Technology-Based Abuse: Understanding the Role of Technology for Furthering Coercion and Control

Background: Despite a large body of research examining online harassment, cyberstalking, and specific technology-based abuse (TBA) among youth, there is a dearth of research examining the experiences of adult victims. Many studies have used qualitative data show the overlap of intimate partner violence (IPV) and TBA, but few have employed quantitative methods to understand TBA among this high-risk subgroup. This paper set out to identify patterns of TBA experienced among a sample of adult, female IPV survivors in the Southwest region of the United States. Specifically, the researchers sought to understand what unique patterns of TBA are present among domestic violence survivors?

Methods: Using an exploratory survey about TBA, survivors accessing IPV services were asked about specific TBA experiences in addition to providing information about the abuse within their relationship. The sample included 249 women who were in domestic violence programs throughout a metropolitan region in the Southwest. Participants ages ranged from 18-66 years (M = 38 years, SD = 11 years) and were primarily low income, with the majority (35%) earning less than $100 per month. The majority of participants (89%) were born in the U.S. And 71% were not employed outside of the home where they earned wages. Participants represented a diverse sample with 38% White, 26% Black, 12% Hispanic, 5% American Indian, 1% Asian, 15% multiracial, and 3% other. Latent Class Analysis (LCA) examined patterns of TBA within the sample.

Findings: Of the TBA survey items, the majority of participants had experienced TBA with 10 of the 16 items receiving a positive endorsement from 50% of respondents. A three-class model was identified as the best model fit of our sample data. The groups included technology-based monitoring, technology-based harassment, and technology-based conflict. Of the three classes the technology-based harassment group (n = 140, 38%) had the highest rates of TBA across all 16 items and demonstrate the perverseness and overlap of different forms of TBA. Patterns of technology-based harassment show the myriad of ways technology can be used with an intention for punishing a victim after leaving, or publicly humiliating the victim. The second grouping, technology-based monitoring (n = 134, 36%) shows abusive techniques that were likely common during the time when the couple were still together. The final group, technology-based conflict (n = 95, 26%) had scored lower across all items, but reported emotional abuse occurring through messages.

Conclusion: The findings from this study show that when surveying IPV victims who have accessed services, the overall prevalence of TBA is high and TBA is associated with patterns of coercion during the course of the relationship and after a survivor has left. In additional to the LCA modeling, prior analysis demonstrated significant association with TBA victimization and serious form forms of IPV including physical assault, severe abuse, and homicide. Future research should look to create validated TBA scales, and examine the impacts of TBA including mental health, financial consequences, and long-term impacts for survivors.