TY - JOUR
T1 - Lifestyles and routine activities
T2 - Do they enable different types of cyber abuse?
AU - Vakhitova, Zarina I.
AU - Alston-Knox, Clair L.
AU - Reynald, Danielle M.
AU - Townsley, Michael K.
AU - Webster, Julianne L.
PY - 2019
Y1 - 2019
N2 - Background: The emergence of new technology-facilitated types of crime following the advent of the Internet necessitated the re-examination of the utility of traditional theories such as lifestyle-routine activity theory to explain crimes that occur in the new and unique environment of cyberspace. Reason for study: The objective of this study was to investigate whether victims’ lifestyles and routine activities can help explain the risk of victimization from direct, indirect and mixed types of cyber abuse. Research design: To achieve this objective, the data from a large nationwide (US) crowd-sourced sample (N = 1463) of adult members of an online labour portal Mechanical Turk was collected using an online survey platform Qualtrics and then analyzed using Bayesian Profile Regression to identify clusters of lifestyles and routine activities of victims associated with victimization from different types of cyber abuse. Findings: Our analyses were able to distinguish between victims and non-victims as well as between victims of different sub-classifications of cyber abuse. Specifically, we have identified five population subgroups based on their lifestyles and routine activities in terms of the associated risk of personal victimization from different types of cyber abuse. This paper discusses the differences in lifestyles and routine activities between the identified groups. Conclusions: Our findings generally support the empirical utility of lifestyle-routine activity theory to explain cyber abuse victimization; as with other traditional types of crime (e.g. robbery or assault), victims’ lifestyles and routine activities play a significant role in their risk of various types of cyber abuse victimization.
AB - Background: The emergence of new technology-facilitated types of crime following the advent of the Internet necessitated the re-examination of the utility of traditional theories such as lifestyle-routine activity theory to explain crimes that occur in the new and unique environment of cyberspace. Reason for study: The objective of this study was to investigate whether victims’ lifestyles and routine activities can help explain the risk of victimization from direct, indirect and mixed types of cyber abuse. Research design: To achieve this objective, the data from a large nationwide (US) crowd-sourced sample (N = 1463) of adult members of an online labour portal Mechanical Turk was collected using an online survey platform Qualtrics and then analyzed using Bayesian Profile Regression to identify clusters of lifestyles and routine activities of victims associated with victimization from different types of cyber abuse. Findings: Our analyses were able to distinguish between victims and non-victims as well as between victims of different sub-classifications of cyber abuse. Specifically, we have identified five population subgroups based on their lifestyles and routine activities in terms of the associated risk of personal victimization from different types of cyber abuse. This paper discusses the differences in lifestyles and routine activities between the identified groups. Conclusions: Our findings generally support the empirical utility of lifestyle-routine activity theory to explain cyber abuse victimization; as with other traditional types of crime (e.g. robbery or assault), victims’ lifestyles and routine activities play a significant role in their risk of various types of cyber abuse victimization.
KW - Bayesian profile regression
KW - Cyber abuse
KW - Lifestyle-routine activity theory
KW - Victimization
UR - http://www.scopus.com/inward/record.url?scp=85069875035&partnerID=8YFLogxK
U2 - 10.1016/j.chb.2019.07.012
DO - 10.1016/j.chb.2019.07.012
M3 - Article
AN - SCOPUS:85069875035
SN - 0747-5632
VL - 101
SP - 225
EP - 237
JO - Computers in Human Behavior
JF - Computers in Human Behavior
ER -