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The transformation of social development modes has led to profound changes in the pattern of intangible cultural heritage, while simultaneously posing significant challenges to its preservation. The rapid development of artificial intelligence (AI) technology has brought new development opportunities in various research fields. This study intends, by constructing and evaluating a theoretical model, to investigate whether AI-generated cultural and creative products can promote the sustainability of intangible cultural heritage. The central focus of this research is to measure the effectiveness of AI technologies in promoting the sustainability of intangible cultural heritage. The context of the research design is rooted in the attention, interest, search, action, and share (AISAS) model, incorporating theories of perceived value and cultural identity, to forecast the long-term viability of AI-generated cultural and creative products in the promotion of intangible cultural heritage. This research was conducted in Tianjin, China and carried out using quantitative methods, a questionnaire survey, and the accidental sampling method, taking a sample of 291 participants for analysis. The results show that 1) the attraction of and interest and participation in AI-generated Yangliuqing New Year Print cultural and creative products have a positive effect on perceived value; 2) the purchase and sharing of these products have a positive impact on cultural identity; 3) the perceived value has a positive impact on cultural identity; and 4) cultural identity has a positive impact on the sustainability of intangible cultural heritage. This study contributes to the theoretical development and practical application of the AISAS model and offers valuable insights into the future development trajectory of intangible cultural heritage, thereby promoting its sustainability. The limitations of this study are its small sample size and geographical restrictions. In future studies, the sample size will be expanded and will include more regions for data analysis.
Zhang et al. (Wed,) studied this question.