AUTHOR=Wan Xueli , Zeng Jie , Zhang Ling 

TITLE=Predicting online shopping addiction: a decision tree model analysis

JOURNAL=Frontiers in Psychology

VOLUME=Volume 15 - 2024

YEAR=2025

URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2024.1462376

DOI=10.3389/fpsyg.2024.1462376

ISSN=1664-1078

ABSTRACT=BackgroundOnline shopping addiction has been identified as a detrimental behavioral pattern, necessitating the development of effective mitigation strategies.ObjectiveThis study aims to elucidate the psychological mechanisms underlying online shopping addiction through constructing and analyzing a C5.0 decision tree model, with the ultimate goal of facilitating more efficient intervention methods.MethodologyA comprehensive survey was conducted among 457 university students in Sichuan, China, utilizing validated psychometric instruments, including the Online shopping addiction Scale, College Academic Self-Efficacy Scale, College Students’ Sense of Life Meaning Scale, Negative Emotion Scale, Social Anxiety Scale, Sense of Place Scale, and Tuckman Procrastination Scale.ResultsThe predictive model demonstrated an accuracy of 79.45%, identifying six key factors predictive of online shopping addiction: academic procrastination (49.0%), sense of place (26.1%), social anxiety (10.1%), college students’ sense of life meaning (7.0%), negative emotions (7.0%), and college academic self-efficacy (0.9%).ConclusionThis pioneering study in online shopping addictiononline shopping addiction prediction offers valuable tools and research support for identifying and understanding this behavioral addiction, potentially informing future intervention strategies and research directions. This study provides research support for improving people’s understanding and management of behavioral addictions and promoting healthier online shopping habits.