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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
Summary
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.
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.
Keywords:
academic procrastinationbehavioral addictionc5.0 decision tree modelonline shopping addictionpredictive analysispsychological mechanismsself-efficacysocial anxietyMore Related Videos
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