Should brands partner with influencers who have massive followings—or smaller, more niche audiences? New research shows that the answer depends on how the message is delivered. Using construal level theory, the study finds that follower size signals psychological “social distance,” which shapes how consumers process influencer content. Smaller influencers are most persuasive when brand information is explicit and shared on their own channels, while mega-influencers perform better when branding is subtle or when content appears on brand-owned channels. The results suggest that aligning influencer follower size with message diagnosticity can significantly improve campaign effectiveness.
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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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As generative AI becomes a central tool for producing marketing content, firms increasingly rely on fine-tuning models using engagement data, such as A/B test results. This MSI working paper argues that optimizing only for “what works” risks reward hacking, clickbait and poor generalization. The authors propose a knowledge-guided alignment framework in which large language models (LLMs) generate and validate hypotheses about why content performs well, and then use this knowledge to guide fine-tuning. Using more than 23,000 A/B-tested news headlines, the study shows that knowledge-guided AI produces higher engagement, avoids clickbait and generalizes better—especially in low-data settings.
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