CEC Theses and Dissertations

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Date of Award

2008

Document Type

Dissertation - NSU Access Only

Degree Name

Doctor of Philosophy in Information Systems (DISS)

Department

Graduate School of Computer and Information Sciences

Advisor

Dr. Yair Levy

Committee Member

Liu Peixiang

Committee Member

Ling Wang

Abstract

While computer technology is generally intended to increase employee productivity and effectiveness that same computer technology may be used in negative ways that reduces productivity and increases cost in the business environment. Computer abuse has occurred in the past 12 months in more than half of the business environments surveyed by the Computer Security Institute. To date, research results still indicate that employee computer abuse is problematic and continues to significantly increase. It is estimated American businesses will lose $63 billion each year due to employees' computer abuse on the Internet.

This study was a predictive study that attempted to predict employees' computer abuse intention (CAI) in the business environment based on the contribution of attitude (ATT), computer security policy awareness (CSPA), and computer self-efficacy (CSE). Working professionals from the south central United States were surveyed to determine their ATT toward computer abuse, CSPA, and CSE, as well as their intention to commit computer abuse in the business environment. A theoretical model was proposed, and two statistical methods were used to formulate models and test predictive power: Multiple Linear Regression (MLR) and Ordinal Logistic Regression (OLR). It was predicted that ATT, CSPA, and CSE will have a significant impact on employee's CAI. Results demonstrated that ATT was a significant predictor in predicting employee CAI on both the MLR and OLR regression models. CSE was a significant predictor on the MLR model only. CSPA was not found to be a significant predictor of CAI on either regression models.

There are two main contributions of this study. First, to develop and empirically validate models for predicting employee's CAI in the business environment. Second, to investigate the most significant construct of the three constructs studied that contribute to the employee's CAI in the business environment.

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