Assessing the Relationship between Lifestyle Routine Activities Theory and Online Victimization Using Panel Data

ABSTRACT

Prior research suggests lifestyle routine activities theory (LRAT) applies to online environments, however, the use of cross-sectional designs may limit the ability to determine whether LRAT behaviors influence online victimization, or if it is victimization that influences the use of LRAT behaviors. The current study used nationally representative panel data from The Netherlands to analyze the relationship between guardianship, exposure, and target attractiveness on hacking and malware victimization, controlling for relevant factors. Using path modeling, two prominent findings emerged. First, of the three LRAT factors, only exposure predicted greater likelihood of cybercrime victimization at a later time. Second, previous cybercrime victimization predicted increases in guardianship and exposure behaviors at a later time. Overall, these findings suggest that cybercrime victimization plays a greater role in changing LRAT behaviors, than LRAT behaviors do in explaining victimization.

Acknowledgments

The LISS panel data were collected by CentERdata (Tilburg University, The Netherlands) through its MESS project funded by the Netherlands Organization for Scientific Research. The authors would like to thank the anonymous reviewers for their insightful comments that led to a stronger paper. Thanks also goes to the Department of Criminal Justice and Criminology at Sam Houston State University for fellowship funding used to complete this research.

Notes

¹ The number of questions used to measure exposure varied across time as some items were either added or combined in later surveys. To ensure each measure was comparable across the three waves, the total sum of the item responses was divided by the total number of non-missing items for that participant. For each index score, we also required that at least 50% of the items were answered by each respondent. For example, if a participant answered eight of eleven items, the total sum of those items would be divided by eight to arrive at that participant’s particular index score. This index score represents the degree to which an individual engages in exposure-related behavior. This same method was applied in creating the guardianship index, and this approach has been used by prior research using this data (Nedelec 2018).

² Since the items used to generate the LRAT measures were binary, we used polychoric correlations as opposed to Pearson’s correlations to conduct factor analyses on these measures. All items were above a 0.32 factor loading and loaded onto one factor for each measure.

³ The inclusion of these four specific sites highlights the digital context of the time in broader Dutch society. A prime example was the wide adoption of Hyves, which had more than 10 million members and a significant following in the Netherlands (see Butcher 2010). Although now defunct, Hyves was included because it represented one of the most popular digital social sites during the study period. As such, these four sites were used to broadly identify the extent to which the personal information of participants had permeated multiple digital locales in that particular context.

⁴ Low self-control was not entirely invariant across all time points of the data as a significant difference was found in this measure between waves 1 and 3. However, the actual difference between the two waves was rather minor (Mean difference = 0.007). As a result, it was concluded that the wave 1 low self-control measure would produce similar results to other time points and so we use it as a control across our analyses in the interest of parsimony.

⁵ While cyber deviance has been used in prior literature as a control variable (Bossler and Holt 2009; Holt and Bossler 2008, 2013), it was excluded from this study due to low participant response for this measure.

⁶ For more about the use of auxiliary variables, see Collins, Schafer, and Kam (2001) and Graham (2009).

⁷ A model for target attractiveness could not be conducted because the measures were not included as part of the wave 1 survey.

Additional information

Notes on contributors

Chris Guerra

Chris Guerra is a Ph.D. student in the Department of Criminal Justice and Criminology at Sam Houston State University. His research interests include cybercrime and cybercrime victimization, crime among immigrant and Hispanic populations, life-course criminology, and police culture.

Jason R. Ingram

Jason R. Ingram is an associate professor in the Department of Criminal Justice and Criminology at Sam Houston State University. His primary research interests focus on policing. His other research interests include testing criminological theories and policy evaluation.