ArticleFrontiers in psychology2024
Predicting online shopping addiction: a decision tree model analysis.
Article in Frontiers in psychology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Online Shopping Addiction in Association with Depression, Anxiety, and Stress: Exploring Independent and Relative Contributions in a Cross-Sectional Study.Behavioral sciences (Basel, Switzerland) · 2026Article
- Machine learning-based prediction model for omental metastasis in right-sided colon cancer patients: a retrospective multicenter study.International journal of colorectal disease · 2025Article
- Chasing the digital high: should hedonic consumption be viewed through the lens of behavioral addiction?Frontiers in psychiatry · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Online shopping addiction has been identified as a detrimental behavioral pattern, necessitating the development of effective mitigation strategies. Objective: This 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. Methodology: A 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. Results: The 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%). Conclusion: This 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.