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
User Innovation and Product Stickiness: Evidence from Video Games
Journal of Economics & Management Strategy
Prior research on user innovation fails to explain its low adoption rate and neglects its impact on increased product stickiness. To bridge these gaps, we conducted an empirical investigation into user innovations within the video game sector. Our study reveals that embracing user innovation leads to an upsurge in the number of active players for a game. Furthermore, the marginal effect of user innovations varies depending on their recency and quality, with low-quality user innovations leading to user attrition. The effect is also contingent on the stage in the product life cycle in which user innovation is adopted.
Yunfei Wang, UMD and Peng Huang, UMD
AdGazer: Improving Contextual Advertising with Theory-Informed Machine Learning
Journal of Marketing
Contextual advertising involves matching features of ads to features of the media context where they appear. We propose AdGazer, a new machine learning procedure to support contextual advertising. It comprises a theoretical framework organizing high and low-level features of ads and contexts, feature engineering models grounded in this framework, an XGBoost model predicting ad and brand attention, and an algorithm optimally assigning ads to contexts. AdGazer includes a Multimodal Large Language Model to extract high-level topics predicting the ad-context match. Our research uses a unique eye-tracking database containing 3531 digital display ads and their contexts, and aggregate ad and brand gaze times. We compare AdGazer’s predictive performance to two feature learning models, VGG16 and ResNet50. AdGazer predicts highly accurately with hold-out correlations of 0.83 for ad gaze and 0.80 for brand gaze, outperforming both feature learning models and generalizing better to out-of-distribution ads. Context features jointly contributed at least 33% to predicted ad gaze and about 20% to predicted brand gaze, good news for managers practicing or considering contextual advertising. We demonstrate that the theory-informed AdGazer effectively matches ads to advertising vehicles and their contexts, optimizing ad gaze more than current practice and alternatives like text-based and native contextual advertising.
Michel Wede (UMD Smith) Jianping Ye (UMD PhD student); and Rik Pieters (Tilburg University, the Netherlands)
Building credible commitments via board ties: Evidence from the supply chain
November 2025
Using a novel dataset that provides a comprehensive coverage of U.S. firms' industrial supply chain relationships, we find that firms with innovation specific to a buyer are more likely to share a common director with that buyer. This association is stronger when the buyer has a larger number of alternative suppliers. We further find that when a supplier–buyer pair shares a common director, the supplier's R&D investment is more sensitive to the investment opportunities of its buyer. Moreover, such pairs tend to have longer supply chain relationships. Taken together, our findings demonstrate that board ties serve as a credible commitment mechanism to support exchange along the supply chain and safeguard suppliers' buyer-specific investments.
Rebecca Hann, University of Maryland-College Park; Musa Subasi, University of Maryland-College Park; Yue Zheng, Hong Kong University of Science and Technology
Biodiversity Entrepreneurship
Review of Finance
We study an emerging class of start-up organizations focused on biodiversity conservation and the challenges they face in financing these ventures. Using a novel machine learning method, we identify 630 biodiversity-linked start-ups in PitchBook and compare their financing dynamics to other ventures. Biodiversity start-ups raise less capital but attract a broader coalition of investors, including not only venture capitalists (“value investors”) but also mission-aligned impact funds and public institutions (“values investors”). Values investors provide incremental capital rather than substituting value investors, but funding gaps persist. We show biodiversity-linked start-ups use social media activity to help connect with value investors. Our findings can inform policy and practice for mobilizing private capital toward biodiversity preservation, emphasizing hybrid financing models and strategic communication.
Sean Cao, Robert H. Smith School of Business, University of Maryland
Transforming Products into Platforms: Unearthing New Avenues for Business Innovation
NIM Marketing Intelligence Review, October 2024
It is impossible for brands to ignore digital platform opportunities. Network effects are one of the strongest sources of power and defensibility ever invented and underlie some of the most valuable businesses in the world. Managers and entrepreneurs can leverage the power of platforms by adding some platform elements to their existing products or services, by distributing their brands via existing platforms or by developing their own new platforms. By using one’s own brands as platforms requires creativity but can help businesses unlock new value and build resilient ecosystems around their products. There are three key methods. The first is to invite third-party sellers to enhance existing products. Examples include selling advertising space around products or creating app stores to extend offers. The second is to connect one’s customers by enabling interactions among users to add value. Third, brands might reach out to customers’ customers by enhancing the end-user experience in a way that benefits both themselves and their direct customers. If thoughtfully implemented, any platform strategy will create self-reinforcing feedback loops sparking growth and keeping competitors at bay.
Andrei Hagiu, Associate Professor of Information System, Boston University; Bobby Zhou, Associate Professor of Marketing, University of Maryland
How to Talk When a Machine Is Listening: Corporate Disclosure in the Age of AI
The Review of Financial Studies, March 2023
Growing AI readership (proxied for by machine downloads and ownership by AI-equipped investors) motivates firms to prepare filings friendlier to machine processing and to mitigate linguistic tones that are unfavorably perceived by algorithms. Loughran and McDonald (2011) and BERT available since 2018 serve as event studies supporting attribution of the decrease in the measured negative sentiment to increased machine readership. This relationship is stronger among firms with higher benefits to (e.g., external financing needs) or lower cost (e.g., litigation risk) of sentiment management. This is the first study exploring the feedback effect on corporate disclosure in response to technology.
Sean Cao, Associate Professor (with tenure), Robert H. Smith School of Business, University of Maryland, United States of America
Bayesian Ensembles of Exponentially Smoothed Life-Cycle Forecasts
Manufacturing and Servoce Operations Management
We study the problem of forecasting an entire demand distribution for a new product before and after its launch. Firms need accurate distributional forecasts of demand to make operational decisions about capacity, inventory and marketing expenditures. We introduce a unified, robust, and interpretable approach to producing these pre- and post-launch distributional forecasts. Our approach is inspired by Bayesian model averaging. Each candidate model in our ensemble is a life-cycle model fitted to the completed life cycle of a comparable product. A pre-launch forecast is an ensemble with equal weights on the candidate models’ forecasts, while a post-launch forecast is an ensemble with weights that evolve according to Bayesian updating. Our approach is part frequentist and part Bayesian, resulting in a novel form of regularization tailored to the demand forecasting challenge. We also introduce a new type of life-cycle or product diffusion model with states that can be updated using exponential smoothing. The trend in this model follows the density of an exponentially tilted Gompertz random variable. For post-launch forecasting, this model is attractive because it can adapt itself to the most recent changes in a product’s life cycle. We provide closed-form distributional forecasts from our model. In two empirical studies, we show that when the ensemble’s candidate models are all in our new type of exponential smoothing model, this version of the ensemble outperforms several leading approaches in both point and quantile forecasting. In a data-driven operations environment, our model can produce accurate fore- casts frequently and at scale. When quantile forecasts are needed, our model has the potential to provide meaningful economic benefits. In addition, our model’s interpretability should be attractive to managers who already use exponential smoothing and ensemble methods for other forecasting purposes.
Xiaojia Guo (Assistant professor, Robert H. Smith School of Business, UMD), Casey Lichtendahl (Google), Yael Grushka-Cockayne (Professor, Darden school of business, University of Virginia)