Abstract Symptom checkers or diagnostic conversational agents have considerable potential in assisting and relieving the medical personnel, especially with advancements in natural language understanding (NLU) and natural language processing (NLP). However, despite technical advancements, the adoption rate of such systems remains limited. Previous research has identified system-related, user-related, and environment-related factors as drivers and barriers to acceptance. This quantitative online user study examines the influence of user-related factors on the intention to use chat-based symptom checkers based on the technology acceptance model (TAM), utilizing the ‘Ada Health’ mobile application as an example. Based on literature and previous empirical research, this study selected variables such as health consciousness (HC), health literacy (HL), internet use for health research (IN), perceived data privacy risk (DPR), perceived compatibility (PC), personal innovativeness (PI), and individual trust beliefs (TB). The results of 91 valid datasets showed a significant positive effect of the factors—IN and PC on users’ intention to use and an interdependence between the effects of HC and HL, with HL negatively moderating the effect of HC on IU. Practical Relevance : This study identifies key user-related factors that influence the acceptance of chat-based symptom checkers, providing guidance for more effective digital health design. The strong effect of perceived compatibility highlights the need for interfaces that resemble familiar digital tools and integrate smoothly into users’ daily routines. The relevance of online health information-seeking behavior suggests that symptom checkers should support clear, easily navigable access to reliable information. The interaction between health consciousness and health literacy shows that adaptive information delivery is essential, with simplified guidance for users with lower literacy and more detailed content for those with higher expertise. These insights can help developers create more user-centered, accessible, and trustworthy symptom checkers for diverse user groups.
Joshi et al. (Mon,) studied this question.