Determining the Buying Motivation for Eco-Friendly Products via Machine Learning Techniques

The purpose of this study was to determine the motivation to buy eco-friendly products via machine learning techniques. With this in mind, a dataset was collected between November and December 2024 from 245 organic consumers in Maramureș County, Romania, via a questionnaire. Consumers’ main motivations to buy eco-friendly products were considered according to three categories: Health Care, Environmental Protection, and Superior Quality. In the analysis of the dataset, among the four feature selection techniques used, Random Forest was determined to be the best with the highest accuracy value. At the beginning of the study, 16 variables were thought to be important categorical factors for consumers’ eco-friendly product-buying motivations, with 5 of these being found to be the most effective with the Random Forest technique. Then, the SHAP method was applied to identify the contribution of driving factors to the buying motivation for eco-friendly products. All analyses were conducted with Python software. The results of the SHAP method indicated that while all factors perform well, consumers considering themselves as eco-friendly is the most important factor for the Environmental Protection category when buying eco-friendly products, while the most important criterion of the original certification category was found to be the Health Care category. The most effective factor for Superior Quality was determined as the high-price category, which is the main barrier to purchasing eco-friendly products.