Developing a psychometric scale for AI service quality: insights from AI retail services

As AI technologies become increasingly embedded in retail services, understanding how customers perceive and evaluate AI-enabled service quality is critical for both theory and practice. This study aims to develop and validate a comprehensive AI service quality framework grounded in human–computer interaction (HCI) and service quality theory. Using a systematic scale development approach including item generation, scale purification, validation, and generalisability assessment, the study conceptualizes AI service quality (AI Retail SQUAL) as a multidimensional construct. Five key dimensions are identified: Human-Centred, Adaptive Delight, Seamless Access, Intuitive Interaction, and Trustworthy Transactions. The findings reveal that customers evaluate AI retail services not only based on technical performance, but also on human-centred design, emotional and adaptive experiences, accessibility, usability, and trust-related attributes. Theoretically, the study extends traditional service quality models by integrating HCI and advancing interdisciplinary research at the intersection of service quality, AI, and HCI. Managerially, the validated AI Retail SQUAL scale provides a practical diagnostic tool to guide the design, evaluation, and continuous improvement of AI retail services.