A Comparative Analysis of Theoretical Frameworks in Predicting Image-based Sexual Assault

• In Event: Explaining the Drivers of Sexual Violence

Abstract

Understanding the complexities surrounding image-based sexual assault, (e.g., revenge porn, nonconsensual pornography) has been hindered by a lack of understanding regarding its underlying motivations. The current study seeks to fill this gap by investigating the strength and applicability of three prominent theoretical frameworks: social learning theory, self-control theory, and general strain theory. Using secondary data (n = 1,227) from the Longitudinal Survey of Cybercriminology Panel (September 2021), the current study examined the impact of each theory on individuals’ perpetration of image-based sexual assault. Specifically, the analyses included examining each theory separately as well as together in one model to assess both its individual and comparative impact on image-based sexual assault offending. Findings revealed that each theory was able to explain some variation of image-based sexual assault perpetration, with self-control being the strongest predictor when compared with the other two frameworks. The theoretical and policy implications of these results will be discussed in detail.

Authors

• Jennifer Ayerza, George Mason University

• Jin R. Lee, George Mason University