As generative AI tools increasingly shape how consumers search, shop, compare and evaluate products, understanding how they make recommendations has become critical for marketers. This seventh experiment in our ongoing Psychology of Gen AI series, is the first phase in a study that examines how large language models (LLMs), like ChatGPT and Claude, determine what qualifies as the “best” product—and reveals that their recommendations are far from neutral. Instead, they tend to rely on narrow, repetitive sets of familiar brands and structured response patterns that may reinforce existing market leaders. The findings highlight important implications for brand visibility, competitive dynamics and how marketers should position their products in AI-driven environments.
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New research reveals that virtual influencers, despite their growing popularity and flexibility, are less effective than human influencers in driving engagement and brand outcomes. The reason lies in consumer psychology: people perceive virtual influencers as less deserving of success, which reduces feelings of envy—an emotion that typically drives social media engagement. However, this disadvantage can be mitigated when virtual influencers are paired with futuristic, technology-focused brands, where their artificial nature feels more congruent.
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As generative AI tools increasingly influence product discovery and decision-making, subtle cues in user language can shape what consumers are shown—and how options are framed. This second phase of the sixth study in the Psychology of Gen AI series examines how implicit and explicit gender signals affect AI-generated product recommendations, revealing systematic differences in categories, brand repetition, descriptive language and price information. The findings raise important questions for advertisers and researchers about bias, brand visibility and the growing cultural role of AI in shaping consumer norms.
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Large language models (LLMs) are rapidly becoming a new gateway to online information, potentially disrupting traditional search engines, websites and advertising markets. Using detailed clickstream data from 2022–2023, this study examines how adopting LLM tools changes consumers’ online behavior. The authors find that LLM adoption gradually reduces traditional search activity and the browsing of smaller websites, while also lowering display advertising exposure. These results suggest that generative AI may reshape how users access information online and alter the distribution of attention and advertising revenue across digital platforms.
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