English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Each year, millions of Americans are becoming the victims of identity-theft and this is one of a number of serious, growing and widespread issues. Examining the factors that affect anxiety levels of identity-theft victims and determining whether there is any significant relationship between these factors are an important issue. In this paper, a research model is presented to determine whether electronic devices self-efficacy, electronic devices usage and information security awareness are the main and direct factors that affect identity-theft anxiety level in college students. This study performed several analyses on a developed questionnaire to ensure validity and reliability. After examining all proposed hypotheses, it was found that electronic devices self-efficacy and electronic devices usage have significant impact on identity-theft anxiety level of the students. The data also support a relationship between information security awareness of the students and their identity-theft anxiety level. This research also showed that gender, employment status, race, and age have moderating effects on all hypotheses. The outcome of this study indicated that more information should be provided to students regarding how to take proactive measures in using their electronic devices in order to avoid identity-theft. Introduction Identity-theft means stealing someone’s personal information and using it without their permission. The list of consumer complaints received by the Federal Trade Commission in 2011 [1] indicates that for the 12 th year in a row, identity-theft complaints are in the top of the list. Among 1.8 million complaints that were filed in 2011, 279,156 or 15% were identity-theft complaints. Nearly 25% of the identity-theft complaints were related to tax or wage-related fraud [1]. In December 2010, the United States Bureau of Justice Statistics announced that about 11.7 million people were the victims of identity theft, which constitutes five percent of people age 16 or older in the U.S. [2]. In 2007, identity-theft was on the list of the top ten consumer complaints to the Federal Trade Commission. According to Paganini [3] in the FBI report of scams in 2011, identity-theft was in second place and had jumped from the top ten crimes in 2007 to the top two in 2011, which obviously must be considered as a serious issue. The Federal Trade Commission report [4] shows that identity-theft was the number one complaint category in the Consumer Sentinel Network for calendar year 2014 with thirteen percent of the overall complaints. Government documents/benefits fraud (39%) was the most common form of reported identitytheft, followed by credit card fraud (17%), phone or utilities fraud (13%), and bank fraud (8%). Other significant categories of identity-theft reported by victims were employment-related fraud (5%) and loan fraud (4%). Thirty-two percent of identity-theft complainants reported they contacted law enforcement. Of those victims, eighty-eight percent indicated a report was taken. Florida is the state with the highest per capita rate of reported identity-theft complaints, followed by Washington and Oregon [4]. A six-factor computer anxiety model has been developed [5] that consists of computer literacy of basic computer skills, self-efficacy on learning how to use computers, physical awareness while using computers such as breathing or sweating, attitudes toward computers, positive belief regarding the benefits of computers to society, and negative beliefs on effects of computers. While there is little information on the perceptions and awareness that college/university students have about identity theft, considerable research has been done with this group on a range of crime and justice-related topics. A review of the literature shows a lack of studies on the perception held by college/university students about identity-theft [6] and there has been a void in research related to information security awareness and identity-theft anxiety levels among students [5]. Also, there has been a void in literature review related to electronic devices self-efficacy. Thus, with increasing identity-theft complaints and with very little research in this area on higher education students, this study investigated the effect of electronic devices usage, electronic devices self-efficacy and information security awareness on identity-theft anxiety level among college students in southeast Michigan. Also, grade level, gender, age and race of the students were examined to determine whether they have any influence on these relationships. The impact of these factors on identity-theft anxiety level has not been tested in prior research using descriptive methodology. Methodology In this research, quantitative methodology was used by utilizing an electronically distributed survey, as presented in the appendix, to measure each construct of the research model utilizing the six-factor computer anxiety model that was developed in [5]. The six factors in computer anxiety model are computer literacy of basic computer skills, self-efficacy on learning how to use computers, physical awareness while using computers such as breathing or sweating, attitudes toward computers, positive belief regarding the benefits of computers to society, and negative beliefs on effects of computers. A sample of 187 students from a university located in southeast of Michigan was considered and a purposive sampling method was used. Cross-sectional or correlation analysis was utilized to examine the research questions. The followings are the hypotheses examined in this study: H1. There is a positive effect of electronic devices self-efficacy on identity-theft anxiety level among students in southeast of Michigan. H2. There is a positive effect of electronic devices usage on identity-theft anxiety level among students in southeast of Michigan. H3. There is a positive effect of information security awareness on identity-theft anxiety level among students in southeast of Michigan. H4. Educational level of students and electronic devices self-efficacy are in a positive relationships with identity-theft anxiety level among students in southeast of Michigan. H5. Educational level of students and information security awareness are in a positive relationship with identity-theft anxiety level among students in southeast of Michigan. H6. Educational level of students and electronic devices usage are in a positive relationship with identity-theft anxiety level among students in southeast of Michigan. Instrument This study consisted of three independent variables and one dependent variable. The dependent variable was anxiety level caused by fear of identity-theft. The independent variables were electronic devices self-efficacy, electronic devices usage and information security awareness. Age, gender, race, and educational level were used as demographical variables, which could be considered as covariates. The survey used a 7-point Likert scale with the ratings of strongly agree, agree, slightly agree, neither agree nor disagree, slightly disagree, disagree, and strongly disagree for anxiety, electronic devices self-efficacy and information security awareness variables. Demographic Characteristics of the Sample The demographics analysis of the participants are presented in Tables 1 – 5 that include gender, age, educational level, race, and device ownership. Table 1: Gender of the Participants Gender Male (82) 43.9% Female (105) 56.1% Table 2: Age of the Participants Age Frequency Percent Cumulative Percent < 19 7 3.7% 7% 20 29 110 58.8% 110% 30 39 37 19.8% 37% 40 49 18 9.6% 18% 50 59 12 6.4% 12% > 60 3 1.6% 3% Total 187 100.0% Table 3: Educational Level of the Participants Educational level Frequency Percent Cumulative Percent Undergraduate 92 49.2% 49.2% Graduate 75 40.1% 89.3% Doctoral 20 10.7% 100.0% Total 187 100.0% Table 4: Race of the Participants Race Frequency Percent Cumulative Percent American Indian/Native American 2 1.1% 1.1% African American 10 5.3% 6.4% Asian 25 13.4% 19.8% Hispanic/Latino 13 7.0% 26.7% White/Caucasian 126 67.4% 94.1% Other 11 5.9% 100.0% Total 187 100.0% Table 5: Device Ownership of the Participants Device Ownership Frequency Percent Netbook 11 5.9% Desktop 58 31% Laptop 170 90.9% Mobile phone 125 66.8% Internet enabled mobile device (Smartphone, tablet, etc.) 144 77% Dedicated e-book device (Kindle, Nook, Sony Reader, etc.) 43 23% None of the above 8 4.3% All the above 2 1.1% For electronic devices usage, the survey provided multiple checkbox options as Likert scaling is designed to measure people's attitudes and awareness [7]. The analysis of device usage are shown in Table 6. Table 6: Device Usage of the Participants Options Frequency Device Usage Always Frequently Occasionally Rarely Never Social Networking 91 55 20 13 8 Reading content (e-books, articles, etc.) 49 72 48 15 3 Accessing email 148 38 1 0 0 Text messaging 130 43 6 5 3 Searching for Information 133 51 3 0 0 Getting directions 97 63 20 7 0 Playing content 63 47 41 25 11 Listening to music/Watching videos 80 57 34 14 2 Banking 68 62 38 14 5 Filing Taxes 34 25 30 26 72 Shopping 32 64 56 26 9 Utility billing 58 43 32 15 39 News 64 73 32 12 6 Weather 87 66 21 7 6 Research 81 66 25 9 6 School Work 105 55 15 7 5 Company Work 68 35 18 27 39 Medical Bill 27 25 31 37 67 Analysis A survey questionnaire was designed to measure each construct of the research model. Out of 251 students who participated in the survey, only 187 completed the entire survey. Thus, only complete responses were used for data analysis and all incomplete responses were excluded. Survey Monkey was used to collect the information and SPSS, Minitab, Statgraphics Centurion software were used to analyze the collected data. The statistical analysis of the surveyed data using different techniques included reliability analysis, normality testing, distribution fitting, factor analysis, validity, and hypothesis testing. The Cro

Factors Affecting Identity Theft Anxiety Level in College Students