This experiment, part of the ARF’s “Psychology of Gen AI” series, reveals how models like ChatGPT not only replicate human cognitive biases, such as loss aversion, but also compress variability into uniform patterns. This raises concerns for advertising researchers who rely on authentic insights into consumer behavior.
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The ARF tested whether generative AI can adopt executive personas and provide credible, role-specific strategies. This experiment highlights how AI performs when “thinking like” organizational leaders, its limitations in institutional logic and feasibility, and how human-in-the-loop feedback can refine outputs and create nuanced and worthwhile results.
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AI-generated advertising promises scalable personalization—but how much is too much? This study explores how different levels of personalization in AI-generated ads influence consumer attitudes. Through a series of experiments, the authors identify an inverted U-shaped effect: while moderate personalization enhances ad effectiveness, excessive tailoring can lead to consumer discomfort, triggering concerns about manipulation and privacy. The findings provide timely guidance for marketers navigating the trade-offs of hyper-personalized messaging in the age of generative AI.
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The ARF Attention Report explores the evolving role of attention metrics in advertising, examining their adoption, effectiveness and challenges across agencies and brand-side advertisers. The data is based on a survey measuring the array of metrics currently used by advertisers and agencies across various platforms and industries. As the advertising industry increasingly values consumer engagement beyond traditional reach-based metrics, attention metrics have gained prominence, though adoption and integration remain uneven. Key findings include that attention metrics are gaining traction, with 64% of agencies and 43% of advertisers reporting frequent or constant use. Engagement rates (58%) and time spent (55%) are the most widely used attention metrics due to their accessibility, while advanced methods like biometric response (16%) and neuro measures (13%) remain relatively niche.
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Retail Media Networks (RMNs) have emerged as one of the fastest-growing advertising channels in the U.S., transforming how brands reach and engage consumers. Leveraging first-party data, RMNs enable targeted, measurable campaigns across retailers’ ecosystems, opening a unique blend of performance-driven and brand-building opportunities. Learn about this emerging channel, including the commonalities and differences in how they are being used between advertisers and agencies, in this deep dive into RMNs.
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Can sanitized AI tools truly capture the nuance required for advertising and brand research? Is a less restrained one more likely to produce skewed results? This comparative deep dive, from ARF and MSI explores how two popular large language models—ChatGPT-4o and Grok 3—respond when prompted with complex topics. The findings highlight how content moderation affects not only tone and specificity, but the very boundaries of inquiry. For advertising researchers navigating sensitive brand perception topics, understanding these model tradeoffs is essential.
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What happens when governments regulate how platforms recommend products? A new study examining Amazon's compliance with the UK’s Competition and Markets Authority rule—banning the use of Prime eligibility to determine “Featured Offers”—finds both gains and trade-offs. While the regulation did reduce self-preferencing and boosted price competition, it also led to fewer consumer purchases and lower product ratings, especially in niche markets.
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Can AI challenge its own conclusions rather than merely reinforcing them? In this ARF experiment, researchers explored whether large language models (LLMs) like ChatGPT can go beyond efficiency and exhibit deeper critical thinking skills. By prompting AI to evaluate and compare hypotheses—including its own—this study reveals how LLMs can serve as interpretive collaborators in research and theoretical reasoning.
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A new study uncovers how advertising agencies are attempting to integrate generative AI into their creative workflows, and the repercussions of that process. Based on interviews and field data, the research presents a four-phase model—readiness, co-creativity, validation and execution. Although the study reveals certain anxieties surrounding this integration, in the end both human and AI elements together are required to boost and enliven the creative process.
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A new framework for measuring advertising impact over time—“intensive longitudinal mediation”—uses AI and big data to reveal when and why ads work. This approach helps marketers capture psychological shifts in real-time, going beyond static analysis to uncover how advertising drives behavior over days or weeks.
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