| Abstract |
Consumer decision-making behaviors play a pivotal role in the realm of purchasing sustainable products. It is crucial for businesses to understand the key factors that influence consumers' choices in this context, especially if they aim to align with eco-friendly trends. Conventional methods are inadequate for accurately and successfully identifying the importance of factors that influence consumers' decision-making behaviors in purchasing sustainable products and stem from a lack of holistic consideration. Conventional methods, like AHP, surveys, questionnaires, interviews, and focus groups, often do not fully consider the many aspects of consumer behavior related to sustainability. To address this gap, our study aims to (1) employ a hybrid approach, integrating conventional methods with cutting-edge machine-learning technology for predicting consumer's decision-making behaviors in purchasing sustainable products; (2) demonstrate the practical application of this hybrid approach through the example of green furniture; and (3) provide a practical guide for identifying the importance of factors influencing consumers' decision-making behaviors in purchasing sustainable products. This study will map out implications for the future of consumer decision-making behaviors in purchasing sustainable products. The hybrid approach to studying consumer decision making in sustainable product purchases, combining quantitative and AI methods. This methodology provides a comprehensive analysis of factors influencing environmentally friendly choices, fostering awareness and informed decision making. Businesses can use these insights to tailor strategies, enhance offerings, and meet the rising demand for sustainable products, contributing to environmentally responsible consumer behaviors and promoting economies of scale for sustainable products and innovation. This holistic understanding is crucial for creating a sustainable and socially responsible marketplace. |
