The current dissertation investigated sexual assault victimization in virtual worlds – environments, where individuals engage through avatars, offering a deep level of immersion and interactivity. As the usage of these virtual spaces grows, so does the concern for negative experiences that mirror the sexual assault encountered in the physical world. Despite this, there is a notable gap in the quantitative analysis of virtual sexual assault (VSA) victimization. This research aimed to fill this void by exploring the prevalence of VSA victimization over the previous year and identifying potential predictors of VSA victimization through the lens of cyberlifestyle-routine activity theory. To achieve this, the current research recruited 829 English-speaking adult participants, who used/have used virtual worlds via Mechanical Turk (MTurk) using CloudResearch, which uses the sample population within MTurk but produces better-quality data. The study found that 46.44% of participants reported at least one instance of VSA victimization. VSA behaviors were categorized into unwanted sexual advances, image-based sexual abuse, and non-consensual sexual avatar manipulation. Unwanted sexual advances were reported by 35.71% of participants, image-based sexual abuse by 33.29%, and non-consensual sexual avatar manipulation by 27.99%. Applying CLRAT, the study found that higher levels of online exposure, online proximity, online deviant lifestyle, and lower levels of online guardianship were correlated with increased VSA victimization. However, the study noted that traditional guardianship concepts needed refinement for virtual environments, as mere vigilance without active intervention was insufficient to prevent victimization. Limitations, suggestions for future studies, and implications are provided based on these findings.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2023-1472 |
| Date | 01 January 2024 |
| Creators | Lee, Narim |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Thesis and Dissertation 2023-2024 |
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