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We study the potential negative impact of imbalanced compensation schemes on firm performance. We analyze data from a radiology workflow platform that connects off‐site radiologists with hospitals. These radiologists select tasks from a common pool, while service level is defined by priority‐specific turnaround time targets. However, imbalances between pay and workload of different tasks could result in higher priority tasks with low pay‐to‐workload ratio receiving poorer service. We investigate this hypothesis, showing turnaround time is decreasing in pay‐to‐workload for lower priority tasks, whereas it is increasing in workload for high‐priority tasks. Crucially, we find evidence of an externality effect: Having many economically attractive tasks with low priority can lead to longer turnaround times for higher priority tasks, increasing their likelihood of delay, thus partially defeating the purpose of the priority classes. Highlights: It has been studied that salaried hospital radiologists working on emergency images tend to sequence images to minimize their work queue. However, the advent of new radiology workflow platforms that aggregate images from multiple hospitals, where independent radiologists choose which emergency and nonemergency images to read, creates novel operational challenges that are becoming pervasive in the sharing economy.We find that independent radiologists behave as previously established when reading emergency images. However, for nonemergency images, they prefer to process images that provide a higher bang‐for‐the‐buck, meaning they lead to a higher monetary payout for the time spent on processing.Crucially, we also find that this preference exerts a negative externality on nonemergency images with high‐operational priority (for example, that can generate bed‐blocking), resulting in longer turnaround times and a higher frequency of delay.The delayed processing exerts a bed‐blocking and patient dissatisfaction cost on client hospitals. We suggest radiology workflow platforms use their information systems capabilities to hide nonemergency, high bang‐for‐the‐buck images from radiologists until the higher priority studies are cleared out. [ABSTRACT FROM AUTHOR]

We study the problem when a firm sets prices for products based on the transaction data, that is, which product past customers chose from an assortment and what were the historical prices that they observed. Our approach does not impose a model on the distribution of the customers' valuations and only assumes, instead, that purchase choices satisfy incentive-compatible constraints. The uncertainty set of potential valuations of each past customer can then be encoded as a polyhedral set, and our approach maximizes the worst case revenue, assuming that new customers' valuations are drawn from the empirical distribution implied by the collection of such polyhedra. We study the single-product case analytically and relate it to the traditional model-based approach. Then, we show that the optimal prices in the general case can be approximated at any arbitrary precision by solving a compact mixed-integer linear program. We further design three approximation strategies that are of low computational complexity and interpretable. In particular, the cutoff pricing heuristic has a competent provable performance guarantee. Comprehensive numerical studies based on synthetic and real data suggest that our pricing approach is uniquely beneficial when the historical data has a limited size or is susceptible to model misspecification. This paper was accepted by Omar Besbes, revenue management and market analytics. Funding: This work was supported by the Rotman School of Management (TD-MDAL Research Grant) and the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757, RGPIN-2020-04038, RGPIN-2020-06054, RGPIN-2021-04295]. Supplemental Material: The e-companion and data files are available at https://doi.org/10.1287/mnsc.2022.4651. [ABSTRACT FROM AUTHOR]