The Perfect Storm of Cyberscam Risk: Examining Personal, Injury, and Psychosocial Risk Factors for People With and Without Acquired Brain Injury
Year: 2025
Type: article
Study design: Observational Study
Abstract: Cyberscams are a pervasive global issue with losses exceeding $1 trillion worldwide and resulting in significant psychosocial impacts, particularly shame. People with disabilities, such as acquired brain injury (ABI), may be additionally vulnerable due to cognitive impairments and social isolation. Increased scam vulnerability and risk factors for people with ABI have not been investigated. This study aimed to (a) determine whether people with ABI have greater risk of being scammed than people without ABI, and (b) explore demographic and psychosocial factors associated with cyberscam risk for people with and without ABI. Using a cross-sectional design, participants with ( n = 149) and without ( n = 153) ABI provided scam experience details and completed a validated measure of self-rated cybersafety and practical scam identification ( The CyberAbility Scale ) and measures of psychosocial risks of loneliness, impulsivity, mood, trust, and community integration. Correlation analyses showed that participants with ABI performed worse on a scam identification task than those without ABI. As expected, higher self-rated scam safety was associated with lower loneliness, impulsivity, and fewer mood symptoms, and higher trust and community integration. In multiple regression analyses, higher loneliness was most significantly associated with higher self-rated cyberscam risk, and older age and presence of ABI were associated with poorer scam identification. This study illustrates the multifaceted nature of cyberscam risk, involving distinct social and knowledge-based risks. Findings underscore the need for scam prevention and recovery initiatives targeting at-risk groups and considering the needs of people with ABI in staying safe online.
Institution: Monash University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4407405385