Knowledge that Transforms

Problem definition: A growing number of firms are encouraging consumers to participate in “bring-your-own-container” (BYOC) behavior in which consumers bring their own reusable packaging to purchase and consume products, thus reducing single-use packaging waste. In this paper, we study the environmental implications of a firm’s BYOC implementation when considering its disposable packaging choice and communication strategy. Methodology/results: We build a stylized model to study a firm’s joint decisions on BYOC, disposable packaging choice, and communication and their implications on the environment. Our main results follow. First, allowing BYOC reduces the firm’s incentive to make fraudulent green claims about its disposable product packaging; however, BYOC implementation may harm the overall environment while improving the firm’s profit, thereby creating a new form of greenwashing. Second, the adoption of third-party certification for green disposable packaging is an effective remedy to mitigate the negative environmental impact of BYOC. In addition, the environmental implications of adopting third-party certification (either voluntarily or because of government mandates) depend on the relationship between the environmental qualities of green disposable packaging and reusable packaging. Whereas it always benefits the environment when the firm’s green disposable packaging has better environmental performance, adopting certification may negatively impact the environment if consumers’ reusable packaging is greener. Furthermore, we find numerically that offering a price discount for BYOC may encourage the firm to adopt certification because of increased profitability, thereby leading to the aforementioned environmental implications. Managerial implications: We offer operational insights on how firms should make joint decisions on BYOC, disposable packaging choice, and communication. We also generate insights on how governments should regulate firms’ green claims when firms start to allow BYOC. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0605 .

Problem definition: Online retailing platforms are increasingly relying on personalized recommender systems to help guide consumer choice. An important but understudied question in such settings is how many products to include in a recommendation set. In this work, we study how the number of recommended products influences consumers’ search and purchase behavior in an online personalized recommender system within a retargeting setting. Methodology/results: Via a field experiment involving 1.6 million consumers on an online retailing platform, we causally demonstrate that consumers’ likelihood of purchasing any product from the recommendation set first increases then decreases as the number of recommended products increases. Importantly, as much as 64% of the decrease in purchase probability (i.e., the choice overload effect) can be attributed to a decrease in consumers’ likelihood of starting a search (i.e., clicking on any recommended product). We discuss the possible behavioral mechanisms driving these results and analyze how these effects could be heterogeneous across different product categories, price ranges, and timing. Managerial implications: This work presents real-world experimental evidence for the choice overload effect in online retailing platforms, highlights the important role of consumer search behavior in driving this effect, and sheds light on when and how limiting the number of options in a recommender system may be beneficial to online retailers. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0659 .

Problem definition: In this paper, we compare laissez-faire and mandatory authorization policy regimes for third-party remanufacturing. Under a laissez-faire policy, an independent remanufacturer (IR) chooses whether to get the original equipment manufacturer (OEM) authorization. Under a mandatory authorization policy, the IR is required to get OEM authorization and to pay the OEM a fee for every item remanufactured. Motivated by China’s regulatory journey that first mandated authorized remanufacturing and then moved to a laissez-faire policy, our goal is to understand which policy is better from the perspectives of different stakeholders. Methodology/results: We use a game-theoretic approach and consider a supply chain consisting of a supplier, an OEM, and an IR under the two policy regimes. Conventional wisdom suggests that the IR would be better off under the laissez-faire policy, but the OEM and the supplier would be better off under the mandatory authorization policy. However, we show that this conventional wisdom may not hold. For products with a low remanufacturing cost, all firms benefit from the mandatory authorization policy, whereas for products with a moderately high remanufacturing cost, all firms are better off under the laissez-faire policy. Further, mandatory authorization may outperform the laissez-faire policy in both economic and environmental dimensions. Managerial implications: Our findings reveal that seemingly advantageous policy regimes may backfire for firms. Therefore, before supporting such policies, the firms need to assess the strategic reactions of other firms and the potential impacts on their profits. Furthermore, a mandatory authorization policy can be beneficial in fostering the development of the remanufacturing sector for products with low remanufacturing costs. Nevertheless, it may also lead to an increase in the total environmental impact. Funding: The work of M. Jin was supported by the National Natural Science Foundation of China [Grants 72071020 and 72471038]. The work of Y. Zhou was supported by the National Natural Science Foundation of China [Grants 71971033 and 72371040] and the Fundamental Research Funds for the Central Universities [Grant 2024CDJSKPT14]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0128 .

Problem definition: In response to the increased use of telehealth to replace traditional office visits with a physician, several U.S. states have recently adopted telehealth pay-parity policies. Such policies state that payers must reimburse healthcare providers for telehealth services at the same rate that would apply if those services had been provided in a traditional office setting. But health policy researchers have pointed out that telehealth may not be as effective as a traditional office visit for acute care. Specifically, telehealth is associated with increased probability of a subsequent office visit (a “duplicate visit”). We examine whether telehealth pay-parity policies are effective at improving access to acute care, and under what conditions. Methodology/results: We use a three-stage game-theoretic model to study the impact of telehealth pay parity. In the first stage, the payer sets a reimbursement policy for telehealth visits. In the second stage, a healthcare provider commits a portion of its capacity to telehealth, and in the third stage, patients arrive and choose between telehealth and office visits according to an equilibrium queueing network. We find structural results for the equilibria and characterize the equilibria in closed-form. When the chance of a duplicate visit is moderate (neither too high nor too low), pay parity leads providers to allocate too much capacity to telehealth, resulting in lower overall patient access than could be otherwise achieved. We characterize a reimbursement level that avoids this misalignment and maximizes patient access, which we show is less than parity. Managerial implications: The literature shows that patients receiving acute care via telehealth may be more likely to require a duplicate, in-person visit to resolve their health concern. In the fee-for-service environment that is common in the United States for acute care, duplicate visits resulting from telehealth lead to an incentive alignment problem because they generate extra work and provider revenue, without any corresponding increase in patient access. Legislating pay parity for telehealth can lead to providers committing more capacity to telehealth, which may not always be good. However, there is good news in that all parties (payers, providers, and patients) would be better off if duplicate visits could be decreased. Policy makers should understand these implications before enacting policies that affect reimbursements for telehealth. Funding: 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 is available at https://doi.org/10.1287/msom.2022.0345 .

Problem definition: We study a feature-based pricing problem with demand censoring in an offline, data-driven setting. In this problem, a firm is endowed with a finite amount of inventory and faces a random demand that is dependent on the offered price and the features (from products, customers, or both). Any unsatisfied demand that exceeds the inventory level is lost and unobservable. The firm does not know the demand function but has access to an offline data set consisting of quadruplets of historical features, inventory, price, and potentially censored sales quantity. Our objective is to use the offline data set to find the optimal feature-based pricing rule so as to maximize the expected profit. Methodology/results: Through the lens of causal inference, we propose a novel data-driven algorithm that is motivated by survival analysis and doubly robust estimation. We derive a finite sample regret bound to justify the proposed offline learning algorithm and prove its robustness. Numerical experiments demonstrate the robust performance of our proposed algorithm in accurately estimating optimal prices on both training and testing data. Managerial implications: The work provides practitioners with an innovative modeling and algorithmic framework for the feature-based pricing problem with demand censoring through the lens of causal inference. Our numerical experiments underscore the value of considering demand censoring in the context of feature-based pricing. Funding: The research of E. Fang is partially supported by the National Science Foundation [Grants NSF DMS-2346292, NSF DMS-2434666] and the Whitehead Scholarship. The research of C. Shi is partially supported by the Amazon Research Award. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.1061 .