Abstract
The blockchain technology has recently started to gain traction in supply chain management. Along with the smart contract which automates payments following a pre-defined protocol, the information recorded in the blockchain can be used to hold the failure-causing suppliers accountable for their own faults and allow the buying firm to pay the suppliers contingently. This could change supply chain quality contracting for industries where supplier accountability is difficult to achieve under traditional technologies (e.g., agri-food and pharmaceutical). In this work, we study the impact of accountability in a multi-sourcing supply chain, where a buying firm procures from multiple suppliers who belong to the same tier of the supply chain. We find that in a multi-sourcing supply chain, a critical value of accountability is that it guarantees cash flow feasibility for the buyer when he offers first-best quality contracts to the suppliers, hence improving the implementability of first-best quality contracts in practice. We further find that the value of accountability is strengthened as the supply chain becomes more complicated, while weakened when suppliers face limited liability constraints.
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Notes
- 1.
- 2.
We focus on the suppliers’ quality decisions and do not consider the buyer’s quality decision, which is consistent with the literature (e.g., Baiman et al., 2000; Hwang et al., 2006; Babich & Tang, 2012; Rui & Lai, 2015; Nikoofal & Gümüş, 2018). The main insights of the paper would carry through if the buyer’s quality decision is incorporated.
- 3.
It is easy to see that if a cash flow feasibility constraint (i.e., \(\sum _{i=1}^n w_i \leqslant p\)) is explicitly incorporated into the model, the buyer will not be able to induce the first-best if \(p<\bar {p}\) in the case without accountability.
- 4.
- 5.
The result that the IRi constraint is binding in equilibrium can be proved by assigning Lagrangian multipliers λ i and μ i to the IRi and ICi constraints, respectively. The Lagrangian of (3) is
$$\displaystyle \begin{aligned} L(\vec{w}, \vec{t}, \vec{q})=&\,\,p\prod_{i=1}^n q_i-l\left(1-\prod_{i=1}^n q_i\right)-\left(\sum_{i=1}^n w_i\right)\prod_{i=1}^n q_i-\left(\sum_{i=1}^n t_i\right)\left(1-\prod_{i=1}^n q_i\right) \\ &+\sum_{i=1}^n\lambda_i\bigg[w_i q_i \prod_{j=1, j\neq i}^n q_j+t_i\left(1-q_i \prod_{j=1, j\neq i}^n q_j\right)-C_i(q_i)\bigg] \\ &+\sum_{i=1}^n\mu_i\bigg[(w_i-t_i)\prod_{j=1, j\neq i}^n q_j-C_i^{\prime}(q_i)\bigg]. \end{aligned}$$The first-order conditions w.r.t. w i and t i lead to λ i = 1 and μ i = 0, implying that the IRi constraint is binding in equilibrium.
References
Ang, E., Iancu, D., & Swinney, R. (2017). Disruption risk and optimal sourcing in multitier supply networks. Management Science, 63(8), 2397–2419.
Aydin, G., & Porteus, E. (2008). Joint inventory and pricing decisions for an assortment. Operations Research, 56(5), 1247–1255.
Babich, V., & Hilary, G. (2020). Distributed ledgers and operations: What operations management researchers should know about Blockchain technology. Manufacturing & Service Operations Management, 22(2), 223–240.
Babich, V., & Tang, C. (2012). Managing opportunistic supplier product adulteration: Deferred payments, inspection, and combined mechanisms. Manufacturing & Service Operations Management, 14(2), 301–314.
Baiman, S., Fischer, P., & Rajan, M. (2000). Information, contracting, and quality costs. Management Science, 46(6), 776–789.
Baiman, S., Fischer, P., & Rajan, M. (2001). Performance measurement and design in supply chains. Management Science, 47(1), 173–188.
Baiman, S., Netessine, S., & Kunreuther, H. (2004). Procurement in supply chains when the end-product exhibits the “weakest link” property. https://doi.org/10.2139/ssrn.2077640
Bajpai, P. (2019, February 14). IBM and Blockchain: What it did in 2018, and where it’s going in 2019. Nasdaq. https://www.nasdaq.com/article/ibm-and-blockchain-what-it-did-in-2018-and-where-its-going-in-2019-cm1100102
Balachandran, K., & Radhakrishnan, S. (2005). Quality implications of warranties in a supply chain. Management Science, 51(8), 1266–1277.
Basu, M. (2015, September 21). For first time, company owner faces life sentence for food poisoning outbreak. CNN. https://edition.cnn.com/2015/09/20/us/peanut-butter-salmonella-trial/index.html
Chao, G., Iravani, S., & Savaskan, R. (2009). Quality improvement incentives and product recall cost sharing contracts. Management Science, 55(7), 1122–1138.
Chen, J., Qi, A., & Dawande, M. (2020). Supplier centrality and auditing priority in socially-responsible supply chains. Manufacturing & Service Operations Management, 22(6), 1199–1214.
Chod, J., Trichakis, N., & Yang, S. (2021). Platform tokenization: Financing, governance, and moral hazard. Management Science, Forthcoming.
Chod, J., Trichakis, N., Tsoukalas, G., Aspegren, H., & Weber, M. (2020). On the financing benefits of supply chain transparency and blockchain adoption. Management Science, 66(10), 4378–4396.
Chu, L., & Lai, G. (2013). Salesforce contracting under demand censorship. Manufacturing & Service Operations Management, 15(2), 320–334.
Chu, Y. (2016, June 23). Getting smart about smart contracts. Wall Street Journal. https://deloitte.wsj.com/cfo/2016/06/23/getting-smart-about-smart-contracts/
Chu, Y. (2017, March 9). Contracts get smarter with blockchains. Wall Street Journal. https://deloitte.wsj.com/cio/2017/03/09/contracts-get-smarter-with-blockchains/
Corbett, C., & DeCroix, G. (2001). Shared-savings contracts for indirect materials in supply chains: Channel profits and environmental impacts. Management Science, 47(7), 881–893.
Cui, Y., Gaur, V., & Liu. J. (2020). Blockchain collaboration with competing firms in a shared supply chain: Benefits and challenges. https://doi.org/10.2139/ssrn.3626028
Cui, Y., Hu, M., & Liu, J. (2021). Value and design of traceability-driven blockchains. https://doi.org/10.2139/ssrn.3291661
Dong, Y., Xu, K., Xu, Y., & Wan, X. (2016). Quality management in multi-level supply chains with outsourced manufacturing. Production and Operations Management, 25(2), 290–305.
Fang, X., Ru, J., & Wang, Y. (2014). Optimal procurement design of an assembly supply chain with information asymmetry. Production and Operations Management, 23(12), 2075–2088.
Gan, J., Tsoukalas, G., & Netessine, S. (2021). Initial coin offerings, speculation, and asset tokenization. Management Science, 67(2), 914–931.
Goldschmidt, D. (2018, October 5). 6.5 million pounds of beef recalled due to salmonella outbreak. CNN. https://edition.cnn.com/2018/10/04/health/salmonella-outbreak-jbs-tolleson-meat-recall-bn/index.html
Gümüş, M., Ray, S., & Gurnani, H. (2012). Supply-side story: Risks, guarantees, competition, and information asymmetry. Management Science, 58(9), 1694–1714.
Hellwig, D., & Huchzermeier, A. (2021). Distributed ledger technology and fully homomorphic encryption: Next-generation information-sharing for supply chain efficiency. In V. Babich, J. Birge, & G. Hilary (eds.), Innovative technology at the interface of finance and operations. Springer Series in Supply Chain Management, (Vol. 13, pp. xx–yy) Springer Nature.
Hilary, G. (2021). Blockchain and other distributed ledger technologies, an advanced primer. In V. Babich, J. Birge, & G. Hilary (eds.), Innovative technology at the interface of finance and operations. Springer Series in Supply Chain Management, Springer Nature.
Hu, B., & Qi, A. (2018). Optimal procurement mechanisms for assembly. Manufacturing & Service Operations Management, 20(4), 655–666.
Hwang, I., Radhakrishnan, S., & Su, L. (2006). Vendor certification and appraisal: Implications for supplier quality. Management Science, 52(10), 1472–1482.
Isidore, C. (2001, May 22). Ford recalls 13 million tires. CNN. https://money.cnn.com/2001/05/22/recalls/ford/
Jiang, L., & Wang, Y. (2010). Supplier competition in decentralized assembly systems with price-sensitive and uncertain demand. Manufacturing & Service Operations Management, 12(1), 93–101.
Karimi, F., & Goldschmidt, D. (2018, July 26). These foods linked to ongoing outbreaks might make you sick. CNN. https://edition.cnn.com/2018/07/20/health/salmonella-cyclospora-outbreak-food-recall/index.html
Li, L. (2012). Capacity and Quality Contract Design in Supply Chain. PhD thesis, Northwestern University
Metcalfe, R. (2019, February 21). The life story of your supermarket chicken: Food tracking goes high-tech. Wall Street Journal. https://www.wsj.com/articles/the-life-story-of-your-supermarket-chicken-food-tracking-goes-high-tech-11550761202
Mu, L., Dawande, M., Geng, X., & Mookerjee, V. (2016). Milking the quality test: Improving the milk supply chain under competing collection intermediaries. Management Science, 62(5), 1259–1277.
Nash, K. (2016, December 16). Wal-Mart readies Blockchain pilot for tracking U.S produce, China pork. Wall Street Journal. https://blogs.wsj.com/cio/2016/12/16/wal-mart-readies-blockchain-pilot-for-tracking-u-s-produce-china-pork/
Nath, T. (2020, March 25). How do recalls affect a company? Investopedia. https://www.investopedia.com/articles/investing/010815/how-do-recalls-affect-company.asp
Nikoofal, M., & Gümüş, M. (2018). Quality at the source or at the end? Managing supplier quality under information asymmetry. Manufacturing & Service Operations Management, 20(3), 498–516.
Petruzzi, N., & Dada, M. (1999). Pricing and the newsvendor problem: A review with extensions. Operations Research, 47(2), 183–194.
Plambeck, E., & Taylor, T. (2016). Supplier evasion of a buyer’s audit: Implications for motivating supplier social and environmental responsibility. Manufacturing & Service Operations Management, 18(2), 184–197.
Prevor, J. (2008, March 25). Wal-Mart announces product removal fee. Perishable Pundit. http://www.perishablepundit.com/index.php?date=03/25/08&pundit=2
Pun, H., Swaminathan, J., & Hou, P. (2021). Blockchain adoption for combating deceptive counterfeits. Production and Operations Management, 30(4), 864–882.
Rui, H., & Lai, G. (2015). Sourcing with deferred payment and inspection under supplier product adulteration risk. Production and Operations Management, 24(6), 934–946.
Tsoukalas, G., & Falk, B. (2020). Token-weighted crowdsourcing. Management Science, 66(9), 3843–3859.
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Appendix: Proofs
Appendix: Proofs
Proof of Lemma 1
The first-order conditions of Π B(q 1, q 2, …, q n) are
where i ∈{1, 2, …, n}. Taking the second-order derivatives of Π B(q 1, q 2, …, q n) w.r.t. q i and q j yields \(\frac {\partial ^2 \varPi _{\mathrm {B}}(q_1, q_2, \ldots , q_n)}{\partial q_i^2}=-C_i^{\prime \prime }(q_i)\) and \(\frac {\partial ^2 \varPi _{\mathrm {B}}(q_1, q_2, \ldots , q_n)}{\partial q_i \partial q_j}=(p+l)\prod _{k=1, k\neq i, j}^n q_k\), where i, j ∈{1, 2, …, n} and i ≠ j. The Hessian of Π B(q 1, q 2, …, q n) is:
By Assumption 1, the solution to (5) is either (0, 0, …, 0) or an interior point \((q_1^{*}, q_2^{*}, \ldots , q_n^{*})\), where \(q_i^{*}\in (0, 1)\) for i ∈{1, 2, …, n}. Specifically, it is impossible for any \(q_i^{*}\) to be 1 since \(C_i^{\prime }(1)>p+l\), and it is also impossible for some but not all \(q_i^{*}\) equals 0 to be the solution because the n equations in (5) cannot be satisfied at the same time. In addition, Assumption 1 also guarantees that (0, 0, …, 0) cannot be a local maximum, because the Hessian of Π B(q 1, q 2, …, q n) is not negative definite at (0, 0, …, 0) due to \(0\leqslant C_i^{\prime \prime }(0)<p+l\).
Next, we prove the existence of an interior solution \((q_1^{*}, q_2^{*}, \ldots , q_n^{*})\) satisfying (2) by induction. First, consider n = 2. We can view \(q_1^{*}\) as a function of \(q_2^{*}\) and view \(q_2^{*}\) as a function of \(q_1^{*}\), i.e., \(q_2^{*}=\frac {C_1^{\prime }(q_1^{*})}{p+l}\) and \(q_1^{*}=\frac {C_2^{\prime }(q_2^{*})}{p+l}\). Thus, to prove the existence of an interior solution \((q_1^{*}, q_2^{*})\) is equivalent to showing the existence of an interior intersection point of the following two lines: \(q_2=F_1(q_1)=\frac {C_1^{\prime }(q_1)}{p+l}\) and q 2 = F 2(q 1), where q 2 = F 2(q 1) is the inverse function of \(q_1=\frac {C_2^{\prime }(q_2)}{p+l}\). We have \(F_1^{\prime }(q_1)=\frac {C_1^{\prime \prime }(q_1)}{p+l}>0\) and \(F_2^{\prime }(q_1)=\frac {p+l}{C_2^{\prime \prime }(q_2)}>0\) for any q 1 > 0. By Assumption 1, we have F 1(0) = F 2(0) = 0, \(F_1^{\prime }(0)=\frac {C_1^{\prime \prime }(0)}{p+l}<1\), and \(F_2^{\prime }(0)=\frac {p+l}{C_2^{\prime \prime }(0)}>1\). Thus, there must exist an infinitesimal 𝜖 > 0 such that F 1(𝜖) < F 2(𝜖). On the other hand, since \(\frac {C_2^{\prime }(1)}{p+l}>1\) by Assumption 1, in order for F 2(q 1) = 1, q 1 must be strictly greater than 1. Then, we have F 2(1) < 1 because \(F_2^{\prime }(q_1)>0\) for any q 1 > 0. Thus, \(F_1(1)=\frac {C_1^{\prime }(1)}{p+l}>1>F_2(1)\). Hence, following the monotonicity of F 1(q 1) and F 2(q 1), they must have an intersection within (0, 1). Therefore, the existence of an interior solution in the case of n = 2 is proved. Then, consider n > 2. Suppose an interior solution \((q_1^{*}, q_2^{*}, \ldots , q_n^{*})\) exists in the case of n > 2, which satisfies
Then, regarding the case of n + 1, considering \(\tilde {q}_i(q_{n+1})\) as functions of q n+1, we have
and
Hence, the interior solution characterized in (6) can be viewed as a special case of (7), where q n+1 is considered as an exogenous parameter and q n+1 = 1. Since \((q_1^{*}, q_2^{*}, \ldots , q_n^{*})\) characterized in (6) is an interior solution in the case of n > 2, we have \(\tilde {q}_i(1)=q_i^{*}<1\) for i ∈{1, 2, …, n}. Then, by (7), we have
for i ∈{1, 2, …, n}. By (8), we have
Solving (9) and (10), we have \(\frac {d \tilde {q}_i(q_{n+1})}{d q_{n+1}}=\frac {C_{n+1}^{\prime \prime }(q_{n+1})q_{n+1}+(p+l)\prod _{j=1}^n \tilde {q}_j(q_{n+1})}{C_i^{\prime \prime }(\tilde {q}_i(q_{n+1}))\tilde {q}_i(q_{n+1})+(p+l)\prod _{j=1, j\neq i}^n \tilde {q}_j(q_{n+1}) q_{n+1}}>0\), for any q n+1 > 0 and for i ∈{1, 2, …, n}. Moreover, from (7), we can obtain \(\tilde {q}_i(0)=0\). Combining with \(\frac {d \tilde {q}_i(q_{n+1})}{d q_{n+1}}>0\), we have \(0=\tilde {q}_i(0)<\tilde {q}_i(q_{n+1})<\tilde {q}_i(1)<1\), for any q n+1 ∈ (0, 1) and for i ∈{1, 2, …, n}. Then, combining with Assumption 1, we have \(C_{n+1}^{\prime }(0)=0<(p+l)\prod _{i=1}^n \tilde {q}_i(q_{n+1})<p+l<C_{n+1}^{\prime }(1)\). Hence, there must exist an interior \(q_{n+1}^{*}\in (0, 1)\) that satisfies (8). Moreover, according to (7), \(\tilde {q}_i(q_{n+1}^{*})\in (0, 1)\) for i ∈{1, 2, …, n}. Therefore, the existence of an interior solution in the case of n + 1 is proved.
Next, we show that the interior solution \((q_1^{*}, q_2^{*}, \ldots , q_n^{*})\) is the unique global maximum. In particular, we will prove that the sufficient condition of the local maximum is able to guarantee the unique global maximum, the underlying idea of which was used previously by Petruzzi and Dada (1999) and Aydin and Porteus (2008). First, we show that \((q_1^{*}, q_2^{*}, \ldots , q_n^{*})\) is a strict local maximum. Denote by \(H(\vec {q})_{i j}\) the entry in row i and column j of the matrix \(H(\vec {q})\), where i, j ∈{1, 2, …, n}. Due to Assumption 2, we have \(\left |H(\vec {q}^{*})_{i i}\right | = C_i^{\prime \prime }(q_i^{*}) > (n-1)(p+l) > \sum _{j=1, j\neq i}^n \left [(p+l) \prod _{k=1, k\neq i, j}^n q_k^{*}\right ] = \sum _{j=1, j\neq i}^n \left |H(\vec {q}^{*})_{i j}\right |\), for i ∈{1, 2, …, n}, which implies that \(H(\vec {q}^{*})\) is a diagonally dominant matrix. Besides, since \(H(\vec {q}^{*})\) is also a symmetric real matrix with negative diagonal entries, we know that the Hessian of Π B(q 1, q 2, …, q n) is negative definite in the neighborhood of any \(\vec {q}^{*}=(q_1^{*}, q_2^{*}, \ldots , q_n^{*})\) satisfying (2), where \(q_i^{*}\in (0, 1)\) for i ∈{1, 2, …, n}. Thus, any interior stationary point is a strict local maximum. Then, we show that the interior stationary point is the unique global maximum. Suppose now that there exist more than one, say two, interior stationary points for the function Π B(q 1, q 2, …, q n). Because both points need to be local maxima, the function should also have an interior local minimum somewhere in between, which is a contradiction to the result that all interior stationary points are local maxima. Consequently, we can conclude that there exists only one stationary point \((q_1^{*}, q_2^{*}, \ldots , q_n^{*})\) that satisfies (2), which is the unique local maximum, and thus, the unique global maximum.
Next, suppose suppliers are symmetric. By (2), considering \(q_i^{*}(n)\) as functions of n, we have \((p+l)(q_i^{*}(n))^{n-1}=C_i^{\prime }(q_i^{*}(n))\). Thus we have \(\frac {d q_i^{*}(n)}{d n}=\frac {(p+l)(q_i^{*}(n))^{n-1}\ln q_i^{*}(n)}{C_i^{\prime \prime }(q_i^{*}(n))-(n-1)(p+l)(q_i^{*}(n))^{n-2}}<0\), where the inequality is due to Assumption 2.
Finally, we prove the last part of the lemma. By (2), we have \(C_i^{\prime }(q_i^{*})q_i^{*}=C_j^{\prime }(q_j^{*})q_j^{*}\). Assuming \(C_i^{\prime }(q)\leqslant C_j^{\prime }(q)\) for all q ∈ (0, 1), we can prove \(q_i^{*}\geqslant q_j^{*}\) by contradiction. Specifically, suppose \(q_i^{*}<q_j^{*}\), we have \(C_i^{\prime }(q_i^{*})q_i^{*}\leqslant C_j^{\prime }(q_i^{*})q_i^{*}<C_j^{\prime }(q_j^{*})q_i^{*}<C_j^{\prime }(q_j^{*})q_j^{*}\), which is contradictory to \(C_i^{\prime }(q_i^{*})q_i^{*}=C_j^{\prime }(q_j^{*})q_j^{*}\). Thus, \(q_i^{*}\geqslant q_j^{*}\) holds. □
Proof of Proposition 1
We first derive the suppliers’ optimal quality decisions. Given contract (w i, t i), supplier i ∈{1, 2, …, n} chooses his quality q i to maximize his expected profit \(\varPi _{\mathrm {S}_i}(q_i|w_i, t_i, q_{-i})\). The first-order condition is \(\frac {d \varPi _{\mathrm {S}_i}(q_i|w_i, t_i, q_{-i})}{d q_i}\bigg |{ }_{q_i=\tilde {q}_i(w_i, t_i, q_{-i})}=(w_i-t_i)\prod _{j=1, j\neq i}^n q_j-C_i^{\prime }(\tilde {q}_i(w_i, t_i, q_{-i}))=0\) . Taking the second-order derivative of \(\varPi _{\mathrm {S}_i}(q_i|w_i, t_i, q_{-i})\) w.r.t. q i yields \(\frac {d^2 \varPi _{\mathrm {S}_i}(q_i|w_i, t_i, q_{-i})}{d q_i^2}=-C_i^{\prime \prime }(q_i)<0\). Thereby, the solution of the first-order condition is supplier i’s optimal quality, in response to contract (w i, t i). The n suppliers’ best response functions form a system of equations, solving which yields the suppliers’ equilibrium quality decisions, \(\tilde {q}_i(\vec {w}, \vec {t})\), as functions of the buyer’s contract decisions \((\vec {w}, \vec {t})\):
Next, consider the buyer’s problem. Since the IRi constraint must be binding in equilibrium,Footnote 5 after plugging \(\tilde {q}_i(\vec {w}, \vec {t})\) into \(\varPi _{\mathrm {S}_i}(q_i|w_i, t_i, q_{-i})\), we have
From the system of equations formed by the n binding IRi constraints, we can obtain \(\tilde {t}_i(\vec {w})\) as functions of \(\vec {w}\). Plugging \(\tilde {t}_i(\vec {w})\) into (11), \(\tilde {q}_i(\vec {w})\) reduces to a function of only \(\vec {w}\) as well. Moreover, from (12), we have
Then, with \(\tilde {t}_i(\vec {w})\) and \(\tilde {q}_i(\vec {w})\) plugged into (3), the buyer’s problem becomes
where the first step follows from (11) and the second step follows from (13).
We now analyze the buyer’s optimal contract decisions. Since \(\vec {w}\) affects \(\varPi _{\mathrm {B}}(\vec {w})\) through \(\tilde {q}_i(\vec {w})\), optimizing \(\vec {w}\) is equivalent to optimizing \(\vec {q}\). Further, notice that after rewriting \(\varPi _{\mathrm {B}}(\vec {w})\) as \(\varPi _{\mathrm {B}}(\vec {q})\), \(\varPi _{\mathrm {B}}(\vec {q})\) is the same as the buyer’s profit function in the first-best problem. Therefore, we have \(q_i^{\mathrm {N}\dagger }=q_i^{*}\) and \(\varPi _{\mathrm {B}}^{\mathrm {N}\dagger }=\varPi _{\mathrm {B}}^{*}\), and the supply chain achieves the first-best in equilibrium. Then, plugging \(q_i^{\mathrm {N}\dagger }\) into (13), we have
where the last step follows from (2). Moreover,
where the first step follows from (11), and the second step follows from (15). Finally, it is easy to see that \(w_i^{\mathrm {N}\dagger }>0\). Regarding \(t_i^{\mathrm {N}\dagger }\), notice that \(\varPi _{\mathrm {B}}^{\mathrm {N}\dagger } =(p+l)\prod _{i=1}^n q_i^{\mathrm {N}\dagger }-l-\sum _{i=1}^n C_i(q_i^{\mathrm {N}\dagger }) =-l-t_i^{\mathrm {N}\dagger }-\sum _{j=1, j\neq i}^n C_j(q_j^{\mathrm {N}\dagger })\), where the last step follows from (15). Since \(\varPi _{\mathrm {B}}^{\mathrm {N}\dagger }\geqslant 0\), we have \(t_i^{\mathrm {N}\dagger }<0\) always holds. □
Proof of Proposition 2
We first derive the suppliers’ optimal quality decisions. Given contract (w i, t i), supplier i ∈{1, 2, …, n} chooses his quality q i to maximize his expected profit \(\varPi _{\mathrm {S}_i}(q_i|w_i, t_i)\). The first-order condition is \(\frac {d \varPi _{\mathrm {S}_i}(q_i|w_i, t_i)}{d q_i}\bigg |{ }_{q_i=\tilde {q}_i(w_i, t_i)}=w_i-t_i-C_i^{\prime }(\tilde {q}_i(w_i, t_i))=0\). Taking the second-order derivative of \(\varPi _{\mathrm {S}_i}(q_i|w_i, t_i)\) w.r.t. q i yields \(\frac {d^2 \varPi _{\mathrm {S}_i}(q_i|w_i, t_i)}{d q_i^2}=-C_i^{\prime \prime }(q_i)<0\). Thereby, the solution of the first-order condition is supplier i’s optimal quality, in response to contract (w i, t i). The n suppliers’ best response functions form a system of equations, corresponding to the suppliers’ equilibrium quality decisions, \(\tilde {q}_i(w_i, t_i)\), as functions of the buyer’s contract decisions (w i, t i):
Next, consider the buyer’s problem. Since the IRi constraint must be binding in equilibrium, after plugging \(\tilde {q}_i(w_i, t_i)\) into \(\varPi _{\mathrm {S}_i}(q_i|w_i, t_i)\), we have
From the system of equations formed by the n binding IRi constraints, we can obtain \(\tilde {t}_i(w_i)\) as functions of w i. Plugging \(\tilde {t}_i(w_i)\) into (16), \(\tilde {q}_i(w_i)\) reduces to a function of only w i as well. Moreover, from (17), we have
Then, with \(\tilde {t}_i(w_i)\) and \(\tilde {q}_i(w_i)\) plugged into (4), the buyer’s problem becomes
where the first step follows from (16) and the second step follows from (18).
We now analyze the buyer’s optimal contract decisions. Since \(\vec {w}\) affects \(\varPi _{\mathrm {B}}(\vec {w})\) through \(\tilde {q}_i(w_i)\), optimizing \(\vec {w}\) is equivalent to optimizing \(\vec {q}\). Further, notice that after rewriting \(\varPi _{\mathrm {B}}(\vec {w})\) as \(\varPi _{\mathrm {B}}(\vec {q})\), \(\varPi _{\mathrm {B}}(\vec {q})\) is the same as the buyer’s profit function in the first-best problem. Therefore, we have \(q_i^{\mathrm {A}\dagger }=q_i^{*}\) and \(\varPi _{\mathrm {B}}^{\mathrm {A}\dagger }=\varPi _{\mathrm {B}}^{*}\), and the supply chain achieves the first-best in equilibrium. Then, plugging \(q_i^{\mathrm {A}\dagger }\) into (18), we have
where the last step follows from (2). Moreover,
where the first step follows from (16), and the second step follows from (20). Finally, it is easy to see that \(w_i^{\mathrm {A}\dagger }>0\). Regarding \(t_i^{\mathrm {A}\dagger }\), notice that \(\varPi _{\mathrm {B}}^{\mathrm {A}\dagger }=(p+l)\prod _{i=1}^n q_i^{\mathrm {A}\dagger }-l-\sum _{i=1}^n C_i(q_i^{\mathrm {A}\dagger }) =-l-t_i^{\mathrm {A}\dagger }-\sum _{j=1, j\neq i}^n C_j(q_j^{\mathrm {A}\dagger })\), where the last step follows from (20). Since \(\varPi _{\mathrm {B}}^{\mathrm {A}\dagger }\geqslant 0\), we have \(t_i^{\mathrm {A}\dagger }<0\) always holds. □
Proof of Corollary 1
Parts (1) and (2) of the corollary follow from comparing the equilibria characterized in Propositions 1 and 2. To show part (3), notice that
By Lemma 1, if \(C_i^{\prime }(q)\leqslant C_j^{\prime }(q)\) for all q ∈ (0, 1), we have \(q_i^{*}\geqslant q_j^{*}\), and thereby, we have \(w_i^{\mathrm {N}\dagger }-w_i^{\mathrm {A}\dagger }\geqslant w_j^{\mathrm {N}\dagger }-w_j^{\mathrm {A}\dagger }\). □
Proof of Propositions 3 and 4
First, consider the case without accountability. We view \(w_i^{\mathrm {N}\dagger }(p)\) as functions of p, and define \(G(p)\equiv \sum _{i=1}^n w_i^{\mathrm {N}\dagger }(p) - p\). Based on the characterization of \(w_i^{\mathrm {N}\dagger }\) in Proposition 1, when p → 0, we have
Hence, there must exist a threshold \(\bar {p}>0\) such that G(p) > 0 for \(0<p<\bar {p}\). In other words, there exists a threshold \(\bar {p}>0\) such that \(\sum _{i=1}^n w_i^{\mathrm {N}\dagger }>p\) if \(0<p<\bar {p}\). Then, suppose suppliers are symmetric. We view \(w_i^{\mathrm {N}\dagger }(n)\) as functions of n. Taking the first-order derivatives of \(w_i^{\mathrm {N}\dagger }(n)\) w.r.t. n yields
where the inequality follows from \(q_i^{*}(n)\in (0, 1)\) and \(\frac {d q_i^{*}(n)}{d n}<0\) in Lemma 1. Since \(w_i^{\mathrm {N}\dagger }(n)\) increases in n, we have \(\sum _{i=1}^n w_i^{\mathrm {N}\dagger }(n)\) increases in n as well. Hence, we know that it is more likely for \(\sum _{i=1}^n w_i^{\mathrm {N}\dagger }>p\) to occur with the increase of n.
Next, consider the case with accountability. Based on the characterization of \(w_i^{\mathrm {A}\dagger }\) in Proposition 2, we have
Define \(F(n)\equiv 1+(n-1)\prod _{i=1}^n q_i^{\mathrm {A}\dagger }-\sum _{i=1}^n \prod _{j=1, j\neq i}^n q_j^{\mathrm {A}\dagger }\). If we can show F(n) > 0 for any \(n\geqslant 2\), then \(\sum _{i=1}^n w_i^{\mathrm {A}\dagger }<p\) always holds since \(\varPi _{\mathrm {B}}^{\mathrm {A}\dagger }=\varPi _{\mathrm {B}}(\vec {q}^{*})\geqslant 0\). We now prove F(n) > 0 by induction. Consider n = 2, we have \(F(2)=\left (1-q_1^{\mathrm {A}\dagger }\right )\left (1-q_2^{\mathrm {A}\dagger }\right )>0\). Suppose F(n) > 0 holds in the case of n > 2. Then, regarding the case of n + 1, since
we have F(n + 1) > 0, and thus, F(n) > 0 for any \(n\geqslant 2\). Hence, \(\sum _{i=1}^n w_i^{\mathrm {A}\dagger }<p\) always holds. □
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Cui, Y., Hu, M., Liu, J. (2022). Impact of Blockchain-Driven Accountability in Multi-Sourcing Supply Chains. In: Babich, V., Birge, J.R., Hilary, G. (eds) Innovative Technology at the Interface of Finance and Operations. Springer Series in Supply Chain Management, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-030-81945-3_4
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