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Smart agriculture technologies are poised to transform farming, yet their adoption remains uneven even in well-resourced peri-urban settings. This study examines the factors shaping both adoption intention and actual use behavior among 206 suburban farmers across nine Shanghai districts via an extended UTAUT framework that incorporates hedonic motivation, trust, and perceived risk alongside the original constructs. To address a common but underexamined limitation in adoption research, both OLS path analysis and full Maximum Likelihood Structural Equation Modeling with latent variables were estimated and compared through systematic specification-sensitivity analysis. Three categories of findings emerged. Performance expectancy and perceived risk were robust predictors of behavioral intention across all model specifications, while facilitating conditions consistently dominated in terms of actual use behavior. By contrast, social influence and hedonic motivation proved unstable: their effects traded off across specifications because of high shared variance, indicating that these constructs may represent overlapping facets of a broader social–hedonic motivational factor rather than independent predictors. Most notably, the intention-to-behavior path weakened to non-significance in the latent variable model, revealing a substantial gap between farmers’ willingness and their actual adoption. This gap was bridged almost entirely by facilitating conditions, i.e., the availability of infrastructure, technical support, and implementation resources. Geographic analysis further revealed significant inter-district variation in trust, suggesting that local institutional context shapes technological confidence. Alternative mediation analysis showed that effort expectancy, trust, and hedonic motivation influenced intention indirectly through performance expectancy rather than acting as independent drivers. These findings suggest that promoting smart agriculture requires shifting policy emphasis from attitude change to developing the infrastructural and service conditions that enable willing farmers to adopt smart agricultural systems.
Li et al. (Fri,) studied this question.