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Because of a growing population and shrinking arable land, the world is facing a global food crisis. One important solution could be to subsidize farmers to sustain their production so that they can produce more food for consumers and earn more money for themselves. An efficient subsidy program should also aim to reduce income inequality among farmers, as measured by the Gini coefficient of farmers' income. In this paper, we examine and compare the effects of input and output farm subsidy programs. The input subsidy reduces the farmers' input purchasing costs, whereas the output subsidy reduces the farmers' output processing costs. By considering a continuum of infinitesimal price-taking farmers who are heterogeneous in their average yield rates, our equilibrium analysis of a game-theoretical model yields three results. First, both subsidy schemes reduce the aggregate income inequality measured by the Gini coefficient. However, they create the following "opposite" effects: the input subsidy decreases the income gap among farmers (under mild conditions), whereas the output subsidy increases it. Second, farmers with low yield rates prefer the input subsidy, whereas farmers with high yield rates prefer the output subsidy. Third, the output subsidy scheme is more effective in improving the total farmer income than the input subsidy scheme, whereas the input subsidy scheme is more effective in reducing income disparities and improving consumer surplus than the output subsidy scheme. Our results provide new insights for policymakers who are crafting subsidy schemes. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71971184, 71972025, and 72032001], the Departmental General Research Fund of the Hong Kong Polytechnic University [Grant P0008761], and the Research Grants Council of Hong Kong [Grants 15504615 and 15500820]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4850. [ABSTRACT FROM AUTHOR]

This paper studies a dynamic principal–agent setting in which the principal needs to dynamically schedule an agent to work or be suspended. When the agent is directed to work and exert effort, the arrival rate of a Poisson process is increased, which increases the principal's payoff. Suspension, on the other hand, serves as a threat to the agent by delaying future payments. A key feature of our setting is a switching cost whenever the suspension stops and the work starts again. We formulate the problem as an optimal control model with switching and fully characterize the optimal control policies/contract structures under different parameter settings. Our analysis shows that, when the switching cost is not too high, the optimal contract demonstrates a generalized control-band structure. The length of each suspension episode, on the other hand, is fixed. Overall, the optimal contract is easy to describe, compute, and implement. This paper was accepted by Ilia Tsetlin, behavioral economics and decision analysis. Funding: The work of P. Cao is financially supported by the National Natural Science Foundation of China [Grants 72122019 and 71771202]. The work of F. Tian is financially supported by the Hong Kong Research Grants Council (RGC) funding (RGC Early Career Scheme 2022/23) [Grant 27500822]. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mnsc.2023.4845. [ABSTRACT FROM AUTHOR]

Disputes over transactions on two-sided platforms are common and usually arbitrated through platforms' customer service departments or third-party service providers. This paper studies crowd-judging, a novel crowdsourcing mechanism whereby users (buyers and sellers) volunteer as jurors to decide disputes arising from the platform. Using a rich data set from the dispute resolution center at Taobao, a leading Chinese e-commerce platform, we aim to understand this innovation and propose and analyze potential operational improvements with a focus on in-group bias (buyer jurors favor the buyer, likewise for sellers). Platform users, especially sellers, share the perception that in-group bias is prevalent and systematically sways case outcomes as the majority of users on such platforms are buyers, undermining the legitimacy of crowd-judging. Our empirical findings suggest that such concern is not completely unfounded: on average, a seller juror is approximately 10% likelier (than a buyer juror) to vote for a seller. Such bias is aggravated among cases that are decided by a thin margin and when jurors perceive that their in-group's interests are threatened. However, the bias diminishes as jurors gain experience: a user's bias reduces by nearly 95% as experience grows from zero to the sample median level. Incorporating these findings and juror participation dynamics in a simulation study, the paper delivers three managerial insights. First, under the existing voting policy, in-group bias influences the outcomes of no more than 2% of cases. Second, simply increasing crowd size through either a larger case panel or aggressively recruiting new jurors may not be efficient in reducing the adverse effect of in-group bias. Finally, policies that allocate cases dynamically could simultaneously mitigate the impact of in-group bias and nurture a more sustainable juror pool. This paper was accepted by Vishal Gaur, operations management. Funding: S. A. Yang and A. Zhang acknowledge the support of the Hong Kong General Research Fund [Grant "Decentralizing Platform Governance: Innovations from China; Project 17614921]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4818. [ABSTRACT FROM AUTHOR]

The concept of proper equilibrium was established as a strict refinement of perfect equilibrium. This establishment has significantly advanced the development of game theory and its applications. Nonetheless, it remains a challenging problem to compute such an equilibrium. This paper develops a differentiable path-following method with a compact formulation to compute a proper equilibrium. The method incorporates square-root-barrier terms into payoff functions with an extra variable and constitutes a square-root-barrier game. As a result of this barrier game, we acquire a smooth path to a proper equilibrium. To further reduce the computational burden, we present a compact formulation of an ε-proper equilibrium with a polynomial number of variables and equations. Numerical results show that the differentiable path-following method is numerically stable and efficient. Moreover, by relaxing the requirements of proper equilibrium and imposing Selten's perfection, we come up with the notion of perfect d-proper equilibrium, which approximates a proper equilibrium and is less costly to compute. Numerical examples demonstrate that even when d is rather large, a perfect d-proper equilibrium remains to be a proper equilibrium. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms-Continuous. Funding: This work was partially supported by General Research Fund (GRF) CityU 11306821 of Hong Kong SAR Government. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0148) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0148). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Both walk-in and online customers appear in many service systems such as restaurants. Online customers are often given a target time for pick up, and the quality of the food/beverage can degrade if it is ready before the arrival of online customers. This distinctive feature brings essential difficulties in the analysis and control of such systems. In "Technical Note: Asymptotically Optimal Control of Omnichannel Service Systems with Pick-up Guarantees," Gao, Huang, and Zhang study the optimal control problem of such service systems based on a two-class single server queueing model. The paper proposes a nearly-optimal policy that keeps the server idle when the queue of online customers drops below a threshold and there are no walk-in customers. Motivated by the recent popularity of omnichannel service systems, we analyze the joint admission and scheduling control of a queueing system with two classes of customers: online and walk-in. Unlike walk-in customers, online customers are given a target time for pick up upon placing an order. Thus, in addition to minimizing the waiting costs of walk-in customers and the rejection cost of both classes, we need to minimize the earliness and tardiness costs of online customers. Such a distinctive objective makes the control problem difficult to analyze. We develop a novel analysis by adopting the idea of proving H = λ G to establish an asymptotic relationship between the waiting time cost and the queue length cost under general control policies in the heavy-traffic regime. We also design a policy and show that it is asymptotically optimal. Funding: X. Gao's research is supported in part by the Hong Kong Research Grants Council [Grants 14201520, 14201421, and 14212522]. J. Huang's research is supported in part by the Hong Kong Research Grants Council [Grants 14500819, 14505820, and 14501621] and the National Natural Science Foundation of China [Grant 72222023]. J. Zhang's research is supported in part by the Hong Kong Research Grants Council [Grants 16208120 and 16214121]. [ABSTRACT FROM AUTHOR]

A Dual Perspective to the Robust Screening Problem Robust screening problem is concerned with the problem of a seller seeking a selling mechanism that maximizes the worst-case revenue obtained from a buyer whose valuation distribution lies in a certain ambiguity set. In the paper "Screening with Limited Information: A Dual Perspective", Z. Chen, Z. Hu, and R. Wang show that strong duality holds between the problem of finding the optimal robust mechanism and a minimax pricing problem, where the adversary first chooses a worst-case distribution and then the seller decides the best posted price mechanism. The duality result connects prior literature that separately studies the primal (robust screening) and problems related to the dual and offers a unified geometric intuition in solving the problem. Consider a seller seeking a selling mechanism to maximize the worst-case revenue obtained from a buyer whose valuation distribution lies in a certain ambiguity set. Such a mechanism design problem with one product and one buyer is known as the screening problem. For a generic convex ambiguity set, we show via the minimax theorem that strong duality holds between the problem of finding the optimal robust mechanism and a minimax pricing problem where the adversary first chooses a worst-case distribution, and then the seller decides the best posted price mechanism. This implies that the extra value of optimizing over more sophisticated mechanisms amounts exactly to the value of eliminating distributional ambiguity under a posted price mechanism. The duality result also connects prior literature that separately studies the primal (robust screening) and problems related to the dual (e.g., robust pricing, buyer-optimal pricing, and personalized pricing). We further analytically solve the minimax pricing problem (as well as the robust pricing problem) for several important ambiguity sets, such as the ones with mean and various dispersion measures, and with the Wasserstein metric, and we provide a unified geometric intuition behind our approach. The solutions are then used to construct the optimal robust mechanism and to compare with the solutions to the robust pricing problem. We also establish the uniqueness of the worst-case distribution for some cases. Funding: The research is funded in part by the Hong Kong Research Grants Council under its General Research Fund (CUHK-11502422) and the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 (Grant MOE-2019-T3-1-010). Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.0016. [ABSTRACT FROM AUTHOR]

Problem definition: In practice, consumers are directly or indirectly connected with branded manufacturers through intermediaries across industries. In this paper, we explore the effects of different selling formats on product quality and price depending on consumer valuations in a market. We employ a distribution family to comprehensively capture the heterogeneity of consumer valuations. Motivated by realistic phenomena, consumer valuations are used to investigate strategic decisions under different selling formats that are not trivial to analyze. Methodology/results: We develop game-theoretical models to examine the equilibrium decisions of stakeholders. The impact of consumer valuations is investigated and validated using sensitivity analysis, and the results are connected to practice. First, we find that agency selling induces a premium quality and maximizes the channel profit; remarkably, a nonmonotonic (approximate U-shaped) relationship exists between the agency fee and consumer valuations. A higher consumer surplus can be achieved in an agency selling channel compared with a reselling channel, particularly when targeting a mass of high-end consumers. Second, by examining distinct consumer valuations, maintaining top-notch quality and the highest price in an agency selling channel is not universally viable under some conditions. Third, in the case of production-level competition, an agency selling format tends to cause product quality to vary noticeably. Moreover, in the hybrid selling channel, in contrast to agency selling, the high-type manufacturer reduces both quality and price, which bolsters the overall profits of the channel and the consumer surplus. Managerial implications: Branded manufacturers can efficiently respond to individualized consumer needs in a centralized distribution channel. In contrast, for selling basic products, the reselling channel could contribute to achieving economies of scale and offering competitive prices. In the agency selling channel, standardized pricing determined by branded manufacturers can create a consistent perception of product quality throughout the distribution network. Funding: The research of L. Hsiao was supported by Ministry of Science and Technology (now National Science and Technology Council), Grant/Award Number: MOST 110-2410-H-005-016-MY3. The research of Y.-J. Chen was supported by the HK RGC General Research Fund [GRF 16500821, GRF 16501722, and HKUST C6020-21GF]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0470. [ABSTRACT FROM AUTHOR]

If a financial asset's price movement impacts a firm's product demand, the firm can respond to the impact by adjusting its operational decisions. For example, in the automotive industry, automakers decrease the selling prices of fuel-inefficient cars when the oil price rises. Meanwhile, the firm can implement a risk-hedging strategy using the financial asset jointly with its operational decisions. Motivated by this, we develop and solve a general risk-management model integrating risk hedging into a price-setting newsvendor. The optimal hedging strategy is calculated analytically, which leads to an explicit objective function for optimizing price and "virtual production quantity" (VPQ). (The latter determines the service level—that is, the demand-fulfillment probability.) We find that hedging generally reduces the optimal price when the firm sets the target mean return as its production-only maximum expected profit. With the same condition on the target mean return, hedging also reduces the optimal VPQ when the asset price trend positively impacts product demand; meanwhile, it may increase the VPQ by a small margin when the impact is negative. We construct the return-risk efficient frontier that characterizes the optimal return-risk trade-off. Our numerical study using data from a prominent automotive manufacturer shows that the markdowns in price and reduction in VPQ are small under our model and that the hedging strategy substantially reduces risk without materially reducing operational profit. This paper was accepted by Kay Giesecke, finance. Funding: Financial support from the Hong Kong Research Grants Council [General Research Fund 17200521] is gratefully acknowledged. Supplemental Material: The data files and e-companion are available at https://doi.org/10.1287/mnsc.2023.4942. [ABSTRACT FROM AUTHOR]

We consider the role of personalized pricing (PP) on product differentiation when PP is costly to implement. Using a stylized yet commonly used formulation, we find that when firms decide on positioning before deciding on PP implementation, PP implementation cost affects not only the amount of differentiation firms choose in their positioning, firm profits, consumer surplus, and social welfare, but also whether firms implement PP. When PP implementation cost is low, firms cannot help but to implement PP and engage in direct price competition. Moreover, firms implementing PP reduce their differentiation, further intensifying price competition, and are worse off. When PP implementation cost is moderate, firms position to reduce their differentiation to commit to not implementing PP, again aggravating price competition. In contrast, when PP implementation cost is higher, firms increase their differentiation due to the threat of PP but do not implement PP. As a result, the availability of PP improves firm profits, even though firms do not implement PP. However, if differentiation is restricted, then PP availability cannot improve firm profits. If an information seller sets the PP implementation cost, then it sets the cost low. Consequently, firms implement PP and are worse off. We also find that when firms decide whether to implement PP before deciding on positioning, they never implement PP. This is the case when PP implementation is complex, and differentiation can be affected by short-run advertising and promotion. Finally, we show that banning PP can benefit consumers when accounting for changes in firm positioning. This paper was accepted by D. J. Wu, information systems. Funding: This work was supported by the Social Sciences and Humanities Research Council of Canada and the Hong Kong Research Grants Council [Grant 21500920]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2021.02740. [ABSTRACT FROM AUTHOR]

For a large class of demand models that allow for multiple consumer types, we present performance guarantees for simple nonpersonalized pricing heuristics relative to optimal personalized pricing. Our results demonstrate that in a general setting, the effectiveness of pricing along a positive vector depends on how the price vector aligns with optimal personalized price vectors. We propose two positive direction vectors: the "economic" and "robust" directions. The economic direction is a convex combination of the optimal personalized price vectors and aims to do well on average. The robust direction offers the best worst-case performance guarantee. By judiciously selecting pricing directions, our results also provide performance guarantees of simple pricing strategies relative to more sophisticated pricing strategies. In particular, we provide performance guarantees for nonpersonalized, optimal linear pricing relative to optimal nonlinear, personalized pricing. Our research also examines the performance of common heuristics for bundle pricing relative to optimal, personalized, bundle-size pricing. Our experiments show that performance often improves when consumer types are clustered and each cluster is offered a price direction. We compared the performance of the k-means clustering heuristic and the farthest point first clustering heuristic. Our findings indicated that k-means clustering has significantly superior performance on average. This suggests that businesses could potentially benefit from implementing k-means clustering in their pricing strategies. In conclusion, our study offers valuable insights and performance guarantees for various pricing strategies and their relative effectiveness. These findings could inform pricing decisions and potentially lead to improved outcomes for firms. This paper was accepted by Chung-Piaw Teo, optimization. Funding: This work was supported by Collaborative Research Funding (CRF) Hong Kong [Grant (CRF) C6032-21G], RGC Hong Kong [Grant 16502819], and Hong Kong University of Science and Technology. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2021.04173. [ABSTRACT FROM AUTHOR]

For an unbalanced cooperative game, its grand coalition can be stabilized by some instruments, such as subsidization and penalization, that impose new cost terms to certain coalitions. In this paper, we study an alternative instrument, referred to as cost adjustment, that does not need to impose any new coalition-specific cost terms. Specifically, our approach is to adjust existing cost coefficients of the game under which (i) the game becomes balanced so that the grand coalition becomes stable, (ii) a desired way of cooperation is optimal for the grand coalition to adopt, and (iii) the total cost to be shared by the grand coalition is within a prescribed range. Focusing on a broad class of cooperative games, known as integer minimization games, we formulate the problem on how to optimize the cost adjustment as a constrained inverse optimization problem. We prove NP -hardness and derive easy-to-check feasibility conditions for the problem. Based on two linear programming reformulations, we develop two solution algorithms. One is a cutting-plane algorithm, which runs in polynomial time when the corresponding separation problem is polynomial time solvable. The other needs to explicitly derive all the inequalities of a linear program, which runs in polynomial time when the linear program contains only a polynomial number of inequalities. We apply our models and solution algorithms to two typical unbalanced games, including a weighted matching game and an uncapacitated facility location game, showing that their optimal cost adjustments can be obtained in polynomial time. History: Accepted by Area Editor Andrea Lodi for Design & Analysis of Algorithms—Discrete. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72022018 and 72091210]; the Research Grants Council of the Hong Kong SAR, China [Grant 16210020]; Hong Kong Polytechnic University [Grant P0032007]; and the Youth Innovation Promotion Association, Chinese Academy of Sciences [Grant 2021454]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0268. [ABSTRACT FROM AUTHOR]

High-dimensional Simulation Metamodeling Stochastic kriging has been widely employed for simulation metamodeling to predict the response surface of complex simulation models. However, its use is limited to cases where the design space is low-dimensional because the sample complexity (i.e., the number of design points required to produce an accurate prediction) grows exponentially in the dimensionality of the design space. The large sample size results in both a prohibitive sample cost for running the simulation model and a severe computational challenge due to the need to invert large covariance matrices. To address this long-standing challenge, Liang Ding and Xiaowei Zhang, in their recent paper "Sample and Computationally Efficient Stochastic Kriging in High Dimensions", develop a novel methodology — based on tensor Markov kernels and sparse grid experimental designs — that dramatically alleviates the curse of dimensionality. The proposed methodology has theoretical guarantees on both sample complexity and computational complexity and shows outstanding performance in numerical problems of as high as 16,675 dimensions. Stochastic kriging has been widely employed for simulation metamodeling to predict the response surface of complex simulation models. However, its use is limited to cases where the design space is low-dimensional because in general the sample complexity (i.e., the number of design points required for stochastic kriging to produce an accurate prediction) grows exponentially in the dimensionality of the design space. The large sample size results in both a prohibitive sample cost for running the simulation model and a severe computational challenge due to the need to invert large covariance matrices. Based on tensor Markov kernels and sparse grid experimental designs, we develop a novel methodology that dramatically alleviates the curse of dimensionality. We show that the sample complexity of the proposed methodology grows only slightly in the dimensionality, even under model misspecification. We also develop fast algorithms that compute stochastic kriging in its exact form without any approximation schemes. We demonstrate via extensive numerical experiments that our methodology can handle problems with a design space of more than 10,000 dimensions, improving both prediction accuracy and computational efficiency by orders of magnitude relative to typical alternative methods in practice. Funding: We gratefully acknowledge financial support from the Hong Kong Research Grants Council (GRF 17206821). Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.2367. [ABSTRACT FROM AUTHOR]

We study an optimal investment and consumption problem with heterogeneous consumption of basic and luxury goods, together with the choice of time for retirement. The optimal heterogeneous consumption strategies for a class of nonhomothetic utility maximizer are shown to consume only basic goods when the wealth is small, to consume basic goods and make savings when the wealth is intermediate, and to consume almost all in luxury goods when the wealth is large. The optimal retirement policy is shown to be both universal, in the sense that all individuals should retire at the same level of marginal utility that is determined only by income, labor cost, discount factor as well as market parameters, and not universal, in the sense that all individuals can achieve the same marginal utility with different utility and wealth. We also show that individuals prefer to retire as time goes by if the marginal labor cost increases faster than that of income. This paper studies an optimal investment and consumption problem with heterogeneous consumption of basic and luxury goods, together with the choice of time for retirement. The utility for luxury goods is not necessarily a concave function. The optimal heterogeneous consumption strategies for a class of nonhomothetic utility maximizer are shown to consume only basic goods when the wealth is small, to consume basic goods and make savings when the wealth is intermediate, and to consume almost all in luxury goods when the wealth is large. The optimal retirement policy is shown to be both universal, in the sense that all individuals should retire at the same level of marginal utility that is determined only by income, labor cost, discount factor and market parameters, and not universal, in the sense that all individuals can achieve the same marginal utility with different utility and wealth. It is also shown that individuals prefer to retire as time goes by if the marginal labor cost increases faster than that of income. The main tools used in analyzing the problem are from a partial differential equation and stochastic control theory including variational inequality and dual transformation. We finally conduct the simulation analysis for the featured model parameters to investigate practical and economic implications by providing their figures. Funding: This work was supported by Hong Kong Research Grants Council General Research Fund [Grants 15202421 and 15202817], the National Research Foundation of Korea [Grant 2021R1C1C1004647], the PolyU-SDU Joint Research Center on Financial Mathematics, the CAS AMSS-PolyU Joint Laboratory of Applied Mathematics, and Hong Kong Polytechnic University, the National Natural Science Foundation of China [Grant 11971409], and the Engineering and Physical Sciences Research Council (UK) [Grant EP/V008331/1]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2328. [ABSTRACT FROM AUTHOR]

Let T=(V,A) be a tournament with a nonnegative integral weight w(e) on each arc e. A subset F of arcs is called a feedback arc set (FAS) if T\F contains no cycles (directed). A collection F of FASs (with repetition allowed) is called an FAS packing if each arc e is used at most w(e) times by the members of F. The purpose of this paper is to give a characterization of all tournaments T=(V,A) with the property that, for every nonnegative integral weight function w defined on A, the minimum total weight of a cycle is equal to the maximum size of an FAS packing. Funding: This work was supported by the National Natural Science Foundation of China [Grants 11801266 and 11971228]; the Chinese Academy of Sciences [Grants XDA27000000 and ZDBS-LY-7008]; the Ministry of Science and Technology of China [Grant 2018AAA0101002]; the Research Grants Council of Hong Kong; the Fundamental Research Funds for the Central Universities [Grant 020314380035]. [ABSTRACT FROM AUTHOR]

Problem definition: A significant percentage of online consumers place consecutive orders within a short duration. To reduce the total order arrangement cost, an online retailer may consolidate consecutive orders from the same consumer. We investigate how long the retailer should hold the consumer's orders before sending them to a third-party logistics provider (3PL) for processing. In this order-holding problem, we optimize the holding time to balance the total order arrangement cost and the potential delay in delivery. Methodology/results: We model the order-holding problem as a Markov decision process. We show that the optimal order-holding decisions follow a threshold-type policy that is straightforward to implement: Hold any pending orders if the holding time is within a threshold or send them to the 3PL otherwise. Whenever the consumer places a new order, the holding time is reset, and the threshold is updated based on a cumulative set of the past consecutive orders in the consumer's shopping journey. Using a consumer's sequential decision model, we personalize the threshold by finding its closed-form expression in the consumer's order features. We determine the model's coefficients and evaluate the threshold-type policy using the data of the 2020 MSOM Data Driven Research Challenge. Extensive numerical experiments suggest that the personalized threshold-type policy outperforms two commonly used benchmarks by having fewer order arrangements or shorter holding times. Furthermore, personalizing the order-holding decisions is significantly more valuable for "enterprise" customers. Managerial implications: Our research suggests a higher threshold for consumers who are more likely to place consecutive orders within a short duration. The consumers' demographic information has a significant effect on the threshold. Specifically, the threshold is higher for "plus" consumers, female consumers, and consumers in the age group of 16–25 years. The threshold for tier 1 cities is lower than that for tier 2 to tier 4 cities but higher than that for tier 5 cities. History: This paper has been accepted as part of the 2020 MSOM Data Driven Challenge. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71931009, 72201237, and 72231009], the Research Grants Council of Hong Kong [Grants 15501920 and 15501221], the Singapore Ministry of Education Academic Research Fund [Tier 1, Grant RG17/21; Tier 2, Grant MOE2019-T2-1-045], the Association of South-East Asian Nations Business Research Initiative Grant [Grant G17C20421], and the Neptune Orient Lines [Fellowship NOL21RP04]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1201. [ABSTRACT FROM AUTHOR]