Knowledge that Transforms

Problem definition: Innovations in digital and mobile health (mHealth) services are transforming chronic care delivery by improving health outcomes and efficiency. However, despite the proven benefits of mHealth, usage remains hindered by a critical challenge: patient engagement after adoption. Without timely and frequent patient data, mHealth apps become ineffective, preventing providers from making optimal interventions. This lack of patient engagement leads to health complications that are expensive, especially for payers. Timely provider interventions are essential to mitigate these costs. Our study explores how payers can leverage targeted incentives to enhance patient usage of mHealth technology. Methodology/results: Using a game-theoretic model, we analyze how payers can design contracts to increase patient usage of mHealth. Our study examines key factors such as disease progression, rewards, premium, copay, and the effectiveness and efficiency of mHealth apps. From our analysis, two optimal strategies emerge: (i) a reward-based strategy in which direct incentives are provided to patients to encourage mHealth usage and (ii) a reminder-based strategy in which contracts are designed to incentivize providers to remind patients to use mHealth apps. The effectiveness of these strategies depends on the ability to facilitate rapid and impactful mobile interventions, which we define as mHealth technology productivity (mHTP). Our findings indicate that, when mHTP is low, payers benefit more from a reminder-based strategy, supported by higher capitation payments to providers. When mHTP is high, a reward-based strategy is more effective with patient rewards increasing as mHTP improves. Managerial implications: Our study highlights how payers can foster a mutually beneficial relationship with patients by involving providers, leading to improved health outcomes and increased payer profitability. By designing optimal incentive structures, payers can drive sustained mHealth engagement, ultimately improving healthcare efficiency and reducing costs.

Problem definition: Although a centralized firm selling durable products benefits from committing to future prices, it remains unclear whether this conclusion extends to a decentralized supply chain where a manufacturer distributes durable products through a retailer. Further, previous research on commitments in decentralized supply chains is sparse and assumes exogenous commitments. To address this gap, we examine the implications of commitment decisions in a supply chain where a manufacturer and a retailer endogenously decide whether to commit to future wholesale and retail prices, respectively. Methodology/results: Using a two-period game-theoretic model, we show that, in equilibrium, either both firms commit to future prices or neither does, indicating that commitment by only one of the firms is not a stable equilibrium. Unlike a centralized firm, both the manufacturer and the retailer in our model may paradoxically suffer because of their commitment capabilities. Additionally, compared with exogenous commitments, endogenously chosen noncommitments can harm the retailer and the supply chain. Interestingly, supply chain coordination can be either better or worse under endogenously determined noncommitment equilibria compared with exogenous commitments. Furthermore, an increase in strategic consumer behavior initially improves but eventually undermines supply chain coordination. Our key insights on commitment strategies, firm profits, and supply chain coordination remain robust to inventory carryover by the retailer. Managerial implications: Our findings suggest that firms must recognize the interdependent nature of commitment decisions and consider how their choices affect—and are affected by—those of their supply chain partners. Moreover, firms should carefully assess the impact of strategic consumer behavior and their impatience for future profits when making commitment decisions. Overall, our study demonstrates that the insights derived from commitment strategies of centralized firms or exogenously given commitments in decentralized supply chains do not necessarily apply to decentralized supply chains with endogenous commitment decisions.

Problem definition: We consider a newsvendor problem with unknown demand distribution, where we distinguish ambiguity under which the newsvendor does not differentiate demand distributions of common characteristics (e.g., mean and variance) and misspecification under which such characteristics might be misspecified (because of, e.g., estimation error and/or distribution shift). Methodology/results: The newsvendor hedges against ambiguity and misspecification by maximizing the worst-case expected profit regularized by a distribution’s distance to an ambiguity set of distributions with some specified characteristics. Focusing on the popular mean-variance ambiguity set and optimal-transport cost for the misspecification, we show that the decision criterion of misspecification aversion possesses insightful interpretations as distributional transforms. We derive the closed-form optimal order quantity that generalizes the solution of the seminal Scarf model under only ambiguity aversion. We establish finite-sample performance guarantees that consist of two parts: an in-sample optimal value and an out-of-sample effect of misspecification, which can be further decoupled into estimation error and distribution shift. We also extend the framework to multiple products, distributional characteristics specified via optimal transport, and misspecification measured by total variation distance and derive analytical optimal solutions. Managerial implications: The closed-form solution highlights the impact of misspecification aversion; the optimal order quantity under misspecification aversion can decrease as the price or variance increases, reversing the monotonicity of that under only ambiguity aversion. Hence, ambiguity and misspecification, as different layers of distributional uncertainty, can result in distinct operational consequences. The finite-sample performance guarantee theoretically justifies the need to incorporate misspecification aversion in a nonstationary environment, as demonstrated in our experiments with real-world data. Funding: Z. Chen is supported in part by the National Natural Science Foundation of China [72422002, 72394395], the Hong Kong Research Grants Council General Research Fund [CUHK-11502422], and the Asian Institute of Supply Chains and Logistics. Ruodu Wang is supported by the Natural Sciences and Engineering Research Council of Canada [CRC-2022-00141, RGPIN-2024-03728]. S. Wang is supported by the National Natural Science Foundation of China [Grants 72471224, 72171221, 71922020, and 71988101], the Fundamental Research Funds for the Central Universities [Grant UCAS-E2ET0808X2], a grant from the MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS, and the MOE Social Sciences Innovative Group on Complex Systems Modeling in Economic Management in the Era of Digital Intelligence, University of Chinese Academy of Sciences [E5820801].

Problem definition: Parcel logistics companies use zoning systems to manage last-mile delivery operations. This practice divides a service area into zones, each served by its own station and drivers. Designing an optimal zoning policy is challenging because the practical service area often includes many customer locations, and vehicle routing problems (VRPs) should be incorporated as a subroutine. Existing methods have limitations in modeling practical fleets with diverse vehicle types and broader routing objectives. Methodology/results: We collaborated with a delivery company to develop a novel data-driven zoning method that minimizes the maximum work span of delivery stations. We define the work span of a station as the duration between the start time of sorting the first parcel and the return time of the last driver upon finishing all assigned delivery tasks. Our method iteratively solves VRPs using observed demand data and partitions the region with additively weighted Voronoi diagrams. We leverage the primal-dual properties and develop a subgradient algorithm with established convergence conditions. Our numerical analyses show that this approach not only reduces the station-level maximum average work span by 20.5% and the average delivery time per driver by 17%, but it also reduces their standard deviations by approximately 25% and 19%, respectively. When tested in actual field conditions, we continue to observe reductions in the work span of the stations and the delivery time of the drivers. Managerial implications: Our approach reduces delivery lead times, better distributes workload among drivers, and limits long working hours, creating a win–win outcome for both the company and its drivers. Besides improving service quality and driver well-being, we estimate annual savings of nearly half a million dollars simply by readjusting the boundaries of service zones. The proposed framework can also be applied in other spatial service settings to achieve equitable distribution of workload among resources.

Problem definition: Many organizations face evidence of discrimination in their hiring and career advancement outcomes: underrepresentation of a group that cannot be explained by quality differences. Representation is generally the result of a funneling process through multiple stages. Therefore, policymakers seeking to intervene (typically by introducing regulations or incentives) must decide which stage to target. Moreover, stakeholders care about both representation and about the quality of hires. How does the multistage funnel structure interact with discriminatory judgment behaviors to impact hire representation and quality? What is the best way to design regulations and incentives within funnel structures to improve representation and performance, and can they improve both? Methodology/results: We develop a stylized mathematical model that captures taste-based and statistical discrimination in a multistage funnel. We show that three distinct discriminatory judgment behaviors work through the funnel in three distinct ways to cause underrepresentation. We then endogenize the funnel’s threshold decisions to study the impact and effective design of regulations and incentives. Whereas common wisdom would suggest targeting regulations and incentives at the stage where underrepresentation appears most severe, our analysis suggests a simple recommendation is surprisingly robust irrespective of where the underrepresentation manifests: regulate the top, subsidize the bottom. We identify several distinct asymmetric dynamics that support this prescription: asymmetry in regulation workarounds, asymmetry in regulation win-win opportunity, and asymmetry in subsidy propagation. Managerial implications: Our results help organizations better understand how discriminatory judgment behaviors affect representation and quality at various stages in the funnel. They help guide policymakers on where to target interventions, and their likely consequences on representation and performance outcomes. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative.

Problem definition: Intense competition in the travel industry has increasingly shifted focus toward ancillary services, particularly seat upgrades in airlines and room upgrades in hotels. In response to this trend, several innovative solutions have emerged, among which Nor1’s eStandby Upgrade program stands out by offering discounted, availability-based room upgrades. Revenue management for these upgrades is complex because customers may request multiple upgrades, whereas hotels allocate them based on availability and typically grant at most one upgrade per customer. Methodology/results: Partnering with Oracle, which acquired Nor1, we develop a state-of-the-art framework for prediction, pricing, and allocation to maximize total revenue from eStandby upgrades. We first model customer decision making using a novel copula-based multivariate choice model that captures complex dependencies among multiple decisions made by the same customer. Next, we develop efficient pricing and allocation algorithms to address the challenges associated with offering multiple availability-based upgrades and tracking customer requests. Managerial implications: Validated with real-world data and data-driven numerical experiments, our choice model for upgrade requests and algorithms for pricing and allocation demonstrate significant revenue potential by capturing dependencies across customers’ multiple decisions. History: This paper has been accepted as part of the 2025 Manufacturing & Service Operations Management Practice-Based Research Competition.

Problem definition: Growing consumer awareness of corporate social responsibility (CSR) has motivated firms to invest in CSR initiatives to gain a competitive edge. However, a phenomenon known as greenwashing has emerged, whereby firms exploit observable CSR activities and advertising solely as a marketing tactic. Methodology/results: We develop a game-theoretic model with two types of firms: a socially responsible firm that intrinsically values CSR and a profit-maximizing firm that may engage in action-based or message-based greenwashing by investing in observable CSR activities or CSR advertising, respectively. Consumers are socially minded but face limited information about the firms’ actual CSR type, inferring the type through observable CSR investment and advertising. We examine how the advertising influence and competition intensity affect equilibrium strategies and social welfare. Our findings show that when advertising influence is high, the profit-maximizing firm engages in greenwashing. When it is moderate, the socially responsible firm overinvests in CSR to deter imitation. When it is low, the two firm types naturally separate. Notably, under high transparency, stronger advertising influence can increase both overall CSR activity and social welfare. Moreover, when transparency is low, the profit-maximizing firm strongly prefers greenwashing; in such settings, if competition intensity is low, greenwashing persists, whereas if competition intensity is high, the socially responsible firm responds by overinvesting to prevent greenwashing. Managerial implications: Greenwashing produces both negative and positive outcomes. Although it erodes consumer surplus, it can spur higher CSR activity in a competitive environment, enhancing social welfare. These results suggest that governments and nongovernmental organizations should carefully design CSR transparency measures; their effect on total CSR investment and social welfare can vary depending on the levels of advertising influence and competitive intensity.

Problem definition: To grow their businesses, entrepreneurs often rely on equity funding. This paper focuses on two elements of entrepreneur-investor equity negotiations: the number of potential investors and the contractual complexity surrounding investor protection. Methodology/results: Our approach involves a theoretical model and a series of laboratory experiments that analyze the effects of different bargaining conditions and contractual terms on the equity (ownership) split between entrepreneurs and their investors. We show that the conventional wisdom that entrepreneurs should seek to negotiate with as many investors as possible, although consistent with the theoretical model, is not true in the data. Indeed, negotiating with multiple investors reduces the entrepreneur’s profits under most conditions. We also show that investor downside protections may disadvantage early-stage startups but can be beneficial to later-stage startups. A refinement of belief modeling in multiparty bargaining, as well as a stylized risk allocation framework, reconcile these results with theory predictions. Managerial implications: Our findings provide a decision framework for entrepreneurs to optimize their approach to investors and negotiate favorable contractual terms.

Problem definition: In the aftermath of disasters, governments must make urgent decisions about how to deploy limited resources for recovery, such as restoring roads or siting emergency facilities. However, these actions often need to be taken before the amount and timing of external funding (e.g., federal disaster relief) are known. This mismatch between the need for immediacy and the delay in budget realization poses a fundamental challenge: How can agencies prioritize recovery actions when budgets are uncertain and decisions, once made, are irreversible? Methodology/results: We develop a practical planning framework that produces a fixed priority list of recovery actions, allowing agencies to act immediately and continue execution as funding arrives over time. The framework identifies early actions that perform well across a range of possible funding paths and preserve the value of later investments. The model is cast as a multiscenario mixed-integer linear program with monotonicity constraints, enforcing consistency in prioritization across all scenarios. To compute such a list efficiently, whereas the natural linear program relaxation of this formulation is weak, we introduce a pegging-based heuristic: For each scenario, we solve the optimal 0-1 allocation, fix it, and relax the remaining scenarios into a linear program. Aggregating across all scenarios yields a robust and interpretable prioritization list. Our analysis provides performance guarantees for committing to a single priority list instead of waiting for full budget information. We derive explicit bounds on the expected performance loss of any prioritization strategy relative to a full-information hindsight benchmark. These results show that, under modest assumptions, the loss from committing to a single priority list is provably small. Furthermore, our pegging-based heuristic yields approximation guarantees under mild conditions and performs remarkably well in empirical evaluations. Managerial implications: This framework offers disaster response planners a rigorous and practical tool for making irreversible decisions under budget uncertainty. The main insight is that the best early action is not always the one that gives the largest immediate gain, but the one that positions the system best when additional funding becomes available. Through experiments on synthetic data and a real-world road network in Manhattan, we demonstrate that the proposed prioritization strategy consistently outperforms conventional heuristics and closely approximates the performance of an ideal benchmark with full budget information. The results highlight the potential of our approach to support timely, resilient, and high-quality disaster recovery planning under uncertain funding conditions.

The journal is pleased to publish the abstracts of the four finalists of the 2025 Manufacturing and Service Operations Management Society’s student paper competition. The 2025 prize committee was chaired by Georgina Hall (INSEAD), Jonas Oddur Jonasson (Massachusetts Institute of Technology), and Vasiliki Kostami (HEC Paris). The judges were Adem Orsdemir, Agni Orfanoudaki, Alex Jacquillat, Alp Akcay, Alp Sungu, Alper Nakkas, Amrita Kundu, Antoine Desir, Antoine Feylessoufi, Anton Braverman, Anton Ovchinnikov, Anyan Qi, Arian Aflaki, Arthur Delarue, Arzum Akkas, Ashish Kabra, Bahar Taskesen, Benjamin Legros, Bilal Gokpinar, Bin Hu, Bing Bai, Bob Batt, Bora Keskin, Brent Moritz, Christopher Chen, Cuihong Li, Daniel Chen, Daniel Freund, Daniela Hurtado-Lange, Dawson Kaaua, Dimitrios Andritsos, Divya Singhvi, Ella Segev, Elodie Adida, Ersin Korpeoglu, Esmaeil Keyvanshokooh, Faidra Monachou, Fanyin Zheng, George Chen, Gian-Gabriel Garcia, Gonzalo Romero, Guangwen Crystal Kong, Guoming Lai, Hamsa Bastani, Hannah Li, Heikki Peura, Hessam Bavafa, Holly Wiberg, Hongyao Ma, Ho-Yin Mak, Huseyin Gurkan, Ilgin Dogan, Ioannis Stamatopoulos, Itir Karaesmen, Ivana Ljubic, Jackie Baek, James Siderius, Jean Pauphilet, Jiahua Wu, Jiankun Sun, Jiaru Bai, Jiayi Yu, Jing Dong, Jing Wu, Jinglong Zhao, John Silberholz, Jose Guajardo, Julia Yan, Julien Grand-Clement, Kenan Arifoglu, Kostas Bimpikis, Kris Ferreira, Leonard Boussioux, Laura Wagner, Lennart Baardman, Levi DeValve, Lin Fan, Lina Song, Luyi Gui, Luyi Yang, Mazhar Arikan, Mehmet Ayvaci, Melvin Drent, Mengzhenyu Zhang, Miao Bai, Michael Lingzhi Li, Michelle Kinch, Mihalis Markakis, Mika Sumida, Ming Hu, Mohsen Bayati, Mostafa Rezaei, Nan Liu, Nan Yang, Neha Sharma, Nektarios Oraiopoulos, Nikos Trichakis, Nil Karacaoglu, Nitin Bakshi, Nur Sunar, Olga Perdikaki, Omar Mouchtaki, Omer Karaduman, Onesun Steve Yoo, Ovunc Yilmaz, Ozge Sahin, Panos Markou, Philip Zhang, Philipp Cornelius, Philippe Blaettchen, Pnina Feldman, Qiuping Yu, Raghav Singal, Rim Hariss, Rouba Ibrahim, Ruslan Momot, Ruth Beer, Ruxian Wang, Ryan Cory-Wright, Saed Alizamir, Sajjad Najafi, Sanjith Gopalakrishnan, Santiago Gallino, Sarah Yini Gao, Scott Rodilitz, Sebastien Martin, Flore Sentenac, Serdar Simsek, Serhan Ziya, Seyed Emadi, Sheng Liu, Shouqiang Wang, Siddharth Singh, Sidika Tunc Candogan, Simone Marinesi, So Yeon Chun, Somya Singhvi, Song-Hee Kim, Stefanus Jasin, Stephen Leider, Suresh Muthulingam, Tamer Boyaci, Tian Chan, Tianyi Peng, Tim Kraft, Tolga Dizdarer, Tom Tan, Vahideh Manshadi, Velibor Misic, Wanning Chen, Will Ma, Woonam Hwang, X. Y. Han, Xiaojia Guo, Xiaoshuai Fan, Xiaoyang Long, Xinyu Liang, Yangfang Helen Zhou, Yao Cui, Yasemin Limon, Yenting Lin, Yiangos Papanastasiou, Yi-Chun Akchen, Ying-Ju Chen, Yixin Iris Wang, Yuan-Mao Kao, Yue Hu, Yuexing Li, Yuqian Xu, Zhaohui (Zoey) Jiang, Zhaowei She, Zhe Liu, Zhen Lian, and Zumbul Atan.