Driving a More Prosperous Future
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)
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