Determinants of Generative AI in Promoting Green Purchasing Behavior: A Hybrid Partial Least Squares–Artificial Neural Network Approach

Abstract

In the era of rapid technological advancement, generative artificial intelligence (AI) has emerged as a transformative force in various sectors, including environmental sustainability. This research investigates the factors and consequences of using generative AI to access environmental information and influence green purchasing behavior. It integrates theories such as the information adoption model, value–belief–norm theory, elaboration likelihood model, and cognitive dissonance theory to pinpoint and prioritize determinants of generative AI usage for environmental information and green purchasing behavior. Data from 467 participants were analyzed using a hybrid methodology that blends partial least squares (PLS) with artificial neural networks (ANN). The PLS outcomes indicate that interactivity, responsiveness, knowledge acquisition and application, environmental concern, and ascription of responsibility are key predictors of generative AI use for environmental information. Furthermore, environmental concerns, green values, personal norms, ascription of responsibility, individual impact, and generative AI use emerge as predictors of green purchasing behavior. The ANN analysis offers a unique perspective and discloses variations in the hierarchy of these predictors. This research provides valuable insights for stakeholders on harnessing generative AI to promote sustainable consumer behaviors and environmental sustainability.