Key Areas of Research
The Changing Nature of Firm Innovation: Short-Termism and Influential Innovation in U.S. Public Firms
Management Science
We examine the link between short-term pressures and technologically significant innovation in U.S. public firms in 1997–2015. Using a market-based measure of short-term pressure, we estimate its relationship with influential and novel patents. We find that firms facing more intense short-term pressures are less likely to patent highly influential or novel innovations. To evaluate whether this relationship is causal, we use changes in ownership styles following financial institution mergers as instruments. Our analysis suggests that changing short-term pressures from investors had a causal impact on firm innovative outcomes; this finding is robust to a wide variety of empirical specifications. While public firms as a whole retained a constant share of highly influential patents, this activity has become more concentrated in fewer firms. This shift does not appear to be fully compensated by an increase in technologically significant patents by nonpublic firms such as venture-capital (VC)-backed start-ups. These findings raise questions about capital markets’ impact on firm R&D strategy and the nature of innovative activities in public firms
Yuan Shi (Cornell University), Rachelle Sampson (University of Maryland), Brent Goldfarb (University of Maryland), Rafael Corredoira (Newcastle University)
Market Formation, Pricing, and Revenue Sharing in Ride Hailing Services
Manufacturing & Service Operations Management, September 2025
Problem definition: We empirically study the market for ride-hailing services. In particular, we explore the following questions: (i) How do the two-sided market and prices jointly form in ride-hailing marketplaces? (ii) Does surge pricing create value and for whom? How can its efficiency be improved? (iii) Can platforms' strategy on revenue sharing with drivers be improved? (iv) What is the value generated by ride-hailing services, including hosting rival taxi services on ride-hailing apps? Methodology/Results: We develop a discrete choice model for the formation of mutually dependent demand (customer side) and supply (driver side) that jointly determine pricing. Using this model and a comprehensive data set obtained from the largest mobile ride platform in China, we estimate customer and driver price elasticities and other factors that affect market participation for the company's two main markets, namely basic ride-hailing and Taxi services. Based on these estimation results and counterfactual analysis, we demonstrate that surge pricing improves customer and driver welfare as well as platform revenues, while counterintuitively reducing Taxi revenues on the platform. However, surge pricing should be avoided during non-peak hours as it can hurt both customer and platform surplus. We show that platform revenues can be improved by increasing drivers' revenue share from the current levels. Finally, we estimate that the platform's basic ride-hailing services generated customer value equivalent to 13.25 Billion USD in China in 2024, and hosting rival Taxi services on the platform boosted customer surplus by 3.6 Billion USD. Managerial Implications: Our empirical framework provides ride-hailing companies a way to estimate demand and supply functions, which can help with optimization of multiple aspects of their operations. Our findings suggest that ride-hailing platforms can improve profits by containing surge-pricing to peak hours only and boosting supply by increasing driver compensation. Finally, our results demonstrate that restricting ride-hailing services create significant welfare losses while including taxi services on ride-hail platforms generate substantial economic value
Liu Ming, Tunay I. Tunca, Yi Xu, and Weiming Zhu
Unintended Consequences of Closing Pay Gaps Across Multiple Groups: A Formal Modeling and Simulation Analysis of Allocation Methods
Organization Science, October 2025
In recent years, many firms have prioritized both pay equity (i.e., closing pay gaps associated with target groups such as women and racial minorities) and equitable representation (i.e., ensuring these target groups are fairly represented across a firm’s hierarchy). We use formal modeling and simulations to show how efforts to close pay gaps across multiple groups can undermine equitable representation. Specifically, our analysis suggests that pressure for pay equity creates a cost-based financial incentive to enact a subtle form of tokenism: A firm may minimize the cost of closing pay gaps if it maintains a workforce with a small number of minority women whom it pays well in order to compensate for underpaying larger numbers of majority women and minority men who resemble each other in terms of job attributes and personal qualifications. A firm can avoid these outcomes if it focuses on ensuring that employees from target groups are equitably rewarded for job attributes and personal qualifications rather than minimizing cost. But an equitable-rewards approach can be substantially more expensive than a cost-minimization approach, especially if pay gaps are larger in high-wage jobs or if there are many target groups. We conclude by offering testable empirical predictions and recommending a practical solution, namely to include terms for intersectional categories (e.g., minority women) in the regressions used to estimate pay gaps.
David Anderson (Villanova University); Margret Bjarnadottir (University of Maryland);
David Ross (University of Florida)
Learning from Earnings Calls: Graph-Based Conversational Modeling for Financial Prediction
Information Systems Research
Earnings conference calls are valuable venues for business communication. Empirical research has shown that the content of earnings calls contains predictive signals about future market risks, which has motivated a line of computational studies that utilize earnings transcripts for financial forecasting tasks. However, earnings call transcripts are typically very long, and the predictive signals within them are often sparsely distributed across different sections of the transcript. As a result, existing computational methods often fail to capture the essential information within the transcript that is relevant to market risks. In this work, we design a novel method to model earnings transcripts as a conversational graph where graph nodes represent discussed topics and graph edges indicate the similarity between topics. By doing so, we aim to explicitly model what is discussed (i.e., topical content), how it is discussed (e.g., cross-referencing or newly introduced topics), and in what manner it is discussed (e.g., sentiment and other linguistic features) within the transcript. We then leverage a graph neural network to learn transcript-level representations for financial risk forecasting. Experimental results show that the proposed method significantly reduces risk forecasting errors, demonstrating its capability of modeling earnings call transcripts. Moreover, this predictive power holds even after considering the firm’s fundamentals, and the performance gain over baseline models continues to grow as transcript length increases. The interpretability analysis shows that the proposed method identifies cross-referencing and newly introduced topics as influential for risk prediction. Moreover, the model tends to associate transcripts with a higher number of new topics in the Q&A section, more cross-referencing discussions, and more positive sentiment with lower predicted financial risks. This work contributes methodologically by proposing a novel predictive approach specifically tailored to the domain-specific challenge of transcript-based risk forecasting. We also discuss key design insights and implications.
Yi Yang (HKUST), Yixuan Tang (HKUST), Yangyang Fan (HK PolyU), and Kunpeng Zhang (UMD)
Cost-Saving Synergy: Energy Stacking in Battery Energy Storage Systems
Management Science
Despite the great potential benefits of battery energy storage systems (BESSs) to electrical grids, most standalone uses of BESS are not economical due to batteries’ high upfront costs and limited lifespans. Energy stacking, a strategy of providing two or more services with a single BESS, has been of great interest to improve profitability. However, some key questions, for example, the underlying mechanism by which stacking works or why and how much it may improve profitability, remain unanswered in the literature. Using two popular battery services, we analytically show that there often exists cost-saving synergy -- the cost of performing both services at the same time (simultaneous stacking) is smaller than the sum of individual costs if we had performed each service alone -- which allows for bigger profits. Furthermore, we perform comparative statics on the optimal mix of the services to systemically characterize grid/market conditions that maximize/minimize this synergy. We also derive a theoretical upper bound on simultaneous stacking’s benefits, showing that it can approximately double the profit of the best standalone service. Several generalizations of the base model not only show that the main lessons continue to hold but also that stacking’s benefits may become even stronger.
Joonho Bae (Indiana University), Roman Kapuscinski (University of Michigan), John Silberholz (UMD)
Can Employees' Past Helping Behavior Be Used to Improve Shift Scheduling? Evidence from ICU Nurses
Management Science, November 2025
Employees routinely make valuable contributions at work that are not part of their formal job description, such as helping a struggling coworker. These contributions, termed organizational citizenship behavior, are studied from many angles in the organizational behavior literature. However, the degree to which the past helping behavior of employees scheduled to a shift impacts that shift’s operational outcomes remains an underexplored question. We define two measures of past helping behavior for members of a shift -- the total past helping of each employee and the past helping between each pair of employees -- and hypothesize that they are associated with shift performance. We empirically confirm our hypotheses with detailed scheduling and patient outcome data from six intensive care units (ICUs) at a large academic medical center, using the hospital’s electronic medical records to identify cases of one nurse helping another. Our empirical results indicate that both measures of past helping are predictive of patient length of stay (LOS), more so than the broadly studied notion of team familiarity. Counterfactual analysis shows that relatively small changes in shift composition can yield significant reduction in total LOS, indicating the managerial significance of the results. Overall, our study suggests the potential value of shift scheduling using data on past helping behaviors, and this may have promise far beyond the selected application to ICU nursing.
Zhaohui (Zoey) Jiang (CMU), John Silberholz (UMD), Yixin (Iris) Wang (UIUC), Deena Kelly Costa (Yale), Michael Sjoding (University of Michigan)
Celebrity messages reduce online hate and limit its spread
Online hate spreads rapidly, yet little is known about whether preventive and scalable strategies can curb it. We conducted the largest randomized controlled trial of hate speech prevention to date: a 20-week messaging campaign on X in Nigeria targeting ethnic hate. 73,136 users who had previously engaged with hate speech were randomly assigned to receive prosocial video messages from Nigerian celebrities. The campaign reduced hate content by 2.5% to 5.5% during treatment, with about 75% of the reduction persisting over the following four months. Reaching a larger share of a user's audience reduced amplification of that user's hate posts among both treated and untreated users, cutting hate reposts by over 50% for the most exposed accounts. Scalable messaging can limit online hate without removing content.
Eaman Jahani, Assistant Professor, UMD
Blas Kolic, Post-doc, Universidad Carlos III de Madrid
Manuel Tonneau, PhD Student, Oxford University
Hause Lin, Post-doc, MIT
Daniel Barkoczi, University of Southern Denmark
Edwin Ikhuoria, Middlesex University
Victor Orozco, World Bank
Samuel Fraiberger, World Bank and NYU
The GenAI Future of Consumer Research
Journal of Consumer Research
We develop a novel generative AI (GenAI) trajectory, “democratization-average trap-model collapse,” to identify data and model challenges posed by GenAI, from which we project the GenAI future of consumer research. This trajectory consists of three key phenomena: democratization broadens consumer participation, the average trap produces generic responses, and model collapses occurs when GenAI outputs lose human sensibilities. Data and model challenges arise as democratization enhances data representation but embeds real-world biases; the average trap, caused by next-token prediction algorithms, leads to generic outputs that lack individuality; and model collapse occurs when GenAI increasingly learns from its own outputs, amplifying machine bias and diverging from human behavior. To address these challenges, researchers can leverage democratization to study marginalized consumers and prioritize human-centered research over purely data-driven methods. The average trap can be mitigated by fine-tuning models with task-specific and marginalized consumption data while engineering responses for uniqueness. Preventing model collapse requires integrating human-machine hybrid data and applying theories of mind to realign AI with human-centric consumption. Finally, we outline three future research directions: preserving data distribution tails to support consumption democratization, countering the average trap in next-token prediction, and reversing the trajectory from democratization to model collapse.
Ming-Hui Huang, Distinguished University Professor, National Taiwan University
Roland T. Rust, Distinguished Professor and David Bruce Smith Chair in Marketing, University of Maryland
The referral penalty: Decreased perceptions of merit undermine helping behavior towards referred employees
Journal of Applied Psychology
Employee referrals are commonly used by organizations due to their numerous benefits. However, it remains unclear how organizational incumbents, who are uninvolved in the hiring process, perceive and react to referral beneficiaries. Although traditional views suggest that the presence of a referral signals merit, incumbents’ perceptions may differ. We theorize that incumbents are more likely to perceive referral beneficiaries as less merited than non-referred employees, due to perceived legitimacy concerns stemming from a simplified view that reliance on network contacts de facto compensates for lower qualifications. Drawing on equity theory, we then theorize that low merit perceptions lead to less positive and more negative behaviors towards referral beneficiaries, as an attempt to restore the equilibrium between beneficiaries’ perceived inputs (e.g., driven by perceived lower merit) and outputs (e.g., being on payroll). Sampling employees from industries in which referrals are normative (Study 1a) and from a cultural context that is positively predisposed toward referrals (Study 1b) confirmed our theorizing. In a subsequent study, aiming to enhance the generalizability of our findings, we found supporting evidence for perceived equity violations, leading incumbents to engage in corrective behaviors toward referral beneficiaries (Study 2). Finally, testing our hypotheses more conservatively, we found that negative attributions toward referral beneficiaries persisted even when the referred employees had demonstrated high performance, thereby underscoring the robustness of our findings (Study 3). This paper elucidates important unintended consequences of one of the most popular hiring methods - employee referrals - and draws implications for both theory and practice.
Tomova Shakur, Teodora, Texas Christian University and Derfler-Rozin, Rellie, University of Maryland
Should I Stand Up for My Mistreated Colleague? When and Why High-Status Team Members Stand Up for Their Coworkers
Organizational Behavior and Human Decision Processes, January 2026
Supervisory mistreatment has adverse consequences for its victims. Coworkers, as observers, can shape victims’ experiences by standing up for them. Yet doing so entails the risk of supervisory retaliation. High-status coworkers should be well-positioned to stand up for victims as they have greater social capital at work. However, such retaliation risks may loom large for them because they are highly motivated to protect what they have. Thus, prior research reports both positive and negative links between status markers and various forms of standing up. We suggest that these inconclusive findings stem from examining individuals’ status only within a single domain (e.g., work) while neglecting how their standing in other groups may shape their experiences in that focal domain. Building on status inconsistency theory (Lenski, 1954) and the concept of status portfolios (Fernandes et al., 2021), we argue that status variance (i.e., inconsistency of status across groups) shapes how high-status employees react to mistreatment. Specifically, we hypothesize that high-status employees with high (compared to low) status variance will experience greater fear of retaliation and reduced willingness to stand up. We argue that this occurs because they perceive their status portfolios as unstable and become more vigilant in protecting their elevated standing at work. Four complementary studies provided support for our hypotheses. We discuss implications for research on bystander intervention, supervisory mistreatment, and status.
Gencay, Oguz, PhD., Bilkent University., Derfler-Rozin, Rellie, PhD. University of Maryland, Arman, Gamze, UWE Bristol