5. EXPLORING USER ADOPTION OF AI-POWERED HEALTH SERVICES IN MOROCCO: A QUALITATIVE INVESTIGATION USING THE AOL METHOD

Ensuring social equity in healthcare remains a critical challenge, especially in developing nations. In Morocco, access is limited by structural constraints: rural areas face distance and cost barriers, while urban populations often lack time. AI-powered health services are increasingly viewed as promising solutions to these challenges, offering the potential for more accessible and personalized care. However, such technologies remain largely unfamiliar to Moroccan citizens, and little is known about how they would perceive and engage with these emerging tools. This study aims to explore how individuals cognitively and emotionally imagine a potential experience of using AI-powered health services, and to identify the motivational and inhibiting factors that could influence their adoption. A qualitative design was employed using the Album On-Line (AOL) method, in which 12 participants engaged with constructed usage scenarios. Data were analyzed using the INDSCAL multidimensional scaling technique to uncover latent perceptual structures. Interpretation was primarily grounded in the Technology Acceptance Model and Innovation Resistance Theory. Findings revealed six cognitive clusters linked to Perceived Usefulness, Convenience, and Security, and six affective clusters tied to Confidence, Familiarity, and Tech-Optimism. Perceived Risk emerged as a salient dimension across both cognitive and affective evaluations. The study contributes conceptually by highlighting user-centered factors that may shape future adoption behaviors and can inform confirmatory quantitative research. Practically, it provides actionable insights for healthcare providers, policymakers, and startups to develop user-centered AI-driven services and adequate communication strategies to promote them.