Impact of AI Affordance on AI Adoption: The Mediating Role of Self-Monitoring and Uniqueness

Abstract

Although artificial intelligence (AI) is currently a prominent technology that has received increasing attention, the factors affecting users’ AI device adoption for healthcare were less discussed. Drawing on affordance-actualization theory and literature on AI, uniqueness theory, and healthcare, we developed a research model to explain users’ adoption intention of AI devices in the healthcare context. Data collected from 307 potential users in China were used to test the proposed model. Our findings indicated that data capture, classification, and social affordances impacted self-monitoring significantly, while data capture and social affordances were the predators of uniqueness enhancement. The self-monitoring, on the other hand, had a positive influence on uniqueness enhancement. Finally, our results revealed that self-monitoring and uniqueness enhancement have significant effects on AI adoption. From the findings, theoretical and practical implications are discussed.

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