Generating Solutions for Society
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
Seed Accelerators, Information Asymmetry, and Corporate Venture Capital Investments
Management Science
Beyond financial incentives, investments by Corporate Venture Capitalists (CVCs) are often motivated by strategic objectives, such as gaining early exposure to emerging technologies. However, in the presence of information asymmetry, CVCs tend to invest in startups with a high degree of business relatedness—startups that are less risky but lacking in knowledge novelty—which are not ideal for achieving their strategic objectives. With startup accelerators showing promise in mitigating the information asymmetry problem, we examine how a CVC’s investment pattern in a region shifts following a startup accelerator’s entry, with a particular interest in the degree of business relatedness between the CVC’s parent corporation and its portfolio companies. Analyses reveal that CVCs increase investments in startups that are dissimilar to their parent’s business following the entry of startup accelerators. We show that the two pathways through which accelerators reduce information asymmetry—quality signals, and mentorship and training—likely contribute to this change. In addition, the change is most pronounced for CVCs whose parent firm operates in an IT-using—rather than an IT-producing—industry, suggesting that accelerators help IT-using firms gain a foothold in the technology space through CVC investments. These findings deepen the understanding of the role that startup accelerators play in the entrepreneurial ecosystem against the backdrop of digital transformation occurring in nearly every industry.
Raveesh Mayya, NYU and Peng Huang, UMD
Prompt Adaptation as a Dynamic Complement in Generative AI Systems
As generative AI systems rapidly improve, a key question emerges: How do users keep up—and what happens if they fail to do so. Drawing on theories of dynamic capabilities and IT complements, we examine prompt adaptation—the adjustments users make to their inputs in response to evolving model behavior—as a mechanism that helps determine whether technical advances translate into realized economic value. In a preregistered online experiment with 1,893 participants, who submitted over 18,000 prompts and generated more than 300,000 images, users attempted to replicate a target image in 10 tries using one of three randomly assigned models: DALL-E 2, DALL-E 3, or DALL-E 3 with automated prompt rewriting. We find that users
with access to DALL-E 3 achieved higher image similarity than those with DALL-E 2—but only about half of this gain (51%) came from the model itself. The other half (49%) resulted from users adapting their prompts in response to the model’s capabilities. This adaptation emerged across the skill distribution, was driven by trial-and-error, and could not be replicated by automated prompt rewriting, which erased 58% of the performance improvement associated with DALL-E 3. Our findings position prompt adaptation as a dynamic complement to generative AI—and suggest that without it, a substantial share of the economic value created when models advance may go unrealized.
Eaman Jahani, UMD
Benjamin Manning, MIT
Hong-Yi TuYe, MIT
Mohammed Alsobay, MIT
Christos Nicolaides, University of Cyprus
Siddharth Suri, Microsoft Research
David Holtz, Columbia
Tracking-Based Advertising After Apple's App Tracking Transparency: Firm-Level Evidence and Policy Implications
TechREG CHRONICLE, November 2025
We discuss the impact of Apple’s App Tracking Transparency's (“ATT”) on targeted, online advertising. We overview the empirical results of Aridor, Che, Hollenbeck, Kaiser & McCarthy (2025) that measured the impact of ATT on e-commerce firms. The results point to a large reduction in the efficacy of targeted advertising and subsequently large revenue losses, borne primarily by smaller firms. We discuss the competition policy implications of this by highlighting the potentially anticompetitive implications of privacy measures implemented by private firms and the lack of substitutability between advertising networks, despite a large exogenous shock in the efficacy of Meta advertising.
Aridor, Guy: Assistant Professor of Marketing, Northwestern University
Hollenbeck, Brett: Associate Professor of Marketing, UCLA
McCarthy, Daniel: Associate Professor of Marketing, University of Maryland
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
Breaking ceilings: Debate training promotes leadership emergence by increasing assertiveness.
Journal of Applied Psychology
To date, little is known about what interventions can help individuals attain leadership roles in organizations. To address this knowledge gap, we integrate insights from the communication and leadership literatures to test debate training as a novel intervention for leadership emergence. We propose that debate training can increase individuals’ leadership emergence by fostering assertiveness—“an adaptive style of communication in which individuals express their feelings and needs directly, while maintaining respect for others” (American Psychological Association, n.d.)—a valued leadership characteristic in U.S. organizations. Experiment 1 was a three-wave longitudinal field experiment at a Fortune 100 U.S. company. Individuals (N = 471) were randomly assigned to either receive a 9-week debate training or not. Eighteen months later, the treatment-group participants were more likely to have advanced in leadership level than the control-group participants, an effect mediated by assertiveness increase. In a sample twice as large (N = 975), Experiment 2 found that individuals who were randomly assigned to receive debate training (vs. nondebate training or no training) acted more assertively and had higher leadership emergence in a subsequent group activity. Results were consistent across self-rated, group-member-rated, and coder-rated assertiveness. Moderation analyses suggest that the effects of debate training were not significantly different for (a) U.S.- and foreign-born individuals, (b) men and women, or (c) different ethnic groups. Overall, our experiments suggest that debate training can help individuals attain leadership roles by developing their assertiveness.
Jackson Lu (MIT), Michelle Zhao (Washington University in St. Louis), Hui Liao (University of Maryland, Long Jiang Endowed Chair in Business), and Lu Zhang (MIT)
Status-Amplified Deterrence: Paul Manafort’s Prosecution Under the Foreign Agents Registration Act
Organization Science, September 2025
Social control agents often struggle to deter organizational deviance. We propose a theory of “status-amplified deterrence” wherein enforcement’s deterrent effects are amplified when carried out against high-status organizational actors. First, this enforcement is interpreted as willingness and ability for far-reaching enforcement. Next, amplified deterrence occurs as these episodes become widely known through (1) extensive media coverage and (2) the marketing efforts of third-party compliance advisors. We examine this theory in the context of the U.S. Department of Justice’s enforcement against Paul Manafort for violating the Foreign Agents Registration Act (FARA). Using a difference-in-differences design, we demonstrate that enforcement against Manafort caused a widespread, sustained, and economically significant reduction in FARA noncompliance. We show supplementary evidence consistent with the idea that deterrence was amplified in significant part by media attention and by law firms referencing the episode while successfully marketing FARA advisory services. We contribute to literature illuminating how organizations, in conjunction with third-party compliance advisors, adjust deviant activities in response to shifting regulatory environments.
Reuben Hurst, Jin Hyung Kim (George Washington University) and Jordan Siegel (University of Michigan)
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
Analytics for Finance and Accounting: Data Structures and Applied AI
Analytics for Finance and Accounting: Data Structures and Applied AI bridges the gap between technical data science education and domain-specific applications in accounting and finance. Designed for students and instructors seeking practical exposure to AI-driven financial analytics, the book prioritizes understanding real-world business data—structured and unstructured—before introducing machine learning techniques. It empowers learners to apply AI tools, such as GPT and pre-trained language models, to analyze corporate disclosures, earnings calls, ESG reports, and other financial documents. Minimizing programming prerequisites, the book integrates video tutorials and applied projects to support hands-on learning. It serves as both a foundational text for graduate-level data analytics courses and a modular supplement for traditional finance and accounting curricula. By combining domain expertise with modern computational tools, this book equips the next generation of financial professionals with the skills to thrive in a data-intensive economy.
Sean Cao, Associate Professor, Robert H. Smith School of Business, University of Maryland, United States of America