{"title":"影响大学生网络成瘾的因素:自我控制和焦虑的中介作用。","authors":"Man Chen, Xinyu Zhang","doi":"10.1016/j.actpsy.2024.104535","DOIUrl":null,"url":null,"abstract":"<div><div>This study aims to explore the key factors affecting internet addiction among college students, analyzing how stress, social support, self-efficacy, self-control, and anxiety influence internet addiction and their interrelationships and mechanisms. A structured survey was administered to 538 students from Zhengzhou Vocational University of Information and Technology, Henan University of Chinese Medicine, Henan Polytechnic, employing a two-stage Structural Equation Modeling-Artificial Neural Network (SEM-ANN) to uncover non-compensatory and non-linear relationships. The findings indicate stress, self-control, pleasure, anxiety, self-efficacy, and social support as significant predictors of internet addiction, providing theoretical and practical insights into mitigating internet addiction.</div></div>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Factors influencing internet addiction among university students: The mediating roles of self-control and anxiety\",\"authors\":\"Man Chen, Xinyu Zhang\",\"doi\":\"10.1016/j.actpsy.2024.104535\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study aims to explore the key factors affecting internet addiction among college students, analyzing how stress, social support, self-efficacy, self-control, and anxiety influence internet addiction and their interrelationships and mechanisms. A structured survey was administered to 538 students from Zhengzhou Vocational University of Information and Technology, Henan University of Chinese Medicine, Henan Polytechnic, employing a two-stage Structural Equation Modeling-Artificial Neural Network (SEM-ANN) to uncover non-compensatory and non-linear relationships. The findings indicate stress, self-control, pleasure, anxiety, self-efficacy, and social support as significant predictors of internet addiction, providing theoretical and practical insights into mitigating internet addiction.</div></div>\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S000169182400413X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S000169182400413X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
Factors influencing internet addiction among university students: The mediating roles of self-control and anxiety
This study aims to explore the key factors affecting internet addiction among college students, analyzing how stress, social support, self-efficacy, self-control, and anxiety influence internet addiction and their interrelationships and mechanisms. A structured survey was administered to 538 students from Zhengzhou Vocational University of Information and Technology, Henan University of Chinese Medicine, Henan Polytechnic, employing a two-stage Structural Equation Modeling-Artificial Neural Network (SEM-ANN) to uncover non-compensatory and non-linear relationships. The findings indicate stress, self-control, pleasure, anxiety, self-efficacy, and social support as significant predictors of internet addiction, providing theoretical and practical insights into mitigating internet addiction.