Prediction of Green Purchase Intention Using Machine Learning Techniques: The Case of Apparel and Clothing Among Filipino Generation Z

This paper seeks to predict a consumer's green purchase intention and classify them as green consumers or not through a set of cognitive and behavioral factors. Data were obtained from 526 Generation Z in the National Capital Region (NCR), Philippines, and evaluated through various machine learning techniques, namely, Decision Trees, Random Forests, Gradient Boosting, XGBoost, K-Nearest Neighbors, and Support Vector Machines. Various performance metrics were used to validate these models. The findings show that most of the models achieved above 80% classification performance. Further, the study revealed that perceived behavioral control, green perceived value, and social media impact were the most crucial factors of green purchase intention, followed by environmental consciousness, green perceived quality, and environmental knowledge. Interestingly, green self-identification achieved the lowest rank, which suggests that green purchase intention among Filipino Generation Z is driven more by practical and psychological factors than just environmental awareness and identity. Finally, theoretical and practical implications are offered at the end of the study.