Psychological predictors of online fraud victimhood in China: a machine learning approach.
Understanding why some individuals are more susceptible to becoming victims of fraud is crucial for developing effective anti-fraud strategies. This study employs a machine learning approach to explore the impact of individual psychological and socio-demographic characteristics on susceptibility to...
| Publicado en: | Psychology, Crime & Law Vol. 32; no. 4; pp. 703 - 727 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
May2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=193489504&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 193489504 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 1068316X 7WL jtl: Psychology, Crime & Law issn: 1068316X maglogo: N pubinfo: dt: May2026 vid: 32 iid: 4 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 193489504 10.1080/1068316X.2024.2389187 ppf: 703 ppct: 24 formats: tig: atl: Psychological predictors of online fraud victimhood in China: a machine learning approach. aug: au: Xu, Liang Wen, Xin Wang, Jie Li, Shutong Shi, Jiaming Qian, Xiuying affil: Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, People's Republic of China Department of Psychology, College of Education, Zhejiang University of Technology, Hangzhou, People's Republic of China su: China Psychological factors Internet fraud Persuasion (Psychology) Self-control Machine learning Random forest algorithms Critical thinking sug: subj: Psychological factors Internet fraud Persuasion (Psychology) Self-control China Machine learning Random forest algorithms Critical thinking keyword: critical thinking machine learning Online fraud victimization perceived benefits on risk personality susceptibility to persuasion critical thinking machine learning Online fraud victimization perceived benefits on risk personality susceptibility to persuasion ab: Understanding why some individuals are more susceptible to becoming victims of fraud is crucial for developing effective anti-fraud strategies. This study employs a machine learning approach to explore the impact of individual psychological and socio-demographic characteristics on susceptibility to fraud. The random forest (RF) models reveal that psychological factors are more influential in determining an individual's vulnerability to fraud than demographic factors. Within the RF models, feature importance analyses highlight that subdimensions of critical thinking – such as truth-seeking, open-mindedness, and cognitive maturity – along with susceptibility to persuasion, perceived benefits on risk, and self-control, are pivotal in influencing an individual's susceptibility to fraud. These insights are critical for informing targeted interventions and enhancing the effectiveness of anti-fraud measures. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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