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Predicting online shopping addiction: a decision tree model analysis.

Xueli Wan1, Jie Zeng1, Ling Zhang1

  • 1College of Chemistry and Life Sciences, Chengdu Normal University, Chengdu, China.

Frontiers in Psychology
|January 23, 2025
PubMed
Summary
This summary is machine-generated.

Academic procrastination and sense of place are key predictors of online shopping addiction. Understanding these factors can help develop targeted interventions for this behavioral addiction.

Keywords:
academic procrastinationbehavioral addictionc5.0 decision tree modelonline shopping addictionpredictive analysispsychological mechanismsself-efficacysocial anxiety

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Area of Science:

  • Behavioral Psychology
  • Digital Health
  • Addiction Research

Background:

  • Online shopping addiction is a growing concern requiring effective mitigation strategies.
  • Understanding the psychological underpinnings is crucial for intervention development.

Purpose of the Study:

  • To identify psychological mechanisms driving online shopping addiction.
  • To develop a predictive model for early identification and intervention.

Main Methods:

  • A C5.0 decision tree model was constructed and analyzed.
  • Survey data from 457 university students in China were collected using validated psychometric scales.

Main Results:

  • The predictive model achieved 79.45% accuracy.
  • Key predictors identified include academic procrastination (49.0%), sense of place (26.1%), social anxiety (10.1%), sense of life meaning (7.0%), negative emotions (7.0%), and academic self-efficacy (0.9%).

Conclusions:

  • This study provides a novel predictive model for online shopping addiction.
  • Findings can inform targeted interventions and future research on behavioral addictions and healthier online shopping habits.