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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]

This paper develops a cost-effective sequential route recommender system to provide real-time routing recommendations for vacant taxis searching for the next passenger. We propose a prediction-and-optimization framework to recommend the searching route that maximizes the expected profit of the next successful passenger pickup based on the dynamic taxi demand-supply distribution. Specifically, this system features a deep learning-based predictor that dynamically predicts the passenger pickup probability on a road segment and a recursive searching algorithm that recommends the optimal searching route. The predictor integrates a graph convolution network (GCN) to capture the spatial distribution and a long short-term memory (LSTM) to capture the temporal dynamics of taxi demand and supply. The GCN-LSTM model can accurately predict the pickup probability on a road segment with the consideration of potential taxi oversupply. Then, the dynamic distribution of pickup probability is fed into the route optimization algorithm to recommend the optimal searching routes sequentially as route inquiries emerge in the system. The recursion tree-based route optimization algorithm can significantly reduce the computational time and provide the optimal routes within seconds for real-time implementation. Finally, extensive experiments using Beijing Taxi GPS data demonstrate the effectiveness and efficiency of the proposed recommender system. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was partially supported by the Hong Kong Research Grants Council [Grants CityU 21500220, CityU 11504322] and the National Natural Science Foundation of China [Grant 72201222]. 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.2021.0112) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0112). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [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]

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]

In this paper, we consider a container leasing firm that has elementary and premium containers, which are downward substitutable and for use by elementary contract customers (ECCs), premium contract customers (PCCs), as well as walk-in customers (WICs). ECCs can be satisfied by elementary containers or premium ones at discounted prices while PCCs only accept premium containers. WICs can be satisfied by any type of container at different prices. The objective is to maximise the expected total rental revenue by managing its limited capacity. We formulate this problem as a discrete-time Markov Decision Process and show the submodularity and concavity of the value function. Based on this, we show that the optimal policy can be characterised by a series of rationing thresholds, a series of substitution thresholds and a priority threshold, all of which depend on the system states. We further give conditions under which the optimal policy can be simplified. Numerical experiments are conducted to show the impact of the substitution of two items on the revenue, to compare the performance of the optimal policy with those of the commonly used policies and to investigate the influence of arrival rates on the optimal policy. Last, we extend the basic model to consider different rental durations, ECCs' acceptance behaviour and endogenous prices for WICs. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: This work was supported by National Natural Science Foundation of China/Research Grants Council of Hong Kong Joint Research Scheme [Grant 71661167009, N_PolyU531/16], National Natural Science Foundation of China [Grants 72171016, 71831001], and British Academy\Leverhulme Small Research [Grant SRG19\190059]. This work was also supported by Zhejiang Shuren University Research [Grant KXJ0121605] and Beijing Logistics Informatics Research Base. Supplemental Material: The e-companion and data files are available at https://doi.org/10.1287/mnsc.2022.4425. [ABSTRACT FROM AUTHOR]

Many warehouses involved in e-commerce order fulfillment use robotic mobile fulfillment systems. Because demand and variability can be high, scheduling orders, robots, and storage pods in interaction with manual workstations are critical to obtaining high performance. Simultaneously, the scheduling problem is extremely complicated because of interactions between decisions, many of which must be taken timely because of short planning horizons and a constantly changing environment. This paper models all such scheduling decisions in combination to minimize order fulfillment time. We propose two decision methods for the above scheduling problem. The models batch the orders using different batching methods and assign orders and batches to pods and workstations in sequence and robots to jobs. Order picking and stock replenishment operations are included in the models. We conduct numerical experiments based on a real-world case to validate the efficacy and efficiency of the model and algorithm. Instances with 14 workstations, 400 orders, 300 stock-keeping units (SKUs), 160 pods, and 160 robots can be solved to near optimality within four minutes. Our methods can be applied to large instances, for example, using a rolling horizon. Because our model can be solved relatively fast, it can be used to take managerial decisions and obtain executive insights. Our results show that making integrated decisions, even when done heuristically, is more beneficial than sequential, isolated optimization. We also find that positioning pick stations close together along one of the system's long sides is efficient. The replenishment stations can be grouped along another side. Another finding is that SKU diversity per pod and SKU dispersion over pods have strong and positive impacts on the total completion time of handling order batches. Funding: This work was supported by National Natural Science Foundation of China [72025103, 72361137001, 71831008, 72071173] and the Research Grants Council of the Hong Kong Special Administrative Region, China [HKSAR RGC TRS T32-707/22-N]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0265. [ABSTRACT FROM AUTHOR]

The ℓ 0 trend filtering ( ℓ 0 -TF) is a new effective tool for nonparametric regression with the power of automatic knot detection in function values or derivatives. It overcomes the drawback of ℓ 1 -TF that is known to have bias issues. To solve the ℓ 0 -TF problem, we propose an alternating minimization induced active set (AMIAS) search method based on the necessary optimality conditions derived from an augmented Lagrangian framework. The proposed method takes full advantage of the primal and dual variables with complementary supports, and decouples the high-dimensional problem into two subsystems on the active and inactive sets, respectively. A sequential AMIAS algorithm with warm start initialization is developed for efficient determination of the cardinality parameter, along with the output of solution paths. Theoretically, the oracle estimator of ℓ 0 -TF is justified to behave like regression splines under the continuous time setting with mild conditions. Our numerical experiments include simulation studies for comparing ℓ 0 -TF to ℓ 1 -TF and free-knot splines on several synthetic examples, and a real data application of time series segmentation on Hong Kong PM2.5 indexes. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms – Continuous. Funding: This work was supported in part by Hong Kong General Research Fund [No. 17306519]. C. Wen's research is partially supported by National Science Foundation of China [12171449] and Fundamental Research Funds for the Central Universities [WK3470000027, YD2040002019]. X. Wang's research is partially supported by National Natural Science Foundation of China [Grants 72171216, 12231017, 71921001, and 71991474], and the National Key R&D Program of China [No. 2022YFA1003803]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoc.2021.0313. 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.2021.0313) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0313). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

We extend the notion of globalized robustness to consider distributional information beyond the support of the ambiguous probability distribution. We propose the globalized distributionally robust counterpart that disallows any (respectively, allows limited) constraint violation for distributions residing (respectively, not residing) in the ambiguity set. By varying its inputs, our proposal recovers several existing perceptions of parameter uncertainty. Focusing on the type 1 Wasserstein distance, we show that the globalized distributionally robust counterpart has an insightful interpretation in terms of shadow price of globalized robustness, and it can be seamlessly integrated with many popular optimization models under uncertainty without incurring any extra computational cost. Such computational attractiveness also holds for other ambiguity sets, including the ones based on probability metric, optimal transport, ϕ-divergences, or moment conditions, as well as the event-wise ambiguity set. Numerical studies on an adaptive network lot-sizing problem demonstrate the modeling flexibility of our proposal and its emphases on globalized robustness to constraint violation. History: Antonio Frangioni, Area Editor for Design & Analysis of Algorithms—Continuous. Funding: Z. Chen was supported by [General Research Fund Grant 9043424, NSFC/RGC Joint Research Scheme N_CityU105/21] from the Hong Kong Research Grants Council. S. Wang was supported by the National Natural Science Foundation of China [Grants 71922020, 72171221, and 71988101, entitled "Econometric Modeling and Economic Policy Studies"] and the Fundamental Research Funds for the Central Universities [Grant UCAS-E2ET0808X2]. 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.2022.0274) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0274). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

A multidepot capacitated vehicle routing problem aims to serve customers' demands using a fleet of capacitated vehicles located in multiple depots, such that the total travel cost of the vehicles is minimized. We study a variant of this problem, the k-depot split delivery vehicle routing problem (or k-DSDVRP in short), for the situation where each customer's demand can be served by more than one vehicle, and the total number of depots, denoted by k ≥ 2 , is a fixed constant. This is a challenging problem with broad applications in the logistics industry, for which no constant ratio approximation algorithm is known. We develop a new approximation algorithm for the k-DSDVRP, ensuring an approximation ratio of (6 − 4 / k) and a polynomial running time for any fixed constant k ≥ 2. To achieve this, we propose a novel solution framework based on a new relaxation of the problem, a cycle splitting procedure, and a vehicle assignment procedure. To further enhance its efficiency for practical usage, we adapt the newly developed approximation algorithm to a heuristic, which runs in polynomial time even when k is arbitrarily large. Experimental results show that this heuristic outperforms a commercial optimization solver and a standard vehicle routing heuristic. Moreover, our newly proposed solution framework can be applied to developing new constant ratio approximation algorithms for several other variants of the k-DSDVRP with k ≥ 2 being a fixed constant. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This work was supported in part by the National Natural Science Foundation of China [Grants 71971177, 71725001, U1811462], Research Grants Council of Hong Kong SAR, China [Grant 15221619], and Guangdong Basic and Applied Basic Research Foundation [Grant 2023A1515030260]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoc.2021.0193. 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.2021.0193) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0193). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

We study adjustable distributionally robust optimization problems, where their ambiguity sets can potentially encompass an infinite number of expectation constraints. Although such ambiguity sets have great modeling flexibility in characterizing uncertain probability distributions, the corresponding adjustable problems remain computationally intractable and challenging. To overcome this issue, we propose a greedy improvement procedure that consists of solving, via the (extended) linear decision rule approximation, a sequence of tractable subproblems—each of which considers a relaxed and finitely constrained ambiguity set that can be iteratively tightened to the infinitely constrained one. Through three numerical studies of adjustable distributionally robust optimization models, we show that our approach can yield improved solutions in a systematic way for both two-stage and multistage problems. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: Financial support by the Early Career Scheme from the Hong Kong Research Grants Council [Project No. CityU 21502820], the CityU Start-Up Grant [Project No. 9610481], the CityU Strategic Research Grant [Project No. 7005688], the National Natural Science Foundation of China [Project No. 72032005], and Chow Sang Sang Group Research Fund sponsored by Chow Sang Sang Holdings International Limited [Project No. 9229076] is gratefully acknowledged. 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.2021.0181), as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0181). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Government bonds might provide reference entities that reduce corporate bond yields. We study China's 2017 issuance of two U.S. dollar (USD)-denominated sovereign bonds when there were (effectively) no outstanding USD sovereigns. We find that Chinese corporate USD bonds experienced a four- to nine-basis-point decline in yield spreads, whereas corporate renminbi (RMB) bonds did not. The effect was stronger for corporate bonds with maturities similar to those of the USD sovereigns. Further consistent with the reference effect, USD-denominated corporate bonds experienced declines in bid-ask spreads and volatility; corporate bond yield spread changes became more sensitive to sovereign yield innovations; and the less-informed foreign mutual funds significantly increased their holdings of offshore USD Chinese corporate bonds. Limited evidence indicates that new corporate bond maturities shifted toward the sovereign bonds' maturity after their issuance. This paper was accepted by Lukas Schmid, finance. Funding: B. Wang received financial support from Hong Kong Institute for Monetary Research. C. Y. Hong received financial support from the National Natural Science Foundation of China [Grant 72003125]. Supplemental Material: The internet appendix and data are available at https://doi.org/10.1287/mnsc.2022.4499. [ABSTRACT FROM AUTHOR]

We study the effects of implicit government guarantee (IGG) on Chinese state-owned enterprises (SOEs). We find that SOEs reduce their investments by 2.4% of book assets, on average, relative to matched non-SOEs after the first SOE default in China's onshore bond markets in 2015. The investment reduction concentrates among SOEs that are financially constrained, yet SOEs financed by large state banks are hardly affected. Bondholders require more stringent default protection in newly issued SOE bonds. We also find that the investment reduction is more pronounced for SOEs with severe agency problems and that SOEs experienced more positive market reactions to acquisition announcements after 2015. Our results suggest that the reduction in IGG has confounding effects on Chinese firms. Although the weakening of IGG may help mitigate overinvestment, it exacerbates financial constraints of those with limited access to alternative sources of financing. This paper was accepted by Lucas Schmid, finance. Funding: Z. Zhang acknowledges the funding support by Research Grants Council of Hong Kong [Project 11503318]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4483. [ABSTRACT FROM AUTHOR]

We consider a mixed duopoly service system with two service providers (SPs): one private and the other public. The public SP's objective is welfare maximization, whereas the private SP is profit driven. Customers are heterogeneous in terms of their quality tastes and choose from three options, namely, joining the private queue, joining the public queue, and balking (or taking an outside option). We first consider the scenario where the private SP provides a premium service and the public SP provides a regular service. Paradoxically, we find that although the public SP's objective is welfare maximization, welfare in the system can be less than that obtained with only profit-seeking private SPs. We further demonstrate that the maximum social welfare is achieved by partially privatizing the public SP, that is, by including both welfare and profit maximization as arguments in its objective function. In extreme cases in which the public SP's capacity is very small, fully privatizing the public SP can be socially desirable. We then consider the alternate setting where the public SP provides a premium service and the private SP provides a regular service and obtain similar conclusions. These findings, however, rely on the assumption that an outside option exists for balking customers, and one should be cautious in generalizing to no-balking situations. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: W. Zhou was supported by the National Natural Science Foundation of China [Grants 71925002 and 71731006]. W. Huang acknowledges the financial support from the National Natural Science Foundation of China [Grants 71801096 and 72101090] and the Guangdong Basic and Applied Basic Research Foundation [Grant 2022A1515011983]. V. N. Hsu was supported by the General Research Fund of the Hong Kong Research Grants Council [Grant CUHK14526216]. P. Guo received financial support from the Hong Kong Research Grants Council [Grant 15502820]. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mnsc.2022.4424. [ABSTRACT FROM AUTHOR]

Corporate bonds with large increases in implied volatility over the past month underperform those with large decreases in implied volatility by 0.6% per month. In contrast to existing studies that show implied volatility changes carry information about fundamental news, our evidence suggests that implied volatility changes contain information about uncertainty shocks to the firm. Our results are consistent with the notion that informed traders with new information about firm risk prefer to trade in the option market and the corporate bond market underreacts to this information. This paper was accepted by Haoxiang Zhu, finance. Funding: We thank the Canadian Derivatives Institute for financial support. J. Cao and X. Zhan acknowledge generous financial support of the Research Grant Council of the Hong Kong Special Administrative Region, China [Grants GRF 14501720, 14500919]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4379. [ABSTRACT FROM AUTHOR]

Problem definition: In this paper, we explore how a firm's concern about profit distribution and the size of downstream firms in supply chains affect corporate social responsibility (CSR) investment strategy. Methodology/results: In a supply chain consisting of one supplier and one manufacturer, both players decide whether to invest to reduce CSR violations, and they negotiate over a wholesale price. Distributive comparison behavior makes the manufacturer compare the profit with his equitable payoff, which is determined by the supplier's profit. Advantageous (resp. disadvantageous) inequality occurs when the manufacturer's profit is higher (resp. lower) than the manufacturer's equitable payoff. We compare this supply chain to the one without distributive comparison behavior. We find that when advantageous inequality occurs, or when neither inequality occurs and the manufacturer's sensitivity to the supplier's profit is low, the manufacturer's distributive comparison behavior makes the manufacturer less (resp. supplier more) likely to invest in CSR, which we call negative (resp. positive) impacts of distributive comparison behavior; otherwise, it makes the manufacturer more (resp. supplier less) likely to invest. In most cases, the weak bargaining power of the small manufacturer leads to larger positive or smaller negative impacts of distributive comparison behavior. Also, the low efficiency of the small manufacturer to reduce CSR violations leads to smaller negative impacts of distributive comparison behavior. Managerial implications: Our results show that governments and nongovernmental organizations (NGOs) should investigate firms' distributive comparison behavior in supply chains. When downstream firms show the aversion to lower (resp. higher) profits than ones from upstream firms, the measures to monitor and support upstream (resp. downstream) firms' CSR investments should be taken to avoid CSR violations. In the supply chains with small downstream firms, extra efforts should be made to induce firms' distributive comparison behavior. Funding: M. Wang was supported partially by the National Natural Science Foundation of China [Grants 71931009 and 71671023]; X. Fang is grateful for the support under a Lee Kong Chian Fellowship and Retail Centre of Excellence Research Grant; Z. Wang was supported partially by the National Natural Science Foundation of China [Grants 72010107002, 71671023, and 72171212]; and Y. Chen was supported partially by the Research Grants Council of the Hong Kong Special Administrative Region, China [HKUST C6020-21GF]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1172. [ABSTRACT FROM AUTHOR]

We develop a game-theoretic model to study the incentive for competing manufacturers to share supplier audit information. Based on the audit information, each manufacturer decides whether to source from a common supplier who has uncertain responsibility violation risk or to switch to a backup supplier who has no responsibility violation risk but charges a higher price. When supplier responsibility violation occurs, some consumers boycott the manufacturers involved. Audit information allows a manufacturer to reduce the uncertainty about the risk of the common supplier. We show that audit information sharing may make the manufacturers' sourcing strategies more or less differentiated. As a result, the information-sharing decision is not monotone in the model parameters. We fully characterize the manufacturers' equilibrium audit information-sharing and sourcing decisions and establish conditions under which audit information sharing induces the manufacturers to adopt more or less responsible sourcing strategies. We also show that a manufacturer could be better off when the cost premium of sourcing from the backup supplier or the risk of the common supplier becomes higher or the audit information becomes less accurate. We consider several extensions of the base model and demonstrate that the main insights remain mostly valid. This paper was accepted by Charles Corbett, operations management. Funding: A. Y. Hawas supported by the Wei Lun Foundation. W. Shang was supported by the Research Grants Council of Hong Kong [GRF Project LU 13501415]. Y. Wang was supported by National Natural Science Foundation of China [Grant 71901209]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4358. [ABSTRACT FROM AUTHOR]

Firms in many industries obtain superior knowledge of customer preferences through industry experience or data analytics, whereas customers often need costly efforts to learn their match values. In this paper, we examine the situations under which a customer chooses whether to inspect upon observing her personalized price from a firm with superior knowledge. On the surface, it seems that the firm can use personalized prices to directly communicate the customers' match value, and thus there is no need for customers to expend inspection efforts. However, we find that in equilibrium the firm may trick low-preference customers into overpaying more than their match value, even when the inspection cost is low. The opportunistic incentives induce customer suspicions, which may lead to excessive customer inspection that would be avoided if the firm were not capable of price discrimination. Therefore, personalized pricing cannot obviate customer inspection. Since inspection cost raises a deadweight loss in social welfare, public policies that prevent firms from price-discriminating against customers may benefit both firms and customers. History: Yuxin Chen served as the senior editor for this article and Zsolt Katona served as associate editor for this article. Funding: This work was supported by the Hong Kong Research Grants Council [Grant 21500920] and the National Natural Science Foundation of China [Grant 72172089]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/mksc.2022.1355. [ABSTRACT FROM AUTHOR]