Chatbot design characteristics and initial stickiness intentions in healthcare mobile applications

The increasing use of AI-driven chatbots in healthcare mobile applications is reshaping how users access health-related information and interact with digital healthcare services. Drawing on the Stimulus-Organism-Response theory, this study examines how three healthcare chatbot characteristics, anthropomorphism, navigability, and task-technology fit (TTF), influence user delight and, subsequently, users' initial stickiness intention toward chatbot-supported healthcare applications. The study further investigates user competency as a moderating factor and compares the proposed relationships across selected public and private healthcare chatbot applications. Data were collected from 360 users of healthcare mobile applications in Pakistan following a controlled app-use exposure procedure. The data were analysed using confirmatory factor analysis and structural equation modelling, along with mediation, moderation, and multigroup analyses. The findings show that anthropomorphism, navigability, and TTF positively influence user delight, which in turn enhances initial stickiness intention. User competency strengthens the relationships between chatbot characteristics and user delight. The multigroup results further suggest that the effects of chatbot characteristics may vary across the selected public and private chatbot-supported healthcare applications. This study contributes to healthcare chatbot research by explaining how design-related chatbot stimuli shape users' affective responses and early engagement intentions in digital healthcare settings.