Driving a More Prosperous Future
Evolution of Ride Services: From Ride- Hailing to Autonomous Vehicles
Management Science
In recent years the ride service industry has been evolving rapidly, driven by disruptive technologies such as mobile apps, AI, and autonomous vehicles (AVs). While platform-based decentralized ride hailing companies have gained significant market share, vertically-integrated robotaxi services using emerging AVs are starting to enter the market. In this paper, we aim to provide insights about the evolution and the future of ride services studying these two competing business approaches. We find that in many cases in larger markets the ride-hailing firm surprisingly gains the upper hand in competition, having higher market share and profits as well as lower service delays and higher prices, even if it has a cost disadvantage. Further, entry of the AV firm into a market with a dominant ride-hailing firm may reduce total vehicle supply and increase customer wait costs. We also find that when customers are impatient, the entry of a high cost AV firm may lead to a decrease in social welfare despite introducing competition, suggesting that regulators should be careful about introduction of robotaxi services in a market if they are not sufficiently cost efficient. From a broader perspective, our results demonstrate that platform business models in general may have significant strategic advantages over firms with traditional vertically-integrated models under competition, and platforms' dominance in a market may even result in welfare gains.
Daehoon Noh (Korea University), Tunay I. Tunca (UMD, Smith), Yi Xu (UMD, Smith)
Stochastic Gradients: Optimization, Simulation, Randomization, and Sensitivity Analysis
IISE Transactions, February 2026
Big data and high-dimensional optimization problems in operations research (OR) and artificial intelligence (AI) have brought stochastic gradients to the forefront. This article provides a view of research and applications in stochastic gradient estimation from multiple perspectives, as seminal advances have come from diverse and disparate research fields, including operations research/management science (OR/MS), industrial/systems engineering (ISE), optimal/stochastic control, statistics, and more recently from the computer science (CS) AI machine learning (ML) community.
Michael C. Fu (University of Maryland), Jiaqiao Hu (Stony Brook University, and Katya Scheinberg (Georgia Institute of Technology)
Simulation Optimization and Artificial Intelligence
Foundations and Trends in Optimization, December 2025
With the relentless increase in computing power and the ubiquitous availability of data in many industries, the fields of simulation optimization and artificial intelligence have emerged at the scientific and engineering forefront in their societal impact, manifested in the pervasiveness of technologies such as large language models, chatbots, digital twins, and agent-based systems. We examine cross-fertilization between simulation optimization and artificial intelligence, with a particular focus on reinforcement learning, highlighting research that has been mutually beneficial. This work presents examples of the synergies between the fields, followed by real-world applications and case studies, including some futurist and forward-looking concepts.
Yijie Peng (Peking University), Chun-Hung Chen (George Mason University, and Michael C. Fu (University of Maryland)
Online Learning with Survival Data
February 2026
Decision-makers frequently use adaptive experiments to optimize time-to-event outcomes, such as accelerating healthcare screenings among patients who are not up to date or delaying customer churn. A common choice to run these adaptive experiments is a multi-armed bandit with a dichotomized outcome -- an experimenter sets some threshold (e.g. 1 month) and then uses the algorithm to identify the intervention with better performance on the dichotomized outcome (e.g. which algorithm maximizes the proportion of participants who get up to date on screening within a month of outreach). We introduce "survival bandits," a principled class of algorithms that integrate the Cox proportional hazards model to better learn from time-to-event outcomes. Both theoretically and numerically (with a case study on cervical cancer screening), we show that these new algorithms have the potential to greatly improve adaptive experimentation for decision makers across industries who seek to speed or slow an event of interest.
Arielle Anderer (Assistant Prof, Cornell), Hamsa Bastani (Associate Prof, UPenn Wharton), John Silberholz (Assistant Prof, UMD Smith)
Holding Horizon: A New Measure of Active Investment Management
Journal of Financial and Quantitative Analysis
This article introduces a new holding horizon measure of active management and examines its relation to future risk-adjusted fund performance (alpha). Our measure reveals a wide cross-sectional dispersion in mutual fund investment horizons, and shows that long-horizon funds exhibit positive future long-term alphas by holding stocks with superior long-term fundamentals. Further, stocks largely held by long-horizon funds outperform stocks largely held by short-horizon funds by more than 3%annually, adjusted for risk, over the following 5-year period. We also find a clientele effect: to reduce liquidity costs, long-horizon funds attract more long-term investors through share classes that carry load fees.
Chunhua Lan (University of New Brunswick), Fabio Moneta (Queen's University), and Russ Wermers
Backfiring AI? AI Deployment in Workplace
Management Science
AI in the workplace has the potential to change the competitive dynamics among employees. The AI system can learn from high-performing employees and make that knowledge available to others. In a competitive environment, this can disincentivize high-performing employees and ultimately backfire, leading to a decline in overall firm productivity. Our results suggest that some ostensibly simple solutions, such as guaranteeing or increasing wages for adversely affected employees, may not effectively solve the problem, and firms would have to judiciously choose optimal AI efficacy levels to achieve better outcomes.
Di Yuan (Assistant Professor, Auburn University), Manmohan Aseri (Assistant professor, University of Maryland), Narayan Ramasubbu (Professor, University of Pittsburgh)
Advisor-Advisee Research Overlap and Its Implications for Scientists’ Early-Career Performance in the U.S.
Organization Science
A genealogical training process, in which senior (advisor) scientists engage in cross-generational transfer of skills and knowledge to junior (advisee) scientists is one of the core organizational features of modern science. In this paper, we examine the consequences of the tension faced by all junior scientists: to build upon an advisor’s skills or to strike out on one’s own? We study the implications of advisor-advisee research overlap for emerging scientists’ performance by constructing a novel, bibliometric-record-based dataset on 15,271 U.S. biomedical scientists (advisees) who were trained in 7,924 PI advisors’ labs between 1972 and 2009. We assessed the junior scientists’ performance in the first ten years of their careers as independent PIs. Tests across multiple research-overlap measures and model specifications reveal a consistently positive relationship between maintaining a higher degree of proximity to advisor’s research areas and the junior scientist’s early-career funding and publication performance. However, evidence is weak regarding scientific impact and non-existent regarding research disruptiveness. We further test how advisor status moderates the research overlap-performance relationship using both a large-sample analysis comparing the performance of academic siblings, and a more stringent difference-in-difference analysis leveraging the exogenous timing of the status elevation events experienced by the advisor scientists when they receive major scientific awards. Both tests yield consistent evidence that the positive relationship between advisor-advisee research overlap and advisee’s early-career performance is reduced as the advisor’s status increases. Taken together, these findings provide a more complete understanding of how advisor-advisee relationships shape new scientists’ performance during early careers.
Waverly W. Ding
Associate Professor of Strategy and Entrepreneurship
R.H. Smith School of Business
University of Maryland
Christopher C. Liu*
Associate Professor
Lundquist College of Business
University of Oregon
Andy (Seungho) Back*
University of Hong Kong
Beril Yalcinkaya
Wharton School
University of Pennsylvania
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)
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)
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