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Paper Type
ERF
Abstract
Generative AI chatbots are increasingly used for health-related information seeking, yet little is known about how users evaluate different platforms in the context of chronic disease management. Unlike episodic health inquiries, chronic conditions require ongoing, reliable, and sensitive information support. Drawing on Task–Technology Fit theory, this work-in-progress study examines how perceived information quality (accuracy, relevance, completeness, timeliness, clarity) and interaction quality (ease of use, engagement, efficiency, cost-effectiveness, privacy protection) influence trust in four GenAI chatbots—ChatGPT, Gemini, Claude, and DeepSeek—and how trust shapes continued use and behavioral intention to act. Using a scenario-based online experiment, participants will interact with one of the assigned chatbots for chronic disease–related information seeking. This study offers a comparative, theory-driven perspective on human–AI interaction in chronic health contexts and provides implications for AI-enabled health information systems design.
Paper Number
1253
Recommended Citation
Maddah, Mahed and Esmaeilzadeh, Pouyan, "When Health Is at Stake: Comparing User Perceptions of Generative AI Chatbots for Chronic Disease Information Seeking" (2026). AMCIS 2026 Proceedings. 5.
https://aisel.aisnet.org/amcis2026/conftheme/conftheme/5
When Health Is at Stake: Comparing User Perceptions of Generative AI Chatbots for Chronic Disease Information Seeking
Generative AI chatbots are increasingly used for health-related information seeking, yet little is known about how users evaluate different platforms in the context of chronic disease management. Unlike episodic health inquiries, chronic conditions require ongoing, reliable, and sensitive information support. Drawing on Task–Technology Fit theory, this work-in-progress study examines how perceived information quality (accuracy, relevance, completeness, timeliness, clarity) and interaction quality (ease of use, engagement, efficiency, cost-effectiveness, privacy protection) influence trust in four GenAI chatbots—ChatGPT, Gemini, Claude, and DeepSeek—and how trust shapes continued use and behavioral intention to act. Using a scenario-based online experiment, participants will interact with one of the assigned chatbots for chronic disease–related information seeking. This study offers a comparative, theory-driven perspective on human–AI interaction in chronic health contexts and provides implications for AI-enabled health information systems design.
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