Knowledge that Transforms

This paper studies how an online seller designs a menu of return contracts to manage consumer heterogeneity arising from product misfit risk. In the model, informed consumers know the product fits, whereas uninformed consumers face uncertainty about fit. We show that when only misfit risk exists, a single full-refund contract can implement the optimal menu. The return price insures consumers against misfit, and the selling price extracts full surplus. This finding aligns with the widespread adoption of lenient return policies and demonstrates that uniform contracts can emerge endogenously from incentive compatibility rather than as ad hoc assumptions. Introducing quality risk—an additional source of uncertainty in the valuation of fit products—fundamentally changes this outcome. The seller then shifts from full to partial refunds. Under high quality risk, the optimal menu becomes differentiated: A low-price, no-frills option for informed consumers and a high-price, insurance-heavy option for uninformed consumers. To induce self-selection, the seller distorts the return price upward for uninformed consumers to strengthen the insurance effect, trading off allocative efficiency for screening. Interestingly, when quality risk becomes extreme, refunds not only mitigate information rents but also enhance social surplus by preventing consumers from retaining low-quality products. Extensions incorporating misfit valuation, seller-side costs, and return hassle costs show that the uniform contract remains robust when only misfit risk is present, rationalizing diverse return policy practices in online markets.

In response to social protests, transportation network companies introduced incentive programs to improve hourly utilization and retain drivers. While such initiatives assume increased driver commitment, their impact on labor allocation across competing gig economy platforms remains unexplored. This study uses a quasi-experimental design and analyzes 20 weeks of data comprising over 75 million trips to examine spillover effects of an East Asian platform's comprehensive personnel on-road insurance (PI) policy on a competitor platform. The research uncovers significant changes in labor allocation patterns by identifying multihoming and dedicated drivers through license plate matches. A difference-in-difference analysis provides evidence of cross-platform spillovers of PI: multihoming drivers allocated 16.4% more hours to food delivery and reduced ride-hailing hours by 10.69% on the competitor platform, while dedicated drivers reduced food-delivery hours by 34.6% but added 12.81% more ride-hailing hours. These shifts in hours corresponded to changes in earnings. To explore the mechanisms of such spillover effects, the study observes multihoming drivers’ labor allocation patterns on the implementing platform: Multihoming drivers shifted toward food delivery, increasing hours by 17.3% and decreasing ride-hailing hours by 11.2% on the platform. The study further links such cross-platform shifts in drivers’ labor allocation by estimating job aggregation among multihoming drivers and provides multiple robustness analyses to validate these effects. The study highlights the interconnectedness of gig economy stakeholders by empirically linking policy changes on one platform to cross-platform labor dynamics. Practical insights are provided on how the PI policy influenced platform utilization, driver behavior, and productivity, emphasizing that gig-economy platforms operate within a highly interdependent ecosystem rather than in isolation.

The opioid epidemic poses widespread societal challenges. In response, electronic prescribing for controlled substances (EPCS), which requires prescribers to use the e-prescribing system, has begun attracting attention to combat the opioid epidemic by helping prescribers detect doctor shoppers and prevent forged prescriptions. However, a concern is that limited access to opioids after EPCS mandates may cause drug users to cross borders and travel to other areas without EPCS. Grounded in the tension on the efficacy of EPCS, this study aims to assess the impact of EPCS on the opioid dispensing rate. Leveraging a U.S. county-level data set from 2010 to 2020 and employing a quasi-experiment setup with matching, we find that counties without an EPCS mandate but adjacent to a state with an EPCS mandate experience an increase in opioid dispensing rates. Specifically, a neighboring-state EPCS mandate is associated with a 7.56% increase in the opioid dispensing rate in an adjacent county on average. The findings reveal that the negative spillover effect spreads deeper in areas with lenient illicit drug controls, in urban regions, and in areas with high poverty levels. Moreover, we estimate the overall efficacy of EPCS mandates by combining both the positive direct effect and the negative spillover effect of EPCS. We discuss theoretical and policy insights for the effective operation of EPCS mandates.

Effective management of election campaigns involves dynamic decision-making under uncertainty. Traditional approaches rely heavily on pre-planned strategies that often fail to adapt to real-time changes in voter sentiment and external factors. This paper introduces the Online Election Campaign Planning Problem (OECPP) to optimize the scheduling of campaign activities in the context of U.S. presidential elections. OECPP incorporates sequentially updated predictions that represent assessments of the impact of campaign activities over the course of the campaign. Since these predictions evolve in response to new information and their accuracy cannot be fully assessed without perfect information, we develop deterministic and randomized online algorithms for OECPP that can operate effectively under unreliable and evolving predictions. We evaluate the performance of our algorithms using the competitive ratio (CR), a metric particularly useful when probabilistic modeling is impractical. We begin by establishing a tight upper bound on the CR of the online algorithms for the OECPP under unreliable reward predictions. We then introduce a sequential setup-based CR metric to capture the value of reoptimization as new predictions arrive, and we design deterministic and randomized algorithms that are optimal under this metric. Using data from U.S. presidential elections, we show that randomized online algorithms can significantly outperform their deterministic counterparts in terms of empirical CR. We also find that the effectiveness of randomized algorithms is driven by two factors: the selection of prediction samples for generating activity scenarios and the randomization cut-off, which determines the scenarios to be randomized. The benefit of randomization is non-monotonic, and the best empirical CR is achieved by selectively adding prediction samples to the randomization set.

The direct impacts of long wait times in elections, such as lost wages for voters and suppressed turnout, are well-documented. Drawing upon the service operations literature, we hypothesize that such operational inefficiencies may have far-reaching consequences beyond the immediate voter experience, in particular, the spread of political misinformation. Using a novel dataset that combines granular measures of voter wait times from cellphone location data, social media content, and demographic information at the county level, we find evidence that longer wait times are associated with greater sharing of political fake news on Reddit in the aftermath of the 2016 US presidential election. Importantly, this relationship exhibits significant heterogeneity based on the racial composition of counties, with more diverse counties experiencing greater increases in misinformation spread due to wait times. To shed light on the mechanisms underlying this link, we leverage individual-level survey data on voters’ polling place experiences and perceptions of electoral integrity. Our analysis reveals that experiencing long wait times erodes voters’ confidence in the integrity of the election process, particularly at the local level. These findings underscore the societal importance of efficient election operations management and highlight how they can have profound implications for the health of democracy. Our work introduces a new factor—the operational efficiency of election administration—into the study of political misinformation, thus opening up avenues for operations management research to contribute to a pressing social challenge.

Religious beliefs have often served as a lens through which communities interpret and cope with disasters. Not surprisingly, many non-governmental organizations (NGOs) that support disaster relief have roots in religious traditions. The religious orientation of an NGO is a key concern as it can influence volunteer outcomes and operational performance. This is important because volunteers are an indispensable asset for NGOs, playing a pivotal role in the efficacy of humanitarian aid efforts. This study employs social capital and person-organization fit theories to examine how NGO religiousness influences social capital, volunteer behaviors, and operational performance. It also analyzes how NGO and volunteer religiousness “fit” affects these relationships. The hypotheses were tested using two scenario-based video experiments: Experiment 1, which collected data from 100 students in a laboratory setting, and Experiment 2, which involved 198 online volunteers. Results from Tobit and Poisson regressions indicate that increased NGO religiousness may diminish volunteer social capital, commitment, and operational performance. However, NGO and volunteer religiousness “fit” mitigates the adverse effects of NGO religiousness, enhancing volunteer behaviors. A large-scale survey of 503 respondents supports these findings and provides insights to guide future research into volunteer motivations. This study contributes to the Humanitarian Operations Management literature and informs the strategies of NGOs regarding religious alignments, volunteer recruitment and retention, and operational performance.

Recently, there has been a notable increase in the prevalence of consumer-to-consumer (C2C) online marketplaces offering integration with social networks. Platforms such as eBay, Facebook Marketplace, Zhuanzhuan, and Poshmark have led such initiatives. In such marketplaces, the structure of social relationships revealed to platform participants can influence the choice of transacting partners as well as the terms of transactions. We study the role of different levels of information design policies on offers and acceptance decisions in a social commerce marketplace setting. We propose a novel information design policy, for C2C marketplaces, called partial anonymity in which the transacting parties are provided with limited social information about each other. Using a theoretical model and experimental data, we show that a partial-anonymity information design allows sellers to be more strategic than a social commerce marketplace with no anonymity. Further, with a partial-anonymity design, the likelihood of a successful transaction is higher than in a fully anonymous marketplace. Our results are robust to different product categories and uncertainty in product quality.

Temporary economic shocks in local employment ecosystems, events that create temporary alternate employment opportunities for workers, tend to create operational challenges for firms. We investigate two dimensions of these challenges faced by firms that engage workers performing low-skilled tasks in the agricultural industry. We seek to understand (i) which workers leave during these shocks, and (ii) whether workers who choose to stay exhibit any change in their productivity. Both of these answers ultimately determine the resilience of firms during the shocks. We present data from a natural experiment in an agricultural company. The data are for two farms, where workers performed the same task but used two different protocols that differ in cognitive load, the mental effort required to manage and coordinate tasks, at two levels: higher and lower. Our findings indicate that, at an aggregate level, shocks in the labor ecosystem increase the strain on farming companies by increasing worker turnover rates. However, during the shock period, the workers who remained with the focal firm demonstrated increased productivity. In a post hoc analysis, we examine whether worker performance before a shock (as measured by pre-shock productivity) impacts turnover and productivity during shock periods. We find that workers following the lower cognitive load protocol were more likely to leave during the shock if their productivity was low. In contrast, workers following the higher cognitive load protocol were equally likely to leave at all productivity levels. Similarly, for the lower cognitive load protocol, the productivity change during shock was proportional to a worker's original productivity. In contrast, workers following the higher cognitive load protocol increased their productivity by the same amount across all levels of pre-shock productivity. These findings highlight the differential impacts of cognitive engagement and workload on worker retention and productivity in low-skill production settings. Our results suggest that work design strategies that keep workers cognitively engaged at work are crucial for mitigating the negative effects of labor shocks and improving organizational resilence.

Over time, firms across industries have adopted quality verification as a costly yet credible tool to signal product excellence and convey reliable information to consumers. With the prosperity of E-commerce, consumers increasingly rely on online reviews—an accessible but inherently biased source of product information—to guide their purchase decisions. In practice, companies now frequently combine quality verification with consumer reviews to influence the perception of product quality. This study examines how firms can strategically determine their optimal quality verification approach to mitigate the negative effects of review bias, and how the timing of verification affects outcomes. We consider two quality verification formats: pre-release verification, conducted before reviews are available, and responsive verification, conducted after reviews are observed. Pre-release verification helps shape early consumers’ expectations and improves late consumers’ interpretation of biased reviews. As the magnitude of review bias increases, firms are more likely to proactively adopt pre-release verification, although this leads to a decline in expected profits due to higher upfront costs. By contrast, responsive verification allows firms to selectively react to negatively biased reviews, preserving the upside potential of favorable reviews. When review volatility is high, this reactive strategy can lead to higher profits. Our results show that each verification format can enhance firm profitability under specific conditions, depending on the cost of verification and the degree of review bias. These findings offer actionable insights into how firms can manage information flows and strategically balance third-party verification with user-generated content in dynamic market environments.

Health information exchanges (HIEs) facilitate the secure, electronic sharing of patient medical records across providers, enabling healthcare professionals to access timely, comprehensive data and thereby improve care coordination and quality. Yet, despite these expected benefits, empirical evidence shows that healthcare professionals often spend substantial time and effort interacting with HIE platforms without discernible productivity gains. Moreover, although HIEs are promoted as a potential solution for addressing geospatial disparities in healthcare, their impact on healthcare professionals’ productivity across urban and rural hospitals remains unclear. Using data envelopment analysis (DEA) to construct a measure of healthcare professionals’ productivity and applying a difference-in-differences (DiD) approach, we investigate the impact of HIE adoption on healthcare professionals’ productivity in urban and rural hospitals in the United States. Our findings show that hospitals that have adopted an HIE experience a significant increase in healthcare professionals’ productivity. However, this effect is more pronounced in urban hospitals than in rural hospitals. We attribute this result to urban hospitals having more information-intensive workflows and greater technological sophistication than rural hospitals. Furthermore, our study reveals that HIE adoption improves communication and the quality of clinical decision-making among urban healthcare professionals, but not among those in rural hospitals. We also find that the productivity gains from HIE adoption are greater for nurses than for physicians. We discuss the theoretical and practical implications of these findings.