THE INFLUENCE OF ELECTRONIC HEALTH LITERACY AND AI SELF-EFFICACY AS MODERATING FACTORS ON BEHAVIORAL INTENTION TO USE AI CHATBOT FOR SELF-DIAGNOSIS
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This research aimed to 1) Examine the levels of Effort Expectancy, Performance Expectancy, Electronic Health Literacy, AI Self-Efficacy, and Behavioral Intention to use AI Chatbots for self-diagnosis, 2) Examine the influence of Effort Expectancy and Performance Expectancy on Behavioral Intention to use, 3) Test the mediating role of Performance Expectancy in linking Effort Expectancy to Behavioral Intention to use, 4) Test the moderating role of Electronic Health Literacy, and 5) Test the moderating role of AI Self-efficacy on the relationships between the two expectancy constructs and Behavioral Intention to use. The sample comprised 670 Thai citizens aged 20 years and older who had experience using AI chatbots for self-diagnosis of disease. Data were collected via an online screening questionnaire consisting of 56 items on a 5-point Likert scale, which was validated for content validity by three experts (overall mean IOC = 0.96), achieved an overall reliability (Cronbach’s alpha) of 0.91, and was analyzed using SPSS and SmartPLS 4. The results revealed that all variables were at a high level, with electronic health literacy showing the highest mean. Effort Expectancy had the greatest effect on Behavioral Intention to use (Total Effect = 0.358), followed by Performance Expectancy (Total Effect = 0.154), together explaining 56.40% of the variance. Performance Expectancy served as a partial mediator between Effort Expectancy and Behavioral Intention to use, while Electronic Health Literacy and AI Self-efficacy significantly moderated the relationships between Effort Expectancy and Behavioral Intention to use. Developers and public health agencies should therefore design AI chatbots that are easy to use, promote online health information literacy skills, and build confidence in using AI to sustainably encourage actual adoption.
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