Abstract
Generative artificial intelligence (GenAI) is reshaping cultural and creative design and raising new questions about how consumers evaluate museum cultural and creative products (MCCPs). Drawing on the Stimulus–Organism–Response (SOR) framework, this study conceptualizes novelty (NOV), originality (ORI), and cultural congruence (CUL) as key GenAI-enabled design stimuli in MCCPs, and models perceived value (PV) and emotional resonance (ER) as organismic states linked to purchase intention (PI). We surveyed Chinese consumers with basic GenAI literacy (N = 312) and analyzed the data using partial least squares structural equation modeling (PLS-SEM). Within the focal context and sample, NOV, ORI, and CUL are all positively associated with PV, ER, and PI, and PV and ER exhibit small but stable partial mediating roles in the relationships between the three stimuli and PI. Among the direct paths, NOV shows the strongest association with PI (β = 0.269, p < 0.001), ORI is more strongly related to ER, and CUL exerts a comparatively larger impact on PV. The model explains 48.0% of the variance in PI (R2 = 0.480) and demonstrates positive predictive relevance (Q2 > 0). These findings suggest that consumer responses to GenAI-enabled MCCPs are jointly shaped by multiple design features and the value judgments and emotional experiences they elicit, rather than by any single cue or pathway. Theoretically, the study applies and tests the SOR framework in a culture–technology hybrid setting and provides an operational measurement and structural model for NOV, ORI, CUL, PV, ER, and PI, while discussing cultural authenticity and disclosure intensity as plausible boundary conditions to be examined in future research. Managerially, the results imply that museums and cultural–creative practitioners may, conditional on cultural congruence, cautiously leverage GenAI to enhance novelty and originality and use value-oriented cues and culturally resonant narratives to support consumer acceptance of GenAI-enabled MCCPs.
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Conceptualization, M.S. and Q.G.; validation, M.S. and K.L.; formal analysis, M.S. and Q.G.; investigation, M.S. and Q.G.; resources, H.L.; data curation, M.S. and Q.G.; writing—original draft, M.S. and Q.G.; writing—review and editing, M.S., K.L. and H.L.; supervision, H.L.; project administration, H.L.; visualization, M.S. and Q.G.
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Shi, M., Guo, Q., Li, H. et al. Understanding purchase intention for genAI-enabled museum cultural and creative products using a SOR model. Sci Rep (2026). https://doi.org/10.1038/s41598-026-36224-8
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DOI: https://doi.org/10.1038/s41598-026-36224-8
