Service Quality of Digital Human Guides at Cultural Heritage Sites: A Socio-Technical Systems Framework Integrating SEM and Importance–Performance Analysis

Existing evaluation tools do not fully capture the service characteristics of digital human guide systems at cultural heritage sites. Treating digital human guiding as a sociotechnical service system, this study develops a six-dimensional, 24-indicator service quality framework based on SERVQUAL, AI service quality scales, and interview data and validates it with survey data from 628 users at six cultural heritage sites in China. The two-stage analysis combines structural equation modeling (SEM) with importance-performance analysis (IPA), first examining the structural paths from each service quality dimension to overall service quality and then using the results to estimate indicator importance and identify improvement priorities. Anthropomorphic performance and personalized service have the strongest structural paths to overall service quality, with coefficients of 0.294 and 0.245, and all eight indicators within these two dimensions fall into the “high-importance, low-performance” quadrant. Credibility assurance and system reliability are basic factors: overall service quality is rated significantly lower when they fail but not noticeably higher when they excel. The dimensions were coupled across subsystems, and the binding constraints lie in the expressive-adaptive layer formed by anthropomorphic performance and personalized service. The framework offers a basis for evaluating digital human guide systems at cultural heritage sites in China and, subject to cross-context validation, other sociotechnical service systems.