This paper studies a long-term power purchase agreement (PPA) between a firm and a new renewable energy generator.

Customer review manipulation is a common strategy employed by sellers of online marketplaces to combat competitors. The impact of this deceptive behavior on the competitive pricing of online marketplaces is intricate. First, buying fake reviews incurs additional costs and alters customer demand in competitive settings by misrepresenting product information. Second, the pricing is determined through internal competitions, where sellers compete with each other within an online marketplace. This is because the winner's price is the default price displayed to customers, representing the price of the online marketplace. Meanwhile, online marketplaces also effectively manage prices in order to stay competitive in external competition against multi-channel retailers, further complicating this problem. To unravel this influence mechanism, we build instrumented econometric models and develop a game-theoretic model to empirically and theoretically analyze this influence mechanism in the context of internal and external competitions, respectively.

This paper uses transaction-level import data at the shipment level to examine how multinational companies importing to the US have restructured their supply chains during the COVID-19 pandemic. We find that companies sourced from fewer locations, reduced the share of imports from China, and increased the share of imports from other Asian countries, such as India and Vietnam, and North American countries, such as Canada and Mexico. For managers, our results imply that a one-size-fits-all mentality regarding supply chain disruption responses is not appropriate, and companies’ disruption-response strategies need to be tailored to individual supply chains’ circumstances.

Customer reviews are essential to online marketplaces. However, reviews typically vary; ratings of a product or service are rarely the same. In many service marketplaces, including the ones for solar panel installations, supply-side participants are active. That is, a seller must make a proposal to serve each customer. In such marketplaces, it is not clear how (or if) the dispersion in customer reviews affects the seller activity level and number of matches in the marketplace. Our paper examines this by considering both ratings and text reviews.

This paper presents the development, validation, and implementation of a data-driven optimization model designed to dynamically plan the assignment of anesthesiologists across multiple hospital locations within a large multi-specialty healthcare system. We formulate the problem as a multi-stage robust mixed-integer program incorporating on-call flexibility to address demand uncertainty. The optimized dynamic staffing plan has been successfully implemented in the University of Pittsburgh Medical Center healthcare system, leading to estimated annual cost savings of 12\% compared to current practice, or about \$800,000 annually.

The rapid growth in the adoption of mobile payments has already begun to reshape bank payment practices. Utilizing a unique data set from a leading bank in Asia that records credit card transactions of its customers before and after the launch of Alipay mobile payment, the largest mobile payment platform in the world, this study aims to understand the impact of mobile payment adoption on bank customer credit card activities and the change of this impact after the mobile payment expansion.

It is common wisdom that practice makes perfect. And, in fact, we find evidence that when given a choice between practicing a task and reflecting on their previously accumulated practice, most people opt for the former. We argue in this paper that this preference is misinformed. Using evidence gathered in ten experimental studies (N = 4,340) conducted across different environments, geographies, and populations, we provide a rich understanding of the conditions under which the marginal benefit of reflecting on previously accumulated experience is superior to the marginal benefit of accumulating additional experience.

About 27% of diabetics 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). An important operational lever in managing Collaborative Care is the allocation of the care manager's time to enrolled patients based on their requirements, which in turn influences the revenue, costs, and patient health outcomes. We present a mathematical modeling approach that determines the optimal allocation of care manager's time and quantifies the costs and benefits of Collaborative Care.

There is a growing interest in the industry around 3D printing. A related phenomenon is personal fabrication (PF) in which a firm sells products' design and lets the customers personalize and manufacture the product using 3D printing services. In this paper, we characterize the market and operational conditions that make PF an attractive operational strategy.

Food ordering and delivery platforms generate online demand for restaurants and deliver food to customers. In return, restaurants pay platforms a commission, typically a percentage of the order amount. Platforms offer partner restaurants the choice of a range of commission rates, rewarding higher commission payments with featured display slots and discounted delivery fees, both of which stimulate demand. Unfortunately, the current environment is grim: platforms scurry to cover delivery costs while restaurants gripe about excessive commissions.

Ignoring consideration sets in modeling customer purchase decisions may lead to biased estimation of customer preferences, yet consideration sets are difficult to infer in brick-and-mortar contexts. We show that the challenge of estimating consideration set models in brick-and-mortar contexts can partially be overcome with an emerging source of data: “heatmap data” collected using in-store sensors.