Knowledge that Transforms

To meet sales targets with limited resources, business-to-business service firms must prioritize promising opportunities within large pipelines. Yet, both theory and practice indicate that such decisions often rely on intuition or ad hoc rules, resulting in suboptimal sales and operations planning. Drawing on the relationship management and organizational buying literature, we develop a theory-informed sales-operations framework that links buyer typology (e.g., new vs. rebid) and opportunity characteristics (e.g., size and relationship strength) to the firm’s bid and win decisions. Using archival data from a global on-site services provider encompassing 4,574 opportunities across 23 countries (2010–2021), we document a persistent tradeoff: while low-risk, relationship-based opportunities yield higher win probabilities, they are insufficient to achieve regional sales goals. We address this challenge through an ensemble machine-learning model that predicts win likelihood and a combinatorial optimization model that allocates bidding capacity strategically. The integrated framework improves predictive accuracy by 11% and could have increased realized sales by 21% while bidding on 38% fewer opportunities. Extensions incorporating stochastic programming and a Heckman-style two-stage correction enhance the framework’s robustness to uncertainty and data selection bias, providing managers with a rigorous, data-driven approach to opportunity management.

The gaming industry has emerged as a critical force in the digital content economy, yet managing user behavior to drive sustained activity and monetization remains a complex operational challenge. In this study, we propose a two-layer hidden Markov model to capture users’ gameplay and payment behaviors by constructing a play-then-pay chain that links user engagement to subsequent purchase intention dynamics. Drawing on a real-world dataset, we uncover three levels of engagement states measuring the degree of stickiness with the focal game, as well as two levels of purchase intention states describing one’s willingness to pay. We find that a higher engagement state is associated with a volatile transition pattern and leads to a higher upward transition tendency in purchase intention, while low and medium engagement states tend to maintain a low purchase intention state. We also examine several factors that affect the transitions of these psychological states. The analysis reveals that user activity in same-type games enhances upward transitions only among users in the medium engagement state, without affecting users in the high engagement state, and exhibits no significant effect on purchase intentions. In contrast, user activity in different types of games has a negative effect on users in both low and high engagement states. Our state-dependent outcomes suggest that the managers’ strategies are more effective when targeted toward users with low engagement and purchase intention states. Further experimental analysis supports the effectiveness of the proposed play-then-pay chain for predicting users’ behaviors. Our policy simulation demonstrates that traffic subsidization effectively redirects user attention to the focal game, with interventions targeting different-type games yielding greater improvements in propensities for both gameplay and payment behavior compared to same-type games. Our work provides managerial implications for platform managers.

In this article, we consider a multi-stage assortment optimization problem with multinomial logit (MNL) choice modeling under resource knapsack constraints. Given the current resource inventory levels, the retailer makes an assortment decision at each period, and the goal of the retailer is to maximize the total profit from purchases. With the exact optimal dynamic assortment solution being computationally intractable, a practical strategy is to adopt the re-solving technique that periodically re-optimizes deterministic linear programs (LPs) arising from fluid approximation. However, the fractional structure of MNL makes the fluid approximation in assortment optimization non-linear, which brings new technical challenges. To address this challenge, we propose a new epoch-based re-solving algorithm that effectively transforms the denominator of the objective into the constraint, so that the re-solving technique is applied to a linear program with additional slack variables amenable to practical computations and theoretical analysis. Theoretically, we prove that the regret (i.e., the gap between the re-solving policy and the optimal objective of the fluid approximation) scales logarithmically with the length of time horizon and resource capacities.

Online healthcare consultation services (OHCS) hold immense potential to transform the healthcare industry. This study seeks to investigate the impact of doctors’ participation in OHCS on offline outpatient and inpatient costs, addressing a critical yet underexplored area in the healthcare IT literature. Utilizing an exhaustive dataset from a high-level comprehensive hospital in China over a period of 42 months, we find that doctors’ OHCS participation increases outpatient costs by 63.2% (¥268.510) and inpatient costs by 22.2% (¥2365.092). These results remain robust after addressing endogeneity concerns through instrumental variable (IV) approaches and propensity score matching (PSM). Notably, mechanism tests reveal that OHCS participation enhances doctors’ service volume expansion, cost-upgrading within services and patient mix changes, leading to induced demand and higher offline medical costs. Moreover, these effects are amplified for doctors with higher professional status. However, the COVID-19 pandemic and higher department crowding levels tend to mitigate these cost increases. This study makes several significant theoretical contributions. First, it expands the healthcare IT literature by uncovering the “dark side” of OHCS participation, showing that it raises medical costs by altering supply–demand dynamics, contrary to the cost-saving potential often associated with traditional healthcare IT. Second, it advances research on OHCS by identifying the mechanisms—service volume expansion, cost-upgrading within services and patient mix changes—through which OHCS participation influences offline costs. Third, it explores the heterogeneous effects of physician, environmental, and departmental characteristics on medical costs. Practically, our findings suggest that hospitals and policymakers should promote widespread OHCS participation to balance supply–demand dynamics, advance treatment standardization and differentiated payment models, establish a multi-dimensional physician performance evaluation system, and leverage AI technology to improve doctor–patient matching and resource scheduling—enabling the realization of online healthcare's potential without inadvertently driving up medical costs. This study highlights the nuanced implications of OHCS on offline healthcare costs, offering insights for improving its implementation in healthcare systems.

Following a manufacturer's large product recall, its supplier's shareholders may perceive uncertain future demand for the supplier's products and react punitively, causing a drop in the supplier's stock return—that is, a contagion (or negative spillover). Moreover, shareholders’ information asymmetry may cause them to “screen” the supplier's information cues to determine the supplier's extent of demand uncertainty. The ideal screen is the supplier's proportion of sales revenue from the recalling manufacturer. However, not all suppliers disclose this information. Therefore, we propose that shareholders use a two-stage screening. The first screen is whether the supplier demonstrates transparency by voluntarily disclosing information about its customer portfolio. The second screen—available only to the subset of suppliers that disclose customer information—is the supplier's sales revenue from the recalling manufacturer. We used a sample of 896 U.S. public manufacturer–supplier dyads impacted by 27 large manufacturer recalls. An event study followed by cross-sectional regressions provides evidence of contagion. In addition, it reveals that the supplier's voluntary disclosure of customer information mitigates contagion, whereas revenue dependence aggravates it. Contextual (i.e., recall) variables also impact contagion. Our research study contributes to the supply-chain contagion literature, screening theory, and customer information disclosure literature. The findings inform supplier firm managers that their prior customer-related disclosures and the contextual variables can moderate contagion.

We investigate the burgeoning trend of prosumers, who have transformed from traditional consumers into active renewable energy producers. While prosumers help reduce greenhouse gas emissions and reliance on fossil fuels, they often remain connected to the grid as a backup. This practice requires that utility companies reserve capacity, and conventional consumers share these associated costs. We develop a stylized model to comprehensively assess the impact of prosumers. Our findings demonstrate that, although prosumers contribute to diminishing nonrenewable energy consumption and offer potential cost savings to utility firms, they simultaneously introduce negative externalities. Specifically, they inject uncertainty into the grid, resulting in higher electricity prices and increased utility bills for regular consumers, even when fixed costs incurred by utility firms are not considered. As the intermittency of prosumer energy generation increases, the socially optimal proportion decreases while the self-selected equilibrium proportion of prosumers increases. Furthermore, we examine the potential implications of a conventional linear incentive scheme for prosumers, exemplified by the 2023 U.S. federal tax credit for solar panel installation costs. We find that such schemes may exacerbate social disparity. To address this issue, we propose a reverse-linear subsidization approach, which paradoxically requires less funding to achieve equivalent prosumer adoption rates and results in smaller social disparity.

About 27% of patients with diabetes also suffer from depression, and the presence of co-morbid depression could increase the cost of care for diabetes by up to 100%. Several randomized clinical trials have demonstrated that physical and mental health are more likely to improve for diabetes patients suffering from depression when regular treatment for depression is provided in a primary care setting (called Collaborative Care). However, Collaborative Care requires additional resource utilization costs and a separate reimbursement model. When managing Collaborative Care, clinics must balance patient health outcomes with the program’s financial sustainability. Important operational levers in Collaborative Care are allocating care managers’ time to patients based on their requirements and the care managers’ staffing level. This staffing and workload allocation influences the revenue, costs, and patient health outcomes. We present a novel Markov Dynamic Programing model that, unlike existing approaches, jointly optimizes both staffing levels and treatment policies for Collaborative Care programs and quantifies the costs and benefits of collaborative care. Mathematically, we model Collaborative Care management at the clinical level as an infinite-horizon Markov Dynamic Program. The objective is a weighted sum of total patient quality-adjusted life years (QALYs) and the clinic profits. The model incorporates insurance payment, resource utilization costs, and disease progression of co-morbid diabetes and depression. We derive structural properties for the joint optimization of the staffing level and allocating care managers’ time to different patient categories. Using these structural properties, we develop a practical and easy-to-implement policy for staffing level and care managers’ time allocation that performs close to the optimal solution. We calibrate the model with data from a large academic medical center and show that our solutions can improve total QALYs and clinic profits compared to current practices. Our analysis also reveals key insights into payment models’ effects on Collaborative Care. Profit under the fixed-fee model responds nonmonotonically to payment rate increases, highlighting complex financial dynamics. Fixed-fee models show a threshold behavior, with high-intensity treatments becoming optimal only above certain payment rates. This threshold varies based on the profit-QALY weight balance, and this threshold is lower under joint-optimization than treatment-only optimization.

Stocks of some food products, such as whiskey, cheese, or port wine, ameliorate during storage, facilitating product differentiation according to age. This induces a trade-off between immediate revenues and further maturation. Inventory management decisions include purchasing volumes of agricultural produce and production volumes for age-differentiated products. Because products can be blended from stocks of different ages, issuance decisions offer operational flexibility. However, whereas some industries (port wine, sherry) only request that the product labels refer to the average age of issued stocks, others (whiskey, rum) have stricter blending regulations, requiring that the product labels represent the minimum age of all components. Further, producers must deal with multiple uncertainties. Purchase prices of agricultural commodities depend on volatile climate-dependent harvest seasons, stocks decay during maturation, and sales market conditions fluctuate. We solve this inventory management problem using a deep reinforcement learning algorithm with three key innovations: (i) A novel actor pipeline that decomposes the action space and flexibly partitions decision dimensions between a neural network and a lookahead optimization model, (ii) an algorithm explicitly maximizing average rewards, and (iii) reward-handling techniques that exploit structural problem insights. Our approach yields near-optimal policies that consistently outperform benchmark heuristics. Beyond the algorithmic contributions, our results offer new managerial insights into the value of blending under uncertainty. Minimum-age blending substantially enhances the profits of firms as compared to no blending because companies can adjust their purchasing policy in response to price fluctuations. The more flexible average-age regime further improves profits by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mn>8.7</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> on average, suggesting that whiskey and rum regulators may wish to reconsider their strict blending rules. We mine black-box policies from deep reinforcement learning using supervised machine learning and Shapley values to analyze near-optimal decision drivers. Exploiting the value of blending requires producers to install sufficient processing capacity, especially when dealing with large variations in harvest seasons. Additionally, blending entails increased planning complexity because the inventory management decisions are driven by a large number of factors.

Media plays a crucial role in shaping public perception of firms, significantly impacting their operations and performance. Firms often attempt to influence media channels to showcase their climate action initiatives, aiming to enhance their public image, reputation, and stakeholder trust. While firms cannot directly control their media coverage, intentional or unintentional dissemination of their climate action efforts can thrust them into the media spotlight. The implications of this media spotlight remain uncertain. While firms may anticipate positive public relations benefits from such exposure, it may also raise environmental expectations for firms and risk highlighting less favorable aspects of their environmental practices. Despite previous research on the impact of media exposure on firms’ financial outcomes, a notable gap exists in understanding how the media spotlight on firms’ climate actions affects their operational and financial performance. A clear hurdle is the lack of a systematic and objective measurement of such a spotlight. Our study addresses this research gap by developing a machine learning-based framework to derive a climate action vocabulary as a novel information artifact. The vocabulary is subsequently used to measure the intensity of media attention on a firm's climate actions. Our research reveals that an increased media spotlight on climate actions, regardless of their sentiment, has an adverse effect on firms’ financial performance, primarily due to the rise in operational costs. Furthermore, we find that the impact of media spotlight is heterogeneous. Low-polluting firms experience negative financial consequences as the downside of the heightened media spotlight. In contrast, high-polluting firms and those operating in polluting industries may witness an overall positive financial impact. We also find that firms’ market orientation (business-to-business vs. business-to-consumer), durability classification, index constituency (i.e., S&P 500), and greenwashing status significantly moderate the relationship. Our research highlights key considerations for corporate leaders with respect to the drawbacks for low-polluting firms in seeking media attention for their climate actions. Given that the media captures society's limited attention, our research suggests that firms should refrain from promoting superficial narratives about climate change that distract society from more impactful sustainability conversations.

Motivated by algorithmic targeting and data management, we explore a scenario where the seller holds an advantage over consumers regarding match-related information about products. The seller optimizes a product line consisting of two vertically differentiated products alongside an opaque product resulting from their mixture, strategically recommending these products to potential consumers. We model algorithmic targeting using an information design framework, and our investigation revolves around understanding how algorithmic targeting shapes consumer purchasing behaviors and influences market equilibrium. Furthermore, we explore the potential orchestration between algorithmic targeting and opaque selling, facilitated by product-line design. These two closely related instruments coincide in ex-ante manipulating information while differing in their targeting objects. Interestingly, only when the basic products exhibit intermediate differentiation does the seller use both instruments. This is because, when the disparity between the two primary products is extreme (either too large or too small), algorithmic targeting makes opaque selling ineffective at increasing profits. However, when these differences are moderate, the two strategies can complement each other. Opaque selling enhances profitability by introducing intermediate product variety, enabling more nuanced market segmentation, while algorithmic targeting is more flexible in promoting the willingness-to-pay of a wider range of consumers. Furthermore, when conducting welfare analysis, the adoption of algorithmic targeting is found sometimes to reduce consumer surplus but can enhance overall social welfare, highlighting the need for careful regulatory oversight in this domain.