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This study aims to predict consumers' sustainable purchase intentions toward green prepared dishes using explainable machine learning techniques.

June 19, 2026SustainabilityOpen Access

Predicting Sustainable Purchase Intention for Green Prepared Dishes Using Explainable Machine Learning: Evidence from Jilin Province, China

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Randomized trial predicts sustainable purchase intention in Jilin Province consumers, indicating important decision influences.

This study aims to predict consumers' sustainable purchase intentions toward green prepared dishes using explainable machine learning techniques.
Collected data from 805 valid questionnaires in Jilin Province, China.
Utilized the stimulus-organism-response framework and applied six classifiers with SMOTE–Tomek resampling.
Employed Optuna for hyperparameter optimization and SHAP for model interpretation.
XGBoost classifier showed the best performance with an F1-score of 0.894, AUC of 0.934, and MCC of 0.702.
Identified behavioral willingness as a non-compensatory barrier for purchase intention.
Recommended strategies prioritize trust-based advocacy and reliable channel design.

Qi et al. (2026) studied this question.

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