Predicting Online Delivery Adoption During COVID-19: A Machine Learning Approach

The significant research contribution of this study lies in its comprehensive analysis of the sociodemographic and behavioral factors influencing online delivery adoption during the COVID-19 pandemic, utilizing advanced machine learning techniques. By employing a structured preprocessing pipeline and comparing multiple models, the study identified LightGBM as the most effective classifier, achieving an accuracy of 97% and an F1-score of 0.96 for both classes, which underscores its capability to capture complex patterns in consumer behavior. The findings revealed that younger age, higher household income, and education level were the most influential predictors of increased delivery use, providing valuable insights for transportation planners and policymakers. This research not only enhances understanding of consumer behavior during a critical period but also offers a framework for future studies on urban logistics and service adaptation in response to evolving consumer needs.