Search

Consumers frequently search for information before making decisions. Because their search and purchase decisions depend on the information environment, firms have a strong incentive to influence it. This paper endogenizes the consumer's information environment from the firm's perspective and endogenizes the search decision from the consumer's perspective. We consider a dynamic model where a firm sequentially persuades a consumer to purchase the product. The consumer only wishes to buy the product if it is a good match. The firm designs the information structure. Given the endogenous information environment, the consumer trades off the benefit and cost of information acquisition and decides whether to search for more information. Given the information acquisition strategy of the consumer, the firm trades off the benefit and cost of information provision and determines how much information to provide. This paper characterizes the optimal information structure under a general signal space. We find that the firm only smooths information provision over multiple periods if the consumer is optimistic about the product fit before searching for information. Moreover, if the search cost for the consumer is high, the firm designs the information such that the consumer will be certain that the product is a good match and will purchase it after observing a positive signal. If the search cost is low, the firm provides noisy information such that the consumer will be uncertain about the product fit but will still buy it after observing a positive signal. This paper was accepted by Dmitri Kuksov, marketing. Funding: This work was supported by University of California, Berkeley, the Institute for Business Innovation [Research Grant], and the Center for Equity, Gender, and Leadership [Research Grants]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.00994. [ABSTRACT FROM AUTHOR]

In this paper, we study the consumers' variety-seeking behavior in recommender system applications and propose a comprehensive framework to measure such behavior based on past consumption records. The effectiveness of the proposed framework is validated through user questionnaire studies conducted at Alibaba, where our constructed variety-seeking measures match well with consumers' self-reported levels of their variety-seeking behaviors. We subsequently present a recommendation framework that combines the identified variety-seeking levels with unexpected recommender systems in the data mining literature to address consumers' heterogenous desire for product variety, in which we provide more unexpected product recommendations to variety-seeking consumers and vice versa. Through off-line experiments on three different recommendation scenarios and a large-scale online controlled experiment at a major video-streaming platform, we demonstrate that those models following our recommendation framework significantly increase various business performance metrics and generate tangible economic impact for the company. Our findings lead to important managerial implications to better understand consumers' variety-seeking behaviors and design recommender systems. As a result, the best performing model in our proposed frameworks is deployed by the company to serve all consumers on the video-streaming platform. Variety seekers are those customers who easily get bored with the products they purchased before and, therefore, prefer new and fresh content to expand their horizons. Despite its prevalence, variety-seeking behavior is hardly studied in recommendation applications because of various limitations in existing variety-seeking measures. To fill the research gap, we present a variety-seeking framework in this paper to measure the level of variety-seeking behavior of customers in recommendations based on their consumption records. We validate the effectiveness of our framework through user questionnaire studies conducted at Alibaba, where our variety-seeking measures match well with consumers' self-reported levels of their variety-seeking behaviors. Furthermore, we present a recommendation framework that combines the identified variety-seeking levels with unexpected recommender systems in the data mining literature to address consumers' heterogenous desire for product variety, in which we provide more unexpected product recommendations to variety-seeking consumers and vice versa. Through off-line experiments on three different recommendation scenarios and a large-scale online controlled experiment at a major video-streaming platform, we demonstrate that those models following our recommendation framework significantly increase various business performance metrics and generate tangible economic impact for the company. Our findings lead to important managerial implications to better understand consumers' variety-seeking behaviors and design recommender systems. As a result, the best-performing model in our proposed frameworks has been deployed by the company to serve all consumers on the video-streaming platform. History: Ahmed Abbasi, Senior Editor; Gautam Pant, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2021.0053. [ABSTRACT FROM AUTHOR]

This paper studies a novel social commerce practice known as "help-and-haggle," whereby an online consumer can ask friends to help her "haggle" over the price of a product. Each time a friend agrees to help, the price is cut by a random amount, and if the consumer cuts the product price down to zero within a time limit, she will get the product for free; otherwise, the product reverts to the original price. Help-and-haggle enables the firm to promote its product and boost its social reach as consumers effectively refer their friends to the firm. We model the consumer's dynamic referral behavior in help-and-haggle and provide prescriptive guidance on how the firm should randomize price cuts. Our results are as follows. First, contrary to conventional wisdom, the firm should not always reduce the (realized) price-cut amount if referrals are less costly for the consumer. In fact, the minimum number of successful referrals the consumer must make to have a chance to win the product can be nonmonotone in referral cost. Second, relative to the deterministic price-cut benchmark, a random price-cut scheme improves firm payoff, extracts more consumer surplus, and widens social reach. Additionally, in most instances, it also reduces the promotion expense while increasing profit from product sales at the same time. Third, help-and-haggle can be more cost effective in social reach than a reward-per-referral program that offers a cash reward for each successful referral. However, using the prospect of a free product to attract referrals cannibalizes product sales, potentially causing help-and-haggle to fall short. Yet, if consumers are heterogeneous in product valuations and referral costs or face increasing marginal referral costs, help-and-haggle can outperform the reward-per-referral program. This paper was accepted by Elena Katok, operations management. Funding: L. Yang acknowledges the Berkeley Haas Center for Growth Markets [2022 Grant Award]. C. Jin acknowledges the Singapore Ministry of Education Academic Research Fund [Tier 1 Grant 251RES2101]. Z. Shao acknowledges support from the National Natural Science Foundation of China [Grant 72071188]. All authors acknowledge the Networks, Electronic Commerce and Telecommunications (NET) Institute [2022 Summer Research Grant]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4948. [ABSTRACT FROM AUTHOR]

This paper shows that consumers prefer organic listings in the top-ranked positions to sponsored listings of the same product/position in an online marketplace. Sponsored product listings on online marketplaces are third-party sellers' ads blended in organic product listings. This paper investigates a seller's managerial questions: whether a sponsored listing outperforms an organic listing and how the performance varies by positions. Our large-scale field study on a mobile app with experimental and natural variation finds that consumers prefer organic listings in the top-ranked positions to sponsored listings of the same product/position. Consumers become indifferent between sponsored and organic listings in the lower-ranked positions. A mechanism check suggests that top-ranked organic listings are perceived as more credible than seller-subsidized sponsored listings. Despite consumers' preference for organic listings, a simulation analysis shows that an advertising seller may benefit financially if a lower-ranked organic listing can be replaced with a top-ranked sponsored listing. History: Olivier Toubia served as the senior editor. Funding: J. Shi acknowledges financial support from INFORMS Society for Marketing Science Doctoral Dissertation Early Stage Research Grants. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mksc.2021.0293. [ABSTRACT FROM AUTHOR]

This paper examines how data-driven personalized decisions can be made while preserving consumer privacy. Our setting is one in which the firm chooses a personalized price based on each new customer's vector of individual features; the true set of individual demand-generating parameters is unknown to the firm and so must be estimated from historical data. We extend the existing personalized pricing framework by requiring also that the firm's pricing policy preserve consumer privacy, or (formally) that it be differentially private: an industry standard for privacy preservation. We develop privacy-preserving personalized pricing algorithms and show that they achieve near-optimal revenue by deriving theoretical (upper and lower) performance bounds. Our analyses further suggest that, if the firm possesses a sufficient amount of historical data, then it can achieve a certain level of differential privacy almost "for free." That is, the revenue loss due to privacy preservation is of smaller order than that due to estimation. We confirm our theoretical findings in a series of numerical experiments based on synthetically generated and online auto lending (CPRM-12-001) data sets. Finally, motivated by practical considerations, we also extend our algorithms and findings to a variety of alternative settings, including multiproduct pricing with substitution effect, discrete feasible price set, categorical sensitive features, and personalized assortment optimization. This paper was accepted by Vishal Gaur, operations management. Funding: R. Momot acknowledges financial support from the HEC Paris Foundation and the Agence Nationale de la Recherche (French National Research Agency) "Investissements d'Avenir" [Grant LabEx Ecodec/ANR-11-LABX-0047] during the initial stages of this project. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4925. [ABSTRACT FROM AUTHOR]

Habits and routines are foundational to several organizational theories. Considering organizational members to be predominately employees, established habit-based models recognize how these members' habits help build organizations and are shaped by them. Departing from this traditional, internal focus, our paper highlights an important aspect of organizing, which has been relatively overlooked by established habit-based models, namely, how firms engineer consumer habits to their advantage and, by extension, strategically shape the habits of other key resource providers. To better theorize consumer habits and their engineering, and to integrate these phenomena within extant organizational theory, we develop a new habit-based perspective relating firms, consumers, and social institutions. Inspired by Dewey's transactional approach and drawing on modern habit science, our transactional framework helps illuminate habit engineering, promotes a richer and more integrated view of organizing, and opens new possibilities for habit-based organizational theories. Our paper also offers several implications for firms' managers, individual consumers, and broader society. [ABSTRACT FROM AUTHOR]

Based on a representative sample of consumer financial transaction data, this paper studies the consumption and savings response to a 2015 permanent increase in the marginal income tax of the high-income taxpayers. Using difference-in-differences regressions, controlling for individual and time fixed effects, we show robust results that the affected consumers experienced little change in their spending. The pattern is prevalent across consumer demographics and is found even among consumers who are sophisticated, have high levels of debt, or register little income changes. Furthermore, the tax increase financed fiscal redistribution leads to a long-lasting increase in the consumption of the lower-income population. This paper was accepted by Lukas Schmid, finance. Funding: The authors gratefully acknowledge financial support from Social Science Research Thematic Grant in Singapore [Future-Proofing Singapore: An Economic Approach MOE2019-SSRTG-024]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4823. [ABSTRACT FROM AUTHOR]

This paper examines mutual funds' dual role as both clients and shareholders of broker banks. Mutual funds are more likely to hold and significantly overweight stocks of their broker banks. In line with the portfolio decisions, fund voting is biased toward broker management in contentious proposals. Such voting bias is inconsistent with maximizing broker banks' shareholder value yet significantly affects voting outcomes and the presence of contentious proposals at the banks' shareholder meetings. Furthermore, we show that although client funds are rewarded with preferential initial public offering allocations from connected brokers for their voting support, fund managers' engagement in reciprocal practices is ultimately determined by the economic tradeoffs they face. Our study not only uncovers a new mechanism—being brokers' friendly shareholders—through which the two parties maintain their quid pro quo relationships but also raises a broader concern about governance of important financial institutions. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4820. [ABSTRACT FROM AUTHOR]

This paper studies an emerging subscription model called ship-then-shop. Leveraging its predictive analytics and artificial intelligence (AI) capability, the ship-then-shop firm curates and ships a product to the consumer, after which the consumer shops (i.e., evaluates product fit and makes a purchase decision). The consumer first pays the up-front ship-then-shop subscription fee prior to observing product fit and then pays the product price afterward if the consumer decides to purchase. We investigate how the firm balances the subscription fee and product price to maximize its profit when consumers can showroom. A key finding is the ship-then-shop firm's nonmonotonic surplus extraction strategy with respect to its prediction capability. As prediction capability increases, the firm first switches from ex ante to ex post surplus extraction (by lowering fees and raising prices). However, if the prediction capability increases further, the firm reverts to ex ante surplus extraction (by raising fees and capping prices). We also find that the ship-then-shop model is most profitable when (i) the prediction capability is advanced, (ii) the search friction in the market is large, or (iii) the product match potential is large. Finally, we show that the marginal return of AI capability on the firm's profit decreases in search friction but increases in product match potential. Taken together, we provide managerially relevant insights to help guide the implementation of the innovative subscription model. This paper was accepted by Dmitri Kuksov, marketing. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4723. [ABSTRACT FROM AUTHOR]

Problem definition: Assortment selection is one of the most important decisions faced by retailers. Most existing papers in the literature assume that customers select at most one item out of the offered assortment. Although this is valid in some cases, it contradicts practical observations in many shopping experiences, both in online and brick-and-mortar retail, where customers may buy a basket of products instead of a single item. In this paper, we incorporate customers' multi-item purchase behavior into the assortment optimization problem. We consider both the uncapacitated and capacitated assortment problems under the so-called Multivariate MNL (MVMNL) model, which is one of the most popular multivariate choice models used in the marketing and empirical literature. Methodology/results: We first show that the traditional revenue-ordered assortment may not be optimal. Nonetheless, we show that under some mild conditions, a certain variant of this property holds (in the uncapacitated assortment problem) under the MVMNL model; that is, the optimal assortment consists of revenue-ordered local assortments in each product category. Finding the optimal assortment even when there is no interaction among product categories is still computationally expensive because the revenue thresholds for different categories cannot be computed separately. To tackle the computational complexity, we develop FPTAS for several variants of (capacitated and uncapacitated) assortment problems under MVMNL. Managerial implications: Our analysis reveals that disregarding customers' multi-item purchase behavior in assortment decisions can indeed have a significant negative impact on profitability, demonstrating its practical importance in retail. We numerically show that our proposed algorithm can improve a retailer's expected total revenues (compared with a benchmark policy that does not properly take into account the impact of customers' multi-item choice behavior in assortment decision) by up to 14%. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0526. [ABSTRACT FROM AUTHOR]

Private sector innovation is critical to mitigating and adapting to climate change. This paper studies innovation in solar energy technology, a key source of clean energy that has experienced rapid price declines over the past decade. To understand the causes and effects of innovation, I estimate a dynamic structural model of competition among solar panel manufacturers. The model captures important features of the industry, including the role of government subsidies for solar adoption, and I employ a unique measure of technological progress that is observable and verifiable. The results produce two main insights. First, ignoring innovation by firms can generate biased estimates of the effects of government policy. Second, decentralized government intervention in a global market generates spillovers; a subsidy in one country causes international firms to innovate more, leading to lower prices and increased adoption elsewhere. This spillover underscores the need for international coordination by governments and the private sector to address climate change. This paper was accepted by Erica Plambeck, Special Section of Management Science on Business and Climate Change. Funding: The author acknowledges support from the U.S. Environmental Protection Agency [Science to Achieve Results (STAR) Fellowship Assistance Agreement FP-91769401-0] and Harvard [Joseph Crump Fellowship]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4662. [ABSTRACT FROM AUTHOR]

Consumers often receive a full or partial refund for product returns or service cancellations. Much of the existing literature studies cash refunds, where consumers get the money back minus a fee upon a product return or service cancellation. However, not all refunds are issued in cash. Sometimes consumers receive credit that can be used for future purchases, oftentimes with an expiration term after which the credit is forfeited. We study the optimal design of credit refund policies. Different from models that consider cash refunds, we explicitly model repeated interactions between the seller and consumers over time. We assume that consumers' valuation for the product/service varies over time and that there is an exogenous probability for product returns. Several interesting results emerge. First, a credit refund policy facilitates intraconsumer price discrimination for a single type of consumers with stochastic valuation. Second, an optimal policy often involves an intermediate credit expiration term, under which a consumer with a high product valuation always makes a purchase, whereas a consumer with a low product valuation may be induced to make a purchase as the credit approaches expiration, leading to a demand induction effect. Finally, a credit refund policy can be more profitable than a cash refund policy and can lead to a win-win outcome for both the firm and consumers under certain conditions. We also consider several extensions to check the robustness of our findings. This paper was accepted by Jeannette Song, operations management. Funding: Y. Liu was substantially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. PolyU15502420). Y. Liu was also supported by a grant from NSFC [72293564/72293560]. Supplemental Material: The data and online appendices are available at https://doi.org/10.1287/mnsc.2020.03042. [ABSTRACT FROM AUTHOR]

In last-mile delivery logistics, peer-to-peer logistic platforms play an important role in connecting senders, customers, and independent carriers to fulfill delivery requests. As the carriers are not under the platform's control, the platform has to anticipate their reactions while deciding how to allocate the delivery operations. Indeed, carriers' decisions largely affect the platform's revenue. In this paper, we model this problem using bilevel programming. At the upper level, the platform decides how to assign the orders to the carriers; at the lower level, each carrier solves a profitable tour problem to determine which offered requests to accept, based on her own profit maximization. Possibly, the platform can influence carriers' decisions by determining also the compensation paid for each accepted request. The two considered settings result in two different formulations: the bilevel profitable tour problem with fixed compensation margins and with margin decisions, respectively. For each of them, we propose single-level reformulations and alternative formulations where the lower-level routing variables are projected out. A branch-and-cut algorithm is proposed to solve the bilevel models, with a tailored warm-start heuristic used to speed up the solution process. Extensive computational tests are performed to compare the proposed formulations and analyze solution characteristics. Funding: The research of E. Fernandez was partially funded through the Spanish Ministerio de Ciencia y Tecnología and European Regional Development Funds (ERDF) [Grant MTM2019-105824GB-I00]. The research of C. Archetti, M. Cerulli, and I. Ljubić was partially funded by CY Initiative of Excellence, France [Grant "Investissements d'Avenir ANR-16-IDEX-0008"]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0129. [ABSTRACT FROM AUTHOR]

Propose a novel business model for parcel fulfillment that benefits all stakeholders. Develop three analytics methods to support the business model. Cainiao proposes a novel business model that consolidates parcels ordered by the same consumer from one or more merchants during the fulfillment process. The objective is to increase delivery speed without incurring additional costs for merchants and consumers. To support this business model, we develop three analytics methods: (1) a two-phase online optimization algorithm to determine which of a consumer's parcels constitute consolidated parcels and to select the shipping methods for the consolidated parcels that maximize the gains while satisfying the constraints (e.g., the 10-day on-time delivery rate of all consumer parcels created within a specified time should reach a target value), (2) a statistical method to calculate delivery time distributions to obtain on-time delivery rates within different days, and (3) a simulation-based optimization method to guide managers in setting appropriate target values for the constraints. In addition, we prove that the expected optimality gap and constraint violation of the online optimization algorithm have sublinear bounds, and we validate its effectiveness and robustness by testing instances generated from real-world data. Since 2020, Cainiao has utilized the system to consolidate numerous parcels shipped from China to more than 50 countries and regions, thus saving tens of millions of dollars annually and reducing delivery time by at least 50%. History: This paper has been accepted for the INFORMS Journal on Applied Analytics Special Issue—2023 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research. [ABSTRACT FROM AUTHOR]

With the growing popularity of e-commerce, nearly every prominent retailer is aiming to turn omni-channel. One crucial decision in this pursuit is the identification of the joint assortment. In this study, we contribute by examining joint assortment and product prices for a retailer that sells products through both brick-and-mortar and online channels. Our analysis indicates that the optimal assortment should be thought of as a portfolio of two types of products: customized and omni-channel. Customized products are priced in such a way that they are targeted toward customers who prefer to shop from the channel the products are sold through. In contrast, omni-channel products are priced attractively so that all customers consider buying them. The relative mix of these products depends on how flexible customers are in shopping from the channel they do not prefer and the number of customers who prefer each channel. Additionally, we investigate whether the conventional wisdom of selling niche products through the online channel is always optimal. We find that this suggestion may be sub-optimal when the online channel has greater cost of including a product in the assortment and fewer preferring customers compared with the brick-and-mortar channel. With growing popularity of e-commerce, nearly every prominent retailer is aiming to turn omni-channel. One crucial decision in this pursuit is the identification of the joint assortment. Despite the importance of this decision, few studies have addressed this issue. In this study, we contribute by examining joint assortment and product prices for a retailer that sells products through both brick-and-mortar and online channels using the locational choice model. We consider two classes of customers such that customers in one class prefer the brick-and-mortar channel and customers in the other class prefer the online channel. Our analysis indicates that the optimal assortment should be thought of as a portfolio of two types of products: customized and omni-channel. Customized products are priced in such a way that they are targeted toward customers who prefer to shop from the channel the products are sold through. In contrast, omni-channel products are priced attractively so that all customers consider buying them. The relative mix of these products depends on how flexible customers are in shopping from the channel they do not prefer and the size of each class. Additionally, we investigate whether the conventional wisdom of selling niche products through the online channel is always optimal. We find that this suggestion may be suboptimal when the online channel has greater cost of including a product in the assortment and fewer preferring customers compared with the brick-and-mortar channel. We also develop insights on price selection. As more and more retailers are converting from single-channel to omni-channel, they need to reexamine their assortment strategy across existing and new channels. The models, results, and insights presented in this paper would help them in designing a joint assortment policy across channels. History: Ram Gopal, Senior Editor; Jianqing Chen, Associate Editor. Funding: A. Sapra received generous financial support from IIMB Chair of Excellence at Indian Institute of Management Bangalore while working on this study. S. Kumar thanks Temple Center for International Business Education and Research for partially supporting this research. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2021.0596. [ABSTRACT FROM AUTHOR]

Problem definition: This paper considers a tandem queueing system in which stage 1 has one station serving multiple classes of arriving customers with different service requirements and related delay costs, and stage 2 has multiple parallel stations, with each station providing one type of service. Each station has many statistically identical servers. The objective is to design a joint capacity allocation between stages/stations and scheduling rule of different classes of customers to minimize the system's long-run average cost. Methodology/results: Using fluid approximation, we convert the stochastic problem into a fluid optimization problem and develop a solution procedure. Based on the solution to the fluid optimization problem, we propose a simple and easy-to-implement capacity allocation and scheduling policy and establish its asymptotic optimality for the stochastic system. The policy has an explicit index-based scheduling rule that is independent of the arrival rates, and resource allocation is determined by the priority orders established between the classes and stations. We conduct numerical experiments to validate the accuracy of the fluid approximation and demonstrate the effectiveness of our proposed policy. Managerial implications: Tandem queueing systems are ubiquitous. Our results provide useful guidelines for the allocation of limited resources and the scheduling of customer service in those systems. Our proposed policy can improve the system's operational efficiency and customers' service quality. Funding: Z. Zhong's research is partially supported by the Fundamental Research Funds for the Central Universities [Grant 2023ZYGXZR074] and the Hunan Provincial Natural Science Foundation of China [Grant 2022JJ40109]. P. Cao's research is partially supported by the National Natural Science Foundation of China [Grant 72122019]. J. Huang's research is partially supported by the Hong Kong Research Grants Council General Research Fund [CUHK-14501621] and the National Natural Science Foundation of China [Grant 72222023]. S. X. Zhou's research is partially supported by the Hong Kong Research Grants Council General Research Fund [CUHK-14500921], the National Natural Science Foundation of China [Grant 72394395], and the Asian Institute of Supply Chains and Logistics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0266. [ABSTRACT FROM AUTHOR]

Problem definition: Loyalty programs have greatly expanded in scale and scope, and loyalty points issued by firms serve as a new form of currency alongside the traditional currency of money. In this paper, we study how consumers decide to pay with points or money for a purchase and how these decisions are affected by consumers' points earning characteristics. Methodology/results: We develop a model of consumers' payment choices and estimate it on proprietary loyalty program data from a major U.S. airline company using a hierarchical Bayesian framework. Our results demonstrate that mental accounting, the subjective perceived value of points, and the reference exchange rate play important roles in consumers' payment choices. Moreover, the primary points earning source and the total earning level are jointly associated with consumers' attitudes toward points and money: Consumers who earn many points and mostly with the focal firm tend to value points more than money, while those who earn few points or mostly through a cobranded credit card tend to value money more than points. To better understand heterogeneity in consumers' attitudes toward points, we propose a probabilistic segmentation of consumers and identify four behavioral segments with distinctive characteristics. Through counterfactual analysis, we demonstrate how a firm can implement money and point pricing policies to optimally target and influence consumers' payment choices. Managerial implications: It is important for firms to understand how consumers think about points and decide to pay with points or money as it affects firms' cash flows, profitability, and consumer loyalty and engagement. We demonstrate that firms can design optimal price targeting policies by utilizing the significant level of heterogeneity in consumers' attitudes toward points. Furthermore, our results indicate that firms that are expanding their partnership networks and offering more points earning sources should consider the impact that this would have on consumers' mental accounting of money and points, and consequently their payment choices. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0287. [ABSTRACT FROM AUTHOR]

Problem definition: We study customer service chat (CSC) systems, in which agents can serve multiple customers simultaneously, with generally distributed service and patience times. The multitasking capability of agents introduces idiosyncratic challenges when making routing and staffing decisions. Methodology/results: To determine the dynamic matching of arriving customers with available agents, we first formulate a routing linear program (LP) based on system primitives. Inspired by the optimal solution of the routing LP, we design a parsimonious dynamic routing policy that is independent of arrival rate and service capacity information. We also use the optimal solution to develop closed-form approximations for crucial performance metrics and show that a similar LP can be utilized to make staffing decisions. Through extensive simulation experiments, we showcase the efficacy of our approximations and staffing decisions. Furthermore, under our proposed policy, the CSC system exhibits a unique stationary fluid model in which the steady-state performance measures align with our approximations. Managerial implications: The extant literature primarily focuses on Markovian systems with exponential distributions. In this paper, customers' service and patience times are allowed to be generally distributed to agree with practical settings. Our findings indicate that the distributions have a significant impact on routing policies, staffing decisions, and system performance. Funding: Z. Long was supported by the National Natural Science Foundation of China [Grants 72101112, 72132005, and 72271119] and Jiangsu Province, China [Grant BK20210171]. J. Zhang was supported by the Hong Kong Research Grants Council, General Research Fund [Grants 16208120 and 16214121]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0114. [ABSTRACT FROM AUTHOR]

Product recommendations can benefit consumers' online product search via multiple underlying mechanisms, such as showing products that offer them high value, facilitating navigation on the website, or exposing more product information. However, it is unclear ex ante which is the primary underlying mechanism that drives the benefits of product recommendations to consumers. We conducted a randomized field experiment to estimate the benefits of an item-based collaborative filtering (CF) recommendation system to consumers. We collect unique data on the affinity scores computed by an item-based CF algorithm to develop measures of a product's net value and horizontal (taste) fit for consumers. Our results indicate that product recommendations help consumers search for higher-value products that are lower priced, fit their tastes better, or both. Besides that, we find that the ability to find higher-value products (rather than easy navigation or exposure to more product information) is the primary driver for consumers' higher purchase probabilities under recommendations. We further find a higher benefit of recommendations in product categories with higher price dispersion and heterogeneity in consumers' tastes, providing additional evidence for the lower price and better horizontal fit mechanisms. Finally, we find that when made available, consumers substitute their usage of other search tools on the website with product recommendations. Our findings have important implications for online retailers, policymakers, regulators, and item-based CF recommendation system design. This paper was accepted by D. J. Wu, information systems. Funding: This work was supported by the Public Utility Research Center of the University of Florida, the Hi! PARIS Fellowship, the HEC Foundation, and the Leavey School of Business at Santa Clara University [Grant 102720]. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2023.4951. [ABSTRACT FROM AUTHOR]

Problem definition: Customer reviews are essential to online marketplaces. However, reviews typically vary; ratings of a product or service are rarely the same. In many service marketplaces, including the ones for solar panel installations, supply-side participants are active. That is, a seller must make a proposal to serve each customer. In such marketplaces, it is not clear how (or if) the dispersion in customer reviews affects the seller activity level and number of matches in the marketplace. Our paper examines this by considering both ratings and text reviews. To our knowledge, this is the first paper that empirically studies how the review dispersion affects a seller's activity level and the number of matches in an online marketplace with active sellers. Distinct from literature, we examine the relationship between the review dispersion and supply-side activities in an online service marketplace. Methodology/results: We collaborated with one of the largest online solar marketplaces in the United States that connects potential solar panel adopters with installers. We obtained a unique data set from the marketplace for 2013 − 2018. We complement this with public data sets. Our analysis uses traditional econometrics methods, a clustering method, and the deep-learning-based natural-language-processing model BERT developed by Google AI. We find that the dispersion in customer reviews has a significant and inverted U-shaped relationship with an installer's marketplace activity level. Intuitively, a marketplace operator would favor having more sellers with perfect ratings. In contrast, we identify a significant and inverted U-shaped relationship between the market-level review dispersion and transactions. Managerial implications: Our paper provides key insights to marketplace operators and sellers. We find that in contrast to general belief, an operator can improve its market transactions by keeping/promoting sellers with low ratings or avoiding (negative) review filtering. Furthermore, sellers' implementation of "rating gating" to avoid negative reviews may backfire for them by reducing their matches. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0104. [ABSTRACT FROM AUTHOR]

In several transportation systems, vehicles can choose where to meet customers rather than stopping in fixed locations. This added flexibility, however, requires coordination between vehicles and customers that adds complexity to routing operations. This paper develops scalable algorithms to optimize these operations. First, we solve the one-stop subproblem in the ℓ 1 space and the ℓ 2 space by leveraging the geometric structure of operations. Second, to solve a multistop problem, we embed the single-stop optimization into a tailored coordinate descent scheme, which we prove converges to a global optimum. Third, we develop a new algorithm for dial-a-ride problems based on a subpath-based time–space network optimization combining set partitioning and time–space principles. Finally, we propose an online routing algorithm to support real-world ride-sharing operations with vehicle–customer coordination. Computational results show that our algorithm outperforms state-of-the-art benchmarks, yielding far superior solutions in shorter computational times and can support real-time operations in very large-scale systems. From a practical standpoint, most of the benefits of vehicle–customer coordination stem from comprehensively reoptimizing "upstream" operations as opposed to merely adjusting "downstream" stopping locations. Ultimately, vehicle–customer coordination provides win–win–win outcomes: higher profits, better customer service, and smaller environmental footprint. This paper was accepted by Chung Piaw Teo, optimization. Funding: This research was supported by the National Natural Science Foundation of China [Grants 72288101, 52221005 and 52220105001]. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mnsc.2023.4739. [ABSTRACT FROM AUTHOR]

Information Disclosure in Platforms: Optimizing Volumes and Prices Can Harm Consumers Two-sided platforms reduce frictions and facilitate trade in many sectors, and, in doing so, they increasingly collect and process data about supply and demand. The paper "Strategic Release of Information in Platforms: Entry, Competition, and Welfare" by Kostas Bimpikis and Giacomo Mantegazza shows that platforms can increase their profits by strategically disclosing (coarse) information that they collected about demand to the supply side. However, this practice may also adversely impact the welfare of consumers. By designing its information disclosure policy, a platform can influence the entry and pricing decisions of its potential suppliers. In general, it is optimal for platforms to disclose their information only partially to either "nudge" entry when it is costly for suppliers to join or discourage it when suppliers do not have valuable outside options. On the other hand, consumers may end up worse off due to higher prices compared with when the platform refrains from sharing any demand information. Two-sided platforms play an important role in reducing frictions and facilitating trade, and in doing so they increasingly engage in collecting and processing data about supply and demand. This paper establishes that platforms have an incentive to strategically disclose (coarse) information about demand to the supply side, as this can considerably boost their profits. However, this practice may also adversely affect the welfare of consumers. By optimally designing its information disclosure policy, a platform can influence the entry and pricing decisions of its potential suppliers. In general, it is optimal for the platform to disclose its information only partially to either "nudge" entry when it is a priori costly for suppliers to join or, conversely, discourage it when suppliers do not have access to attractive outside options. On the other hand, consumers may end up being worse off, as they have access to fewer trading options and/or face higher prices compared with when the platform refrains from sharing any demand information to its potential suppliers. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.2402. [ABSTRACT FROM AUTHOR]

Customers in sales processes increasingly encounter automated sales agents that complement or replace human sales agents. Yet, little is known about whether, how, and why customers respond to automated agents in contrast to human agents across successive decision stages of the same sales process. Even less is known about customer responses to combinations where both agents assume distinct roles and focus on complementary tasks that are traditionally performed by only one single agent. Against this backdrop, this paper explores the influence of increasingly common sales representative types on customer decisions across sales stages. Our findings demonstrate that customer responses to automated (versus human) sales agents are not stable in sales processes and instead, shift as customers move across sales stages. What is more, the paper shows that combinations of sales agents versus single sales agents do matter, yet their differential effects depend on contextual features of the sales setting. These insights are important because vendors may assume that a certain type of sales agent is always more appreciated by customers, whereas in fact, different sales agent types bring distinct attributes to the table, and customers' appreciation of these attributes shifts across sales stages. Customers in sales processes increasingly encounter automated sales agents (ASAs) that complement or replace human sales agents (HSAs). Yet, little is known about whether, how, and why customers respond to ASAs in contrast to HSAs across successive decision stages of the same sales process. Even less is known about customer responses to HSA-ASA combinations, where both agents assume distinct roles and focus on complementary tasks that are traditionally performed by only one single sales agent. Against this backdrop, this paper explores the influence of increasingly common sales representative (rep) types (i.e., ASA, HSA, and HSA-ASA) on customer decisions across sales stages. Drawing on information processing theory and the literature on hedonic-utilitarian decision making, we investigate customer responses to text-based ASAs from vendor companies in two common early stages of email sales processes (i.e., sales initiation stages) when customers successively decide whether to indicate their initial interest in an offer and then, whether to provide their contact information. Specifically, we conducted two complementary multi-decision experiments, namely (1) a randomized field experiment in a high-stakes sales initiation setting (n = 325) and (2) a subsequent randomized online experiment to complement the real-world insights (n = 408). Our core findings reveal reversing effect patterns of sales rep types across stages: although customers are more likely to indicate their initial interest to HSAs (versus ASAs) because of HSAs' higher levels of social presence, they are less likely to provide contact information to HSAs because of HSAs' lower levels of performance expectancy and effort expectancy. We also show that HSA-ASA combinations can be reasonable options for single ASAs, yet contextual features of the sales setting may affect differential customer responses to HSA-ASA combinations (versus ASAs) in each sales stage. Taken together, we uncover shifting effect patterns in customer responses to sales rep types across successive sales stages and shed light on the consecutive underlying mechanisms that explain these shifts. These findings have significant implications for vendor companies seeking to allocate HSAs and/or ASAs effectively across various decision stages in sales processes and beyond. History: Wonseok Oh, Senior Editor and Khim Yong Goh; Associate Editor. Funding: This work was supported by the Center for Responsible Digitality (ZEVEDI) and the German Research Foundation (DFG) [Grant 471168026]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1171. [ABSTRACT FROM AUTHOR]

This paper addresses how and when to extrapolate experimental results for normative policy analysis, particularly in the context of compliance promotions in which consumers can self-select into treatment. This paper considers an experiment-based approach to the optimal design and targeting of compliance promotions. Compliance promotions involve optional participation on the behalf of customers. For example, physicians must consent to see detailers, and consumers must redeem coupons to obtain discounts. Individual compliance decisions affect the mix of customers participating in the promotion and, therefore, how the promotion affects sales. Optional compliance is an especially acute problem in the context of field experiments as policy optimization often necessitates extrapolation beyond the observed cells of the experiment to a different mix of complying customers. Our approach to optimizing the design and targeting of compliance promotions involves (i) an experiment to exogenously vary promotion features; (ii) a means to identify which promotion features can be causally extrapolated; (iii) an approach to extrapolate those causal effects; and (iv) an optimization over the promotion features, conditioned on the extrapolation. The approach is easy to estimate, accommodates two-sided noncompliance due to unobserved heterogeneity, and establishes partial identification bounds of causal effects. When applying the approach to a hotel loyalty promotion, wherein customers must visit enough hotels to earn bonus loyalty points, we find profits are improved considerably. History: Olivier Toubia served as the senior editor for this article. Supplemental Material: The data files are available at https://doi.org/10.1287/mksc.2022.1420. [ABSTRACT FROM AUTHOR]

Due to the sheer number of available choices, online retailers frequently use tiered assortment to present products. In this case, groups of products are arranged across multiple pages or stages, and a customer clicks on "next" or "load more" to access them sequentially. Despite the prevalence of such assortments in practice, this topic has not received much attention in the existing literature. In this work, we focus on a sequential choice model that captures customers' behavior when product recommendations are presented in tiers. We analyze multiple variants of tiered assortment optimization (TAO) problems by imposing "no-duplication" and capacity constraints. For the offline setting involving known customers' preferences, we characterize the properties of the optimal tiered assortment and propose algorithms that improve the computational efficiency over the existing benchmarks. To the best of our knowledge, we are the first to study the online setting of the TAO problem. A unique characteristic of our online setting, absent from the one-tier MNL bandit, is partial feedback. Products in the lower priority tiers are not shown when a customer has purchased a product or has chosen to exit at an earlier tier. Such partial feedback, along with product interdependence across tiers, increases the learning complexity. For both the noncontextual and contextual problems, we propose online algorithms and quantify their respective regrets. Moreover, we are able to construct tighter uncertainty sets for model parameters in the contextual case and thus improve the performance. We demonstrate the efficacy of our proposed algorithms through numerical experiments. This paper was accepted by J. George Shanthikumar, data science. Supplemental Material: The data files and e-companion are available at https://doi.org/10.1287/mnsc.2023.4940. [ABSTRACT FROM AUTHOR]

We study the problem of maximizing payoff generated over a period of time in a general class of closed queueing networks with a finite, fixed number of supply units that circulate in the system. Demand arrives stochastically, and serving a demand unit (customer) causes a supply unit to relocate from the "origin" to the "destination" of the customer. The key challenge is to manage the distribution of supply in the network. We consider general controls including customer entry control, pricing, and assignment. Motivating applications include shared transportation platforms and scrip systems. Inspired by the mirror descent algorithm for optimization and the backpressure policy for network control, we introduce a rich family of mirror backpressure (MBP) control policies. The MBP policies are simple and practical and crucially do not need any statistical knowledge of the demand (customer) arrival rates (these rates are permitted to vary in time). Under mild conditions, we propose MBP policies that are provably near optimal. Specifically, our policies lose at most O(KT+1K+ηK) payoff per customer relative to the optimal policy that knows the demand arrival rates, where K is the number of supply units, T is the total number of customers over the time horizon, and η is the demand process' average rate of change per customer arrival. An adaptation of MBP is found to perform well in numerical experiments based on data from NYC Cab. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Funding: Y. Kanoria was supported by the National Science Foundation's Division of Civil, Mechanical, and Manufacturing Innovation [Grant CMMI-1653477]. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4934. [ABSTRACT FROM AUTHOR]

Many firms delegate pricing decisions to sales agents that directly interact with customers. A premise behind this practice is that sales agents can gather informative signals about the customer's valuation for the good of interest. The information acquired through this interaction with the customer can then be used to make better pricing decisions. We study the underlying principal-agent problem that arises in such situations. In this setting, the agent can exert costly effort to learn a customer's valuation and then decide on the price to quote to the customer, whereas the firm needs to offer a contract to the agent to induce its desired joint learning and pricing behavior. We analyze two versions of this problem: a base model where there is a single customer and a single good, and a generalization where there are multiple customers and limited inventory of the good. For both problems, we find a family of contracts whose payoffs can approach first-best payoffs arbitrarily closely even if the agent has limited liability, that is, garners nonnegative payments in all states of the world, and shed light on the structure and implementation of such contracts. Under reasonable assumptions, these contracts can be implemented with commissions that are convex increasing in revenues up to some cap. These contracts continue to perform well under practical adjustments such as commissions with a revenue-sharing structure. This paper was accepted by Itai Ashlagi, revenue management and market analytics. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mnsc.2023.4939. [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]

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]

Problem definition: We study open-shop service networks where customers go through multiple services. We were motivated by a partnering health screening clinic, where customers are routed by a dispatcher and operational performance is measured at two levels: micro-level, via waits for individual services, and macro-level, via overall wait. Both measures reflect customer experience and could support its management. Our analysis revealed that waits were long and increased along the service process. Such long waits give rise to negative waiting experience and the increasing shape is detrimental as it is known to create perceived waits that are even longer. Our goal is hence to analyze strategies that shape and improve customers' perceived experience. Methodology/results: Analytically, we use a stylized two-station open-shop network to show that prioritizing advanced customers, jointly with pooling (virtual) queues, can improve both macro- and micro-level performance. We validate these findings with a simulation model, calibrated with our clinic's data. Practically, we find that an automated routing system (ARS), recently implemented in the clinic, had a negligible impact on overall wait—It simply redistributed waiting among wait-for-routing and wait-for-service. However, ARS renders applicable sophisticated priority and routing policies (that were infeasible under the manual routing practice), specifically the ones arising from the present research. Managerial implications: Our study amplifies performance benefits of accounting for individual customers' system status in addition to station-level load information. We offer insights into the implementation of new technologies: Firms better plan for fundamental changes in their operation rather than harness new technology to their existing operation, which may be suboptimal due to past technical limitations. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Funding: M. Chen acknowledges the support from the National Natural Science Foundation of China [Grant 72301280]. O. Baron acknowledges the support from the Natural Science and Engineering Research Council of Canada. J. Wang acknowledges the support from the City University of Hong Kong [Grant 11505421]. A. Mandelbaum is partially supported by the Israel Science Foundation [Grant 491/22]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0590. [ABSTRACT FROM AUTHOR]

A natural experiment reveals that an ad ban reduces sales of a grocery retailer by 6% but does not affect overall market size. Advertising bans typically target products that deceive consumers in ways that can threaten their physical and mental health. An alternative policy objective might seek environmental protection through a ban on print advertising. Such measures would profoundly affect grocery retailers relying on printed leaflets to communicate weekly promotions. We measure the causal effect of banning advertising on retail performance by studying a temporary advertising ban implemented in a German federal state during the COVID-19 pandemic. The ban resulted in the suspension of all print advertising by grocery retailers, and the exogenous variation in advertising created by this natural experiment serves as our identification strategy. We apply difference-in-differences regressions to data from a national grocery retailer and find that the ban resulted in a 6% sales decrease in the treated state compared with an adjacent state. GfK Household Panel data reveal no effect of the advertising ban on the market level but a negative impact on retailers offering and advertising weekly promotional product assortments. We study the sensitivity of these results to the COVID pandemic and find that neither changes in COVID-19 incidence, vaccination rates, nor customers' mobility moderate the ad ban effect. The findings offer practical insights for regulators and retailers regarding the impact of ad bans and the value of advertising. History: Catherine Tucker served as the senior editor for this article. This paper was accepted through the Marketing Science: Frontiers review process. Conflict of Interest Statement: None of the authors have any financial or non-financial interests in this manuscript's subject matter or materials. Sebastian Gabel consults with a company linked to the retailer that provided the data. Access to the GfK Household Panel data is provided by AiMark (http://www.aimark.org), which is a not-for-profit foundation that supports top academic research with scanner data provided by its data partners GfK and Kantar. Funding: This work was supported by Deutsche Forschungsgemeinschaft (CRC TRR 190 [Project Number 280092119]) and Nederlandse Organisatie voor Wetenschappelijk Onderzoek. S. Gabel acknowledges funding from the Dutch Research Council (NWO) under a Veni grant. M. Spann acknowledges funding from the German Research Foundation. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mksc.2023.0019. [ABSTRACT FROM AUTHOR]

This paper frames the choice of messenger in advertising content as a Bayesian persuasion problem and characterizes the optimal messenger choice. We propose a model of advertising content that focuses on messenger selection, where advertising can generate product-match signals for consumers. We consider advertising as a problem of Bayesian persuasion with costly information processing, where the type of communication messenger is costless to observe and determines the information structure consumers will face, thereby affecting their attention decisions. Messengers are classified as high type or low type based on their likelihood of generating positive signals about product match. Our findings highlight that the optimal choice of messengers depends on their signal elasticities and the firm's decision on whether to induce consumer attention. In particular, we find that when it is crucial to raise prices and high-type messengers overshadow the product match value by providing generally positive signals, a low-type messenger can effectively capture consumer attention and persuade them to pay a higher premium. This holds true even if high-type messengers can better grab consumers' attention by providing additional entertainment value or when some consumers are naive in belief updating. History: Antony Dukes served as the senior editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mksc.2022.0405. [ABSTRACT FROM AUTHOR]

Merchants need not fear platform entry because that can raise merchants' profits by resolving quality uncertainty for consumers. Merchants are wary of a platform's competitive entry reducing sales and margins. Profit-maximizing platforms seek to provide credible quality information to gain consumer trust and confidence. Can a platform enter so that resulting multisender prices alleviate consumers' uncertainty by signaling quality? Can merchants also benefit from platform entry? We provide answers by analyzing strategic platform and merchant pricing under quality uncertainty. A modeling innovation is a leader-follower framework for platform-merchant competition. Consumers resolve uncertainty using both platform and merchant prices. Using what we label perfect Bayes-consistent beliefs, we identify perfect Bayesian equilibrium that satisfies the intuitive criterion, and also out-of-equilibrium beliefs that are unprejudiced. A substantive finding is that platform entry can raise merchant profits when the quality is high by moving the equilibrium from pooling to separating, and supporting higher prices. Another strategic consequence of platform entry could be a separating equilibrium with incomplete market coverage due to price distortion moving to one of complete market coverage due to seller competition. Indeed, conditions exist in our model for an equilibrium outcome identical to complete information. Finally, facing entry cost, a platform may choose to enter only with high quality but quality revelation remains price dependent and not based on mere entry. History: Yuxin Chen served as the senior editor for this article. Funding: Y. Qiu was supported by the Natural Science Foundation of China [Grant 71902142] toward completion of this paper. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mksc.2021.0227. [ABSTRACT FROM AUTHOR]

We study a courier dispatching problem in an on-demand delivery system in which customers are sensitive to delay. Specifically, we evaluate the effect of temporal pooling by comparing systems using the dedicated strategy, with which only one order is delivered per trip, versus the pooling strategy, with which a batch of consecutive orders is delivered on each trip. We capture the courier delivery system's spatial dimension by assuming that, following a Poisson process, demand arises at a uniformly generated point within a service region. With the same objective of revenue maximization, we find that the dispatching strategy depends critically on customers' patience level, the size of the service region, and whether the firm can endogenize the demand. We obtain concise but informative results with a single courier and assuming that customers' underlying arrival rate is large enough, meaning a crowded market, such as rush hour delivery. In particular, when the firm has a growth target and needs to achieve an exogenously given demand rate, using the pooling strategy is optimal if the service area is large enough to fully exploit the pooling efficiency in delivery. Otherwise, using the dedicated strategy is optimal. In contrast, if the firm can endogenize the demand rate by varying the delivery fee, using the dedicated strategy is optimal for a large service area. The reason is that it is optimal for the firm to sustain a relatively low demand rate by charging a high fee for a large service radius: within this large area, the pooling strategy leads to a long wait because it takes a long time for multiple orders to accumulate. Moreover, with an exogenous demand rate to meet, customers' patience level has no impact on the dispatch strategy. However, when the demand rate can be endogenized, the dedicated strategy is preferable if customers are impatient. Furthermore, we extend our model to account for social welfare maximization, a hybrid contingent delivery policy, a general arrival rate that does not have to be large, a nonuniform distribution of orders in the service region, and multiple couriers. We also conduct numerical analysis and simulations to complement our main results and find that most insights in our base model still hold in these extensions and numerical studies. This paper was accepted by Jeannette Song, operations management. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757 and RGPIN-2021-04295]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4858. [ABSTRACT FROM AUTHOR]

Consumers encounter costs and inconvenience in the purchase and consumption of goods. Time, effort, training, and expertise, also referred to as access costs, are examples of factors that constrain the ability of consumers to derive utility from offerings beyond the price of a good. Economies of scope exist when household production of these factors share commonalities among choice alternatives, making it more likely that some products would be purchased and consumed in conjunction with others. For example, consumers planning out-of-town travel might consider visiting multiple tourist attractions during their trip, and course offerings might appeal more to students who are already in the mindset of taking courses. We develop a model with economies of scope that captures a leveraging effect where the purchase of one good makes the purchase of other goods more likely. Our model offers an alternative explanation of complementary purchases that are driven by cost reductions rather than utility enhancements. The model is applied to four conjoint datasets where we find that economies of scope and access costs play an important role in consumer purchases and have high economic value relative to the role played by price. This paper was accepted by Eric Anderson, marketing. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4841. [ABSTRACT FROM AUTHOR]

We propose a monopoly and Stackelberg duopoly model for "new economy" markets—with a Beckerian S-shaped demand curve at its center—that allows for intermediate degrees of firm focality and consumer heterogeneity. Because of network externalities, firms compete for the dominant market share, rather than the marginal consumer. This leads to a type of limit pricing—from within, rather than from outside the market—where the nondominant firm captures a positive market share that serves as a consolation prize. We characterize how firms' technologies, "consumer impulses" (which might be influenced by past sales, defaults, or advertising), and network externalities affect competition, and derive implications for firm strategy. Broadly speaking, we find that the dominant firm should adopt an aggressive "top-dog" stance (akin to, but not identical to, that of a firm seeking to deter rival entry), whereas the nondominant firm should respond with an accommodating "puppy-dog" approach. This paper was accepted by Alfonso Gambardella, business strategy. Funding: R. Akerlof received financial support from the Institute for New Economic Thinking. [ABSTRACT FROM AUTHOR]

Trialing an experience good allows consumers to learn their value for the sampled good and also informs beliefs about their value for similar products. These demand-side information spillovers across products create a relatively well-informed group of potential future consumers for rival firms. When both switching consumers and repeat buyers are profitable, firms face reduced incentives to set a low initial price to attract inexperienced consumers. Switchers and repeat buyers are more likely to be profitable in new product categories that build on major innovations and when firms can price discriminate based on purchasing history. We suggest that competing products and services arising from new innovations often have demand-side information spillovers from any product trial and are, hence, settings where competing firms can make overall profits even when selling products that consumers perceive to be indistinguishable prior to initial trial. This paper was accepted by Joshua Gans, business strategy. Funding: This work is supported by the National Natural Science Foundation of China (No. 71903046) and the "Shenzhen Peacock Program" (No. GA11409002). Supplemental Material: The e-companion is available at https://doi.org/10.1287/mnsc.2021.02754. [ABSTRACT FROM AUTHOR]

Cybersecurity breaches pose a substantial concern in the digital era. We investigate how customers respond to multiple unexpected data breaches of their information in India. Difference-in-differences estimates show that digital payments declined by 9% relative to cash payments immediately after an unexpected data breach in a food delivery platform, but the gap disappeared three months later. Customer entry and exit also exhibit weak, short-lived changes. Additional analyses on bank and online grocery data breaches uncover even weaker effects of data breaches. Our findings imply that the perceived benefits of convenience outweigh the costs of payment security risks. This paper was accepted by David Sraer, finance. Funding: This work was supported by the MOE (Ministry of Education in China) Project of Humanities and Social Sciences [Project 20YJC790183], National University of Singapore [Start-Up Grant A-0003870-00-00], the National Natural Science Foundation of China [Grant 72150004], and the National Nature Science Foundation of China [Grant 72203112]. Supplemental Material: The online appendices and data are available at https://doi.org/10.1287/mnsc.2021.01335. [ABSTRACT FROM AUTHOR]

Ad targeting technology has enabled a highly personalized delivery of online ads. Behind this development is the belief that better targeting will lead to more relevant ads. This paper challenges this lay belief by showing that irrelevant advertising can arise not necessarily from technological imperfection but also from the incentive problem embedded in the ad agency-advertisers relationship. We first demonstrate that the ad agency serving multiple advertisers may strategically allocate an ad impression to a lesser-matched, sometimes totally irrelevant, niche advertiser because future impressions can match better with the mainstream advertiser. We further find that, without a contractual obligation to serve both advertisers, the agency may not deliver completely irrelevant ads to consumers. However, another type of inefficiency can arise where the agency may not send any ad to potentially interested consumers who have a strictly positive match probability with advertisers. These inefficiencies arise due to contractual restrictions, either contractual obligations or budget constraints, when the agency serves multiple advertisers. As such, we endogenize the advertisers' contractual requirement choices and show how the contractual obligation(s) can arise in equilibrium. Finally, we show that irrelevant ads will not disappear simply because more impressions are available in the market. Our analysis suggests that as the number of impressions increases, the irrelevant ads can persist, but the probability of receiving irrelevant ads decreases. This paper was accepted by Dmitri Kuksov, marketing. Funding: W. Shin gratefully acknowledges financial support from the Brian R. Gamache Endowed Professorship at the University of Florida. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4605. [ABSTRACT FROM AUTHOR]

Whether to purchase a product is of fundamental importance to marketing, but purchasing behaviors vary widely across individuals and contexts. This paper proposes that a sizeable fraction of this variation is associated with differences in the time at which a product's desirability and its price are processed and utilized by consumers. To test this hypothesis, participants purchased different products while their mouse cursor movements associated with purchasing an option were recorded across three laboratory studies. These natural cursor movements and estimates from a cognitive model identified the time at which product desirability and price each began to influence decisions. On average, we found that product desirability impacted the decision-making process significantly earlier than price. Moreover, the difference in the time at which product and price influenced choice explained a sizeable fraction of the variation in the option that was purchased. Additional analysis and studies revealed that the time at which an attribute begins to influence decisions can be altered by simple marketing actions, such as a product's visual display and price discount framing, and that these actions have consequences for choice. Together, these results add to our understanding of how consumers make simple purchasing decisions. This paper was accepted by Matthew Shum, marketing. Funding: This work was supported by the Foundation for Food and Agriculture Research. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mnsc.2022.4598. [ABSTRACT FROM AUTHOR]

Advancements in targeting technology have allowed firms to engage in more precise targeting based on several aspects of consumers' preferences. Exposed to more targeted ads, consumers are becoming increasingly aware of being targeted and respond accordingly. This paper provides a theoretical analysis of multidimensional targeting under which consumers can draw inferences about multiple components of their utility from the advertised product. We show that the firm can be worse off under multidimensional targeting than under single-dimensional targeting, in which the firm targets consumers based only on a single component of their utility. This is because, with multidimensional targeting, targeted consumers may face greater uncertainty about on which specific dimension(s) they can expect to enjoy the advertised product. Therefore, they may be less willing to exert a costly effort of clicking the ad and making a purchase decision. When this result holds, the firm may want to adopt a single-dimensional targeting strategy. However, we show that the firm cannot credibly commit to such a strategy once given access to multiple dimensions of customer data. Interestingly, a higher unit cost of advertising can mitigate the firm's commitment problem for using customer data and thus increase the firm's profit. Moreover, the firm can sometimes lower the price to recover some of, but not entirely offset, the drawbacks of multidimensional targeting. We discuss the implications of our results regarding the current practice of targeted advertising and data privacy protection policies. This paper was accepted by Dmitri Kuksov, marketing. Funding: This work was supported by the Korea Advanced Institute of Science and Technology [Grant G04210017]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4604. [ABSTRACT FROM AUTHOR]

Online marketplaces, such as Amazon, Alibaba, Google Shopping, and JD.com, allow sellers to promote their products by charging them for the right to be displayed on top of organic search results. In this paper, we study the problem of designing auctions for sponsored products and highlight some new challenges emerging from the interplay of two unique features: substitution effects and information asymmetry. The presence of substitution effects, which we capture by assuming that consumers choose sellers according to a multinomial logit model, implies that the probability a seller is chosen depends on the assortment of sellers displayed alongside. Additionally, sellers may hold private information about how their own products match consumers' interests, which the platform can elicit to make better assortment decisions. We first show that the first-best allocation, that is, the welfare-maximizing assortment in the absence of private information, cannot be implemented truthfully in general. Thus motivated, we initiate the study of incentive-compatible assortment optimization by characterizing prior-independent and prior-dependent mechanisms and quantifying the worst-case social cost of implementing truthful assortment mechanisms. An important finding is that the worst-case social cost of implementing truthful mechanisms can be high when the number of sellers is large. Structurally, we show that optimal mechanisms may need to downward distort the efficient allocation both at the top and the bottom. This paper was accepted by Victor Martínez-de-Albéniz, operations management. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4603. [ABSTRACT FROM AUTHOR]

Research in psychology shows that consumption goals can help consumers avoid excessive consumption of vice goods and the associated long-term harm. In this paper, we propose a model of self-control with consumption goals and examine how goals moderate the behavior of consumers and the firm's strategy. We find that consumers' personal goals lead to a lower price for a less unhealthy product but a higher price for a more unhealthy product. Furthermore, even though personal goals reduce the sales of a product, the firm can be better off if consumers have goals rather than no goals. The improvement in the firm's profits need not be at consumers' expense. In fact, consumer welfare increases with personal goals. In some contexts, consumption is not driven by personal goals but shaped by social norms, such as the advice of experts or social groups. We find that, unlike personal goals, normative goals make consumers less sensitive to price and do not always improve consumer welfare. Furthermore, normative goals can hurt the firm's profits in contexts in which personal goals could improve profits. Finally, we show that our framework with dynamically inconsistent preferences yields results that are consistent with alternative formulations of consumer self-control problems, such as the dual-self model of Thaler and Shefrin and the costly self-control model of Gul and Pesendorfer. This paper was accepted by Duncan Semester, marketing. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4567. [ABSTRACT FROM AUTHOR]

Modern consumers are concerned about not only their material payoff, but also the fairness of the transaction when making purchasing decisions. In this paper, we investigate how consumers' inequity aversion affects a manufacturer who sources inputs from upstream suppliers. We find that, when the manufacturer sources from a single supplier or when consumers observe the manufacturer's cost, inequity aversion hurts both the supplier's and manufacturer's profits. However, when the manufacturer sources from multiple suppliers and consumers do not observe the manufacturer's cost, inequity aversion reduces both the suppliers' and manufacturer's margins, which significantly alleviates the double marginalization problem, increases consumer demand, and improves channel efficiency. As a result, inequity aversion benefits the suppliers, manufacturer, and consumers alike, leading to a "win–win–win" outcome. By comparing cases in which consumers observe and do not observe the manufacturer's cost, we also find that, when faced with inequity-averse consumers, a manufacturer may find it optimal to withhold its cost information to help secure lower procurement costs from upstream suppliers. This paper was accepted by Dmitri Kuksov, marketing. Funding: This work was supported by the Hong Kong Research Grants Council [Grant 21500920]. [ABSTRACT FROM AUTHOR]

This paper addresses how referral generation and referral value evolve throughout the customer's life cycle as a function of service usage, experience level, and past referral behavior. We look at the referral behavior of 400,000 users of a large ride-sharing platform over the duration of a year. The upshot is that users make more and higher value referrals as they become more experienced with the service and when they are using the service intensively. However, as users make referrals, they are more likely to run out of friends to refer, leading to fewer (and lower value) referrals in the future. Based on these results, we suggest how digital platforms can improve their referral programs by tailoring them to how referral generation and referral value evolve over time. The richness of our data set allows us to address two shortcomings from previous studies: modeling dynamic behavior, such as the relationship between past and future referrals, and accounting for unobserved heterogeneity across users. Online platforms often ask their users to refer friends in exchange for a reward. This paper addresses how referral generation and referral value evolve throughout the customer's life cycle as a function of service usage, experience level, and past referral behavior. Our analysis is based on a longitudinal data set that comprises the transactions and referral actions of 400,000 users in a ride-sharing platform over a year. The richness of our data set allows us to address two shortcomings from previous studies: modeling dynamic behavior (i.e., the relationship between past and future referrals) and accounting for unobserved heterogeneity across users. Our results show that users make more referrals when they have used the service recently and intensively. For example, users become 9% less likely to make referrals for each week they have not used the service. Furthermore, users make more high-value referrals as they become more experienced with the service. The referral generation and referral value of the top 10% most experienced users are more than 18% higher relative to when they first used the service. Finally, as users make more referrals, they become more likely to run out of friends to whom they can refer the service, leading to less referrals in the future. After users make their first referral, the probability of making additional referrals decreases by more than 78% and the value of subsequent referrals reduces by 19% on average. The results imply that platforms should consider tailoring their referral programs according to how referral generation and referral value evolve over time. History: Xiaoquan (Michael) Zhang, Senior Editor; Khim Yong Goh, Associate Editor. [ABSTRACT FROM AUTHOR]

Incentivizing reviewers to write product reviews is a widespread yet controversial practice. Whereas outright fake reviews are clearly unacceptable and should be removed from any review platform, reviews contributed by incentivized consumers with otherwise authentic product experiences fall in a gray area. This paper offers a fresh perspective for us to understand conceptually the relationship between incentivized reviews and its two counterparts (i.e., organic reviews and advertisements) on the two ends of the spectrum, and it studies whether incentivized reviews are of higher text quality. The authors argue that incentivized reviewers may shift their "writing mode" from back stage to front stage and may also "compensate" for their reduced impartiality through better text quality. Drawing on recent advancements in computational linguistics, the authors demonstrate that incentivized reviews tend to have more coherent writing and cover more details, suggesting higher review text quality. Their finding highlights the often-overlooked value of incentivized reviews, which can complement an organic review system and also alleviate the cold-start problem for new products, thereby promoting healthy competition in the e-commerce era. For review platforms, the authors suggest they explicitly group and label incentivized reviews without their numerical ratings and separate them from organic reviews. In this way, we can take the best of both worlds. Incentivized reviews have become increasingly prevalent on product review sites such as Amazon. Whereas outright fake reviews are clearly unacceptable and should be removed from review platforms, reviews by incentivized consumers with otherwise authentic product experiences fall into a gray area. On the one hand, many critics and researchers have warned of their harm by pointing out their biased ratings. On the other hand, these reviews might complement organic reviews with review text of higher quality. The current paper studies whether incentivized reviews on Amazon are more coherent and offer richer detail. We use Amazon's platform-incentivized reviews, known as Vine reviews, for our primary sample and use seller-incentivized reviews for checking robustness. Estimations from a two-way fixed-effect model consistently show that incentivized reviews do compensate for their reduced impartiality through better text quality, measured by discourse coherence and level of relevant detail. This finding is further supported by a randomized experiment using Amazon Mechanical Turk. Hence, current literature findings on the poor quality of text of incentivized reviews, based on review length and lexical complexity, portray only an incomplete picture of incentivized reviews. Given that numerical ratings for products via incentivized reviews are likely biased whereas their text content is of high quality, a natural way to embrace incentivized reviews is to keep their text content, suppress their numerical ratings, and always highlight the label of "incentivized reviews." History: Juan Feng, Senior Editor; Jianqing Chen, Associate Editor. Funding: This work was supported by Singapore Ministry of Education Tier 1 Research Grant [Grant A-0008174-00-00]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1146. [ABSTRACT FROM AUTHOR]

Online retail has become more prominent around the world in the last decade. As a result, online retailers' website performance is increasingly important. Previous literature has extensively studied customer sensitivity to service speed and wait times in offline services. In "Need for Speed: The Impact of In-Process Delays on Customer Behavior in Online Retail," Gallino, Karacaoglu, and Moreno extend this literature to online retail. They study the impact of delays in online retail on customer behavior. They estimate sizable negative effects of website slowdowns on online sales and conversion rates. Moreover, they explore how customer sensitivity to online delays varies throughout customers' shopping journeys. They find that the impact of waiting times varies along the different stages of the shopping journey, with customers becoming more sensitive to slowdowns at the checkout stage. Their findings have implications for website design decisions. This research is especially relevant in the current regulatory environment with ongoing policy debates about net neutrality. The impact of delays has been widely studied in various offline services. The focus of this study is online services, and we explore the impact of in-process delays—measured by website speed—on customer behavior. We leverage novel retail and website speed data to investigate how delays impact online sales and how customer sensitivity to in-process delays varies across the different stages of a customer's shopping journey. We estimate sizable adverse effects of website slowdowns on online sales. Using threshold regression models, we show that customers exhibit diminishing sensitivity to increases in website slowdowns. Our results suggest that waiting times affect customer abandonment differently at different stages of the shopping journey. Customers are more sensitive to slowdowns at the checkout stage. Our findings have implications for website design decisions such as improving website speed at the checkout stage, selecting third-party content providers, and customizing the design of mobile and desktop channels. The paper's results are especially relevant in the current regulatory environment with ongoing policy debates about net neutrality. History: This paper has been accepted for the Operations Research Special Issue on Behavioral Queueing Science. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2262. [ABSTRACT FROM AUTHOR]

Problem definition: We examine the impact of logistics performance metrics such as delivery time and customer's requested delivery speed on logistics service ratings and third-party sellers' sales on an e-commerce platform. Academic/practical relevance: Although e-commerce retailers like Amazon have recently invested heavily in their logistics networks to provide faster delivery to customers, there is scant academic literature that tests and quantifies the premise that convenient and fast delivery will drive sales. In this paper, we provide empirical evidence on whether this relationship holds in practice by analyzing a mechanism that connects delivery performance to sales through logistics ratings. Prior academic work on online ratings in e-commerce platforms has mostly analyzed customers' response to product functional performance and biases that exist within. Our study contributes to this stream of literature by examining customer experience from a service quality perspective by analyzing logistics service performance, logistics ratings, and its impact on customer purchase probability and sales. Methodology: Using an extensive data set of more than 15 million customer orders on the Tmall platform and Cainiao network (logistics arm of Alibaba), we use the Heckman ordered regression model to explain the variation in customers' rating of logistics performance and the likelihood of customers posting a logistics rating. Next, we develop a generic customer choice model that links the customer's likelihood of making a purchase to the logistics ratings provided by prior customers. We implement a two-step estimation of the choice model to quantify the impact of logistics ratings on customer purchase probability and third-party seller sales. Results: We surprisingly find that even customers with no promise on delivery speed are likely to post lower logistics ratings for delivery times longer than two days. Although these customers are not promised an explicit delivery deadline, they seem to have a mental threshold of two days and expect deliveries to be made within that time. Similarly, we find that priority customers (those with two-day and one-day promise speed) provide lower logistics ratings for delivery times longer than their anticipated delivery date. We estimate that reducing the delivery time of all three-day delivered orders on this platform (which makeup ≈ 35% of the total orders) to two days would improve the average daily third-party seller sales by 13.3% on this platform. The impact of delivery time performance on sales is more significant for sellers with a higher percentage of three-day delivered orders and a higher spend per order. Managerial implications: Our study emphasizes that delivery performance and logistics ratings, which measure service quality, are essential drivers of the customer purchase decision on e-commerce platforms. Furthermore, by quantifying the impact of delivery time performance on sales, our study also provides a framework for online retailers to assess if the increase in sales because of improved logistics performance can offset the increase in additional infrastructure costs required for faster deliveries. Our study's insights are relevant to third-party sellers and e-commerce platform managers who aim to improve long-term online customer traffic and sales. History: This paper has been accepted as part of the 2018 MSOM Data Driven Research Challenge. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.1045. [ABSTRACT FROM AUTHOR]

This paper investigates how a manufacturer should respond to the retailer's and consumers' stockpiling ability by contracting with the retailer. Manufacturers of consumer-packaged goods invest heavily in trade promotions (i.e., temporary wholesale price discounts), but retailer stockpiling often yields trade promotions unprofitable. In this paper, we investigate how a manufacturer should respond to the retailer's and consumers' stockpiling ability by contracting with the retailer. Specifically, we examine when the manufacturer should restrict the retailer's stockpiling ability and when it should issue trade promotions. Our analysis suggests the following. First, the manufacturer should restrict the retailer's stockpiling ability when the storage cost is low; such restriction also benefits the retailer, resulting in a win-win outcome. Second, the manufacturer should offer trade promotions when the retailer cannot stockpile products and the storage cost is low but raise the wholesale price when the retailer can stockpile products. Third, stockpiling improves channel coordination and increases the manufacturer's profit; therefore, the manufacturer should design products to be more storable. History: Ganesh Iyer served as the senior editor, and Anthony Dukes served as associate editor for this article. Supplemental Material: The e-companion is available at https://doi.org/10.1287/mksc.2022.1394 [ABSTRACT FROM AUTHOR]

This paper exploits a major food safety crisis to estimate a full demand model for the unsafe product and its substitutes, recovering consumers' preference parameters for different product characteristics. Counterfactual exercises quantify the relevance of different mechanisms driving consumers' responses, such as changes in safety perceptions, idiosyncratic taste, product characteristics, and price. We find that consumers' reactions are limited by their preferences for the product's observable and unobservable characteristics. Because of the costs associated with switching from the affected product, the decline in demand following a product-harm crisis tends to understate the true weight of such events in consumers' utility. We find that unobservable taste is a crucial driver of consumers' responses. Our counterfactual exercises illustrate that the demand would have declined further if consumers had had access to a closer substitute. For an accurate assessment of product-harm crises, managerial strategies should therefore account for how different demand drivers bind consumers' substitution patterns. This paper was accepted by Matthew Shum, marketing. Funding: This work was supported by the Ministerio de Ciencia e Innovación [Grants ECO2008-01116 and ECO2010-15052], the Ministerio de Economía y Competitividad [Grant ECO2013-43011-P], and the Deutsche Forschungsgemeinschaft [Grant CRC TR 224]. R. Ferrer received support from [Grant ECO2016-76998-P], and the Severo Ochoa Programme for Centers of Excellence in R&D [SEV-2015-0563]. [ABSTRACT FROM AUTHOR]