The study, published in npj Heritage Science, applied a cognition-emotion-behavior framework and combined two analytical methods: structural equation modeling and artificial neural networks. The survey data were examined to understand the psychological processes associated with users’ continuance and recommendation intentions, which the authors note had remained unclear for digital cultural heritage platforms.

According to the structural equation modeling results, experiential dimensions were positively associated with emotional experience, satisfaction, and cultural identity. These three psychological variables were in turn positively associated with both continuance and recommendation intentions. Among the three, cultural identity showed the strongest association with both outcomes.

The mediation models indicated partial mediation through emotional experience, satisfaction, and cultural identity, suggesting that these variables account for part of the relationship between experiential dimensions and engagement intentions. The artificial neural network sensitivity analysis broadly supported the rankings produced by the structural equation modeling and quantified the relative importance of predictors under nonlinear modeling.

The authors state that the findings identify cultural identity as a central correlate of intended digital heritage engagement and that the results can inform the design and management of digital cultural heritage platforms. The study is an open access article published on 27 October 2026, and the version shared is subject to further edits before the final version of record.

Subject of Research: Psychological predictors of user engagement with the Digital Dunhuang digital heritage platform

Article Title: Predicting digital heritage engagement through cultural identity: a hybrid SEM-ANN study of Digital Dunhuang

Article References: Tang, X., Wei, X., Zhan, C., Lee, H., & Kim, S. (2026). Predicting digital heritage engagement through cultural identity: a hybrid SEM-ANN study of Digital Dunhuang. npj Heritage Science. https://doi.org/10.1038/s40494-026-03071-9

Image Credits: AI Generated

DOI: 10.1038/s40494-026-03071-9

Keywords: Digital Dunhuang, digital cultural heritage, cultural identity, structural equation modeling, artificial neural networks, user engagement, continuance intention, recommendation intention, heritage science, survey study, Predicting, digital