The Influence of Smart Home Service’s Explainable Interface Attributes on Service Quality and Satisfaction

Artificial intelligence (AI)-enabled smart home services increasingly require explainability to support users’ understanding and acceptance of automated decisions. While explainable AI (XAI) research has primarily focused on technical interpretability and task-oriented contexts, less attention has been paid to explanation interfaces in everyday smart home services. This study examines the relationships among explanation interface attributes, user perceptions, service experience, and usage intention. Six attributes were initially identified: Transparency, Accuracy, Clarity, Consistency, Customization/Adaptability, and Privacy. Data from 450 users across Home Management, Health Care, and Care were analyzed using partial least squares structural equation modeling. During measurement model refinement, Accuracy and Consistency were excluded due to insufficient discriminant validity, resulting in four final attributes. Clarity and Privacy were significantly associated with Perceived Ease of Use, Perceived Usefulness, and Perceived Trustworthiness, whereas Transparency and Customization/Adaptability showed more selective relationships. Perceived Usefulness and Perceived Trustworthiness were significantly associated with Service Experience Quality and Satisfaction. Service Experience Quality and Satisfaction were significantly associated with Usage Intention. Between-domain comparisons indicated largely common structural relationships, with significant differences limited to the relationships of Customization/Adaptability and Privacy with Perceived Usefulness. These findings support common explanation design principles alongside selective context-specific adaptation.