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AI Self-diagnosis Systems Adoption: A Socio-Technical Perspective
Katia Guerra, Victor Prybutok
This research investigates technical characteristics and individual and environmental factors that influence the evaluation of technological factors as mediated by ethical considerations toward adopting AI self-diagnosis systems in the USA. The study embraces a socio-technical perspective and develops a research model that integrates the Technological - Personal - Environmental (TPE) framework's constructs, with new constructs, i.e., AI technical characteristics and AI ethics. A survey will be developed and distributed among undergraduate and graduate students in the USA. Our research findings will contribute to capturing the different roles of technical characteristics and individual and environmental factors on technological factors as mediated by ethical principles toward the adoption of AI self-diagnosis systems.

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