Key Areas of Research
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
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)
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)
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
AI for Customer Journeys: A Transformer Approach
Journal of Marketing Research
AI for Customer Journeys: A Transformer Approach
Zipei Lu and P. K. Kannan, Smith School of Business, University of Maryland
(forthcoming Journal of Marketing Research)
AI for Customer Journeys: A Transformer Approach introduces a novel artificial intelligence (AI) framework for modeling customer journeys in digital marketing. Leveraging transformer-based models – originally developed for natural language processing - this approach analyzes complex sequences of customer interactions across multiple channels (e.g., search, email, display ads). Unlike traditional models, this method considers both the timing and type of interactions, making it uniquely suited to modern multi-touchpoint environments.
“Transformers give us the ability to see the journey as a whole, not just as a series of isolated interactions. That’s a major leap in marketing analytics.”
— PK Kannan
The core innovation lies in its use of multi-head self-attention mechanisms, which model each customer’s journey as a dynamic sequence of touchpoints. This allows marketers to not only predict the likelihood of purchase but also identify when and through which channels interventions are most effective. Furthermore, the model is extended to capture individual-level heterogeneity, enabling personalized insights into how different customers respond to marketing efforts.
“We designed the model to capture the complexity and individuality of digital customer journeys—something traditional models often overlook.”
— Zipei Lu
Using rich data from a major hospitality firm – including over 92,000 users and over half a million visits – the model demonstrates substantial improvements over traditional approaches (e.g., Hidden Markov Models, Poisson Point Process Models, and LSTMs). For example, the proposed model achieves an AUC of 0.92 for out-of-sample conversion predictions compared to 0.85 for LSTM and <0.70 for others. Moreover, it identifies high-potential customers with far greater precision – top-decile predictions yield an 88% true conversion rate versus 34% for LSTM.
Beyond prediction, the model offers descriptive marketing insights, such as how the effectiveness of email or display ads varies over time and across customers. For instance, the study finds that customer-initiated interactions (like direct visits) have stronger and longer-lasting effects than firm-initiated ones (like emails), and the optimal window for intervention is typically within 7 to 14 days before purchase.
The model’s structure also enables profiling and customer segmentation based on latent self-attention patterns, helping marketers understand nuanced motivations like last-minute business bookings versus long-term vacation planning. This insight can inform targeted messaging and A/B testing strategies.
Overall, this AI framework not only enhances predictive accuracy but also delivers actionable insights that can improve ROI, optimize channel mix, and enable real-time personalization in customer engagement.
Zipei Lu, Ph. D. Candidate in Marketing; P. K. Kannan, Dean's Chair in Marketing Science, both at the Smith School
The Influential Solo Consumer: When Engaging in Activities Alone (vs. Accompanied) Increases the Impact of Recommendations
Journal of Marketing Research
Information about the social context of consumption is often seen on review websites or social media when consumers sharing word-of-mouth about an experience indicate whether they engaged in the activity solo or with companions. Across a secondary dataset scraped from Tripadvisor.com, five main experiments, and one supplemental experiment, the current research finds that individuals who engage in consumption activities alone can be a more influential source of recommendations than people who engage in these same activities with others. The results support an attribution-based process, such that people are more likely to attribute a solo (vs. accompanied) review to the quality of the activity itself, leading the solo (vs. accompanied) person’s review to be particularly influential. Further, the studies test the theorizing that perceived interest on the part of the solo (vs. accompanied) consumer leads to the stronger attribution to quality, and therefore that additional cues to intrinsic interest (e.g., presence of a cue to intrinsic or extrinsic motivation) attenuate the influence of solo (vs. accompanied) word-of-mouth. This work has theoretical and managerial relevance for those who seek to understand how the social context of consumption influences other consumers.
Rebecca Ratner, Dean's Professor of Marketing, Robert H. Smith School of Business; Yuechen Wu, assistant professor, Spears School of Business, Oklahoma State
From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses
Journal of Financial Economics, July 2024
An AI analyst trained to digest corporate disclosures, industry trends, and macroeconomic indicators surpasses most analysts in stock return predictions. Nevertheless, humans win “Man vs. Machine” when institutional knowledge is crucial, e.g., involving intangible assets and financial distress. AI wins when information is transparent but voluminous. Humans provide significant incremental value in “Man + Machine”, which also substantially reduces extreme errors. Analysts catch up with machines after “alternative data” become available if their employers build AI capabilities. Documented synergies between humans and machines inform how humans can leverage their advantage for better adaptation to the growing AI prowess.
Sean Cao, Robert H. Smith School of Business, University of Maryland
EPA Scrutiny and Voluntary Environmental Disclosures
Review of Accounting Studies
Market participants have called on the SEC to address the lack of disclosures about firms’ environmental impacts, investments, and exposures. However, the frictions that obstruct the flow of environmental information are not well understood. I shed light on these frictions by examining whether scrutiny by the Environmental Protection Agency (EPA) restricts the firm’s voluntary environmental disclosures in earnings conference calls. Consistent with the notion that EPA scrutiny gives rise to disclosure frictions, I find a negative relation between EPA scrutiny and the environmental disclosures of scrutinized firms. This negative relation is concentrated among firms without environmental expert directors, suggesting that environmental governance mitigates the chilling effect of EPA scrutiny. In terms of disclosure quality, I show that environmental disclosures include fewer quantitative details under EPA scrutiny. Collectively, these findings provide insights into the frictions that restrict the flow of environmental information to market participants, an important issue given the SEC’s efforts to improve current disclosure practices.
Mark Zakota, Assistant Professor, Robert H. Smith School of Business, University of Maryland
Identifying Competitors in Geographical Markets Using the CSIS Method
Journal of Marketing Research
Businesses with physical footprints – hotels, retailers, and restaurants – must identify the competitors that matter most. Traditional approaches using brand tier or proximity often fail in dynamic or asymmetric markets. We introduce the Conditional Sure Independence Screening (CSIS) method to marketing to identify true competitors based on their pricing influence on a focal firm’s demand. CSIS is computationally efficient, robust to spatial mis-specifications, and effective for identifying, asymmetric, even non-local, and segment-specific competition. It is also an effective variable selection technique.
In applying CSIS to U.S. hotel data our analysis shows that competition intensity varies not only by location or market segments, but that asymmetry is common – many hotels influence others without being influenced in return. Our methodology enables smarter, data-driven pricing and benchmarking and helps tailor strategy to segment and seasonality. In addition, it is scalable across industries such as retail, services, and hospitality.
Xian Gu, Assistant Professor, Kelley School of Business, Indiana University; P.K. Kannan, Dean's Chair in Marketing Science, Smith School of Business, University of Maryland