A Study of the Contributions of Attitude, Computer Security Policy Awareness, and Computer Self-Efficacy to the Employees' Computer Abuse Intention in Business Environments
2008
- 60Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage60
- Abstract Views59
- Downloads1
Thesis / Dissertation Description
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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