and then increase in varying degrees. (2) When a RS contract is acceptable for all supply chain members, all members benefit from the cooperation between the manufacturer and retailers. (3) Although all supply chain members may benefit from an increase in the number of retailers, when the number of retailers is greater than a certain threshold, retailers would be caught in a “prisoner’s dilemma” of product information disclosure due to consumer information overload. Moreover, to maximize business profits, manufacturers should sometimes strictly limit and control the number of their reseller partners, rather than blindly expand their markets. (4) Retailers may benefit from their own fairness concerns if and only if the level of fairness concerns is sufficiently low, otherwise such concerns would be harmful to all supply chain members.





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Notes
This is because if \(a\le \frac{q_i}{\beta _{i}}\), then the sales of the retailer i is \(D_i(\alpha _0, \alpha _i, \alpha _j)=\beta _{i}a\), otherwise the sales of the \(q_{i}\). Thus, we can obtain that the expected sales of the retailer i is \(S_{i}(q_{i},\alpha _{i})=\beta _{i}E(min(a, \frac{q_i}{\beta _{i}}))=\beta _{i}(\int _{0}^{\frac{q_i}{\beta _{i}}}af(a)da+\int _{\frac{q_i}{\beta _{i}}}^{\infty }\frac{q_i}{\beta _{i}}f(a)da)=\beta _{i}\int _{0}^{\frac{q_i}{\beta _{i}}}\bar{F}(a)da=q_{i}-\beta _{i}\int _{0}^{\frac{q_i}{\beta _{i}}}F(a)da\). Specially, when \(\beta _{i}\) is equal to 1, the expected sales of the retailer i is \(S_{i}(q_{i},\alpha _{i})=\int _{0}^{q_i}\bar{F}(a)da=q_{i}-\int _{0}^{q_i}F(a)da\). Similarly, we can also derive Eqs. (3) and (4).
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Acknowledgements
The authors would like to thank the editors and the review team for their valuable comments and suggestions which have significantly improved the quality of this paper. This research was supported partially by the National Natural Science Foundation of China [Grant Nos. 71620107002, 71821001, 71971095 and 71771138] and Special Foundation for Taishan Scholars of Shandong Province, China [Grant No. tsqn201812061].
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Appendices
Appendices
The proof of Theorem 1
Proof
Under Model W, substituting Eq. (2) into Eqs. (5) and (6), respectively. Thus, the profits of supply chain members are given as follows:
and
And \(\beta _{1}=(1-\lambda )+\lambda (\alpha _{0}+\alpha _{1}-\gamma \alpha _{2})\), \(\beta _{2}=(1-\lambda )+\lambda (\alpha _{0}+\alpha _{2}-\gamma \alpha _{1})\).
Solving this two-stage game using backward induction technology. Taking the first-order partial derivative of \(\pi _{Ri}^{W}(q_{i}, \alpha _{i})\) (\(=1, 2\)) with respect to \(q_i\) and \(\alpha _i\), respectively. Thus, we have
Taking the second-order partial derivative of \(\mathop \pi \nolimits _{Ri}^{W}(q_{i}, \alpha _{i})\) (\(=1, 2\)) with respect to \(\alpha _i\) and \(q_i\), respectively. Thus, the corresponding Hessian matrix is given as follows:
Thus, we have
Eqs. (A.9) and (A.10) yield that the Hessian matrix is a negative definite matrix. Consequently, the profit of the retailer i (\(=1\), 2) is a joint concave function of \(\alpha _i\) and \(q_i\), and the first-order conditions can guarantee optimality.
Next, at the Nash equilibrium, we solve the first-order conditions \(\frac{{\partial \mathop \pi \nolimits _{Ri}^{W} (q_{i}, \alpha _{i})}}{{\partial \mathop \alpha \nolimits _i }} = 0\) and \(\frac{{\partial \mathop \pi \nolimits _{Ri}^{W}(q_{i}, \alpha _{i}) }}{{\partial q_i}} = 0\). Thus, we can get four solutions
and \(A=F^{-1}\left( \frac{p-c_r-w+g_r}{p-v+g_r}\right) \).
Then, substituting Eqs. (A.11) and (A.12) into Eq. (2), and taking the first-order partial derivative of \(\mathop \pi \nolimits _M^{W}(\alpha _{0})\) with respect to \(\mathop \alpha \nolimits _0 \), we have
Taking the second-order partial derivative of \(\pi _{M}^{W}(\alpha _{0})\) with respect to \(\alpha _0\), we have
Thus, the profit function of the manufacturer is a concave function of \(\mathop \alpha \nolimits _0 \), and the first-order condition can guarantee optimality.
Next, at the Nash equilibrium, we solve the first-order condition \(\frac{{\partial \mathop \pi \nolimits _{M}^{W}(\alpha _{0}) }}{{\partial \mathop \alpha \nolimits _0 }} = 0\). Thus, we can get three solutions
\(\square \)
The proof of Theorem 2
Proof
Substituting Eqs. (A.11), (A.15) and (A.16) into Eqs. (A.1), (A.2) and (A.3), respectively. Thus, we have
\(\square \)
The proof of Corollary 1
Proof
Taking the first-order partial derivatives of \(\alpha _{0}^{W*}\), \(\alpha _{i}^{W*}\), \(q_{i}^{W*}\), \(\pi _{Ri}^{W*}(q_{i}^{W*}, \alpha _{i}^{W*})\) and \(\pi _{M}^{W*}(\alpha _{0}^{W*})\) with respect to \(\lambda \), where \(i=1\), 2, respectively. Thus, we have
Thus, if \(0\le \lambda \le min(\eta _1, 1)\), then \(\frac{\partial q_{i}^{W*}}{\partial \lambda }\le 0\), otherwise \(\frac{\partial q_{i}^{W*}}{\partial \lambda }> 0\); If \(0\le \lambda \le min(\eta _2, 1)\), then \(\frac{\partial \pi _{i}^{W*}}{\partial \lambda }\le 0\), otherwise \(\frac{\partial \pi _{i}^{W*}}{\partial \lambda }> 0\); And if \(0\le \lambda \le min(\eta _3, 1)\), then \(\frac{\partial \pi _{M}^{W*}}{\partial \lambda }\le 0\), otherwise \(\frac{\partial \pi _{M}^{W*}}{\partial \lambda }> 0\), where
\(\eta _{1}=\frac{k}{((1+\gamma )w+2g_m-2c_m)A-2 g_m(\mu +\int _{0}^{A}F(a)da)+(1-\gamma )\left\{ (p-c_r+g_r)A-g_r\mu -(p-v+g_r)\int _{0}^{A}F(a)da\right\} }\),
\(\eta _{2}=\frac{k}{((1.5+\gamma )w+2g_m-2c_m)A-2g_m(\mu +\int _{0}^{A}F(a)da)+(0.5-\gamma )\left\{ (p-c_r+g_r)A-g_r\mu -(p-v+g_r)\int _{0}^{A}F(a)da\right\} }\),
\(\eta _{3}=\frac{k}{\{\gamma w+g_m-c_m\}A-g_m(\mu +\int _{0}^{A}F(a)da)+(1-\gamma )\left\{ (p-c_r+g_r)A-g_r\mu -(p-v+g_r)\int _{0}^{A}F(a)da\right\} }\). \(\square \)
The proof of Theorem 3
Proof
Under Model RS, substituting Eqs. (2), (3) and (4) into Eqs. (12) and (13), respectively. Thus, the profits of the retailer i (=1, 2) and the manufacturer are given as follows:
and
Substituting Eqs. (14) and (15) into Eqs. (D.1), (D.2) and (D.3), respectively. Thus, we have
and
Solving this two-stage game using backward induction technology. Taking the first-order partial derivative of \(\pi _{Ri}^{RS}(q_{i}, \alpha _{i})\) (\(=1, 2\)) with respect to \(q_i\) and \(\alpha _i\), respectively. Thus, we have
Taking the second-order partial derivative of \(\mathop \pi \nolimits _{Ri}^{RS}(q_{i}, \alpha _{i})\) (\(=1, 2\)) with respect to \(\alpha _i\) and \(q_i\), respectively. Thus, the corresponding Hessian matrix is given by as follows:
Thus, we have
Eqs. (D.12) and (D.13) yield that the Hessian matrix is a negative definite matrix. Consequently, the profit of the retailer i (\(=1, 2\)) is a joint concave function of \(\alpha _i\) and \(q_i\), and thus the first-order conditions can guarantee optimality.
Next, at the Nash equilibrium, we solve the first-order conditions \(\frac{{\partial \mathop \pi \nolimits _{Ri}^{RS} (q_{i}, \alpha _{i})}}{{\partial \mathop \alpha \nolimits _i }} = 0\), \(\frac{{\partial \mathop \pi \nolimits _{Ri}^{RS}(q_{i}, \alpha _{i}) }}{{\partial q_i}} = 0\), where \(i=1\), 2. Thus, we can get four solutions
and \(D=F^{-1}\left( \frac{p-c+g}{p-v+g}\right) \).
Then, substituting Eqs. (D.14) and (D.15) into Eq. (2), respectively, and taking the first-order partial derivative of \(\mathop \pi \nolimits _M^{RS}(\alpha _{0})\) with respect to \(\mathop \alpha \nolimits _0 \), we have
Taking the second-order partial derivative of \(\pi _{M}^{RS}(\alpha _{0})\) with respect to \(\alpha _0\), we have
Thus, the profit function of the manufacturer is a concave function in \(\mathop \alpha \nolimits _0 \), and the first-order condition can guarantee optimality.
Next, at the Nash equilibrium, we solve the first-order condition \(\frac{{\partial \mathop \pi \nolimits _{M}^{RS}(\alpha _{0}) }}{{\partial \mathop \alpha \nolimits _0 }} = 0\). Thus, we can get three solutions
\(\square \)
The proof of Theorem 4
Proof
Substituting Eqs. (D.14), (D.18) and (D.19) into Eqs. (D.4), (D.5) and (D.6), respectively. Thus, we have
\(\square \)
The proof of Corollary 2
Proof
Taking the first-order partial derivatives of \(\alpha _{0}^{RS*}\), \(\alpha _{i}^{RS*}\), \(q_{i}^{RS*}\), \(\pi _{Ri}^{RS*}(q_{i}^{RS*}, \alpha _{i}^{RS*})\) and \(\pi _{M}^{RS*}(\alpha _{0}^{RS*})\) with respect to \(\lambda \), where \(i=1\), 2, respectively. Thus, we have
Thus, if \(0\le \lambda \le min(\eta _4, 1)\), then \(\frac{\partial q_{i}^{RS*}}{\partial \lambda }\le 0\), otherwise \(\frac{\partial q_{i}^{RS*}}{\partial \lambda }> 0\); If \(0\le \lambda \le min(\eta _5, 1)\), then \(\frac{\partial \pi _{i}^{RS*}}{\partial \lambda }\le 0\), otherwise \(\frac{\partial \pi _{i}^{RS*}}{\partial \lambda }> 0\); And if \(0\le \lambda \le min(\eta _6, 1)\), then \(\frac{\partial \pi _{M}^{RS*}}{\partial \lambda }\le 0\), otherwise \(\frac{\partial \pi _{M}^{RS*}}{\partial \lambda }> 0\), where
\(\eta _{4}=\frac{k}{(2-\theta -\theta \gamma )\{(p-c+g)D-(p-v+g)\int _{0}^{D}F(a)da\}-\mu ((1-\gamma )g_r+2g_m)}\),
\(\eta _{5}=\frac{2k}{(4-3\theta -2\theta \gamma )\{(p-c+g)D-(p-v+g)\int _{0}^{D}F(a)da\}-\mu ((1-2\gamma )g_r+4g_m)}\),
\(\eta _{6}=\frac{k}{(1-\theta \gamma )\{(p-c+g)D-(p-v+g)\int _{0}^{D}F(a)da\}-\mu ((1-\gamma )g_r+g_m)}\). \(\square \)
The proof of Theorem 5
Proof
Under Model WRS, substituting Eqs. (2), (3), (4), (14) and (15) into Eqs. (21), (22) and (23), respectively. Thus, the profits of the retailer i (\(=1\), 2) and the manufacturer are given as follows:
and
Solving this two-stage game using backward induction technology. Taking the first-order partial derivative of \(\pi _{Ri}^{WRS}(q_{i}, \alpha _{i})\) (\(=1, 2\)) with respect to \(q_i\) and \(\alpha _i\), respectively. Thus, we have
Taking the second-order partial derivative of \(\mathop \pi \nolimits _{Ri}^{WRS}(q_{i}, \alpha _{i})\) (\(=1, 2\)) with respect to \(\alpha _i\) and \(q_i\), respectively. Thus, the corresponding Hessian matrices are given as follows:
Thus, we have
Eqs. (G.10), (G.11), (G.12) and (G.13) yield that the above Hessian matrices are negative definite matrices. Consequently, the profit of the retailer i (\(=1, 2\)) is a joint concave function of \(\alpha _i\) and \(q_i\), and the first-order conditions can guarantee optimality.
Next, at the Nash equilibrium, we solve the first-order conditions \(\frac{{\partial \mathop \pi \nolimits _{Ri}^{WRS} (q_{i}, \alpha _{i})}}{{\partial \mathop \alpha \nolimits _i }} = 0\), \(\frac{{\partial \mathop \pi \nolimits _{Ri}^{WRS}(q_{i}, \alpha _{i}) }}{{\partial q_i}} = 0\), where \(i=1, 2\). Thus, we can get four solutions
Then, substituting Eqs. (G.14), (G.15), (G.16) and (G.17) into Eq. (G.3), and taking the first-order partial derivative of \(\mathop \pi \nolimits _M^{WRS}(\alpha _{0})\) with respect to \(\mathop \alpha \nolimits _0\), we have
Taking the second-order partial derivative of \(\pi _{M}^{WRS}(\alpha _{0})\) with respect to \(\alpha _0\), we have
Thus, the profit function of the manufacturer is a concave function in \(\mathop \alpha \nolimits _0 \), and the first-order condition can guarantee optimality.
Next, at the Nash equilibrium, we solve the first-order condition \(\frac{{\partial \mathop \pi \nolimits _{M}^{WRS}(\alpha _{0}) }}{{\partial \mathop \alpha \nolimits _0 }} = 0\). Thus, we can get three solutions
\(\square \)
The proof of Theorem 6
Proof
Substituting Eqs. (G.14), (G.15), (G.20), (G.21) and (G.22) into Eqs. (G.1), (G.2) and (G.3), respectively. Thus, we have
\(\square \)
The proof of Corollary 3
Proof
Taking the first-order partial derivatives of \(\alpha _{0}^{WRS*}\), \(\alpha _{i}^{WRS*}\), \(q_{i}^{WRS*}\), \(\pi _{Ri}^{WRS*}(q_{i}^{WRS*}, \alpha _{i}^{WRS*})\) and \(\pi _{M}^{WRS*}(\alpha _{0}^{WRS*})\) with respect to \(\lambda \), where \(i=1\), 2, respectively. Thus, we have
Thus, if \(0\le \lambda \le min(\eta _7, 1)\), then \(\frac{\partial q_{1}^{WRS*}}{\partial \lambda }\le 0\), otherwise \(\frac{\partial q_{1}^{WRS*}}{\partial \lambda }> 0\); if \(0\le \lambda \le min(\eta _8, 1)\), then \(\frac{\partial q_{2}^{WRS*}}{\partial \lambda }\le 0\), otherwise \(\frac{\partial q_{2}^{WS*}}{\partial \lambda }> 0\); If \(0\le \lambda \le min(\eta _9, 1)\), then \(\frac{\partial \pi _{R1}^{WRS*}}{\partial \lambda }\le 0\), otherwise \(\frac{\partial \pi _{R1}^{WRS*}}{\partial \lambda }> 0\); If \(0\le \lambda \le min(\eta _{10}, 1)\), then \(\frac{\partial \pi _{R2}^{WRS*}}{\partial \lambda }\le 0\), otherwise \(\frac{\partial \pi _{R2}^{WRS*}}{\partial \lambda }> 0\); And if \(0\le \lambda \le min(\eta _{11}, 1)\), then \(\frac{\partial \pi _{M}^{WRS*}}{\partial \lambda }\le 0\), otherwise \(\frac{\partial \pi _{M}^{WRS*}}{\partial \lambda }> 0\), where
\(\square \)
The proof of Theorem 7
Proof
Under Model M, there are N competing retailers in the luxury market. Substituting Eqs. (2) into (32) and (33), respectively. Thus, the profits of the retailer i and the manufacturer are given as follows:
where \(\beta _{i}=(1-\lambda )+\lambda \bigg (\alpha _{0}+(1+\gamma )\alpha _{i}-\sum _{j=1}^{N}\gamma \alpha _{j} \bigg )\).
Solving this two-stage game using backward induction technology. Taking the first-order partial derivative of \(\pi _{Ri}^{M}(q_{i}, \alpha _{i})\) with respect to \(q_i\) and \(\alpha _i\), respectively. Thus, we have
Taking the second-order partial derivative of \(\mathop \pi \nolimits _{Ri}^{M}(q_{i}, \alpha _{i})\) with respect to \(\alpha _i\) and \(q_i\), respectively. Thus, the corresponding Hessian matrix is given as follows:
Thus, we have
Eqs. (10) and (J.7) yield that this Hessian matrix is a negative definite matrix. Consequently, the profit of the retailer i is a joint concave function of \(\alpha _i\) and \(q_i\), and the first-order conditions can guarantee optimality.
Next, at the Nash equilibrium, we solve the first-order conditions \(\frac{{\partial \mathop \pi \nolimits _{Ri}^{M} (q_{i}, \alpha _{i})}}{{\partial \mathop \alpha \nolimits _i }} = 0\), \(\frac{{\partial \mathop \pi \nolimits _{Ri}^{M}(q_{i}, \alpha _{i}) }}{{\partial q_i}} = 0\). Thus, we can get the following solutions
Then, substituting Eqs. (J.8) and (J.9) into Eq. (2), and taking the first-order partial derivative of \(\mathop \pi \nolimits _M^{M}(\alpha _{0})\) with respect to \(\mathop \alpha \nolimits _0 \), we have
Taking the second-order partial derivative of \(\pi _{M}^{M}(\alpha _{0})\) with respect to \(\alpha _0\), we have
Thus, the profit function of the manufacturer is a concave function of \(\mathop \alpha \nolimits _0 \), and the first-order condition can guarantee optimality.
Next, at the Nash equilibrium, we solve the first-order condition \(\frac{{\partial \mathop \pi \nolimits _{M}^{M}(\alpha _{0}) }}{{\partial \mathop \alpha \nolimits _0 }} = 0\). Thus, we can get the following solutions
\(\square \)
Substituting Eqs. (J.8), (J.12) and (J.13) into Eqs. (J.1) and (J.2), respectively. Thus, we have
The proof of Corollary 4
Proof
Subtracting \(\alpha _{0}^{M*}(N)\) from \(\alpha _{0}^{M*}(N+1)\), subtracting \(\alpha _{i}^{M*}(N)\) from \(\alpha _{i}^{M*}(N+1)\), subtracting \(q_{i}^{M*}(N)\) from \(q_{i}^{M*}(N+1)\), subtracting \(\pi _{Ri}^{M*}(q_{i}^{M*}(N), \alpha _{i}^{M*}(N))\) from \(\pi _{Ri}^{M*}(q_{i}^{M*}(N+1), \alpha _{i}^{M*}(N+1))\), and subtracting \(\pi _{M}^{M*}(\alpha _{0}^{M*}(N))\) from \(\pi _{M}^{M*}(\alpha _{0}^{M*}(N+1))\), respectively, where i=1, 2. Thus, we have
Thus, if \(\gamma \in [0, min(1,\eta _{12})]\), then \(\pi _{Ri}^{M*}(q_{i}^{M*}(N+1), \alpha _{i}^{M*}(N+1))\ge \pi _{Ri}^{M*}(q_{i}^{M*}(N), \alpha _{i}^{M*}(N))\) and \(q_{i}^{M*}(N+1)\ge q_{i}^{M*}(N)\), otherwise \(q_{i}^{M*}(N+1)< q_{i}^{M*}(N)\) and \(\pi _{Ri}^{M*}(q_{i}^{M*}(N+1), \alpha _{i}^{M*}(N+1))<\pi _{Ri}^{M*}(q_{i}^{M*}(N), \alpha _{i}^{M*}(N))\). If \(\Delta \ge 0\), then \(\pi _{M}^{M*}(\alpha _{0}^{M*}(N+1))\ge \pi _{M}^{M}(\alpha _{0}^{M*}(N))\), if \(\Delta <0\) and \(N < \lfloor N^{*}\rfloor \), then \(\pi _{M}^{M*}(\alpha _{0}^{M*}(N+1))\ge \pi _{M}^{M}(\alpha _{0}^{M*}(N))\); otherwise \(\pi _{M}^{M*}(\alpha _{0}^{M*}(N+1))< \pi _{M}^{M}(\alpha _{0}^{M*}(N))\), where
\(\Delta =(g_m+w-c_m)A-g_m(\int _{0}^{A}F(a)da+\mu )-2\gamma \{ (p-c_r-w+g_r)A-(p-v+g_r)\int _{0}^{A}F(a)da-g_r\mu \}\),
\(\square \)
The calculation of the extended model in Sect. 4.2
Proof
Under Model F, the two competing retailers are concerned about fairness. Substituting Eqs. (A.1), (A.2) and (A.3) into Eq. (37), respectively. Thus, the utility of the retailer i (\(=1, 2\)) is given as follows:
Solving this two-stage game using backward induction technology. Taking the first-order partial derivative of \(u_{Ri}^{F}(q_{i}, \alpha _{i})\) (\(=1, 2\)) with respect to \(q_i\) and \(\alpha _i\), respectively. Thus, we have
Taking the second-order partial derivative of \(\mathop u \nolimits _{Ri}^{F}(q_{i}, \alpha _{i})\) (\(=1, 2\)) with respect to \(\alpha _i\), \(q_i\), respectively. Thus, the corresponding Hessian matrices are given as follows:
Thus, we have
and
Using the conditions \(\frac{{\partial ^{2} \mathop u \nolimits _{Ri}^{F} (q_{i}, \alpha _{i})}}{{\partial \mathop \alpha ^{2}\nolimits _i }}<0\) and \(\left| {\mathop H_{Ri}\nolimits ^{F} } \right| >0\), we can find that the above Hessian matrices are negative definite matrices. Consequently, the profit of the retailer i (\(=1, 2\)) is a joint concave function of \(\alpha _i\) and \(q_i\), and the first-order conditions can guarantee optimality.
Next, at the Nash equilibrium, we solve the first-order conditions \(\frac{{\partial \mathop u \nolimits _{Ri}^{F} (q_{i}, \alpha _{i})}}{{\partial \mathop \alpha \nolimits _i }} = 0\) and \(\frac{{\partial \mathop u \nolimits _{Ri}^{F}(q_{i}, \alpha _{i}) }}{{\partial q_i}} = 0\), where \(i=1\), 2. Thus, we can get four solutions
where \(G=F^{-1}\left( \frac{(1+f)(p-c_r-w+g_r)-f(g_m+w-c_m)}{(1+f)(p-v+g_r)-fg_m}\right) \).
The profit of manufacturer is as follows:
Then, substituting Eqs. (L.15) and (L.16) into Eq. (2), and taking the first-order partial derivative of \(\mathop \pi \nolimits _M^{F}(\alpha _{0})\) with respect to \(\mathop \alpha \nolimits _0 \), we have
Taking the second-order partial derivative of \(\pi _{M}^{F}(\alpha _{0})\) with respect to \(\alpha _0\), we have
Thus, the profit function of the manufacturer is a concave function of \(\mathop \alpha \nolimits _0 \), and the first-order condition can guarantee optimality.
Next, at the Nash equilibrium, we solve the first-order condition \(\frac{{\partial \mathop \pi \nolimits _{M}^{F}(\alpha _{0}) }}{{\partial \mathop \alpha \nolimits _0 }} = 0\). Thus, we can get three solutions
\(\square \)
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Li, Z., Xu, X., Bai, Q. et al. Optimal joint decision of information disclosure and ordering in a blockchain-enabled luxury supply chain. Ann Oper Res 329, 1263–1314 (2023). https://doi.org/10.1007/s10479-022-04703-6
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DOI: https://doi.org/10.1007/s10479-022-04703-6