The impact of generative AI search result design on user perceptions and information adoption

Artificial intelligence (AI) summaries appear in many Web searches, but there have been errors and questionable quality in the AI responses and source attributions. This dissertation project consists of two studies that explore the impact of generative AI summary features on user perceptions and decision-making. The first experimental study examines the effects of two designs of generative AI search responses (narrative point of view, source authority) on user perceptions and information adoption about consumer health topics. AI summaries were collected, modified, and used in a randomised, within-subjects experiment with crowdsourced workers (N=234). The Information Adoption Model (IAM) informed the conceptual model of the experiment, and the Comprehensive Model of Information Seeking (CMIS) guided the health task design. User perceptions are assessed through self-reported measures of persuasive communication (argument quality, source credibility), information usefulness, domain knowledge, and involvement. The second qualitative study aims to explore how AI summaries and source attributions are utilised alongside traditional Web search results for tasks of personal interest. This is an in-person study involving search sessions, retrospective think-alouds, and semi-structured interviews with graduate students (N=16).