An artificial intelligence-driven smart home interaction framework for technology acceptance prediction and user experience optimization: A raw-data confirmatory reanalysis
Smart-home adoption depends not only on technical performance but also on users’ perceptions of affordability, compatibility, usefulness, ease of use, trust, social influence, and support. However, when these acceptance factors overlap, their relative predictive contributions and implications for user-experience design remain unclear. This research proposes an artificial intelligence-based smart-home interaction framework to predict technology acceptance and translate evidence of acceptance into actionable user-experience priorities for young adults aged 18 to 35 in eastern coastal China. The open respondent-level questionnaire data reported by Wang et al. (2024) were analyzed for 475 valid cases of behavioral intention, perceived usefulness, perceived ease of use, social influence, facilitating conditions, perceived trust, perceived cost, and compatibility. Pathway relevance is distinguished from predictive contribution through reliability checking, standardized regression, bootstrap inference, cross-validation, and exact Shapley decomposition in the reanalysis, rather than in the original structural-model study. The seven-predictor model accounted for 41.8% of the variance of behavioral intentions, with perceived cost being the strongest negative predictor and facilitating conditions, compatibility, and perceived usefulness being strong and consistent positive predictors. The Shapley decomposition analysis showed that cost, compatibility, facilitating conditions, and usefulness were the most important factors explaining the variance, with trust primarily through shared predictive information. The result is an Acceptance–Experience Priority Matrix that delineates a structured innovation sequence that prioritizes affordability, interoperability, support, and visible daily value. It provides a framework for the evidence-based design of smart-home products and services, and the development of user-centered innovations without invoking causal claims for young users in technology-supported domestic environments in a safe way.
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