Predicting Consumer Perceptions of Metaverse Shopping Through Insights from Machine Learning Models

This study investigates consumer perceptions of Metaverse shopping and the factors that influence these perceptions, using machine learning models to classify and analyze the data. Four models—Logistic Regression, Random Forest, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN)—were employed to predict whether consumers view Metaverse shopping favorably or unfavorably. Among these, the SVM model achieved the highest performance, with an accuracy of 94.17%, precision of 97.14%, and an AUC-ROC score of 98.13%. These results indicate that machine learning can reliably classify consumer perceptions based on demographic and experience-related data. Furthermore, the Random Forest model was used to analyze the importance of features influencing consumer attitudes. The findings revealed that experience-related factors—such as interactivity, personalization, and consumer engagement—were more significant in shaping perceptions than product-specific attributes. The most important feature, MC2 (interactivity), contributed 23.6% to the model’s predictive power, highlighting the importance of user experience in driving positive sentiment. These insights suggest that businesses aiming to enter the Metaverse retail space should focus on enhancing the overall shopping experience to foster positive consumer perceptions. Machine learning models provide valuable tools for understanding consumer behavior and tailoring virtual shopping environments accordingly. This research offers a data-driven approach to predicting and understanding consumer perceptions of the Metaverse, providing actionable insights for businesses in this emerging market.