International Journal of Information Technology· 2026Q1
Structural equation model of E-commerce live broadcasting influencing customers purchase intention prediction using machine learning
- 50citations
- Q1SCImago
- 2026year
Short summary
A structural equation model (SEM) combined with machine learning (ML) algorithms (Bayesian, Random Forest, XGBoost, KNN, SVM) accurately predicts purchase intention in e-commerce live broadcasting, with live broadcast attributes increasing perceived value, trustworthiness, and impulsivity.
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Key points
- A combined SEM and ML approach predicts purchase intention in e-commerce live broadcasting.
- Live broadcast attributes positively influence perceived value, trustworthiness, and consumer impulsivity.
- The study utilized Bayesian, Random Forest, XGBoost, KNN, and SVM algorithms for prediction.
- Feature transformation (MinMax, Zscore) and selection (InfoGain, Correlation) methods were applied to enhance ML accuracy.
- Data was collected from 500 participants who purchased via E-commerce Live Broadcast in China.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. International Journal of Information Technology, 2026 · DOI ↗
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Field: Information Systems and Management
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