Knowledge that Transforms

Student evaluations of teaching are widely used in higher education operations to assess instructional quality and inform personnel decisions, yet they remain vulnerable to systematic bias. This study examines whether the rapid transition to online education during COVID-19 changed perceived teaching effectiveness across instructors from different race-gender groups. Using 598,252 student evaluations from the top 150 U.S. universities, we estimate a difference-in-differences model with propensity score weighting and supplement the analysis with large-language-model-based content analysis of student comments. We find that the shift to online teaching disproportionately penalized non-white male instructors: their ratings declined by 0.69 points on a 5-point scale, a reduction 16.2% larger than that for white male instructors. The effect is stronger in less racially diverse institutions and among reviews reflecting more hedonic expectations of education, and it remains robust across a wide range of alternative specifications and subsample analyses. Comment-based evidence is consistent with the interpretation that online instruction increased psychological distance and reduced interpersonal cues, thereby increasing reliance on stereotype-based judgments in student evaluations. These findings show that digital transformation in teaching can unintentionally worsen inequities in performance evaluation. They highlight the need for higher education institutions to redesign teaching evaluation systems so that they rely less on bias-prone subjective ratings and more on structured, multidimensional indicators of teaching effectiveness.

Firms are exposed to varying levels of supply chain risk and engage in efforts to resolve such risk. This paper examines how disclosures of supply chain risk and resolution during earnings calls affect firms’ stock returns. Using natural language processing, we develop measures of supply chain risk and resolution from quarterly earnings call transcripts for a total of 129,981 firm-quarter observations between 2008 and 2019. We find that higher levels of supply chain risk are associated with lower stock returns around earnings calls, while disclosures of supply chain risk resolution attenuate these negative effects. In particular, stock returns of firms in the highest supply chain risk quintile are 1.07% lower compared to the stock returns of firms in the lowest quintile, and regression analyses indicate that a one-standard-deviation increase in supply chain risk is associated with a 0.56% decline in stock returns. The stock returns of firms in the highest supply chain risk resolution quintile are 0.12% higher compared to the stock returns of firms in the lowest quintile. A one-standard-deviation increase in resolution increases stock returns by 0.08%, and to 0.29% for the subsample of observations where the resolution measure is positive. Exploratory analyses indicate that the effect of supply chain risk on stock returns is significantly greater for smaller firms than for larger firms. In addition, when there is evidence that larger firms’ resolution-related statements are mere rhetoric, the effect of resolution on stock returns is diminished.

The increasing prevalence of patent infringement litigation in recent decades has imposed significant costs on firms. This paper empirically examines how this legal risk influences a key operational decision: vertical integration. Drawing on the real options theory (ROT), we argue that managers reduce the extent of vertical integration to preserve flexibility and make operational adjustments in anticipation of unfavorable outcomes from patent lawsuits. Using a dataset of public firms and a text-based measure of vertical integration, our findings show that firms facing higher patent litigation risk are less vertically integrated. At the same time, these firms maintain broader supplier networks and product market scopes, consistent with a preference for leveraging market mechanisms to reduce the costs of exercising switch and exit options. The negative relationship between litigation risk and vertical integration is particularly pronounced in durable goods sectors and declining industries, where the ability to divest or halt operations is highly valued. Additionally, we find that firms diversify their supplier base and product and technology portfolios after facing patent lawsuits. However, more vertically integrated firms are slower to make these adjustments, suggesting that they encounter greater friction in exercising real options. Overall, our study highlights the significant role that intellectual property disputes play in shaping corporate vertical integration strategies.

Educational services contribute $315.65 billion to the U.S. GDP, with online education representing the fastest-growing segment. This paper examines how operational and market factors influence universities’ decisions to introduce online degree programs. We study the strategic introduction of such programs in a vertically differentiated market, where universities differ in online program rankings (quality) and compete for a diverse student population with varying willingness to pay for perceived quality. Our analysis focuses on a simultaneous market entry scenario, yielding robust insights that also hold under alternative settings—such as when universities are equally ranked or differ in their variable costs of technology. We also examine two additional contexts: (1) A mixed competition setting in which one university operates independently while the other is guided by a social planner, and (2) an incumbent—entrant setting in which a university considers launching an online program when its competitor has already entered the market. Our findings reveal that symmetric market entry—where both universities introduce online programs—is more likely when technology integration costs exceed a certain threshold and student valuation heterogeneity is significant. In contrast, when these costs fall below the threshold, asymmetric equilibria arise in which only one university introduces an online program. When a social planner regulates tuition at the lower-ranked university, it faces tighter constraints on entering the market. However, when the higher-ranked university is subject to tuition regulation, broader market coverage and improved social welfare outcomes are achieved. Additionally, lower-ranked universities can strategically enter by targeting lower-end segments through moderate technology investments and competitive pricing. Yet, the entry of a higher-ranked rival can exert downward pressure on tuition fees for both institutions, promoting a more accessible educational environment. These insights offer strategic guidance for universities navigating quality-based competition and provide policy implications for balancing competitive dynamics with educational equity through regulatory interventions.

Online product reviews play a pivotal role in empowering consumers by reducing uncertainty about product attributes, a phenomenon widely studied from the demand perspective. However, the supply-side implications remain less understood—particularly how firms can leverage consumer reviews to develop an integrated pricing–quality strategy. To address this gap, we examine a firm’s dynamic pricing and product-quality decisions over two selling periods, where consumer reviews play a central role in shaping the market’s perception of a new experience good. Both the firm and its consumers are initially uncertain about the product’s perceived (market-based) quality and rely on early reviews to update their beliefs. Our analysis identifies two critical review metrics— volume and valence —as jointly shaping the firm’s optimal strategy. Review volume reflects initial sales, while valence measures the average rating. Together, these metrics generate what we call the learning precision effect , whereby review information enhances the accuracy of quality inference and, in turn, guides the firm’s dynamic pricing and quality decisions. We find that, without the option to refine (adjust) quality after launch, the firm prefers a higher initial product quality and a lower introductory price to boost review volume and improve learning. When post-launch quality refinement is feasible, the learning precision effect intensifies, as the firm further increases initial quality to enhance learning from reviews. However, the resulting optimal pricing strategy in the first period can depart from conventional intuition. Depending on market conditions, the firm may either raise or lower the introductory price—relative to the case without quality refinement—in order to enhance review generation. A notable outcome is that the firm’s optimal strategy not only increases its overall profit but also improves consumer surplus for both early and late buyers. Although quality refinement is often expected to favor the firm at consumers’ expense, our results show that learning from reviews can generate a genuine win–win outcome. Finally, we extend our model to incorporate under-reporting bias, uninformed consumers, nonzero marginal quality costs, and alternative distributions of perceived quality, and find that our main insights remain robust across these variations.

Ensuring fair platform practices has become a critical challenge in Operations Management. Increasing anecdotal evidence suggests that digital platforms may leverage consumer data to engage in data-driven price discrimination (DDPD)—charging discriminatory prices to customers based on inferred willingness to pay. Yet, despite widespread concern, no platform has publicly acknowledged the operation of DDPD, and academic research has thus far lacked the means and empirical evidence to quantify its magnitude in practice. Against this backdrop, the first objective of this study therefore is to provide direct evidence on the presence of DDPD. To this end, we conducted a field experiment with a leading e-retailing platform in China. The results reveal that customers for whom the platform possesses more data are subject to a higher level of price discrimination. Recently, various regulations have been introduced to protect consumers from unfair use of their data, but there is a lack of evidence on platform compliance to these regulations. The second research objective is then to investigate whether and to what degree platforms complied with these regulations. We leverage a unique natural experiment in which the Chinese government implemented a new regulation banning DDPD in 2020. The findings reveal that while DDPD practice did not disappear entirely, the level decreased significantly post-regulation. As the first empirical study to present direct evidence of DDPD presence and platform compliance, the findings have significant implications for policymakers, platforms, and customers.

As malpractice liability increases, physicians order more and more diagnostic tests/procedures to avoid potential lawsuits. Given the waste/costs of defensive medical practices, some U.S. states enacted tort reform to reduce malpractice liability. As key healthcare providers, hospitals face significant malpractice liability. Despite the fact that a vast amount of medical malpractice occurs in hospitals, the effects of tort reform on hospital performance have not been studied. This study examined tort reform's effects on four hospital cost/efficiency related measures (defensive medicine (DM) costs, occupancy rate (OR), operating cost (OC), and full-time equivalent (FTE) employees/bed) and two patient-centered measures (experiential quality (EQ) and patient satisfaction (PS)). Considering both cost and quality measures enabled an examination of whether a trade-off arises under tort reform—a trade-off uniquely relevant at the hospital-level. The research applied difference-in-differences methodology to longitudinal data from multiple sources and addressed methodological weaknesses in prior studies preventing causal attribution. The results showed that tort reform reduced DM costs, with an average savings of $372 per patient stay and a savings of $238 million for an average-sized hospital, decreased hospitals’ OC, with an average savings of $664 per patient stay and $4.23 million for an average-sized hospital and is associated with 0.22 fewer FTEs per bed. However, tort reform's reduction in OR was not statistically significant. The findings indicate that conclusions drawn from single medical specialty studies do not necessarily translate to hospitals with a full spectrum of healthcare services. With respect to the patient-oriented measures, experiential quality and patient satisfaction fell by 3.53% and 5.63%, respectively. The study is particularly germane given the challenges hospitals face in meeting both the cost and quality mandates of the Affordable Care Act's (ACA's) Value-based Purchasing (VBP) program and provides valuable insights for policymakers (at both government and hospital levels) for improving hospital performance.

Telemedicine has transformed healthcare delivery, yet its impact on the quality and consistency of clinical decision-making remains underexplored in operations management. While heralded for efficiency and access, it is unclear whether virtual care systematically alters adherence to clinical practice guidelines. This study investigates whether the mode of care, telemedicine versus in-person, affects clinicians’ guideline-recommended prescribing of anticoagulants for atrial fibrillation, where treatment adherence is crucial to prevent severe outcomes. Using electronic medical record data from 16,603 patient encounters at a major academic health system in the United States (2020–2023), we employ a two-way fixed-effects linear probability model to compare prescribing patterns. We find that telemedicine visits are associated with a 5.2-percentage-point lower probability of guideline-recommended anticoagulant prescription than in-person visits. The gap is driven by clinicians with historically lower adherence; top-quartile clinicians show no difference across modalities, exhibiting behavioral robustness (adherence resilient to modality). We propose that telemedicine’s leaner information channel heightens perceived uncertainty, activating cognitive biases: For some clinicians, telemedicine exacerbates ambiguity aversion and omission bias , favoring inaction in virtual settings. Linking service design to variation in professional judgment, we show how operational context moderates decision quality. Practically, healthcare organizations should not treat telemedicine as a simple substitute for in-person care; they should implement targeted operational interventions, such as enhanced decision support and training on cognitive biases, to maintain high standards of care so efficiency and access gains do not come at the cost of adherence. In a supplementary analysis, we estimate that, at the population level in the United States, a 5.2-percentage-point telemedicine-related gap in anticoagulant prescribing could result in more than 150 preventable strokes and roughly $50 million in avoidable lifetime costs annually for each newly diagnosed cohort, underscoring its substantial clinical and economic significance.

We consider a production-inventory system in which production relies on a self-renewable natural resource. The resource regenerates at a rate that depends on its current stock as well as random environmental factors. In each period, the firm decides on the production quantity, facing stochastic demand and limited resource. This type of system is common in industries such as fishing and logging. We model the resource’s self-renewal using a generating function and formulate the production optimization problem as a dynamic program. We find that because of the renewable nature of the resource, the optimal stock level does not necessarily increase with the available resource quantity, and could become higher than the optimal stock level for a benchmark system where resource is unlimited. In a special case with deterministic demand and environment, we show that it could be optimal to halt production even with zero inventory left, in order to preserve the growth of resource. On the other hand, it could also be optimal to raise the inventory level to be higher than demand, when overpopulation becomes an issue. Moreover, a higher market price, which stems from adverse environmental conditions and signals lower future yields, can incentivize the firm to reduce production and conserve resources for future growth. In a deterministic environment where the inventory is fully perishable, we analytically characterize the long-run dynamics of the resource quantity. In particular, sustainable production is more achievable for low-margin products, and a myopic production firm does not necessarily deplete the resource. We conduct numerical experiments using real-world salmon population data to test the robustness of our findings in general systems with stochastic environment and non-perishable products. The results show that ignoring either the resource constraint or the self-renewal dynamics, could lead to high operational costs and undermine resource sustainability.

In modern consumer technology industries, manufacturers make substantial upfront investments in product quality, despite facing significant demand uncertainty—particularly regarding the size of the high-valuation segment that values quality highly. Dynamic pricing, where retailers adjust prices based on demand learning in the early period, may help mitigate this uncertainty. We develop a game-theoretic model to examine how a retailer's dynamic pricing capability impacts a manufacturer's wholesale pricing and product quality investment and whether it benefits both firms. In our model, the retailer can learn the proportion of high-valuation consumers by setting a high early price to target only that segment. This demand learning opportunity affects the manufacturer's wholesale pricing strategy and its expected return on quality investment. We find that the retailer's dynamic pricing improves product quality when the market is moderately likely to be good but has no effect when a good market is nearly certain. Dynamic pricing can also lead to either win-win or lose-lose outcomes: it benefits both firms when it mitigates double marginalization and enables broader market coverage, but harms both when it induces inefficient premium pricing and reduces total demand. We extend our main model to several settings, including cases where both firms have dynamic pricing capabilities and where consumer valuations follow a continuous distribution. While our main qualitative insights remain robust, we uncover several new findings. For example, when both firms (rather than only the retailer) can adjust prices dynamically, dynamic pricing is more likely to enhance product quality and increase the manufacturer's profits.