Determinants of Digital Museum Users’ Continuance Intention—An Integrated Model Combining an Enhanced TAM3 and UTAUT

Abstract

Using the “Cloud Tour Dunhuang” digital museum as a case, this study integrates an enhanced TAM3 with UTAUT and introduces two external variables—cultural identity and technological innovation—to construct a comprehensive framework for users’ continuance intention. Based on 484 valid responses, we employ a sequential mixed-method design combining structural equation modeling (SEM), artificial neural networks (ANNs), necessary condition analysis (NCA), and grounded theory (GT). The results show that (1) cultural identity and technological innovation significantly promote behavioral intention and continuance behavior by strengthening perceived usefulness; (2) performance expectancy and social influence exert significant effects, whereas effort expectancy and facilitating conditions are comparatively weaker; and (3) the integrated model delivers superior explanatory power and predictive performance relative to single-path baselines. This research enriches user-behavior scholarship in digital cultural heritage and offers theory-informed, practical guidance for improving user retention and optimizing platform design.