Shubhranshu Singh

Associate Professor of Marketing

Johns Hopkins Carey Business School

Joint faculty appointment: Department of Economics, Krieger School of Arts & Sciences

Phone: 410-234-9247

Email: shubhranshu.singh@jhu.edu

Research interests:

Societal Impact Driven Marketing

Expert Service and AI

Strategic Communication

CV

BIOGRAPHY

Shubhranshu Singh is an Associate Professor of marketing at the Carey Business School, Johns Hopkins University. He holds a joint faculty appointment in the Economics Department, Krieger School of Arts and Sciences and is a core member of the Hopkins Business of Health Initiative (HBHI). He received his PhD in Business Administration from the University of California at Berkeley, MBA from National University of Singapore, Singapore, and MSc in Physics from Indian Institute of Technology Delhi, India.

He is an applied theorist and his main research interests are in the areas of societal impact driven marketing (e.g., healthcare, poverty, inequality, and corruption) and strategic communication. His research has appeared in Journal of Marketing Research, Marketing Science, and Management Science journals. He serves as a senior editor for the Product and Operations Management Journal and as a member of the editorial board of Marketing Science. His research has won the 2013 John A. Howard/AMA doctoral dissertation award and the 2012 ISMS doctoral dissertation competition. His paper on competition in corruption markets was finalist for both Bass and Little awards in 2017. He was chosen a Marketing Science Institute (MSI) young scholar in 2021. 

RECENT HONORS & DISTINCTIONS

PUBLICATIONS

1. "Overdiagnosis and Undertesting for Infectious Diseases," 2024 (with Tinglong Dai), Marketing Science, Forthcoming.

Abstract: The COVID-19 pandemic brought the availability of diagnostic tests to the forefront of public attention and highlighted the prevalence of undertesting (i.e., insufficient test supply relative to demand). Another important yet little studied systematic issue is overdiagnosis (i.e., positive diagnoses for patients with negligible viral loads): evidence suggests U.S. laboratories have adopted highly sensitive diagnosis criteria, such that up to an estimated 90% of positive diagnoses are for minuscule viral loads. Motivated by this situation, we develop a theory of diagnostic testing for infectious diseases that explains both undertesting and overdiagnosis. We show a commercial laboratory has an incentive to inflate its diagnosis criterion, which generates a higher diagnosis-driven demand as a result of contact-tracing efforts, albeit while dampening demand from disease transmission. An inflated diagnosis criterion prompts the laboratory to build a higher testing capacity, which may not fully absorb the inflated demand, so undertesting arises. Finally, we examine a social planner’s problem of whether to mandate that the laboratory report the viral load along with its diagnosis, so that a physician or contact tracer can make informed triage decisions. Our results show the social planner may choose not to mandate viral-load reporting initially; this choice induces a higher testing capacity and can help reduce disease transmission.

2. "Educational Inequality and Reservation Policy in Developing Markets," 2023 (with Weining Bao and Jian Ni), Management Science, Forthcoming.

Abstract: Educational inequality undermines the pivotal role of education in increasing the ability of the poor to move up the income ladder. This paper investigates educational inequality that arises from low-income students’ lack of monetary resources that higher-income students invest in education. We show low-income students’ inability to make monetary investments in education reduces their incentive to study and contributes to educational inequality. In the context of developing markets, we study implications of a reservation policy that aims to reduce inequality by reserving some college seats for students of the disadvantaged group. The policy essentially transfers college seats from the advantaged student group to the disadvantaged student group and as a result increases the welfare of students from the disadvantaged group. In some cases, the seat transfer also improves the welfare of higher-income students. We show such a transfer, if small, can enhance overall student welfare in developing markets. However, a large transfer may reduce overall student welfare. Finally, we show the welfare-maximizing transfer is usually smaller than inequality-minimizing transfer of seats.

3. "Service Provision in Distribution Channels," 2022 (with Haresh Gurnani, Sammi Tang, and Huaqing Wang), Journal of Marketing Research, 59(5), 926-940.

Abstract: Consumers may need help using an inherently complex product after purchase. This paper studies a manufacturer’s and a retailer’s incentives to provide pre-sales service and after-sales support in a distribution channel. The authors consider a model in which a manufacturer makes wholesale-price and channel-service decisions. Subsequently, a retailer makes retail-price and channel-service decisions. They find that, in the equilibrium, both channel members provide pre-sales service. If the fixed-cost investment needed to enhance the effectiveness of after-sales support is small, the manufacturer lets the retailer provide after-sales support. But when it is above a threshold and the retailer becomes unwilling to invest in providing after-sales support, the manufacturer steps in and invests in providing it. As expected, when the fixed cost is too large, the manufacturer also opts out of providing after-sales support. Interestingly, when the retailer provides after-sales support, the level of pre-sales service and the demand for after-sales support can simultaneously be the highest among all configurations. Finally, the authors demonstrate the robustness of their main results by studying alternative channel-service configurations. 

4. "Persuasion Contest: Disclosing Own and Rival Information," 2022 (with Ganesh Iyer), Marketing Science, 41(4), 254-281.

Abstract: This paper investigates a contest in information revelation between firms that seek to persuade consumers by revealing positive own information and negative information about the rival. In the face of limited bandwidth, firms are forced to make a trade-off between disclosing their own positive information and their rival’s negative information. A negative-communication equilibrium, in which firms disclose rival’s negative information whenever possible, exists when consumers have poor outside options or when firms are better informed. Bandwidth limitations make competitive firms more likely to disclose information compared to when they have no limitations. When firms strictly prefer consumers to choose the outside option over the rival (as in political contests), there is a greater prevalence of the negative communication equilibrium while the incidence of positive communication is lowered. Finally, when firms are asymmetric in their ex-ante quality valuations, the higher quality firm is less likely to engage in negative communication.

5. "Conspicuous by Its Absence: Diagnostic Expert Testing Under Uncertainty," 2020 (with Tinglong Dai), Marketing Science, 39(3), 540–563. Online Appendix

Abstract: We study the problem a diagnostic expert (e.g., a physician) faces when offering a diagnosis to a client (e.g., a patient) that may be based only on her own diagnostic ability or supplemented by a diagnostic test—conventional and artificial intelligence (AI) tools alike—revealing the client’s true condition. The expert’s diagnostic ability (or type) is her private information. The expert is impurely altruistic in that she cares about both the client’s utility and her own reputational payoff that depends on the peer perception about her diagnostic ability. The decision of whether to perform the test, which is costly for the client, provides the expert with an opportunity to influence that perception. We show a unique separating equilibrium exists in which the high-type expert does not resort to diagnostic testing and offers a diagnosis based only on her own diagnostic ability, whereas the low-type expert performs the test. Furthermore, we establish that the high-type expert may skip necessary diagnostic tests to separate her from the low-type expert. Interestingly, the effect of reputational payoff on under-testing is non-monotonic, and the desire to appear of high type leads to under-testing only when the reputational payoff is intermediate. Our results also suggest a more altruistic expert may be more likely to engage in under-testing. Furthermore, efforts to encourage testing by providing financial incentives or by raising malpractice-lawsuit concerns may, surprisingly, help fuel under-testing in the equilibrium. Our paper sheds new light on barriers to the adoption of AI tools aimed at enhancing physicians’ diagnostic decision making. 

6. "Voluntary Product Safety Certification," 2018 (with Ganesh Iyer), Management Science, 64(2), 695-714.

Abstract: This paper describes the incentives for firms to seek voluntary product safety certifications. We consider a firm which makes the decision of whether or not to seek certification prior to selling the product. We show that, even when the firm and the consumers have same beliefs about the product safety there are incentives for the firm to seek safety certification. The main analysis investigates the role of consumer moral hazard and shows that it can lead to greater incentives for voluntary certification when inherent product safety and effort are substitutes, but smaller incentives when they are complements. The analysis of consumer moral hazard provides a nuanced perspective on the so-called risk-compensation or the “Peltzman effect” phenomenon which postulates higher levels of accident for safer products. In our paper, products that are successfully certified can end up with higher incidence of accidents. We also uncover an interesting non-monotonic relationship between effectiveness of consumers’ effort and the firm’s incentives to seek certification. Finally, we find that certification can be welfare enhancing in the presence of consumer moral hazard.

7. "Informal Lending in Emerging Markets," 2018 (with Weining Bao and Jian Ni), Marketing Science, 37(1), 123-137.

Abstract: Micro-entrepreneurs in emerging markets often rely on informal lenders for their routine borrowing needs. This paper investigates informal lenders’ and micro-entrepreneurs’ incentives to participate in a lender-borrower relationship in a market in which repayments are neither law-protected nor asset-secured. We consider a borrower who seeks a short-term loan, invests in a project, and repays in full using her project earnings if the project is successful. If the project fails, the borrower uses her outside option to repay over a period of time. The analysis uncovers an interesting effect of the borrower’s outside option on the loan rate offered by the lender - the loan rate first increases and then decreases with the borrower’s outside option. An important policy implication is that an increase in the outside option of the poor micro-entrepreneurs might actually reduce their surplus. Finally, we find that lenders in emerging markets may be more likely to engage in informal lending compared to those in developed or poorer markets.

8. "Competition in Corruptible Markets," 2017, Marketing Science, 36(3), 361-381.

Abstract: Firms seeking business opportunities often face corruptible agents in many markets. This paper investigates the marketing strategy implications for firms competing for business, and for the buyer in a corruptible market. We consider a setting in which a buyer (a firm or government) seeks to purchase a good through a corruptible agent. Supplier firms that may or may not be a good fit compete to be selected by the agent. Only the agent observes whether a firm is a good fit. Corruption arises due to the agent’s incentive to select a non-deserving firm in exchange for bribes. Intuitively and as expected, a sufficiently large monitoring of the agent eradicates corruption. Interestingly, however, increasing the monitoring from an initial low level can backfire, making the agent more likely to select a non-deserving firm. This non-monotonic agent behavior makes it difficult for the buyer to reduce corruption. The implication is that the buyer should choose either to be ignorant or to take drastic measures to limit corruption. Further, we show that unilateral anti-corruption controls, such as the Foreign Corrupt Practices Act of 1977, on a U.S. firm seeking business in a corrupt foreign market can actually increase the firm’s profits.

WORKING PAPERS

1. "Artificial Intelligence on Call: The Physician's Decision of Whether to Use AI in Clinical Practice," 2023 (with Tinglong Dai)

Abstract: Whether patients benefit from artificial intelligence (AI) depends on physicians' endogenous use behavior. In contrast to rapidly growing applications of AI to medicine, how physicians use AI in their day-to-day practices, and its implication for patient safety, remain little explored. In this paper, we respond to this emerging concern by modeling a situation in which a physician has access to AI in recommending a treatment plan for a patient subject to clinical uncertainty. The physician can recommend a “standard” or “nonstandard” treatment plan, either of which may be optimal; a suboptimal treatment plan can cause harm to the patient. Recommending the “standard” treatment plan shields the physician from legal liability; in contrast, recommending a “nonstandard” treatment plan subjects the physician to liability if patient harm occurs. Using AI helps the physician to generate a more precise signal that lessens clinical uncertainty in recommending the treatment plan. However, the use of AI is complicated by its implications for legal liability. Our analysis shows that under commonly proposed patient-protection schemes, the physician has an incentive to use AI in low-uncertainty situations despite little value derived from using AI. At the same time, the physician may rationally avoid using AI in high-uncertainty situations in which AI could have improved patient surplus. As the precision of the AI algorithm improves, such an avoidance behavior may exacerbate.

2. "The Pricing and Financing of Education — Student Loans and Income Share Agreements," 2023 (with Weining Bao and Kinshuk Jerath)

Abstract: This paper examines the pricing and design of one of the most important services that an individual can consume in their lifetime, and one with tremendous lifetime impact—university education. University education in the US is usually priced as a lump-sum tuition amount, which is typically financed through a student loan. Such a loan subjects the student to the possibility of loan default in the case of an unfavorable post-education job outcome, leading to adverse financial effects for the student. Partially to address this issue, the income share agreement (ISA) pricing format, in which the university gets a fraction of the student’s post-education income, is recently being increasingly offered as an alternative to (or in combination with) student-loan financing. We examine a university’s decision of offering tuition and ISA pricing with endogenous student learning effort, which affects job market outcomes and expected future income. We find that, conditional on the student joining the university, an ISA results in lower student effort and lower expected future income than a student loan. The university prefers ISA financing if the student faces a large adverse impact from loan default; otherwise, the university prefers student-loan financing. Generally speaking, the university offers higher quality education when it charges higher lump-sum tuition. From a policy point of view, student surplus is always higher in the case of ISA financing than student-loan financing, but if the adverse impact of loan default can be sufficiently reduced, using student-loan financing without an ISA component maximizes expected social welfare.

3. "Ambiguous Expert Communication," 2024 (with Xudong Zheng)

Abstract: In many consulting environments, the expert often assertively recommends the client to take an action but is vague about the probability of that action’s outcomes, making the recommendation ambiguous. In this paper, we analytically investigate this phenomenon by incorporating the client’s optimism and attitudes toward ambiguity into a strategic communication model. A primitive premise is that a more ambiguous message can lower the expert’s communication cost by freeing the expert from further explanations. In equilibrium, the expert will claim the possibility of a range of probability distributions with a lower end strictly below his or her precise observation. By choosing an optimal level of ambiguity, the expert trades off the client’s expected payoffs for self-benefits from cost reduction and the extra perks from the focal action. When the client cannot exert effort to disambiguate the expert’s message, the expert benefits from the client’s greater optimism and lower aversion to ambiguous information. Interestingly, when the client exerts costly effort, the expert can leverage the client’s ambiguity attitude. As the client becomes more ambiguity averse, precise information about the less preferable option becomes even less preferable—high ambiguity aversion mitigates the ambiguity of the information about the focal option, inducing the expert to send a more ambiguous message.

4. "Product Design by Competing Public and Private Providers," 2023 (with Yogesh Joshi)

Abstract: Certain consumption contexts involve the design of products which can be fairly resource intensive to produce. In such contexts, it is common to observe public agencies make substantial investments required for designing products that improve social welfare. In these environments, it is also not uncommon to observe the presence of private firms that leverage these substantial investments in order to design products that generate profits. We model such public-private interactions and analyze the implications of these interactions on product quality and prices. We have four main results: first, the presence of a private firm does not always lead to enhancements in product quality; for quality to go up, the firm needs to be significantly more efficient than the public agency. Second, private presence can lead to a significant reduction in consumer surplus, especially at the lower end of the market. Third, depending on the relative design efficiency of the private firm, the public agency might implement policies that prevent private firms from entering the market. Finally, there exist circumstances under which private presence can be beneficial in that it leads to product designs that not only enhance social welfare but also improve overall consumer surplus.

WORK IN PROGRESS

1. "Algorithmic Bias and Physician Liability" (with Shujie Luan and Tinglong Dai)

Abstract: AI algorithms are increasingly used to help support clinical decision making. Their potential bias, as reflected in disparities in accuracy across patient populations, has received growing attention. The U.S. Department of Health and Human Services (HHS) recently proposed an anti-discrimination liability rule, which states that providers may be liable for erroneous medical decisions made in reliance on biased algorithms. In this paper, we model and analyze how such anti-discrimination liability rules affect both upstream AI development decisions (by an AI developer) and downstream AI deployment decisions (by a physician). The AI developer first decides on the algorithm’s accuracy for two types of patients, where training the algorithm for the disadvantaged patients requires higher development costs; in response, the physician decides whether and how to use the algorithm, which may be biased against the disadvantaged patient, when prescribing a treatment plan. Using a biased algorithm can help reduce clinical uncertainty but may expose a physician to legal liability in the event of a treatment error. We show several results with important policy implications. First, anti-discrimination liability leads to discriminatory use of AI by inducing the physician to (1) underuse AI and (2) disproportionately reject AI recommendations for disadvantaged patients. Second, we show a non-monotonic effect of liability on the physician’s decision to use AI: As liability increases, the physician is less likely to use AI for disadvantaged patients and then more likely to use it. Finally, we show that mandating equal accuracy can make all patient populations worse off, because it removes liability concerns and leads to more AI use, but the physician may overuse AI for disadvantaged patients.

2. "Virtual Brands and Platform Intermediation" (with Ruizhi Zhu and Yakov Bart)

Abstract: Virtual brands are gaining prominence on various food delivery platforms. Restaurant owners use the same physical storefront but establish multiple virtual brands, often showcasing identical sets of products but with varied brand names, on the same platform. Consumers search brand by brand, without observing beforehand the product variety each brand offers. Using a search model, we demonstrate that multi-product firm can utilize multiple virtual brands with different leading products to communicate information about its product variety, enticing more consumers to explore its brands. The platform aids in reducing such information asymmetry by consistently presenting all available brands to all consumers. Surprisingly, such communication through virtual brands also raises consumer surplus and even the profit of single-product firm that does not utilize virtual brands. We find that banning identical-menu virtual brands can benefit both the multi-product firm and consumers when the ban pushes the multi-product firm to create virtual brands specializing in distinct products. However, when the ban leads the multi-product firm to keep only one brand with all its products, the ban hurts consumers and all firm types.

3. "Corporate Social Justice and Brand Strategy" (with Ganesh Iyer and Tongil Kim)

Abstract: Recent social justice movements have led firms to revisit their commitments to social justice and consumers to self-examine the extent to which they value it when buying products in the market. In this paper, we examine brands’ incentive to invest in the diversity of workforce in a market where consumers are heterogenous in their preference for social justice and use the brand image of products they purchase to confirm their social-justice identities (specifically, the extent to which they care about the proportion of minorities in the workforce). We find that a brand’s workforce composition critically depends on the role its image can play in reminding consumers about their social-justice identities. When consumers are more likely to rely on their purchase decisions to confirm their social-justice identities (1) brands are less likely to become niche brands that sell exclusively to a specific type of consumer, and (2) a brand’s likelihood of hiring minorities may increase. The implication is that as consumers’ social justice identities become constantly accessible to them, brands may become more niche, targeting products exclusively to specific consumer types and hiring fewer minorities overall.

4. "Microentrepreneur Skill Training in Developing Markets" (with Weining Bao)

5. "Safe Adoption and Supervision of Generative AI Systems" (with Alessandro De Chiara and Ester Manna)