Graduation Year

2017

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Criminology

Major Professor

John K. Cochran, Ph.D.

Co-Major Professor

Michael J. Lynch, Ph.D.

Committee Member

Lyndsay N. Boggess, Ph.D.

Committee Member

George W. Burruss, Ph.D.

Keywords

online property crime, cybercrime, macro-social correlates, state-level analysis, partial least square

Abstract

Despite the recent decreasing trend of most traditional types of crime, online property crime (OPC), referring to crime committed online with a financial orientation such as online frauds, scams, and phishing, continues to increase. According to the Internet Crime Complaint Center, the number of reported complaints about OPC have increased by approximately sixteen fold from 16,838 cases in 2000 to 288,012 cases in 2015, and referred financial losses have also increased about sixty times from $17.8 million in 2001 to $1 billion in 2015. The increase in OPC might be directly related to advanced online accessibility due to the accelerated progress of information and communication technology (ICT). Since the progress of ICT continues forward and the advanced ICT infrastructure can affect our routine activities more significantly, issues regarding OPC may become more various and prevalent.

The present study aims to explore a macro-social criminogenic structure of OPC perpetration. Specifically, this study focused on exploring probable macro-social predictors of OPC rates and examining how effectively these possible macro-social predictors account for variance in OPC perpetration rates. In addition, this study explored possible predictors of macro-level online opportunity structure, which is expected to have a direct relationship with OPC rates. It also examined how much variance in online opportunity structure was explained by the included possible predictors. With these research purposes, the current study analyzed state-level data of the fifty states in the U.S. by applying a partial least square regression (PLSR) approach.

The results indicated that predictors related to macro-social economic conditions such as economic inequality, poverty, economic social support, and unemployment had a significant association with OPC. As expected, indicators in the domain of economic inequality predicted greater OPC rates and those in the domain of economic social support were related to lower OPC rates. However, poverty and unemployment predictors were negatively associated with OPC, which is the opposite direction of the relationships between these predictors and traditional street crime. In addition, indicators of online opportunity structure were found to have a significantly positive relationship to OPC as expected. The PLSR model for predicting OPC applied in the current study accounted for approximately 50% of variance in OPC rates across states.

For predictors of online opportunity structure, the results indicated that online opportunity was associated with state-level economic and socio-demographic characteristics. States with less poverty, more urban population, and more working age adults were more likely to report more online opportunities. The PLSR model for predicting online opportunity structure explained about 80% of variance in measured online opportunity. These results may imply that some types of macro-social conditions may have an indirect effect on OPC through online opportunity structure as well as their direct effects on OPC. Future study should pay more attention to examining structural relationships of macro-social contexts, online opportunity structure, and OPC to understand macro-level criminogenic mechanism of OPC.

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