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Consumer Behaviour and Digital Health Innovation: The Case of AI Symptom Checkers
Almaazmi, Sara
Almaazmi, Sara
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33.232-2025.24a Sara Almaazmi.pdf
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Description
A Master of Business Administration (MBA) by Sara Almaazmi entitled, “Consumer Behaviour and Digital Health Innovation: The Case of AI Symptom Checkers”, submitted in November 2025. Thesis advisor is Dr. Aaron Gazley. Soft copy is available (Thesis, Approval Signatures, Completion Certificate, and AUS Archives Consent Form).
Abstract
As artificial intelligence reshapes healthcare delivery, symptom checker applications powered by artificial intelligence are increasingly used for early self-assessment and triage. However, limited research has examined how consumers perceive and adopt these technologies, particularly within the United Arab Emirates. This study addresses two key gaps in the literature: the lack of empirical research on consumer attitudes toward artificial intelligence–based symptom checkers in the United Arab Emirates, and the limited application of established theoretical frameworks, such as the Technology Acceptance Model, in analysing their adoption. Using a quantitative cross-sectional survey of residents in the United Arab Emirates (one hundred and forty respondents), this study examined behavioural intention, perceived risk, trust, social influence, privacy concerns, and core constructs of the Technology Acceptance Model. The findings indicate that generative artificial intelligence tools, including ChatGPT, Gemini, and Microsoft Copilot, were the most frequently used platforms for symptom checking, substantially surpassing specialized medical tools. Social influence emerged as the strongest positive predictor of future adoption among non-users, while perceived risk was associated with a reduction in intention among both users and non-users. Constructs related to perceived usefulness, perceived ease of use, and trust did not significantly influence the persistence of intention among current users. Age was also not a significant predictor of symptom checker usage. These results provide new insights into consumer acceptance of artificial intelligence–based symptom checkers in the United Arab Emirates and highlight the importance of social influence, familiarity with generative artificial intelligence, and perceived risk relative to traditional usability factors. The findings offer practical implications for healthcare practitioners and developers seeking to enhance trust, safety, and cultural appropriateness in artificial intelligence–enabled health technologies within the United Arab Emirates.