AI-Assisted Greenwashing Detection and Rational Green Food Purchase Intention in Online Shopping: A Hybrid PLS-SEM and ANN Approach

Artificial intelligence (AI) is increasingly used to help consumers detect greenwashing in online food markets, yet how AI-use motivations relate to rational green food consumption intention (RCI) remains unclear. Integrating information processing theory, the consumer decision-making process model, and the theory of planned behavior, this study examines how risk avoidance (RA), performance expectations (PE), and health benefits (HB) are associated with RCI through subjective norm (SN) and perceived behavioral control (PBC). Based on 619 valid responses from a cross-sectional online survey, the data were analyzed using partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN). The PLS-SEM results show that PE, RA, SN, and PBC are significantly associated with RCI, whereas HB has no significant direct association. SN is strongly associated with PBC, and the SN–PBC sequential mediation path is supported for RA, PE, and HB. The RA–PBC–RCI path is not supported, indicating that risk awareness does not automatically translate into perceived control. The ANN results identify PE as the strongest nonlinear predictor, followed by RA, while HB shows the weakest predictive importance. The findings advance AI-mediated sustainable consumption research and provide intention-level evidence for responsible online green food purchasing.