The Influence of Cognitive Factors and Personality Traits on Mobile Device User\u27s Information Security Behavior

As individuals have become more dependent on mobile devices to communicate, to seek information, and to conduct business, their susceptibility to various threats to information security has also increased. Research has consistently shown that a user’s intention is a significant antecedent of information security behavior. Although research on user’s intention has expanded in the last few years, not enough is known about how cognitive factors and personality traits impact the adoption and use of mobile device security technologies. The purpose of this research was to empirically investigate the influence of cognitive factors and personality traits on mobile device user’s intention in regard to mobile device security technologies. A conceptual model was developed by combining constructs from both the Protection Motivation Theory (PMT) and the Big Five Factor Personality Traits. The data was collected using a web-based survey according to specific inclusion and exclusion criteria. Respondents were limited to adults 18 years or older who have been using their mobile devices to access the internet for at least one year. The Partial Least Square Structural Equation Modeling (PLS-SEM) was used to analyze the data gathered from a total of 356 responses received. The findings of this study show that perceived threat severity, perceived threat susceptibility, perceived response costs, response efficacy, and mobile self-efficacy have a significant positive effect on user’s intention. In particular, mobile self-efficacy had the strongest effect on the intention to use mobile device security technologies. Most of the personality traits factors were not found significant, except for conscientiousness. The user’s intention to use mobile device security technologies was found to have a significant effect on the actual usage of mobile device security technologies. Hence, the results support the suitability of the PMT and personality factors in the mobile device security technologies context. This study has contributed to information security research by providing empirical results on factors that influence the use of mobile device security technologies

DEVELOPMENT OF DIAGNOSTIC AND PROGNOSTIC METHODOLOGIES FOR ELECTRONIC SYSTEMS BASED ON MAHALANOBIS DISTANCE

Diagnostic and prognostic capabilities are one aspect of the many interrelated and complementary functions in the field of Prognostic and Health Management (PHM). These capabilities are sought after by industries in order to provide maximum operational availability of their products, maximum usage life, minimum periodic maintenance inspections, lower inventory cost, accurate tracking of part life, and no false alarms. Several challenges associated with the development and implementation of these capabilities are the consideration of a system's dynamic behavior under various operating environments; complex system architecture where the components that form the overall system have complex interactions with each other with feed-forward and feedback loops of instructions; the unavailability of failure precursors; unseen events; and the absence of unique mathematical techniques that can address fault and failure events in various multivariate systems. The Mahalanobis distance methodology distinguishes multivariable data groups in a multivariate system by a univariate distance measure calculated from the normalized value of performance parameters and their correlation coefficients. The Mahalanobis distance measure does not suffer from the scaling effect--a situation where the variability of one parameter masks the variability of another parameter, which happens when the measurement ranges or scales of two parameters are different. A literature review showed that the Mahalanobis distance has been used for classification purposes. In this thesis, the Mahalanobis distance measure is utilized for fault detection, fault isolation, degradation identification, and prognostics. For fault detection, a probabilistic approach is developed to establish threshold Mahalanobis distance, such that presence of a fault in a product can be identified and the product can be classified as healthy or unhealthy. A technique is presented to construct a control chart for Mahalanobis distance for detecting trends and biasness in system health or performance. An error function is defined to establish fault-specific threshold Mahalanobis distance. A fault isolation approach is developed to isolate faults by identifying parameters that are associated with that fault. This approach utilizes the design-of-experiment concept for calculating residual Mahalanobis distance for each parameter (i.e., the contribution of each parameter to a system's health determination). An expected contribution range for each parameter estimated from the distribution of residual Mahalanobis distance is used to isolate the parameters that are responsible for a system's anomalous behavior. A methodology to detect degradation in a system's health using a health indicator is developed. The health indicator is defined as the weighted sum of a histogram bin's fractional contribution. The histogram's optimal bin width is determined from the number of data points in a moving window. This moving window approach is utilized for progressive estimation of the health indicator over time. The health indicator is compared with a threshold value defined from the system's healthy data to indicate the system's health or performance degradation. A symbolic time series-based health assessment approach is developed. Prognostic measures are defined for detecting anomalies in a product and predicting a product's time and probability of approaching a faulty condition. These measures are computed from a hidden Markov model developed from the symbolic representation of product dynamics. The symbolic representation of a product's dynamics is obtained by representing a Mahalanobis distance time series in symbolic form. Case studies were performed to demonstrate the capability of the proposed methodology for real time health monitoring. Notebook computers were exposed to a set of environmental conditions representative of the extremes of their life cycle profiles. The performance parameters were monitored in situ during the experiments, and the resulting data were used as a training dataset. The dataset was also used to identify specific parameter behavior, estimate correlation among parameters, and extract features for defining a healthy baseline. Field-returned computer data and data corresponding to artificially injected faults in computers were used as test data

An Empirical Investigation of the Relationship between Computer Self-Efficacy and Information Privacy Concerns

The Internet and the growth of Information Technology (IT) and their enhanced capabilities to collect personal information have given rise to many privacy issues. Unauthorized access of personal information may result in identity theft, stalking, harassment, and other invasions of privacy. Information privacy concerns are impediments to broad-scale adoption of the Internet for purchasing decisions. Computer self-efficacy has been shown to be an effective predictor of behavioral intention and a critical determinant of intention to use Information Technology. This study investigated the relationship between an individual\u27s computer self-efficacy and information privacy concerns; and also examined the differences among different age groups and between genders regarding information privacy concerns and their relationships with computer self-efficacy. A paper-based survey was designed to empirically assess computer self-efficacy and information privacy concerns. The survey was developed by combining existing validated scales for computer self-efficacy and information privacy concerns. The target population of this study was the residents of New Jersey, U.S.A. The assessment was done by using the mall-intercept approach in which individuals were asked to fill out the survey. The sample size for this study was 400 students, professionals, and mature adults. The Shapiro-Wilk test was used for testing data normality and the Spearman rank-order test was used for correlation analyses. MANOVA test was used for comparing mean values of computer self-efficacy and information privacy concerns between genders and among age groups. The results showed that the correlation between computer self-efficacy and information privacy concerns was significant and positive; and there were differences between genders and among age groups regarding information privacy concerns and their relationships with computer self-efficacy. This study contributed to the body of knowledge about the relationships among antecedents and consequences of information privacy concerns and computer self-efficacy. The findings of this study can help corporations to improve e-commerce by targeting privacy policy-making efforts to address the explicit areas of consumer privacy concerns. The results of this study can also help IT practitioners to develop privacy protection tools and processes to address specific consumer privacy concerns

Using an airborne hyperspectral and LiDAR integrated sensor approach to spectrally discriminate and map savanna bush encroaching species in the Greater Kruger National Park region

Includes abstract.Includes bibliographical references (leaves 105-113).Bush encroachment is an environmental phenomenon which affects arid and semi-arid savanna rangelands across the world. Bush encroachment has numerous negative and positive impacts on these savanna ecosystems depending on the land use practices and associated rangeland management regimes

An Investigation of Factors that Affect HIPAA Security Compliance in Academic Medical Centers

HIPAA security compliance in academic medical centers is a central concern of researchers, academicians, and practitioners. Increased numbers of data security breaches and information technology implementations have caused concern over the confidentiality, integrity, and availability of electronic personal health information. The federal government has implemented stringent HIPAA security compliance reviews and significantly extended the scope and enforcement of the HIPAA Security Rule. However, academic medical centers have shown limited compliance with the HIPAA Security Rule. Therefore, the goal of this study was to investigate the factors that may affect HIPAA security compliance in academic medical centers. Based on a review of the literature of technology acceptance and security effectiveness, this study proposed a theoretical model that uses management support, security awareness, security culture, and computer self-efficacy to predict security behavior and security effectiveness and thus HIPAA security compliance in academic medical centers. To empirically assess the effect of the above-noted variables on HIPAA security compliance in academic medical centers, a Web-based survey was developed. The survey instrument was designed as a multi-line measure that used Likert-type scales. Previous validated scales were adapted and used in the survey. The sample for this investigation was health care information technology professionals who are members of the Group on Information Resources within the Association of American Medical Colleges. Two statistical methods were used to derive and validate predictive models: multiple linear regression and correlation analysis. The results of the investigation demonstrated that security awareness, management support, and security culture were significant predictors of both security effectiveness and security behavior. Security awareness was the most significant predictor of security effectiveness and security behavior. Due to the presence of collinearity, Pearson correlation analysis was used to develop a composite factor, consisting of management support and security culture, for the final multiple linear regression model. By enhancing the understanding of HIPAA security compliance in academic medical centers, the outcomes of this study will contribute to the body of knowledge of security compliance. The empirical results of this research also will provide guidance for individuals and organizations involved with HIPAA security compliance initiatives in health care