Dual Drivers of Experience and Trust: Exploring the Mechanisms of Elderly User Adoption of AI-HVAs from a UTAUT Perspective
DOI:
https://doi.org/10.4108/eetpht.11.11028Keywords:
AI-HVAs, elderly users, UTAUT, PAIE, PAIT, dual perspectiveAbstract
INTRODUCTION: With the accelerating aging of the population and the integrated development of artificial intelligence (AI) technology, AI health voice assistants (AI-HVAs) present a novel approach for enhancing health management among older adults. However, the adoption of this technology by the elderly population still faces multiple barriers, including cognition, trust, and user experience, and its adoption mechanisms have yet to be fully elucidated.
OBJECTIVES: This study aims to construct an AI-HVA adoption model applicable to China's elderly population, focusing on revealing the dual driving role of “experience (experiential rationality)” and “trust (relational rationality)” in the decision-making process of elderly users.
METHODS: Integrating the Unified Theory of Acceptance and Use of Technology (UTAUT) model, this study introduces two key variables—"Perceived AI Experience (PAIE)" and "Perceived AI Trust (PAIT)"—to form a dual-path hypothesis of "internal experience-external influence." Through a questionnaire survey of 413 elderly users, structural equation modeling (SEM) was employed to analyze the data and examine the influence relationships among variables.
RESULTS: (1)Internal Experience Path:PAIE significantly and positively influenced Performance Expectancy (PE) and Effort Expectancy (EE), and also directly promoted Behavioral Intention (BI). This indicates that the quality of the interaction experience is a key antecedent for elderly users forming perceptions of usefulness and ease of use . (2) External Influence Path:Social Influence (SI) did not exert a direct effect on BI but required mediation through PAIT, highlighting the pivotal bridging role of trust in the adoption decision . (3) BI and Facilitating Conditions (FC) jointly significantly promoted Usage Behavior (UB), supporting the applicability of the UTAUT model in the context of AI technology adoption among the elderly.
CONCLUSION: This study extends the explanatory boundaries of the UTAUT model in the field of digital technology adoption by older adults, revealing the complex psychological processes underlying their acceptance of AI-HVAs. On a practical level, the findings provide important insights for the age-friendly design, trust-building, and promotion strategies of AI health products.
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