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Victimization Analysis Based On Routine Activitiy Theory for Cyber-Love Scam in Malaysia

Saad, ME. ; Norul Huda Sheikh Abdullah, S. (2018) — 2018 Cyber Resilience Conference (CRC)

DOI: 10.1109/cr.2018.8626818
Type:   Proceedings Article
Country:   Malaysia
Tags:   victim experience, law enforcement, platform policy, prevention, cyber-love scam, online dating fraud, Routine Activity Theory, victimization, cybercrime awareness, computer skills, Malaysia, police reporting

Synopsis (AI-Generated)

This publication examines cyber-love scam victimization in Malaysia through the lens of Routine Activity Theory, aiming to identify factors that influence an individual’s susceptibility. The authors position cyber-lovel scam as a growing cybercrime problem in Malaysia and seek to clarify how demographic characteristics (such as age, education, marital status, and income) together with variables like computer skills and cyber-fraud awareness relate to the likelihood of becoming a victim. The study adopts a quantitative approach centered on a questionnaire survey designed to measure levels of cybercrime awareness, computer skills, and the propensity to be victimized by cyber-love scams. The researchers implement a pioneer test to assess instrument reliability and use statistical analysis to explore relationships between the identified variables and victimization risk. The analysis is anchored in Routine Activity Theory, which emphasizes the convergence of a motivated offender, an available target, and the absence of capable guardianship as drivers of crime, and the authors apply this framework to cyber-enabled romance scams. The authors discuss implications for policy and enforcement, noting that findings may inform measures to protect Internet users in Malaysia. They also acknowledge limitations related to the study’s scope and measurement approach, suggesting that the work provides a foundation for understanding factors that influence vulnerability to cyber-love scams, while not asserting broad generalizations beyond the sampled context. The publication thus contributes to the discourse on cybercrime risk factors and potential prevention strategies within Malaysia.

Identified Gaps (AI-Generated)

The study identifies a need for clearer analysis of cyber-love-scam victimization patterns through financial-loss data and offender-side factors. Its victim-focused design addresses demographic characteristics, computer skills, and cybercrime awareness, but does not examine cybercriminals’ target-selection environment or motivations.

Methods (AI-Generated)

Quantitative questionnaire survey of 280 cyber-love-scam victims who reported to police in Selangor, Malaysia, drawn from a target population of 2,508. The questionnaire measured demographics, cybercrime awareness (six items), and computer skills (three items). A 20-person pilot assessed reliability using Cronbach’s alpha (0.83–0.92). The authors used SPSS descriptive, normality, Pearson correlation, and casewise/Cook’s-distance analyses.

Limitations (AI-Generated)

The sample comprises only police-reporting victims in Selangor, so findings may not represent unreported victims or victims elsewhere in Malaysia. Because all respondents were already victims and the analysis is correlational, the design supports associations within this victim sample rather than causal estimates of susceptibility or comparisons with non-victims. The study also does not measure financial losses or offender motives/environment.

Future Work (AI-Generated)

Future research should examine the extent and pattern of victims’ financial losses. It should also incorporate the environment, motives, and major motivations of cybercriminals when selecting targets.

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AI-Generated Content Notice

The synopsis and research notes on this page were generated with AI from available publication information and, when available, the uploaded paper text. They may contain errors, omissions, or interpretation issues. Readers should follow the DOI or source link, review the original publication, and make their own judgment about the content.

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