A Hybrid SEM-ANN Model for Predicting Purchase Intention Toward Recycled PET Products: Evidence from Indonesian Generational Segments

The escalating plastic waste crisis in Indonesia calls for a transition toward a circular economy, particularly through the adoption of recycled Polyethylene Terephthalate (rPET) products. Despite their environmental benefits, consumer acceptance remains limited due to concerns over quality, cost, and insufficient product information. This study employs a hybrid framework that combines partial least squares structural equation modeling (PLS-SEM) and artificial neural networks (ANN), complemented by multi-group analysis (MGA), to identify the behavioral, ethical, and cognitive determinants of purchase intention (PI) toward rPET products. A survey involving 406 respondents from Generations X, Y, and Z provides empirical evidence of both linear and nonlinear behavioral mechanisms. PLS-SEM captures direct and indirect effects among extended Theory of Planned Behavior (TPB) constructs, while ANN uncovers hidden nonlinear relationships and variable importance rankings. The results show that Willingness to Pay (WTP) is the most decisive predictor of PI, followed by Environmental Literacy (EL) and Moral Obligation (MO), which indirectly influence PI through Attitude (ATT). Moreover, MGA reveals generational heterogeneity: Generation Z responds most strongly to WTP and Perceived Behavioral Control (PBC), whereas Generation X emphasizes Subjective Norms (SN). These findings underscore the importance of affordability, ethical framing, and literacy enhancement as critical levers for policymakers and industry stakeholders seeking to accelerate the adoption of sustainable rPET in emerging economies.

Artificial neural networks; Circular economy; Multi-generation; PLS-SEM; Purchase intention; rPET

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