How AI apps' attributes influence social stigma, trust and usage intentions for mental health support: a cross-cultural study based on an intellectual capital perspective

Purpose Despite the widespread occurrence and adverse effects of mental health issues, many people refrain from seeking assistance owing to stigma, limited access, financial barriers, and insufficient awareness of available resources. Artificial intelligence (AI) has advanced quickly in recent years, leading to the development of a number of AI-supported mental health interventions, which provide benefits including ease, affordability, confidentiality, and accessibility. This study adopts an intellectual capital perspective by conceptualizing AI app attributes as key intangible resources, i.e. human-like capabilities (Empathy and, performance expectancy), structural assurances (Privacy assurance and transparency) and relational mechanisms (trust) that influence user adoption in two culturally distinct contexts, the United Kingdom and Saudi Arabia. Design/methodology/approach The data collected through an online survey, which resulted in 404 usable responses from the UK and 443 from Saudi Arabia, were analyzed using PLS-SEM. Findings The findings demonstrate that performance expectancy, perceived empathy (representing AI-enabled human capital), and privacy assurance (structural capital) are critical drivers of trust in AI systems, while privacy assurance significantly influences social stigma associated with traditional mental health therapy. Both trust in AI (relational capital) and social stigma strongly affect intention to use AI mental health apps, underscoring their central role in adoption behavior. Perceived AI transparency further moderated the relationship between trust and intention in both countries. Despite high cross-cultural similarity in most structural paths, notable differences emerged for behavioral intention. Originality/value Unlike previous work focused on clinical or technical performance, this study offers one of the first user-centric, cross-cultural models that explains how AI mental health app attributes build trust, influence social stigma, and drive usage intention, while positioning AI transparency as a moderator shaping these relationships.