American Journal of Criminal Justice

, Volume 41, Issue 3, pp 583–601 | Cite as

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

  • Hyojong SongEmail author
  • Michael J. Lynch
  • John K. Cochran


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.


Cybercrime Cyber-theft victimization Criminal opportunity theory Household activity Online routine activity Macro-level crime analysis State-level analysis 


  1. Anderson, T. L., & Bennett, R. R. (1996). Development, gender, and crime: The scope of the routine activities approach. Justice Quarterly, 13, 31–56.CrossRefGoogle Scholar
  2. 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.CrossRefGoogle Scholar
  3. Bennett, R. R. (1991). Routine activities: A cross-national assessment of a criminological perspective. Social Forces, 70, 147–163.CrossRefGoogle Scholar
  4. Birkbeck, C., & LaFree, G. D. (1993). The situational analysis of crime and deviance. Annual Review of Sociology, 19, 113–137.CrossRefGoogle Scholar
  5. 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
  6. Choi, K. (2008). Computer crime victimization and integrated theory: An empirical assessment. International Journal of Cyber Criminology, 2, 308–333.Google Scholar
  7. Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 44, 588–608.CrossRefGoogle Scholar
  8. 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.CrossRefGoogle Scholar
  9. 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
  10. 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.CrossRefGoogle Scholar
  11. 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.CrossRefGoogle Scholar
  12. Felson, M., & Santos, R. B. (2009). Crime and everyday life (4th ed.). Thousand Oaks, CA: Sage.Google Scholar
  13. File, T. (2013). Computer and internet use in the United States. Washington, DC: U.S. Census Bureau.Google Scholar
  14. Fritz, M. S., & MacKinnon, D. P. (2007). Required sample size to detect the mediated effect. Psychological Science, 18, 233–239.CrossRefGoogle Scholar
  15. Gaquin, D. A., & Dunn, G. W. (2013). State and metropolitan area data book. Lanham, MD: Bernan.Google Scholar
  16. 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.CrossRefGoogle Scholar
  17. Higgins, G. E. (2007). Digital piracy: An examination of low self-control and motivation using short-term longitudinal data. Cyberpsychology & Behavior, 10, 523–529.CrossRefGoogle Scholar
  18. 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.CrossRefGoogle Scholar
  19. Hilbe, J. M. (2007). Negative binomial regression. New York: Cambridge University Press.CrossRefGoogle Scholar
  20. 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
  21. Holt, T. J., & Bossler, A. M. (2008). Examining the applicability of lifestyle-routine activities theory for cybercrime victimization. Deviant Behavior, 30, 1–25.CrossRefGoogle Scholar
  22. 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.CrossRefGoogle Scholar
  23. Holt, T. J., & Turner, M. G. (2012). Examining risks and protective factors of on-line identity theft. Deviant Behavior, 33, 308–323.CrossRefGoogle Scholar
  24. Holtfreter, K., Reisig, M., & Pratt, T. (2008). Low self-control, routine activities, and fraud victimization. Criminology, 46, 189–220.CrossRefGoogle Scholar
  25. Internet Crime Complaint Center. (2012). 2011 Internet Crime Report. Retrieved June 15, 2015, from
  26. Internet Crime Complaint Center. (2013). 2012 Internet Crime Report. Retrieved June 15, 2015, from
  27. Internet Crime Complaint Center. (2014). 2013 Internet Crime Report. Retrieved June 15, 2015, from
  28. 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.CrossRefGoogle Scholar
  29. Kaufman, R. L. (2013). Heteroskedasticity in regression: Detection and correction. Thousand Oaks, CA: Sage.CrossRefGoogle Scholar
  30. Kennedy, L. W., & Forde, D. R. (1990). Routine activities and crime: An analysis of victimization in Canada. Criminology, 28, 137–152.CrossRefGoogle Scholar
  31. Kigerl, A. (2012). Routine activity theory and the determinants of high cybercrime countries. Social Science Computer Review, 30, 470–486.CrossRefGoogle Scholar
  32. 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.CrossRefGoogle Scholar
  33. Liau, A. K., Khoo, A., & Ang, P. H. (2005). Factors influencing adolescents engagement in risky internet behavior. Cyberpsychology & Behavior, 8, 513–520.CrossRefGoogle Scholar
  34. 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.CrossRefGoogle Scholar
  35. Long, J. S. (1997). Regression models for categorical and limited dependent variables. Thousand Oaks, CA: Sage.Google Scholar
  36. 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
  37. Long, J. S., & Freese, J. (2014). Regression models for categorical dependent variables using stata (3rd ed.). College Station, TX: Stata Press.Google Scholar
  38. Lynch, J. P. (1987). Routine activity and victimization at work. Journal of Quantitative Criminology, 3, 283–300.CrossRefGoogle Scholar
  39. MacKinnon, J. G., & White, H. (1985). Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties. Journal of Econometrics, 29, 305–325.CrossRefGoogle Scholar
  40. 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
  41. 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.CrossRefGoogle Scholar
  42. 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.CrossRefGoogle Scholar
  43. Maume, D. J. (1989). Inequality and metropolitan rape rates: A routine activity approach. Justice Quarterly, 6, 513–527.CrossRefGoogle Scholar
  44. 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.CrossRefGoogle Scholar
  45. 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.CrossRefGoogle Scholar
  46. Maxfield, M. G. (1987). Household composition, routine activity, and victimization: A comparative analysis. Journal of Quantitative Criminology, 3, 301–320.CrossRefGoogle Scholar
  47. Menard, S. (1995). Applied logistic regression analysis. Thousand Oaks, CA: Sage.Google Scholar
  48. Messner, S. F., & Blau, J. R. (1987). Routine leisure activities and rates of crime: A macro-level analysis. Social Forces, 65, 1035–1052.CrossRefGoogle Scholar
  49. 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.CrossRefGoogle Scholar
  50. Miethe, T. D., Hughes, M., & McDowall, D. (1991). Social change and crime rates: An evaluation of alternative theoretical approaches. Social Forces, 70, 165–185.CrossRefGoogle Scholar
  51. Miethe, T. D., & McDowall, D. (1993). Contextual effects in models of criminal victimization. Social Forces, 71, 741–759.CrossRefGoogle Scholar
  52. 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.CrossRefGoogle Scholar
  53. 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.CrossRefGoogle Scholar
  54. 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.CrossRefGoogle Scholar
  55. 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
  56. 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.CrossRefGoogle Scholar
  57. Osgood, D. W. (2000). Poisson-based regression analysis of aggregate crime rates. Journal of Quantitative Criminology, 16, 21–43.CrossRefGoogle Scholar
  58. Patchin, J. W., & Hinduja, S. (2011). Traditional and nontraditional bullying among youth: A test of general strain theory. Youth & Society, 43, 727–751.CrossRefGoogle Scholar
  59. Pew Research Center. (2014). Internet use over time. Retrieved June 10, 2015, from
  60. 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.CrossRefGoogle Scholar
  61. Ren, F., Kwan, M., & Schwanen, T. (2013). Investigating the temporal dynamics of internet activities. Time & Society, 22, 186–215.CrossRefGoogle Scholar
  62. 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.CrossRefGoogle Scholar
  63. 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.CrossRefGoogle Scholar
  64. 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.CrossRefGoogle Scholar
  65. 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.CrossRefGoogle Scholar
  66. 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.CrossRefGoogle Scholar
  67. 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.CrossRefGoogle Scholar
  68. StataCorp. (2011). Stata statistical software: Release 12. College Station, TX: StataCorp LP.Google Scholar
  69. 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.CrossRefGoogle Scholar
  70. 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.CrossRefGoogle Scholar
  71. van Wilsem, J. (2013a). ‘Bought it, but never got it’ assessing risk factors for online consumer fraud victimization. European Sociological Review, 29, 168–178.CrossRefGoogle Scholar
  72. 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.CrossRefGoogle Scholar
  73. White, H. L. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48, 817–838.CrossRefGoogle Scholar
  74. Yar, M. (2005). The novelty of ‘cybercrime’. European Journal of Criminology, 2, 407–427.CrossRefGoogle Scholar
  75. 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.CrossRefGoogle Scholar

Copyright information

© Southern Criminal Justice Association 2015

Authors and Affiliations

  • Hyojong Song
    • 1
    Email author
  • Michael J. Lynch
    • 1
  • John K. Cochran
    • 1
  1. 1.Department of CriminologyUniversity of South FloridaTampaUSA

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