Knowledge that Transforms

Problem definition: Ride-hailing poses significant challenges to Original Equipment Manufacturers (OEMs) as it offers an affordable mobility option, and thus may lead to lower new car ownership. Yet, its more intense competition with the sales of used cars in secondary markets (another low-cost mobility option) may reduce the cannibalization of new car sales to consumers, and therefore benefit OEMs. Moreover, ride-hailing creates additional new car demand for OEMs from drivers providing service on ride-hailing platforms. With these complex interactions, the effects of ride-hailing on the car sales markets and associated environmental impacts are not clear. Recent practices also indicate that ride-hailing platforms may impose vehicle age limits to increase the quality of cars providing service. A vehicle age limit may increase the total new car sales as it increases the drivers’ new car purchase frequency; however, as it requires drivers to replace their used cars with new ones, it also creates another source of used car supply to secondary markets, and thus may increase the cannibalization of OEMs’ new car sales to consumers. Methodology: We establish a game-theoretic model to endogenize these interactions by accounting for the durable nature of cars and the effect of secondary market. Managerial implications: We show that ride-hailing can in fact lead to higher new car ownership among consumers and total new car sales for the OEM; it can also increase the total environmental impact, despite its perceived environmental benefit from usage pooling. Moreover, despite increasing the quality of ride-hailing cars, vehicle age limits can lead to higher profit and total new car sales for the OEM, but they can also reduce the total usage impact of cars.

Problem definition: Enabling market access is widely recognized as a priority for local governments aiming to reduce poverty among smallholder farmers. Traditional market access strategies, which connect farmers to wholesale intermediaries and reach consumers indirectly, have been criticized for exposing farmers to exploitation. Although it is commonly believed that enabling farmers to sell directly to consumers can mitigate this issue, farmers often rely on service intermediaries to facilitate direct sales and continue to face exploitation. Consequently, it remains unclear which type of market access strategy—indirect or direct—is more beneficial to smallholder farmers. We address this question by comparing representative strategies of the two types: contract farming, where farmers sell to a buying firm at a predetermined wholesale price, and rural livestreaming, where farmers sell directly to consumers via live broadcasts hosted by a media company that charges a percentage commission fee. Methodology/results: We construct game-theoretic models of the two strategies and compare farmers’ income in equilibrium. We show that, relative to contract farming, rural livestreaming can mitigate exploitation and improve farmers’ income for niche crops or crops with limited diseconomies of scale in planting, but may have the opposite effect for crops with mass appeal or steeply increasing marginal costs. Yield uncertainty can strengthen the relative advantage of rural livestreaming, whereas subsidies to farmers can weaken or even eliminate it. We validate our results via a case study of the market access strategies for smallholder farmers in Western China. Managerial implications: Policymakers should be mindful of the operational and market characteristics of local crops when choosing between direct and indirect market access strategies to improve farmers’ income. Moreover, coordination among poverty reduction instruments is important to avoid unintended consequences.

Problem definition: Digital educational technologies have the potential to address educational inequality by providing affordable and accessible learning resources. However, it remains unclear whether access to a wide range of learning resources through digital technologies corrects or exacerbates existing disparities, in both the short and the long run, among children from different socioeconomic backgrounds. Methodology/results: Using data from a unique large-scale field study conducted by a digital reading app for K-12 children, we apply a staggered difference-in-differences design to identify the causal effect of subsidizing a broad scope of digital reading resources. Our results show that providing free access to a broad scope of digital reading materials leads to an immediate increase of 428% in daily reading time, with the largest short-term effects observed among children from less developed cities. However, this initial boost in engagement declines sharply over time, particularly for children in less developed areas. We find suggestive evidence that the long-term difference in reading patterns reflects differing levels of parental involvement in the education of children of different socioeconomic status. The long-term contrast between children from poor and rich cities is stronger during the weekend and holidays when parents are more likely to be present. Compared with children in rich cities, children from poor cities perform worse in selecting the “right difficulty” content to read and are less able to sustain long reading sessions, especially with materials that demand more cognitive resources and parental support. Managerial implications: Our findings provide operational insights for EdTech firms and policy recommendations for policymakers, highlighting the critical role of parental involvement in fostering children’s learning persistence and their long-term participation in subsidized educational programs

Agile product development relies on rapid iteration and customer feedback to guide product improvement, yet firms face challenges in coordinating when to solicit feedback and when to invest in quality improvement. While each action adds value on its own, coordinating the two introduces an additional layer of complexity beyond optimizing either decision in isolation. We model this problem as a semi-Markov decision process, with product quality and accumulated customer feedback as state variables. To build intuition, we first analyze two single-action settings: one where the firm continuously invests in quality improvement while optimizing feedback solicitation, and another where it always solicits feedback while optimizing quality investment. In both cases, the optimal policy exhibits a clean monotone-threshold structure. We then study the full joint optimization problem, where the firm must simultaneously decide when to solicit feedback and when to invest in quality. These two interdependent actions create significant analytical complexity. Despite this, we uncover a key structural insight: under mild regularity conditions, the optimal joint policy preserves the threshold-based properties of the single-action settings. Feedback solicitation follows an optimal stopping rule, while quality investment is selectively applied based on the evolving product state. This structure partitions the state space into four regions, each guiding a distinct course of action. We also conduct sensitivity analysis to show how the thresholds shift in response to changes in model parameters. Our analysis reveals that effective agile product development benefits from selective agility—instead of defaulting to nonstop iteration, firms may benefit from pausing quality investment to await more feedback, or focusing solely on quality improvement once sufficient customer input has been collected. The resulting threshold-based policy offers clear, actionable guidance for balancing learning and execution under uncertainty.

Problem Definition. Rapid medical response is critical for out-of-hospital cardiac arrest (OHCA) cases. Using drones to deliver Automated External Defibrillators (AEDs) can significantly enhance the chances of survival by reducing delivery time. This paper aims to optimize the strategic deployment of drones and disposable AEDs within a budget-constrained Emergency Medical Services (EMS) system, using incomplete OHCA data. Unlike previous research, we focus on maximizing the number of timely AED deliveries within a critical window, rather than improving average or tail delivery time metrics. Methodology/Results. We frame this problem as a Modular Capacitated Maximal Covering Location Problem (MC-MCLP), incorporating a time constraint for AED delivery within a narrow therapeutic window. Our model can help alleviate resource imbalances across diverse service regions. We address demand variability using a distributionally robust optimization approach, which enhances decision resilience amid real-world uncertainties. Extensive testing reveals the impact of key parameters on the model, highlighting trade-offs between operational efficiency and both reliability and fairness. A case study of OHCA incidents in Virginia Beach demonstrates our model’s effectiveness in significantly increasing the number of patients reached within the critical time period. Managerial Implications. Our framework ensures prompt assistance to OHCA cases within the vital intervention window while promoting equitable resource allocation across regions. This approach addresses the primary challenges in EMS planning by improving response times within the crucial timeframe and establishing backup emergency resources. Our proposed methodology will enhance OHCA survival rates and optimize EMS resource distribution.

Problem Definition: Growing environmental awareness is prompting consumers to consider reusable alternatives to disposable packaging, driving firms in the takeaway food and beverage sector to explore reusable packaging models. In addition to encouraging consumers to use their personal reusable packaging, some firms now offer firm-owned reusable packaging as an alternative reuse option. This paper examines how price incentives and convenience enhancements shape consumers’ packaging choices and the resulting environmental and profitability implications of reusable packaging models. Methodology/Results: Using a game-theoretical model, we analyze a firm’s pricing and reuse-program decisions when consumers choose among disposable packaging, consumer-owned reusable packaging, and firm-owned reusable packaging. Our key findings are as follows. First, a more eco-conscious market does not necessarily strengthen the firm’s incentive to introduce a firm-owned reusable packaging program. When disposable packaging is inexpensive, the program’s main value lies in price discrimination rather than market expansion; as the market becomes more eco-conscious, this price-discrimination benefit weakens. Second, when disposable packaging is costly, the introduction of firm-owned reusable packaging can increase packaging waste, as the firm may strategically reduce the price discount for consumer-owned reuse. Finally, convenience improvements have asymmetric effects. Improving the convenience of consumer-owned reuse generally reduces packaging waste. However, making the return process more convenient for firm-owned reuse can increase packaging waste by shifting some consumers from consumer-owned to firm-owned reuse, which remains subject to non-return risk. Managerial Implications: Firms in the takeaway food and beverage sector should jointly manage firm-owned and consumer-owned reusable packaging. Price incentives and convenience design should be evaluated based on how they shift consumers across packaging options and affect the tension between firm profitability and packaging-waste reduction.

Problem definition: This paper studies an emerging omnichannel on-demand economy, where consumers decide how their orders are delivered through a platform: fulfilled by independent couriers or picked up in-store by themselves. We analyze the platform’s optimal pricing strategies across service channels and its wage-setting decisions for couriers, and assess the resulting welfare implications. Methodology/results: We develop a stylized model to study how buy-online-and-pick-up-in-store (BOPS) influences pricing on both the courier and consumer sides. We then estimate model primitives using data from a leading meal-delivery platform in China, and use these estimates to quantify the effects of BOPS and wage regulations. Our main results are as follows. First, while it is widely recognized that price is positively related to demand for gig services, this conventional wisdom no longer holds in an omnichannel environment with BOPS, particularly when consumers are less sensitive to delivery congestion. Second, although BOPS shifts the burden of delivery cost from the platform to consumers, the BOPS-channel price need not be lower than the delivery-channel price in a gig-economy setting. Third, although BOPS always improves platform and merchant profits, it enhances consumer surplus only when market demand is sufficiently great, while consistently reducing courier welfare. Managerial implications: In an omnichannel environment, pricing strategies proven effective in traditional gig economy may become suboptimal. Moreover, because the gains from BOPS are unevenly distributed, platform managers and policymakers should look beyond aggregate gains and consider the welfare losses that BOPS adoption imposes on couriers.

Problem Definition: Shared decision-making processes, in which doctors and patients work together to choose among treatment options, have gained substantial support from clinicians, policymakers, and health systems. Shared decision-making personalizes care by combining health outcome predictions from doctors with preference-based input from patients. Despite this broad advocacy, important challenges remain: outcome predictions are noisy, patients often misinterpret trade-offs, and it is unclear when personalization improves or worsens outcomes. Methodology/Results: We develop a stylized analytical model to characterize how personalizing outcome predictions and incorporating patient preferences affect patient utility under bounded rationality. We show that personalizing outcome predictions can backfire when doctors overweight noisy signals or when underlying health-outcome heterogeneity is low relative to doctor error. With patient participation, the interaction between doctor and patient errors becomes critical: when patients have strong preferences, this interaction reduces—and can even reverse—the value of personalizing outcome predictions; in contrast, when patients have weak preferences, the same interaction enhances its value. We also uncover a counterintuitive non-monotonic effect: utility losses from personalizing outcome predictions peak not at the lowest, but at moderate levels of health-outcome heterogeneity relative to doctor error. Finally, we show that preference-based personalization can reduce utility when treatments appear similar across patient types and patients misinterpret the associated trade-offs. Managerial implications: Contrary to the common belief that advocates for increasing personalization uniformly, our results show the trade-offs between personalization and standardization that arise from limitations in prediction accuracy and human cognition. Our results describe conditions under which personalization can backfire.

Problem Definition. Loyalty programs (LPs) are widely used to enhance customer retention and firm profitability. With technological advancements like blockchain, many brands are exploring tokenized LPs. Unlike traditional LPs, where rewards can only be redeemed for future purchases, tokenized LPs introduce tradability, allowing customers to trade their loyalty points for others or cash. This paper examines the value and potential drawbacks of tradability in LPs. Methodology/Results. We develop a model in which a firm sells to strategic customers who make repeated purchases over two periods, which allow us to uncovering the underlying mechanisms behind the traditional and tradable LPs. While traditional LPs primarily boost firm profitability by encouraging repeat purchases, tradable LPs provide an additional benefit-they enable low-valuation customers to participate in the market through token trading. However, tradability may also encourage strategic waiting, potentially reducing profitability. Under a static pricing strategy, the firm prefers tradable LPs when customers are relatively myopic or when the repurchase discount factor-how customers value repeat purchases relative to first-time ones-is either low (e.g., durable or experiential products) or high (e.g., frequently consumed products). Notably, tradable LPs can achieve win-win outcomes for both the firm and customers when the repurchase discount factor is moderately high. Conversely, traditional LPs yield higher profits when customers are highly strategic and the repurchase discount factor is moderate. With dynamic pricing, however, traditional LPs consistently generate higher firm profits than tradable LPs, though this advantage may come at the cost of consumer welfare under moderate repurchase discount factors. Managerial Implications. These findings provide economic rationales for the excitement and adoption challenges surrounding tradable LPs. They also offer practical guidelines for optimizing LP design under different market conditions and pricing strategies.

Problem definition: We study the inventory placement problem of splitting [Formula: see text] units of a single item across warehouses in advance of a downstream online matching problem that represents the dynamic fulfillment decisions of an e-commerce retailer. This is a challenging problem both theoretically, due to the computational complexity of the downstream matching problem, and practically, as the fulfillment team continuously updates its algorithm while the placement team lacks direct evaluation of placement decisions. Methodology/results: We compare the performance of three placement procedures based on optimizing surrogate functions that have been studied and applied: Offline, Myopic, and Fluid placement. On the theory side, we show that optimizing inventory placement for the Offline surrogate leads to an [Formula: see text]-approximation for the joint placement and fulfillment problem under any demand model that admits an [Formula: see text]-competitive fulfillment policy. We assume [Formula: see text] is an upper bound on how many warehouses can serve any demand location. The crux of our theoretical contribution is to use randomized rounding to derive a tight [Formula: see text]-approximation for the integer programming problem of optimizing the Offline surrogate. We further show how to extend this result to a multi-SKU setting, improving upon the best known approximation of [Formula: see text]. We use statistical learning to show that rounding after optimizing a sample-average Offline surrogate, which is necessary due to the exponentially-sized support, indeed has vanishing loss. On the experimental side, we evaluate how different combinations of placement and fulfillment procedures perform on a wide array of synthetic instances. When coupled with a good fulfillment procedure, optimizing the Offline surrogate performs best even compared to computationally-intensive simulation procedures, corroborating our theory. Managerial implications: Theoretical guarantees and extensive numerics both suggest that the placement team should optimize the (optimistic) Offline surrogate, assuming the fulfillment team has a good algorithm. Otherwise, the placement team could optimize the (pessimistic) Myopic surrogate instead.