Assessment of Smart Assistance on Voice-Driven Online Shopping

The rapid growth of voice-driven online shopping, powered by smart assistants such as Alexa, Google Assistant, and Siri, has transformed digital commerce by enabling personalized and hands-free shopping experiences. However, challenges related to trust, usability, and technological adaptability remain underexplored. This study aims to assess the effectiveness of smart assistance in enhancing consumer satisfaction, adoption, and engagement in voice-driven e-commerce. Methodologically, the study employs a predictive analysis framework integrating machine learning algorithms (Random Forest, SVM, Logistic Regression, among others) and multiple regression analysis to examine consumer behavior across diverse demographic and technological contexts. Exploratory Factor Analysis (EFA) and Principal Component Analysis (PCA) are also applied to identify key latent factors. Findings reveal that trust, accuracy of voice recognition, and seamless personalization strongly influence adoption, offering critical insights for optimizing smart assistant-enabled shopping systems.