Factors influencing green, environmentally-friendly consumer behaviour

Excessive consumption of non-green products contributes to increasing levels of environmental damage. One effort to overcome this problem is to influence consumer behavior in a more environment-friendly direction. Therefore, it is necessary to identify factors that influence environment-green consumer behavior. The main aim of this research is to build a more comprehensive consumer behavior model inductively using artificial intelligence algorithms. This research aims to identify factors based on questionnaire instruments and interviews as data collection methods. Questionnaires were distributed to the public in the province of West Java, Indonesia, where only 253 respondents gave valid answers. This research measures stated behavior and not actual behavior. This research focuses on variables that influence environment-green consumer behavior, including environmental knowledge, environmental concern, health concern (HC), attitude toward behavior (ATB), subjective norm (SN), perceived price (PP), perceived value and quality, and green consumer behavior (GCB). All variables were validated using the partial least square-structural equation model method using SmartPLS 4.0 software. Furthermore, the validated variables were modeled and classified using the support vector machine (SVM) method. The test results show that all variables, both direct and indirect, have a positive and significant influence on environment-green consumer behavior, with a significant level of p < 0.05 and t > 1.96. The SVM modeling predictions reach a high level of accuracy of about 96%. This means that the variables ATB, SN, and PVC have a direct effect on GCB. Additionally, most respondents fell into the "less environment-green behavior" classification, indicating there is a space for improvement in promoting environment-green choices.