Traditional social learning predicts cyber deviance? Exploring the offending versatility thesis in social learning theory
| Author(s) | Zhou, You | |
| Author(s) | Liu, Weidi | |
| Author(s) | Lee, Claire | |
| Author(s) | Xu, Boyang | |
| Author(s) | Sun, Ivan | |
| Date Accessioned | 2024-06-26T16:45:10Z | |
| Date Available | 2024-06-26T16:45:10Z | |
| Publication Date | 2024-05-20 | |
| Description | This is the peer reviewed version of the following article: Zhou, Y., Liu, W., Lee, C., Xu, B., & Sun, I. (2024). Traditional social learning predicts cyber deviance? Exploring the offending versatility thesis in social learning theory. Behavioral Sciences & the Law, 1–18. https://doi.org/10.1002/bsl.2664, which has been published in final form at https://doi.org/10.1002/bsl.2664. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited. © 2024 John Wiley & Sons Ltd. This article will be embargoed until 05/20/2025. | |
| Abstract | Social learning theory has been widely implemented to understand cyber deviance. Nevertheless, the antecedent scholarship homogenously nested in the perspective of offending specification, leaving the offending versatility thesis unattained. The lack of such studies may undermine the capability of comprehensively understanding the social learning patterns of online offending. Using a sample of 3741 Chinese college students, this study estimated an array of binary logistic regressions to compare the effects of traditional and online social learning in four types of online offending (online sexual harassment, cyberbullying, hacking, and digital piracy). The results suggest that offending versatility and offending specification co-exist in the social learning process of cyber deviance, while offending specification explains a marginally greater variance. Besides, online learning variables act as potential mediators in the relationships between traditional learning and cyber deviance. Furthermore, traditional social learning shows greater predictive power in cyber-enabled crimes than in cyber-dependent crimes. Our study provides fresh empirical evidence for the non-exclusive association between offending versatility and offending specification in the social learning process of cyber deviance. | |
| Sponsor | Supported by the China Youth Research Association Project in 2024, Grant Number: 2024B12; Marxist Theory Research and Construction Fund of Sichuan Provincial Philosophy and Social Science Foundation, Grant Nunmber: SCJJ23MGC49; Zhejiang Office of Philosophy and Social Sciences' Zhejiang Provincial Philosophy and Sciences Planning Project, Grant Nummber: 24NDQN197YBM; Support Program for Academic Innovation Teams of Young Teachers at China University of Political Science and Law, Grant/Award Number: No.24CXTD02. | |
| Citation | Zhou, Y., Liu, W., Lee, C., Xu, B., & Sun, I. (2024). Traditional social learning predicts cyber deviance? Exploring the offending versatility thesis in social learning theory. Behavioral Sciences & the Law, 1–18. https://doi.org/10.1002/bsl.2664 | |
| ISSN | 1099-0798 | |
| URL | https://udspace.udel.edu/handle/19716/34538 | |
| Language | en_US | |
| Publisher | Behavioral Sciences & the Law | |
| Keywords | cyber deviance | |
| Keywords | offending specification | |
| Keywords | offending versatility | |
| Keywords | social learning theory | |
| Title | Traditional social learning predicts cyber deviance? Exploring the offending versatility thesis in social learning theory | |
| Type | Article |
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