Problem definition: Shift workers make up a significant portion of the labor force in major countries and are widespread across industries. Their work schedules often involve odd hours and inconsistencies over time, deviating from regular day workers. Lu et al. (2022) introduced a two-dimensional measure for shift worker schedule consistency, namely, the hour-of-the-day and the day-of-the-week consistencies, and demonstrated their efficacy in improving labor productivity for grocery store cashiers. We expand this nascent literature in several ways. Methodology/results: Using data collected from warehouse workers performing picking and loading tasks, our context is differentiated by the inclusion of night hours and a piece-rate pay structure. The former enables us to show that hour-of-the-day consistency improves productivity only during day hours; it is in fact detrimental to productivity, at an even larger magnitude, during sleep hours. We revise the rhythm-synchronizing theory proposed by Lu et al. (2022) to accommodate this heterogeneity. The latter affords a comparison of effect sizes across the two most common pay structures for shift workers. We demonstrate that hour-of-the-day and day-of-the-week consistencies’ impact on productivity are four and eight times larger, respectively, under piece-rate pay than under hourly pay. We further consider how two environmental factors, inclement weather and extreme temperature, both of which are easily accessible via weather forecasts by schedulers, attenuate hour-of-the-day consistency’s effect. Managerial implications: Departing from existing perspective that both schedule consistency dimensions are effective, we show robust evidence that day-of-the-week consistency not only has a larger impact, improving productivity by 9.20% for a first- to third-quartile change compared with hour-of-the-day consistency’s 2.03%, but is also relatively stable across days and resistant to moderation by environmental factors. Thus, it should be the primary operational lever for schedulers, especially given its relative ease of use in practice. We also find schedule consistency to be most beneficial for new hires, with its impact waning as workers accumulate experience. However, the speed of this attenuation is much slower than previously reported. We illustrate how these insights can be easily incorporated into a consistency-aware scheduling policy through numerical studies. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Funding: This study was supported by the National Natural Science Foundation of China [Grants 72172027, 72293563, and 72232001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2025.0019 .

Problem definition: Supply chain financing solutions allow financially weaker suppliers to secure funding based on the stronger financial standing of their buyers, presenting an alternative to conventional financing. This paper explores the preferences between immediate-tier financing (involving a direct buyer of the supplier) and remote-tier financing (involving a buyer’s buyer). Methodology/results: Using a model of a three-tier supply chain and a financial market, we solve for the subgame-perfect Nash equilibrium for a game comprising supply chain financing supergame and financing subgames between a firm and investors, subject to equilibrium pricing of firms’ assets-in-place and moral hazard frictions. The game is dynamic and stochastic because production shocks and financing subgames occur over time, coinciding with trade transactions between firms, and because of the stochastic evolution of information about firms’ assets-in-place affecting demand for products of each firm. We identify three economic mechanisms that stem from the relative position of firms in the supply chain and that work against remote-tier financing: risk spillover, noise accumulation, and loss of hedging options. Risk spillover occurs when a firm’s pledge of assets to secure supplier financing increases its own financing risk, affecting upstream firms and transactions. Noise accumulation refers to the greater uncertainty in forecasts of asset values, which are realized later for remote-tier buyers. The loss of the hedging option occurs because, by helping the supplier initially, the remote-tier buyer loses some of the capacity to help the immediate-tier buyer later. These mechanisms may explain challenges faced by remote-tier supply chain financing in practice. Managerial implications: The paper highlights the challenges faced by remote-tier financing and identifies conditions under which it can be favorable. It extends traditional finance models to incorporate supply chain considerations, demonstrating the importance of these factors in economic and financial decision making. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.1262 .

Problem definition: Motivated by the significance of side information in numerous operations management problems, this paper studies conditional stochastic optimization to enable more informed decisions. The side information constitutes observable exogenous covariates that alter the conditional probability distribution of the random problem parameters. Decision makers who adapt their decisions according to the observed side information solve a stochastic optimization problem where the objective function is specified by the conditional expectation of the random cost. If the joint probability distribution is unknown, then the conditional expectation can be approximated in a data-driven manner using kernel regression. Although this approximation scheme has found successful applications in diverse decision problems under uncertainty, it is largely unknown whether the scheme can provide any reasonable out-of-sample performance guarantees, and how such statistical guarantees can guide the decision-making process. Methodology/results: We employ the Nadaraya–Watson kernel regression for data-driven approximation of the conditional expectation and leverage moderate deviations theory to establish its performance guarantees. Our analysis and resultant statistical bounds motivate the use of a conditional standard deviation regularization scheme to enhance out-of-sample performances. As the designed regularization scheme leads to a nonconvex optimization problem, we further adopt ideas from distributionally robust optimization to obtain tractable formulations. We examine our proposed models on portfolio optimization, inventory management, and wind energy commitment problems. The numerical results demonstrate the effectiveness of our proposed regularization scheme. Managerial implications: Our paper illustrates the importance of side information in real-world decision-making problems. Incorporating side information through a regularized Nadaraya–Watson scheme offers managers a robust framework to enhance decision making under uncertainty. The theoretical guarantees provide guidance on the number of samples required to obtain high-quality solutions and how to optimally adjust the regularization parameter. For problem instances with high-dimensional covariates, we further present a simple dimensionality reduction procedure that helps improve the sample complexity of the scheme. All our proposed formulations are concise and straightforward for the operations manager to implement using any popular programming language interfaced with standard off-the-shelf solvers. Funding: Y. Wang was supported by the National Natural Science Foundation of China [Grant 72501204] and the Fundamental Research Funds for the Central Universities. G. A. Hanasusanto was funded by the National Science Foundation [Grants CCF-2343869 and ECCS-2404413]. C. P. Ho was supported by the Research Grants Council [General Research Fund 11508623] and the CityUHK Start-Up Grant [9610481]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.0997 .

Problem definition: The transition from fossil-fuel generators to renewable energy requires significant growth of flexible resources to manage weather-dependent output variations. Key among these are large-scale storage assets. Although storage is mostly carbon neutral in its direct operations, its arbitrage activities influence the scheduled quantities of other producers, thereby affecting market-level carbon emissions indirectly. This raises questions about the extent of these indirect emissions and how to limit them effectively. Methodology/results: We develop a model to analyze the emission impact of profit-maximizing large-scale storage agents in a competitive electricity market. We derive a tight condition for the worst-case rate of added emissions from a storage transaction. Accordingly, we characterize the minimum sufficient carbon levy to keep emissions below a desired threshold. We support our theoretical findings with numerical studies based on the Dutch electricity market. Our results show that both emissions and the corresponding carbon levy depend on the round-trip efficiency of the storage asset and the characteristics of technologies in the energy mix (e.g., marginal costs, emissions, and capacities). The findings remain robust under uncertainty in demand and renewables. Managerial implications: The framework developed in this work enables market participants and regulators to assess, interpret, and potentially control the unintended carbon impacts of storage asset operations. Several findings are nontrivial and carry important implications for regulation. For instance, we show that counterintuitively, storage assets with higher round-trip efficiency can increase system emissions more—and require higher carbon levies to curb them—than less efficient ones. Additionally, although a carbon levy reduces the worst-case emission rates of a storage transaction, we identify scenarios where the emission impact of a storage agent may rise with higher levies. Notably, the indirect emissions of storage agents are also sensitive to whether solar or wind is the dominant renewable. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.1411 .

Problem definition: Integrating contests into established firm-driven User Generated Content (UGC) communities presents a complex interplay between innovation and communal engagement. The impact of such contests on both the creative outcomes and the community’s dynamics remains largely unexplored. Methodology/results: We combine two different data sources stemming from the 3D printing platform Thingiverse, allowing us to compare (a) regular content creation activities shared with the existing platform community with (b) content creation activities explicitly pertaining to the contests launched in the existing platform community. Our empirical analyses show that contests increase community reaction given a “spotlight effect” and contestants’ increasing efforts. Second, we find that post-contest participation, submissions to future contests are increased, and submissions to the general community are decreased. Furthermore, our analysis shows that the net effect of contest participation on future content creation overall is negative. Third, our results show that contestants develop more exploratory outcomes, suggesting that contests redirect participants’ attention to venture into new, unfamiliar domains when problem-solving. Managerial implications: Our study provides important insights pertaining to the operations management of digital platforms by comparing the amount and diversity of user-generated content stemming from two platform initiatives: contests versus community. If managers aim to increase overall content quantity, introducing contests to an existing community may be detrimental as they seem to primarily trigger contest related UGC and not “regular community” content. If they aim for content diversity instead, contests are a vehicle to promote contestants’ exploratory behavior. These results provide detailed guidance for managers interested in applying communal and competitive initiatives on user-generated content platforms and highlight the importance of technologies in facilitating social interactions between and among users and firms more broadly. Funding: This work was supported by the Henry Crown Institute of Business Research in Israel, the Jeremy Coller Foundation, and the Field Family Foundation. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0375 .

Problem definition: Whereas artificial intelligence (AI) algorithms may perform well on data that are representative of the training set (inliers), they may err when extrapolating on nonrepresentative data (outliers). How can humans and algorithms work together to make better decisions when faced with outliers and inliers? Methodology/results: We study a human–AI collaboration on prediction tasks using a bias adjustment framework and hypothesize that humans tend toward naïve adjusting behavior: humans make adjustments to AI predictions that are too similar across inliers and outliers when, ideally, adjustments should be larger on outliers than inliers. In an online experiment, we demonstrate that participants are indeed unable to sufficiently differentiate their adjustments to an AI algorithm when faced with both inliers and outliers, leading to a 143%–176% increase in their absolute deviation from the optimal prediction compared with participants facing either all inliers or all outliers. We design a warning (an endorsement) that alerts participants when feature values constitute outliers (inliers), and in a second experiment, we show that this warning (endorsement) helps participants differentiate adjustments, reducing their absolute deviation from the optimal prediction by an average of 35% (28%). Deploying both interventions together reduces participants’ absolute deviation from the optimal prediction by 49%. In a third experiment, we demonstrate the robustness of warnings and endorsements in the presence of “fringeliers”—data points with features marginally outside the range of the training data set. Managerial implications: Our work details an important behavioral bias and identifies a simple educational intervention for mitigation. Ultimately, we hope that this work will help managers better equip their employees for human–AI collaboration. Funding: Funding was provided by Harvard Business School. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.0854 .

Problem definition: The ongoing large-scale deployment of autonomous vehicle (AV) technology has the potential to fundamentally change the transportation landscape. Due to the high cost of AV hardware, the most likely path to widespread AV use is via platforms that can sustain high utilization, such as ride-hailing and delivery services. Our work studies the incentives of stakeholders in such deployments, possible misalignments, and contracting solutions to overcome them. Methodology/results: We consider four potential operational models to commercialize AVs, which we model as a supply chain game between a platform, an AV supplier, and human individual contractors (ICs), including (1) an open platform in which the AV supplier and ICs bring their own vehicles to the system, (2) an AV-supplier operated platform without ICs, (3) a platform that sources AVs through leasing contracts, and (4) an integrated supply chain. We find that except for the AV-only platform, all deployment models are subject to a risk of AV underutilization due to the need to maintain the ICs’ utilization sufficiently high to ensure ICs remain engaged. As AV underutilization propagates in a nonintegrated supply chain, an open platform can become arbitrarily worse than supply chain integration, and careful usage commitments are needed to overcome the efficiency loss. Managerial implications: This paper identifies a new kind of supply chain misalignment that is likely to emerge as AVs become deployed technology, which both platform operators and AV suppliers should be mindful of. Moreover, we demonstrate the value of usage commitment in the deployment of AVs through an open platform, which makes it more efficient than AV-only/AV leasing platforms. History: This paper was selected as part of the 1RR initiative between the M&SOM Journal and the MSOM Society. This particular paper was part of the 2024 MSOM Service Operations SIG Conference. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.1175 .

Problem definition: We consider the dynamic pricing problem of multiple products under (asymmetric) reference effects over an infinite horizon. Unlike existing literature, which is mostly focused on the single-product setting, our multiproduct setting takes into account the cross-product effects among substitutes and incorporates the memory-based reference prices into the multinomial logit (MNL) demand model. Even with the single-product logit demand, the structure of the optimal pricing policy is intractable. Therefore, we focus on the long-run patterns of the optimal pricing policy and also discuss the performance of the myopic pricing policy. Methodology/results: We first provide a comprehensive characterization of the myopic pricing policy, including its solution, long-run convergence behavior, and optimality gap. For the optimal pricing policy, we show an intricate connection between its long-run dynamics and types of reference effects. We demonstrate that the presence of any gain-seeking product renders a long-run constant pricing policy suboptimal. Conversely, the constant policy (or optimal steady state) can exist in both loss-neutral and loss-averse scenarios, where we provide a sufficient condition for such existence and give the analytical expression for the optimal steady state. We further show that when pricing perfect substitutes, the true optimal policy under the multiproduct framework is more likely to yield a long-run cyclic pattern than the policy derived from the single-product framework, a phenomenon that aligns well with the periodic discounts in real-world markets. Managerial implications: This discrepancy in the long-run behaviors between multi- and single-product-based policies highlights the importance of employing the multiproduct framework and addressing the cross-product effects, as sticking to the single-product framework while managing multiple substitutes can misrepresent long-run dynamics and result in suboptimality. In the multiproduct domain, our model suggests that retailers are more likely to benefit from appropriate price variations than maintaining a constant pricing policy. Funding: H. Jiang acknowledges support from the Natural Sciences and Engineering Research Council of Canada [NSERC Discovery Grant RGPIN 2024 05796]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.0801 .

Problem definition: Substance use disorder (SUD) is a pressing health concern in the United States, and connecting communities with treatment is a growing public health imperative. We study the optimal allocation of substance use treatment centers across counties within U.S. states over a multiyear time horizon. We use a three-part objective function that minimizes (i) statewide overdose deaths, (ii) mismatch between counties’ treatment center access and overdose death rates (inequity), and (iii) mismatch between counties’ treatment center access and population (inequality). We also consider the temporal interdependence of counties’ treatment center allocations and their death-rate-based equity targets. Methodology/results: We use a predict-then-optimize approach based on data from more than 2,500 U.S. counties between 1999 and 2016. We first estimate SUD treatment centers’ causal impact on county-level overdose death rates, using mental health parity laws as an instrument. We then use these estimates in a nonlinear program that reallocates states’ observed levels of SUD treatment funding. Results suggest that this approach can meaningfully reduce statewide overdose deaths and between-county disparities in treatment access. Managerial implications: Prioritizing death rate reductions over equity/equality results in more resources being directed to high-population counties, where treatment centers are most effective. Prioritizing equity/equality in treatment access results in more resources being directed to underserved, low-population counties. We also find that states differ in their Pareto-optimal trade-offs between death rates and inequity/inequality. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.0963 .