Marketing Misinformation in the Age of AI
Misinformation is a big problem online, and AI is making it worse. With AI, people/organizations can create and disseminate inaccurate information instantaneously at large scale. We call on brands, platforms, AI companies, consumers, and the government to address the problem.
Behavioral Intervention Construal: A Framework for Understanding Inferences from Behavioral Interventions
Managers and policymakers frequently use behavioral interventions—including incentives and messaging campaigns—to influence people’s behavior. They often choose an intervention by asking whether it will make a desired behavior easier, cheaper, or more attractive. Our research suggests they should also ask a second question: “What could this intervention unintentionally communicate?” For instance, interventions can signal that an organization is self-interested or trying to control people’s choices.
First-Party Content Production in a Competitive Media Market
Streaming platforms are pouring money into original content, but whether it pays off depends on two things: how much their content already overlaps with competitors and how flexible their pricing is. When prices are fixed (e.g., standard subscription tiers), platforms are more likely to invest in originals—especially if competitors offer similar libraries—because originals help differentiate. But when platforms can easily adjust prices, heavy content overlap actually reduces the incentive to invest in originals, since pricing can be used instead to compete.
Three Strategic Bets on AI’s Future
This paper examines competition in the consumer AI assistant market using worldwide iOS and Android app-store data from seven major AI assistants from May 2023 through December 2025. Rather than finding a winner-take-all market, we show that major product launches tend to coincide with growth in the overall category, with little evidence of direct cannibalization across leading models. In other words, the “AI war” appears less zero-sum than commonly assumed.
The Caring Machine: Feeling AI for Customer Care
Feeling skills have always been a human domain, but AI is advancing rapidly. This article shows how AI can be used to develop feeling intelligence and build empathetic customer relationships.
The GenAI Future of Consumer Research
We develop a novel generative AI (GenAI) trajectory, “democratization-average trap-model collapse,” to identify data and model challenges posed by GenAI, from which we project the GenAI future of consumer research. This trajectory consists of three key phenomena: democratization broadens consumer participation, the average trap produces generic responses, and model collapses occurs when GenAI outputs lose human sensibilities.
Tracking-Based Advertising After Apple's App Tracking Transparency: Firm-Level Evidence and Policy Implications
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.
AdGazer: Improving Contextual Advertising with Theory-Informed Machine Learning
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.
AI for Customer Journeys: A Transformer Approach
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)
The Influential Solo Consumer: When Engaging in Activities Alone (vs. Accompanied) Increases the Impact of Recommendations
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.