Nghiên cứu sinh Đào Minh Hoàng bảo vệ luận án tiến sĩ
ORIGINAL CONTRIBUTIONS OF THE DISSERTATION
Dissertation title: Determinants of intention to use artificial intelligence in healthcare: An empirical study in Vietnam
Specialization: Business Administration (E-PhD) Specialization code: 9340101
PhD candidate: Dao Minh Hoang
Supervisor(s): Prof.Dr. Nguyen Thi Tuyet Mai
Institution: National Economics University
Original contributions on academic and theoretical aspects
This dissertation provides several contributions to the literature on consumer adoption of AI-based Medical Decision Support Systems. First, it advances the application of Behavioral Reasoning Theory by incorporating two beliefs, techno-optimism and anthropocentrism, and demonstrating that each belief can activate both supportive and opposing reasoning processes. By illustrating these concurrent pathways, the study expands existing BRT applications that have predominantly examined a single belief/value, and assumed more unidirectional links between beliefs and reasoning. Second, this study has uncovered context-specific reasons for (e.g. initial trust, personal innovativeness in the domain of health technology and modern self), and reasons against (e.g. perceived threats and traditional self) adopting medical AI. Furthermore, integrating self-concepts into consumers’ reasons enriches the understanding of how sociocultural identity influences medical AI adoption in transitional economies and underresearched contexts such as Vietnam. In addition, by drawing on the Belief-to-Behavior Inference perspective, the study clarifies how beliefs are translated into specific reasons, which subsequently shape attitudes and intentions. The validated indirect effects reinforce the centrality of reasoning mechanisms within BRT and provide a more detailed explanation of how abstract beliefs inform technology adoption judgments. Last, the study refines the conceptualization of anthropocentrism within AI adoption research. The findings show that anthropocentric beliefs can exert both facilitating and inhibiting influences, depending on whether AI is interpreted as augmenting or diminishing human agency. This contributes to current discussions on ambivalence and identity-relevant evaluations in consumer responses to emerging technologies.
Recommendations derived from the findings of the dissertation
To successfully integrate medical AI, a multifaceted strategy involving healthcare providers and policymakers is essential. First, hospitals should initiate adoption by targeting tech-optimistic, younger, and tech-savvy demographics through digital campaigns and hands-on experience hubs that clearly highlight the concrete benefits of medical AI, such as improved diagnostic accuracy and greater patient convenience. To address human-centered concerns (anthropocentric values), the healthcare providers must explicitly frame AI as a supportive tool that empowers rather than replaces human physicians, utilizing medical experts as ambassadors to reinforce human-AI collaboration. To mitigate consumer resistance stemming from a preference for traditional methods and perceived threats, educational visualization videos and relatable testimonials should be used to help patients envision safe, human-centered AI integration in their medical services. Furthermore, hospitals are advised to develop comprehensive AI guidelines, adopt a gradual implementation roadmap, and train staff in AI literacy to ensure patients feel informed and respected throughout the process. Finally, policymakers must establish a clear legal framework governing data protection, accountability, and informed consent to safeguard patient rights and facilitate the responsible expansion of AI within the healthcare system.
