What consumers feel, not what they are told: reframing the drivers of augmented reality purchase intention—a PLS-SEM, ANN, and NCA approach

The adoption of augmented reality (AR) shopping is transforming the consumer shopping experience, but it is unclear how exactly it affects consumers' purchase intention, and whether providing either an informational or a risk-based cue would facilitate or impede the adoption of this technology. This study adopts a hybrid method of PLS-SEM, artificial neural network (ANN) and necessary condition analysis (NCA) to investigate the impact of seven antecedents of AR purchase intention: diagnosticity, information quality, memorable experience, hedonic motivation, perceived risk, social approval, and trust. The results demonstrate that the variables of memorable experience, hedonic motivation, trust and social approval significantly explain purchase intention, while diagnosticity, information quality and perceived risk do not significantly explain purchase intention despite having a negative direction. When nonlinearity is assumed, the four significant predictors are as expected again included in the ANN analysis. In NCA results, all seven constructs are found to be medium necessity effects in the absence of sufficiency effects in PLS-SEM. The study contributes theoretically by offering a more balanced understanding of the positive and negative dimensions of AR adoption, while methodologically demonstrating the value of combining symmetric and asymmetric analytical techniques to explain complex consumer behavior in immersive digital environments. Practical implications for the design of more transparent, user-centered, and cognitively sustainable AR retail experiences are also discussed. The results indicate that the primary motivators towards AR purchase intention are the relational and experiential mechanisms.