CRACKING THE CODE: AN EMPIRICAL EXPLORATION OF SOCIAL LEARNING THEORY AND COMPUTER CRIME.
Computer crime and related behaviors have received increased attention in recent years, yet there is still a paucity of research focusing on the behaviors surrounding computer hacking In the present study, we extend Akers' social learning theory (SLT) to explore the etiology of four forms of compute...
| Publicado en: | Journal of Crime & Justice Vol. 32; no. 1; pp. 1 - 35 |
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| Autores principales: | , |
| Formato: | Artículo |
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Taylor & Francis Ltd
2009
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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=42876047&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 42876047 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0735648X 16GG jtl: Journal of Crime & Justice issn: 0735648X maglogo: Y pubinfo: dt: 2009 vid: 32 iid: 1 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 42876047 10.1080/0735648X.2009.9721260 ppf: 1 ppct: 34 formats: tig: atl: CRACKING THE CODE: AN EMPIRICAL EXPLORATION OF SOCIAL LEARNING THEORY AND COMPUTER CRIME. aug: au: Morris, Robert G. Blackburn, Ashley G. affil: University of Texas at Dallas University of North Texas, Emeritus su: Computer crimes Social learning theory Computer hacking Computer hackers Malware Computer passwords Internet piracy sug: subj: Computer crimes Social learning theory Computer hacking Computer hackers Malware Computer passwords Internet piracy ab: Computer crime and related behaviors have received increased attention in recent years, yet there is still a paucity of research focusing on the behaviors surrounding computer hacking In the present study, we extend Akers' social learning theory (SLT) to explore the etiology of four forms of computer hacking (guessing passwords, attempted hacking, malicious file manipulation, and using/creating computer malware), thus filling a gap in the literature. Based on self-report data (n=600), our findings lend modest support to SLT, however the impact of social learning components may vary across different types of computer hacking Limitations are discussed and suggestions for future research are provided. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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