Promoting Sustainable Digital Cultural Heritage Preservation: A Study on Designers’ Satisfaction with a Digital Platform Using TAM, TTF, and ANN

Cultural heritage plays a crucial role in maintaining cultural diversity and historical identity. However, preservation faces challenges from natural and human-induced factors, prompting increased adoption of digital technologies. Digital cultural heritage platforms provide innovative pathways for sustainable preservation, yet factors influencing user engagement remain underexplored. This study examines the WenZang Chinese Pattern Online Museum database using an integrated Technology Acceptance Model (TAM) and task–technology fit (TTF) framework, supplemented by Artificial Neural Networks (ANN), to explore relationships between key factors affecting designers’ satisfaction and engagement. A combined Structural Equation Modeling (SEM) and ANN approach was employed to survey 267 Chinese designers. Results indicate that design aesthetics (DA) and perceived ease of use (PEOU) enhance perceived convenience (PC); performance impacts (PIM) and information quality (IQ) influence perceived usefulness (PU); PC and PU drive attitude toward using (AU) and purchase intention (PI), jointly enhancing satisfaction with (SAT). Mission technology matching (MTM) positively influences SAT, perceived task–technology fit (PTTF), and technical task fitting (TTF). ANN analysis reveals that PI is the most significant determinant of SAT, followed by DA and PIM, demonstrating nonlinear relationships not captured by linear SEM alone. The introduction of ANN provides comprehensive understanding of user satisfaction, revealing indirect effects of key experience factors (such as DA and PIM) on SAT through PC and PU. This study emphasizes the need to comprehensively consider user experience, technological performance, and behavior transformation mechanisms when optimizing digital cultural heritage platforms to achieve sustained improvements in user satisfaction and engagement.