Exploring Factors of Artificial Intelligence-Enabled Service Quality: Scale Development and Empirical Validation

The rapid integration of Artificial Intelligence (AI) into service delivery necessitates a re-evaluation of traditional service quality models. This study develops and validates a comprehensive scale to measure Artificial Intelligence-Enabled Service Quality (AI-SQ), addressing the limitations of existing frameworks like SERVQUAL in non-human, technology-driven contexts. Grounded in a thorough review of literature and empirical data, the research proposes a five-dimensional AI-SQ scale: Reliability, Responsiveness, Personalization & Intelligence, Transparency & Trust, and Empathy & Emotional Intelligence. Data were collected from users of AI-powered services through a structured survey (N = 350), and psychometric analysis was conducted using exploratory factor analysis (EFA), Cronbach’s alpha, and KMO/Bartlett’s tests. Results indicate excellent reliability (Cronbach’s Alpha = 0.987) and strong construct validity, with a Kaiser-Meyer-Olkin (KMO) measure of 0.976 and significant Bartlett’s Test of Sphericity (p < 0.001). The rotated component matrix confirms clean item loadings across the five factors, supporting the scale’s dimensional structure. Findings reveal that users expect AI systems to be not only accurate and fast but also transparent, trustworthy, personalized, and emotionally aware. The validated AI-SQ scale offers a reliable tool for researchers and practitioners to assess and improve AI-driven customer experiences. This study contributes to service science by introducing a context-specific, empirically supported model that captures the evolving nature of service quality in the age of AI. The scale can be applied across industries to enhance AI design, build user trust, and ensure ethical, human-centered service delivery.