The Social Psychology of Adopting AI-Based Health Sensors: Insights from a Qualitative Study

Abstract

This study investigates users’ perceptions and acceptance of AI-integrated radar health monitoring devices by applying an extended Technology Acceptance Model (TAM)–Protection Motivation Theory (PMT) framework. While previous research has largely relied on surveybased data, this study employs a quantitative content analysis using NVivo to examine linguistic expressions of user attitudes toward AI-enabled health technologies. Data were collected from 15 participants who viewed a demonstration video of the device, and their feedback was systematically coded into four refined themes: Perceived Ease and Usefulness (PEU), Attitude and Intention toward Use (ATI), Health Risk Perception (HRP), and Perceived Risk (PR). Frequency and thematic analyses revealed that PEU was the most dominant factor influencing user acceptance, followed by ATI and HRP, while PR appeared less prominent but remained a moderating concern. The findings highlight that users’ acceptance is shaped by a balance between functional confidence, emotional reassurance, and ethical trust in AI. The study contributes theoretically by extending the TAM–PMT model through linguistic quantification, demonstrating how perceived usability and health motivation jointly influence technology adoption. Managerially, it provides insights for developers and policymakers on improving usability, transparency, and risk communication in AI-based healthcare systems.