Artificial intelligence (AI) chatbots are increasingly used in healthcare, yet questions remain about consumer trust, particularly in providing over-the-counter (OTC) medication advice. This dissertation evaluates consumer trust and satisfaction with AI chatbots and how cultural, regulatory, and experiential factors shape trust. A mixed-methods design grounded in a realist philosophical approach was adopted. The research collected quantitative data through surveys (n=153) and qualitative data via semi-structured interviews. Quantitative analysis employed descriptive statistics, t-tests, correlations, and regression modelling, while qualitative data were analysed thematically through the lens of the trust–accuracy paradox and cultural frameworks. Findings show that trust in chatbots is conditional and there are significant differences between the two countries. Indian consumers reported higher acceptance of chatbots by using chatbots often for minor ailments such as coughs or fever linked to limited healthcare access. However, advice was usually cross-checked with family or doctors showing conditional trust. Irish consumers relied more on pharmacists due to easier access to professional care. Survey data showed moderate trust in chatbot accuracy (22% scored 3/5) but stronger trust in pharmacists (38% scored 5/5). Pharmacists were aware of chatbot use, yet many expressed concerns about misleading or incomplete advice. Across both countries, pharmacists remained the benchmark of credibility. The study contributes to understanding how trust in AI healthcare tools differs across different cultural and regulatory settings for example, Indian participants demonstrated necessity-driven trust valuing accessibility and speed while Irish participants exhibited regulation-driven caution emphasising privacy and accountability. Gender differences were also observed, with men showing greater trust than women. The study contributes to theory by extending models of trust in automation demonstrating that satisfaction and reassurance often outweigh accuracy in driving adoption.

Overall, the research highlights that trust in healthcare AI is shaped not solely by technical accuracy but by user experience, cultural context, and systemic healthcare realities, underscoring the importance of responsible and contextualised deployment.