Date of Award

January 2015

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer and Information Technology

First Advisor

Marcus Rogers

Committee Member 1

Kathryn Seigfried-Spellar

Committee Member 2

John Springer

Abstract

Presently, computer crime is rampant and costly. Combating these crimes is not only focused on the technical aspect but also the individual behind the computer. Researchers agree the way to fight computer crime is to gain a better understanding of those behind the keyboard. In an effort to aid investigators in profiling computer criminals, the current study aims to add empirical literature relating to characteristics which predict computer behavior. The current study aims to test the Rogers, Seigfried and Tidke (2006) predictive model and determine if Internet addiction is related to self reported computer deviant behavior. By utilizing a snowball sampling method the current study (n=95) was comprised of 49 self reported computed deviants and 46 non-computer deviants. Over all, Internet addiction was the best predictive variable for computer behavior. Those who scored high on the Internet Addiction Test (IAT) were 1 time more likely to be self-reported computer deviants. Limitation and future research is also discussed.

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