A Study on Trust Deficiency and Discontinuance Intention toward Wearable-Based AI Sleep Coaching Features

Purpose: This study investigated how innovation resistance factors and trust deficit factors influence the lack of trust in wearable-based AI sleep coaching features and how such lack of trust affects discontinuance intention.Methods: Based on Innovation Resistance Theory and Trust Transfer Theory, this study proposes a research model incorporating privacy concerns, technology anxiety, lack of personalization, algorithm distrust, usage burden, usage avoidance tendency, accuracy concerns, lack of trust in wearable devices, and lack of trust in sleep data. Data were collected from 300 users who had experience using wearable-based AI sleep coaching features, and structural equation modeling was employed to test the proposed hypotheses.Results: The results indicated that lack of personalization and usage avoidance tendency significantly increase the lack of trust in AI sleep coaching features. Privacy concerns and usage burden show significant negative effects on the lack of trust in AI sleep coaching features. In addition, lack of trust in wearable devices and lack of trust in sleep data have significant positive effects on the lack of trust in AI sleep coaching features. However, technology anxiety, algorithm distrust, and accuracy concerns are not found to be significant. Furthermore, the lack of trust in AI sleep coaching features significantly increases discontinuance intention. These findings suggest that discontinuance intention is influenced more by lack of personalization, usage avoidance tendency, and lack of trust in wearable devices and sleep data than by concerns about AI technology itself.Conclusion: The findings highlight that enhancing personalization and strengthening trust in wearable devices and sleep data are essential for enhancing users' trust in AI sleep coaching features and reducing discontinuance intention.