Mixed-methods research reveals user attitudes toward rural smart furniture, indicating pathways for cultural adaptation and effective design in public spaces.
As the development of “smart villages” and “sustainable rural tourism” increasingly becomes a focal point on the global policy agenda, tourism-oriented villages are experiencing a growing demand for digital infrastructure transformation. Against this backdrop, smart outdoor furniture emerges as a noteworthy intervention. However, existing designs for smart outdoor furniture predominantly originate from urban contexts, often failing to align with the distinct preferences, behavioural patterns, and cultural identity of rural users. This study employs a mixed-methods approach, combining Q-methodology with an extended Technology Acceptance Model (TAM), to explore rural users’ technology acceptance pathways. Through Q-sorting, four typical attitude structures were identified: Pragmatic Function-Oriented, Cultural Concern-Oriented, Smart Enhancement-Oriented, and Technology Anxiety-Oriented. These qualitative insights were integrated into an extended TAM framework and validated through a structured survey (n = 319) using Partial Least Squares Structural Equation modelling (PLS-SEM). Findings confirm that Perceived Usefulness and Perceived Ease of Use remain the strongest predictors of user attitude and behavioural intention. Among contextual factors, Function Configuration exerts significant positive influence on both PU and PEOU; Cultural Adaptation significantly enhances PU; Social Influence primarily affects PEOU; Smart Features moderately influence both dimensions; and Perceived Cost Structure affects only PU. This research extends the applicability of the TAM model within rural socio-technical contexts. It provides empirical reference for inclusive and sustainable digital infrastructure design in tourism-oriented villages, while offering practical insights and dissemination pathways for smart design strategies in public spaces within similar socio-cultural environments.
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Duan et al. (2025) studied this question.
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