ArticleFrontiers in psychology2026
Media amplification, model source cues, and expectancy violation in public acceptance of generative AI: evidence from a health-consultation experiment.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Generative artificial intelligence (GenAI) is increasingly used for health-information seeking, where evaluations may depend on prior media amplification, the model identity presented, and expectancy violation. Methods: We conducted a 2 (media amplification: benefit vs. risk) × 2 (model source: general-purpose vs. professional) × 2 (expectancy violation: positive vs. negative) between-subjects online experiment with 491 participants. Participants read prior-user reports of better- or worse-than-expected performance without using the AI or observing a response. Type III factorial models tested attitude toward use (ATT), behavioral intention (BI), and perceived risk (PRISK). A secondary analysis estimated conditional indirect associations between expectancy violation and BI through PRISK. Results: Media amplification increased PRISK but did not change ATT or BI on average. Professional model-source cues and positive expectancy violation produced favorable average effects across all three outcomes. In the primary Type III models, the three-way interactions reached significance for ATT, BI, and PRISK (partial η Conclusion: In this health-consultation vignette, media amplification most clearly altered perceived risk, while model source and expectancy violation shaped prospective acceptance across communication conditions. The three-way evidence was most consistent for perceived risk.
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