Neves, Catarina Paisana Pires Costa dasPihas, Sara2025-11-072025-11-072025-10-27http://hdl.handle.net/10362/190255Dissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Digital Marketing and AnalyticsThis study investigates the factors shaping patient trust in the adoption of AI chatbots for healthcare purposes, using an extended UTAUT2 framework integrated with constructs from Bagozzi’s self-regulation model. Drawing on 256 valid responses from Portugal and Israel, the research explores how performance expectancy, effort expectancy, hedonic motivation, habit, price value, trust, system quality and information quality influence both behavioral intention and actual use of AI chatbots in healthcare. Trust emerges as a critical driver, directly impacting use behavior and moderating key relationships such as those between service quality and intention to use. Multigroup analysis reveals cultural differences, with Portugal showing stronger alignment between intention and behavior, while Israel displays an intention behavior gap, emphasizing the importance of contextual and cultural factors in technology acceptance. The study contributes to theoretical understanding by highlighting the role of trust as both a direct and moderating influence and confirms that adoption dynamics vary across populations. Practically, it provides actionable insights for developers and healthcare providers aiming to enhance AI chatbot adoption by prioritizing usability, trust building features, and localized strategies. Ultimately, this research supports the design of inclusive, trustworthy AI tools that align with patient expectations in sensitive healthcare contexts.engArtificial Intelligence (AI)AI chatbotAI in HealthcareAI trustUTAUT2Patients’ perceptionsSDG 3 - Good health and well-beingSDG 8 - Decent work and economic growthSDG 9 - Industry, innovation and infrastructuremaster thesis204071011