A systematic literature review on predictive analytics for sustainable consumer behavior : Investigating how predictive analytics can help understand and promote sustainable consumer behaviors, focusing on consumer data to predict eco-friendly product shifts, and how businesses leverage these insights

Abstract

Sustainable consumer behavior is increasingly important as societies face urgent environmental challenges. This thesis explores how predictive analytics can support and promote sustainable consumption by analyzing patterns in consumer data. Using a systematic literature review approach guided by PRISMA 2020, the study reviews 40 peer-reviewed articles published between 2010 and 2025. The research addresses three main questions: how predictive analytics can help understand and forecast sustainable consumer behavior; which data sources and analytical methods are most effective in predicting eco-friendly product shifts; and what outcomes, both positive and negative, result from applying these tools. The findings show that predictive analytics, including methods like logistic regression, random forest, and machine learning, is used to segment consumers, predict demand for sustainable products, and guide business and policy decisions. These tools rely on various data types, such as purchase records, surveys, social media, and sensor data. A framework is developed to illustrate how data, analytics, and behavioral factors interact to shape sustainability outcomes. The results highlight benefits like improved targeting, innovation, and operational efficiency. However, the study also identifies challenges, such as privacy concerns, model bias, and limited generalizability across contexts. While predictive analytics has strong potential to support sustainability goals, the thesis emphasizes the need for more ethical, transparent, and adaptable applications. The review also points to gaps in research, especially a lack of theory-driven models and cross-sector collaboration. Overall, the thesis concludes that predictive analytics can be a valuable tool for understanding and encouraging sustainable consumer behavior, but its success depends on responsible use and continued development