The obesity rate has grown to epidemic proportions in the United States. Recent scientific studies suggest that excess intake of sugar-sweetened beverages (SSBs) is one of the primary contributors to weight gain. One option to address the growing obesity epidemic is to discourage SSB consumption through fiscal policies such as imposing a soda excise tax. In this paper, we apply the Becker-Murphy model of rational addiction to present evidence that consumers are rationally addicted to the consumption of soda drinks that contain caffeine and/or sugar. Motivated by the evidence, we build a dynamic structural model to quantify the impact of soda taxes on soda purchases and consumption and study the role of addiction in influencing this impact. We extend the canonical rational addiction model by introducing a multidimensional framework of addiction, which allows for the decomposition of addiction in the context of soda consumption into two distinct components: caffeine and sugar. Using the parameter estimates, we conduct counterfactual analyses based on realistic policy scenarios and evaluate how different tax policies would affect soda purchases. For example, one policy experiment indicates that a tax of one cent per ounce only on sugary soda categories would lead to a 50.2% decrease in sugary soda consumption. In addition, we find that sugar seems to play a bigger role in influencing substitution patterns than caffeine. We further investigate how these taxes affect welfare through a change in addiction level and emphasize the importance of addiction by comparing estimation results with and without addiction in the model. To understand the externalities associated with soda taxation, we integrate our counterfactual results with causal estimates from recent medical literature. Our findings suggest that individuals could decrease their medical costs by $642 to $717 per year under various tax schemes. These results underscore the potential of soda taxes to significantly improve public health and reduce obesity-related healthcare costs. This paper was accepted by Raphael Thomadsen, marketing. Funding: This work was supported by a Nanyang Technological University start-up grant [Grant 023220-00001]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.02422 .

Real-time decisions are usually irrevocable in many contexts of online decision making. One common practice is delaying real-time decisions so that the decision maker can gather more information to make better decisions. For example, in online retailing, there is typically a time delay between when an online order is received and when it gets picked and assembled for shipping. However, decisions cannot be delayed forever. In this paper, we study this fundamental trade-off and aim to theoretically characterize the benefits of delaying real-time decisions. We provide a theoretical foundation for a broad family of online decision-making problems by proving that the gap between our proposed online algorithm (called “delayed Bayesian prophet”) and the offline optimal hindsight policy decays exponentially fast in the length of delay. We also conduct extensive numerical experiments on the benefits of delay, using both synthetic data and publicly available real data. Both our theoretical and empirical results demonstrate an important managerial insight: a little delay is all we need. Finally, we extend our analysis and results to the setting where the arrival distribution is independent but nonidentical, the setting where the arrival distribution is unknown, and the setting where decisions are made in batches. This paper was accepted by Jeannette Song, operations management. Funding: The second author was partially funded by a grant from Amazon.com Inc., awarded through collaboration with the Columbia Center of AI Technology. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00549 .

We examine traders’ order submission strategies during the Euronext preopen, which uses price-time priorities to arrange opening trades via a call auction. Preopening order submissions follow a J-shape pattern. Sophisticated proprietary traders arrive late, consistent with information leakage concerns. Slow clients arrive early and place aggressive orders to advertise their trading needs and strategically gain time priority. Their early order submissions often occur in stocks with an increase in the tick size and on days with expected liquidity shocks. They also contribute to daily price discovery. Using a preopening outage as a natural experiment, we show that the Euronext preopen improves price discovery on a rival venue. These benefits hold even in the absence of an opening auction taking place. This paper was accepted by Lin William Cong, finance. Funding: Financial support from the Agence Nationale de la Recherche [Grants ANR-16-CE26-0008, ANR-17-EURE-0010 (Investissements d’Avenir program)]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03998 .

This paper considers introducing asymmetric privacy in the design of central bank digital currencies (CBDC) and digital currencies more generally to preserve the privacy of money spent while keeping the benefits of digital records for money received. It is shown that this feature would help minimize real distortions between consumers, firms, and financiers while enabling tax optimization and better access to external financing. Protecting the privacy of consumers is desirable from a welfare and efficiency standpoint as long as there exist noticeable privacy concerns. Implementing asymmetric privacy is technologically feasible, using, for instance, zero-knowledge proofs or other privacy tools. This paper has been accepted by Lin William Cong for the Special Issue on Digital Finance. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2024.06830 .

Expert opinion exerts tremendous influence on the purchase journey, but its effect on overall consumer experience is ambiguous because it can give rise to both “expectation” and “reputation” effects. This paper explores the effect of expert opinions on consumer experience via the lens of consumer reviews in the restaurant industry, where the expert opinions are conveyed by Michelin stars. The paper uses a unique data set based on the Michelin Guide for Great Britain & Ireland from 2010 to 2020. The data include consumer reviews on TripAdvisor for all restaurants that were awarded Michelin stars during this period and a large pool of potential control restaurants. We apply two synthetic control–based methods to estimate the effect of Michelin star changes on the sentiment and content of consumer reviews. We find that decreases in Michelin stars improve consumer review ratings. We examine three potential explanations for this finding. First, the positive expectation effect of lowered expert opinions outweighs the negative reputation effect. Second, there could be a change in the customer mix. Third, there may be a changes on the supply side, for example, in service levels. We find support for the first explanation, rule out the second, but cannot entirely rule out the third. The analysis of review content further shows that a loss in Michelin stars leads consumers to become less focused on value and become less demanding regarding service. We discuss the implications of our findings for restaurant managers, the Michelin Guide, and other businesses that provide experience goods. This paper was accepted by Eric Anderson, marketing. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2023.04302 .

We examine a unique experiment of anonymizing online loan applications to test whether race-blind screening procedures reduce racial disparities in consumer credit. With names on applications, minority applicants are 10% less likely to receive online loan offers and receive worse offer terms than otherwise identical majority applicants. Anonymizing applications reduces such disparities substantially. High-income minority applicants benefit more than low-income minorities. We show that the racial disparities are not driven by differences in credit demand. Overall, anonymous loan applications reduce racial disparities in access to credit by increasing lender reliance on objective credit risk measures. This paper was accepted by Kay Giesecke, finance. Funding: This research is supported by the Ministry of Education, Singapore, under its Social Science Research Thematic Grant [Grant MOE2019-SSRTG-024]. P. Kabir acknowledges financial support from the National University of Singapore (NUS) [Start-Up Grant A-0003875-00-00] and the Singapore Ministry of Education [Academic Research Fund (AcRF) Tier 1 Research Grant A-8000758-00-00 & A-8000760-00-00]. T. Ruan acknowledges financial support from the Singapore Ministry of Education [AcRF Tier 1 Research Grant A-8000757-00-00]. Supplemental Material: The internet appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05026 .

Most organizations encourage employees to provide feedback to one another to support learning, personal growth, and career advancement. However, employee feedback often fails to improve performance because it lacks concrete, specific guidance. We provide a temporal explanation for why workplace input processes routinely fail to produce valuable and concrete developmental insights: they are insufficiently focused on the future. In this paper, we theorize and demonstrate that encouraging input providers to think about the future leads them to produce more concrete developmental input. Across a large scale, preregistered field experiment (n = 27,432 comments) and two laboratory studies (n = 806), people provide more concrete and actionable developmental input when they are prompted to provide future-looking “advice” rather than “feedback,” a common method of soliciting input in organizations. The effect of soliciting advice on input concreteness was mediated by providers’ future focus. Moreover, in a follow-up study, such concrete input was assessed by independent raters as more useful. These findings highlight the role of temporal orientation in driving the content of developmental input. In doing so, our data suggest that individuals and organizations have the potential to promote higher-quality developmental input by attending to the temporal orientation that their input systems encourage. This paper was accepted by Isabel Fernandez-Mateo, organizations. Funding: Funding for the laboratory experiments was provided by Harvard Business School. Supplemental Material: The supplementary appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03207 .

Optimization problems with discrete decisions are nonconvex and thus lack strong duality, which limits the usefulness of tools such as shadow prices. It was shown in Burer (2009) that mixed-binary quadratic programs can be written as completely positive programs, which are convex. We apply this perspective by writing unit commitment in power systems as a completely positive program and then using the dual copositive program and strong duality to design new pricing mechanisms. We show that the mechanisms are revenue-adequate and, under certain conditions, support a market equilibrium. To facilitate implementation, we also employ a cutting plane algorithm for solving copositive programs exactly, which we further speed up via a second-order cone programming approximation. We provide numerical examples to illustrate the potential benefits of the pricing mechanisms and algorithms. This paper was accepted by Chung Piaw Teo, optimization. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00906 .

We study the behavioral effects of process automation on human workers interacting with automated tasks. We introduce a stylized normative model with two workers who complete their tasks sequentially, working toward a joint project to obtain a fixed payment plus a variable bonus that depends on how early the project is completed. The normative model prescribes that, if workers are fully rational, they will complete their tasks as soon as possible if the early completion bonus is high enough. However, following the literature, we hypothesize that workers will suboptimally delay project completion. Following the insights from a behavioral model, we further predict that automation will alleviate this problem by reducing strategic uncertainty, resulting in an indirect behavioral benefit of a higher worker productivity, in addition to the direct benefit of a higher project completion rate and a shorter project duration. To test these predictions, we conduct an experiment replicating the theoretical model, varying (i) whether a worker collaborates with a coworker or a robot, and (ii) in the case of collaborating with a robot, whether the upstream or downstream task is the one automated. First, we find that workers largely deviate from the optimal policy, as they take longer than what the normative theory prescribes to complete their tasks or do not complete the project. Second, we show that process automation increases the project completion rate and reduces the project completion time, confirming the benefits of process automation. Interestingly, workers who collaborate with robots take longer to complete their tasks, contradicting our initial hypothesis that process automation has a positive effect on the productivity of human workers. In addition, we find that upstream automation is more beneficial than downstream automation. We also show that social preferences are an important driver of these results because prosocial subjects tend to be more productive when collaborating with a human coworker than with a robot. Finally, we show that our findings remain robust in a continuous processing setting. This paper was accepted by Felipe Caro for the Management Science Special Issue on the Human-Algorithm Connection. Funding: The authors gratefully acknowledge financial support from Zicklin School of Business, Baruch College, the City University of New York, and the University of Texas at Dallas. Support for this project was provided by a PSC-CUNY Award, jointly funded by the Professional Staff Congress and the City University of New York. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05408 .