Skip to main content
SpringerLink
Log in

A Macro-Social Exploratory Analysis of the Rate of Interstate Cyber-Victimization

  • Published:
American Journal of Criminal Justice Aims and scope Submit manuscript

Abstract

This study examines whether macro-level opportunity indicators affect cyber-theft victimization. Based on the arguments from criminal opportunity theory, exposure to risk is measured by state-level patterns of internet access (where users access the internet). Other structural characteristics of states were measured to determine if variation in social structure impacted cyber-victimization across states. The current study found that structural conditions such as unemployment and non-urban population are associated with where users access the internet. Also, this study found that the proportion of users who access the internet only at home was positively associated with state-level counts of cyber-theft victimization. The theoretical implications of these findings are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. Regarding the effects of structural variables on cyber-space, Yar (2005) discussed that websites or online services frequently used can be influenced by structural contexts because the virtual spaces are “physically rooted and produced in ‘real space’” (p.416). In other words, individual’s patterns of internet use (e.g., kinds of online service frequently used, spent hours of internet) vary depending on culture, language, gender, ethnicity, and class etc. If the individual’s patterns of internet use affect cyber-victimization, accordingly, structural characteristics indirectly affect cyber-victimization.

  2. The rate of cyber-theft victimization was rounded to the nearest whole number to apply a regression model for count data. Both the rate of male and GDP per capita were significantly skewed (p < .01) so that the log transformation was applied to them for normality.

  3. To examine mediated effects, Fritz and MacKinnon (2007) recommended that ample sample sizes be obtained to guarantee more than .8 of statistical power.

  4. Alaska has 256 per 100,000 population of cyber-theft victimization, which is six standard deviations away from the average, 76.12 per 100,000 (See Table 1). Since the sample size of the data is small, one outlier can have a significant effect on estimation. Hilbe (2007) conducted a simulation to examine effects of outliers on overdispersion and found that 0.1 percentage of outliers increased 30 percentage of the Pearson dispersion statistic. Accordingly, the outlier in the dependent variable, which is estimated as 2 % in the current study may have an influential effect on estimation.

References

  • Anderson, T. L., & Bennett, R. R. (1996). Development, gender, and crime: The scope of the routine activities approach. Justice Quarterly, 13, 31–56.

    Article  Google Scholar 

  • Ang, R. P., Huan, V. S., & Florell, D. (2014). Understanding the relationship between proactive and reactive aggression, and cyberbullying across United States and Singapore adolescent samples. Journal of Interpersonal Violence, 29, 237–254.

    Article  Google Scholar 

  • Bennett, R. R. (1991). Routine activities: A cross-national assessment of a criminological perspective. Social Forces, 70, 147–163.

    Article  Google Scholar 

  • Birkbeck, C., & LaFree, G. D. (1993). The situational analysis of crime and deviance. Annual Review of Sociology, 19, 113–137.

    Article  Google Scholar 

  • Bossler, A. M., & Holt, T. J. (2009). On-line activities, guardianship, and malware infection: An examination of routine activities theory. International Journal of Cyber Criminology, 3, 400–420.

    Google Scholar 

  • Choi, K. (2008). Computer crime victimization and integrated theory: An empirical assessment. International Journal of Cyber Criminology, 2, 308–333.

    Google Scholar 

  • Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 44, 588–608.

    Article  Google Scholar 

  • Cohen, L. E., Kluegel, J. R., & Land, K. C. (1981). Social inequality and predatory criminal victimization: An exposition and test of a formal theory. American Sociological Review, 46, 505–524.

    Article  Google Scholar 

  • Dowdell, E. B. (2011). Risky Internet behaviors of middle-school students: Communication with online strangers and offline contact. CIN: Computers, Informatics, Nursing, 29, 352–359.

    Google Scholar 

  • Dowdell, E. B. (2013). Use of the Internet by parents of middle school students: Internet rules, risky behaviours and online concerns. Journal of Psychiatric & Mental Health Nursing, 20, 9–16.

    Article  Google Scholar 

  • Dowell, E. B., Burgess, A. W., & Cavanaugh, D. J. (2009). Clustering of internet risk behaviors in a middle school student population. Journal of School Health, 79, 547–553.

    Article  Google Scholar 

  • Felson, M., & Santos, R. B. (2009). Crime and everyday life (4th ed.). Thousand Oaks, CA: Sage.

    Google Scholar 

  • File, T. (2013). Computer and internet use in the United States. Washington, DC: U.S. Census Bureau.

    Google Scholar 

  • Fritz, M. S., & MacKinnon, D. P. (2007). Required sample size to detect the mediated effect. Psychological Science, 18, 233–239.

    Article  Google Scholar 

  • Gaquin, D. A., & Dunn, G. W. (2013). State and metropolitan area data book. Lanham, MD: Bernan.

    Google Scholar 

  • Hemphill, S. A., & Heerde, J. A. (2014). Adolescent predictors of young adult cyberbullying perpetration and victimization among australian youth. Journal of Adolescent Health, 55, 580–587.

    Article  Google Scholar 

  • Higgins, G. E. (2007). Digital piracy: An examination of low self-control and motivation using short-term longitudinal data. Cyberpsychology & Behavior, 10, 523–529.

    Article  Google Scholar 

  • Higgins, G. E., Hughes, T., Ricketts, M. L., & Wolfe, S. E. (2008). Identity theft complaints: Exploring the state-level correlates. Journal of Financial Crime, 15, 295–307.

    Article  Google Scholar 

  • Hilbe, J. M. (2007). Negative binomial regression. New York: Cambridge University Press.

    Book  Google Scholar 

  • Hindelang, M. J., Gottfredson, M. R., & Garofalo, J. (1978). Victims of personal crime: An empirical foundation for a theory of personal victimization. Cambridge, MA: Ballinger.

    Google Scholar 

  • Holt, T. J., & Bossler, A. M. (2008). Examining the applicability of lifestyle-routine activities theory for cybercrime victimization. Deviant Behavior, 30, 1–25.

    Article  Google Scholar 

  • Holt, T. J., & Bossler, A. M. (2013). Examining the relationship between routine activities and malware infection indicators. Journal of Contemporary Criminal Justice, 29, 420–436.

    Article  Google Scholar 

  • Holt, T. J., & Turner, M. G. (2012). Examining risks and protective factors of on-line identity theft. Deviant Behavior, 33, 308–323.

    Article  Google Scholar 

  • Holtfreter, K., Reisig, M., & Pratt, T. (2008). Low self-control, routine activities, and fraud victimization. Criminology, 46, 189–220.

    Article  Google Scholar 

  • Internet Crime Complaint Center. (2012). 2011 Internet Crime Report. Retrieved June 15, 2015, from http://www.ic3.gov/media/annualreport/2011_IC3Report.pdf

  • Internet Crime Complaint Center. (2013). 2012 Internet Crime Report. Retrieved June 15, 2015, from http://www.ic3.gov/media/annualreport/2012_IC3Report.pdf

  • Internet Crime Complaint Center. (2014). 2013 Internet Crime Report. Retrieved June 15, 2015, from http://www.ic3.gov/media/annualreport/2013_IC3Report.pdf

  • Jang, H., Song, J., & Kim, R. (2014). Does the offline bully-victimization influence cyberbullying behavior among youths? application of general strain theory. Computers in Human Behavior, 31, 85–93.

    Article  Google Scholar 

  • Kaufman, R. L. (2013). Heteroskedasticity in regression: Detection and correction. Thousand Oaks, CA: Sage.

    Book  Google Scholar 

  • Kennedy, L. W., & Forde, D. R. (1990). Routine activities and crime: An analysis of victimization in Canada. Criminology, 28, 137–152.

    Article  Google Scholar 

  • Kigerl, A. (2012). Routine activity theory and the determinants of high cybercrime countries. Social Science Computer Review, 30, 470–486.

    Article  Google Scholar 

  • Land, K. C., McCall, P. L., & Cohen, L. E. (1990). Structural covariates of homicide rates: Are there any invariances across time and social space? American Journal of Sociology, 95, 922–963.

    Article  Google Scholar 

  • Liau, A. K., Khoo, A., & Ang, P. H. (2005). Factors influencing adolescents engagement in risky internet behavior. Cyberpsychology & Behavior, 8, 513–520.

    Article  Google Scholar 

  • Liu, C., Ang, R. P., & Lwin, M. O. (2013). Cognitive, personality, and social factors associated with adolescents’ online personal information disclosure. Journal of Adolescence, 36, 629–638.

    Article  Google Scholar 

  • Long, J. S. (1997). Regression models for categorical and limited dependent variables. Thousand Oaks, CA: Sage.

    Google Scholar 

  • Long, J. S., & Ervin, L. H. (2000). Using heteroscedasticity consistent standard errors in the linear regression model. The American Statistician, 54, 217–224.

    Google Scholar 

  • Long, J. S., & Freese, J. (2014). Regression models for categorical dependent variables using stata (3rd ed.). College Station, TX: Stata Press.

    Google Scholar 

  • Lynch, J. P. (1987). Routine activity and victimization at work. Journal of Quantitative Criminology, 3, 283–300.

    Article  Google Scholar 

  • MacKinnon, J. G., & White, H. (1985). Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties. Journal of Econometrics, 29, 305–325.

    Article  Google Scholar 

  • Madero-Hernandez, A., & Fisher, B. S. (2012). Routine activity theory. In F. Cullen & P. Wilcox (Eds.), The Oxford handbook of criminological theory (pp. 513–534). New York: Oxford University Press.

    Google Scholar 

  • Maimon, D., Kamerdze, A., Cukier, M., & Sobesto, B. (2013). Daily trends and origin of computer-focused crimes against a large university computer network: An application of the routine-activities and lifestyle perspective. British Journal of Criminology, 53, 319–343.

    Article  Google Scholar 

  • Maimon, D., Wilson, T., Ren, W., & Berenblum, T. (2015). On the relevance of spatial and temporal dimensions in assessing computer susceptibility to system trespassing incidents. British Journal of Criminology, 55, 615–634.

    Article  Google Scholar 

  • Maume, D. J. (1989). Inequality and metropolitan rape rates: A routine activity approach. Justice Quarterly, 6, 513–527.

    Article  Google Scholar 

  • Marcum, C., Higgins, G., Freiburger, T., & Ricketts, M. (2014). Exploration of the cyberbullying victim/offender overlap by sex. American Journal of Criminal Justice, 39, 538–548.

    Article  Google Scholar 

  • Marcum, C. D., Ricketts, M. L., & Higgins, G. E. (2010). Assessing sex experiences of online victimization: An examination of adolescent online behaviors using routine activity theory. Criminal Justice Review, 35, 412–437.

    Article  Google Scholar 

  • Maxfield, M. G. (1987). Household composition, routine activity, and victimization: A comparative analysis. Journal of Quantitative Criminology, 3, 301–320.

    Article  Google Scholar 

  • Menard, S. (1995). Applied logistic regression analysis. Thousand Oaks, CA: Sage.

    Google Scholar 

  • Messner, S. F., & Blau, J. R. (1987). Routine leisure activities and rates of crime: A macro-level analysis. Social Forces, 65, 1035–1052.

    Article  Google Scholar 

  • Messner, S. F., Lu, Z., Zhang, L., & Liu, J. (2007). Risks of criminal victimization in contemporary Urban China: An application of lifestyle/routine activities theory. Justice Quarterly, 24, 496–522.

    Article  Google Scholar 

  • Miethe, T. D., Hughes, M., & McDowall, D. (1991). Social change and crime rates: An evaluation of alternative theoretical approaches. Social Forces, 70, 165–185.

    Article  Google Scholar 

  • Miethe, T. D., & McDowall, D. (1993). Contextual effects in models of criminal victimization. Social Forces, 71, 741–759.

    Article  Google Scholar 

  • Miethe, T. D., & Meier, R. F. (1990). Opportunity, choice, and criminal victimization: A test of a theoretical model. Journal of Research in Crime and Delinquency, 27, 243–266.

    Article  Google Scholar 

  • Miethe, T. D., Stafford, M. C., & Long, J. S. (1987). Social differentiation in criminal victimization: A test of routine activities/lifestyle theories. American Sociological Review, 52, 184–194.

    Article  Google Scholar 

  • Moon, B., McCluskey, J., & McCluskey, C. (2010). A general theory of crime and computer crime: An empirical test. Journal of Criminal Justice, 38, 767–772.

    Article  Google Scholar 

  • Ngo, F. T., & Paternoster, R. (2011). Cybercrime victimization: An examination of individual and situational level factors. International Journal of Cyber Criminology, 5, 773–793.

    Google Scholar 

  • Oksanen, A., & Keipi, T. (2013). Young people as victims of crime on the internet: A population-based study in Finland. Vulnerable Children and Youth Studies: An International Interdisciplinary Journal for Research, Policy and Care, 8, 298–309.

    Article  Google Scholar 

  • Osgood, D. W. (2000). Poisson-based regression analysis of aggregate crime rates. Journal of Quantitative Criminology, 16, 21–43.

    Article  Google Scholar 

  • Patchin, J. W., & Hinduja, S. (2011). Traditional and nontraditional bullying among youth: A test of general strain theory. Youth & Society, 43, 727–751.

    Article  Google Scholar 

  • Pew Research Center. (2014). Internet use over time. Retrieved June 10, 2015, from http://www.pewInternet.org/data-trend/Internet-use/Internet-use-over-time

  • Pratt, T., Holtfreter, K., & Reisig, M. (2010). Routine online activity and internet fraud targeting: Extending the generality of routine activity theory. Journal of Research in Crime and Delinquency, 47, 267–296.

    Article  Google Scholar 

  • Ren, F., Kwan, M., & Schwanen, T. (2013). Investigating the temporal dynamics of internet activities. Time & Society, 22, 186–215.

    Article  Google Scholar 

  • Reyns, B. (2013). Online routines and identity theft victimization: Further expanding routine activity theory beyond direct-contact offenses. Journal of Research in Crime and Delinquency, 50, 216–238.

    Article  Google Scholar 

  • Reyns, B., Burek, M., Henson, B., & Fisher, B. S. (2013). The unintended consequences of digital technology: Exploring the relationship between sexting and cybervictimization. Journal of Crime and Justice, 36, 1–17.

    Article  Google Scholar 

  • Reyns, B., Henson, B., & Fisher, B. S. (2011). Being pursued online: Applying cyberlifestyle-routine activities theory to cyberstalking victimization. Criminal Justice and Behavior, 38, 1149–1169.

    Article  Google Scholar 

  • Sampson, R. J., & Wooldredge, J. D. (1987). Linking the micro- and macro-level dimensions of lifestyle—Routine activity and opportunity models of predatory victimization. Journal of Quantitative Criminology, 3, 371–393.

    Article  Google Scholar 

  • Shapka, J. D., & Law, D. M. (2013). Does one size fit all? Ethnic differences in parenting behaviors and motivations for adolescent engagement in cyberbullying. Journal of Youth and Adolescence, 42, 723–738.

    Article  Google Scholar 

  • Skinner, W. F., & Fream, A. M. (1997). A social learning theory analysis of computer crime among college students. Journal of Research in Crime and Delinquency, 34, 495–518.

    Article  Google Scholar 

  • StataCorp. (2011). Stata statistical software: Release 12. College Station, TX: StataCorp LP.

  • Tseloni, A., Wittebrood, K., Farrell, G., & Pease, K. (2004). Burglary victimization in England and wales, the United States and the Netherlands: A cross-national comparative test of routine activities and lifestyle theories. British Journal of Criminology, 44, 66–91.

    Article  Google Scholar 

  • van Wilsem, J. (2011). Worlds tied together? Online and non-domestic routine activities and their impact on digital and traditional threat victimization. European Journal of Criminology, 8, 115–127.

    Article  Google Scholar 

  • van Wilsem, J. (2013a). ‘Bought it, but never got it’ assessing risk factors for online consumer fraud victimization. European Sociological Review, 29, 168–178.

    Article  Google Scholar 

  • van Wilsem, J. (2013b). Hacking and harassment—Do they have something in common? Comparing risk factors for online victimization. Journal of Contemporary Criminal Justice, 29, 437–453.

    Article  Google Scholar 

  • White, H. L. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48, 817–838.

    Article  Google Scholar 

  • Yar, M. (2005). The novelty of ‘cybercrime’. European Journal of Criminology, 2, 407–427.

    Article  Google Scholar 

  • Ybarra, M. L., Finkelhor, D., Mitchell, K. J., & Wolak, J. (2009). Associations between blocking, monitoring, and filtering software on the home computer and youth-reported unwanted exposure to sexual material online. Child Abuse & Neglect, 33, 857–869.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Criminology, University of South Florida, 4202 E. Fowler Ave., SOC107, Tampa, FL, 33620, USA

    Hyojong Song, Michael J. Lynch & John K. Cochran

Authors
  1. Hyojong Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael J. Lynch
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John K. Cochran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hyojong Song.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, H., Lynch, M.J. & Cochran, J.K. A Macro-Social Exploratory Analysis of the Rate of Interstate Cyber-Victimization. Am J Crim Just 41, 583–601 (2016). https://doi.org/10.1007/s12103-015-9308-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12103-015-9308-4

Keywords

Search

Navigation