Structural equation model of E-commerce live broadcasting influencing customers purchase intention prediction using machine learning

Abstract

There is a growing need to understand how live streaming e-commerce influences consumers’ purchasing behavior. Perceived value, engagement, and live streaming quality are crucial components that utilized structural equation modeling (SEM) to examine the factors that influence purchase intentions. This study presents a methodology for analyzing the variables that influence live streaming e-commerce purchase decisions. The study uses SEM and Machine Learning algorithms like Bayesian model, Random Forest, XGBoost, KNN and SVM to assess the prediction. This paper uses two feature transformation methods (MinMax and Zscore) and two feature selection models (InfoGain and Correlation) to improve the prediction of purchase intention. This paper gathers questionnaire responses from 500 participants who have purchased goods through E-commerce Live Broadcast in China and validates the results using a SEM. The study provides a reliability and validity analysis for the suggested model using SEM analysis. The attributes of live broadcasts can elevate the perceived value and trustworthiness, as well as consumer impulsivity, hence increasing customers’ likelihood to purchase.