Predicting online shopping addiction: a decision tree model analysis
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 C...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1462376/full |
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| author | Xueli Wan Jie Zeng Ling Zhang |
| author_facet | Xueli Wan Jie Zeng Ling Zhang |
| author_sort | Xueli Wan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c90f91ac75cc4c7b92e22c5c6c49e8bc |
| institution | Kabale University |
| issn | 1664-1078 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj-art-c90f91ac75cc4c7b92e22c5c6c49e8bc2025-01-08T06:11:44ZengFrontiers Media S.A.Frontiers in Psychology1664-10782025-01-011510.3389/fpsyg.2024.14623761462376Predicting online shopping addiction: a decision tree model analysisXueli WanJie ZengLing ZhangBackgroundOnline 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.https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1462376/fullonline shopping addictionc5.0 decision tree modelpredictive analysisbehavioral addictionacademic procrastinationsocial anxiety |
| spellingShingle | Xueli Wan Jie Zeng Ling Zhang Predicting online shopping addiction: a decision tree model analysis Frontiers in Psychology online shopping addiction c5.0 decision tree model predictive analysis behavioral addiction academic procrastination social anxiety |
| title | Predicting online shopping addiction: a decision tree model analysis |
| title_full | Predicting online shopping addiction: a decision tree model analysis |
| title_fullStr | Predicting online shopping addiction: a decision tree model analysis |
| title_full_unstemmed | Predicting online shopping addiction: a decision tree model analysis |
| title_short | Predicting online shopping addiction: a decision tree model analysis |
| title_sort | predicting online shopping addiction a decision tree model analysis |
| topic | online shopping addiction c5.0 decision tree model predictive analysis behavioral addiction academic procrastination social anxiety |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1462376/full |
| work_keys_str_mv | AT xueliwan predictingonlineshoppingaddictionadecisiontreemodelanalysis AT jiezeng predictingonlineshoppingaddictionadecisiontreemodelanalysis AT lingzhang predictingonlineshoppingaddictionadecisiontreemodelanalysis |