UTAUT Framework Analysis of Transcultural Heritage Education in Digital Libraries: Artificial Intelligence in Action

Abstract The digitisation of cultural heritage resources contributes to the development of transcultural learning, while the integration of AI within online library environments supports more dynamic and participatory user engagement. Despite this potential, empirical evidence examining the acceptance of AI in such contexts remains limited. This study addresses this gap by examining the principal determinants influencing both the intention to use and the actual utilisation of AI tools in transcultural heritage education, through an extension of the Unified Theory of Technology Acceptance and Use (UTAUT). Data were analysed using partial least squares structural equation modelling (PLS-SEM), drawing on survey responses from 365 academic librarians, educators, and students. Construct reliability and internal consistency were assessed using average variance extracted (AVE), composite reliability (CR), and Cronbach’s alpha (α), while discriminant validity was confirmed through established discriminant validity criteria and the HTMT ratio. Mediation and moderation effects were examined, and overall model adequacy was evaluated using standard fit indices. The results demonstrate strong predictive capability, with R² values of 0.677 for behavioural intention and 0.574 for actual usage. Facilitating conditions (β = 0.345) and effort expectancy (β = 0.284) exerted significant positive effects on behavioural intention, whereas performance expectancy did not exhibit a statistically meaningful influence. Attitude functioned as a mediating mechanism across key relationships, while personal innovativeness significantly moderated acceptance (p = 0.003), and individual readiness also acted as a mediator. The model exhibited high validity, as reflected by SRMR = 0.052 and NFI = 0.921. Overall, the findings indicate that favourable attitudes, user preparedness, perceived ease of use, and institutional support are critical enablers of effective AI adoption in digital heritage contexts. Anchored in the UTAUT framework, the study supports the deployment of culturally adaptable and sustainable AI solutions within online library systems.