While criminality is digitizing, a theory-based understanding of the impact of cybercrime on victims is lacking. Therefore, this study addresses the psychological and financial impact of cybercrime on victims, applying the shattered assumptions theory (SAT) to predict that impact. A secondary analysis was performed on a representative data set of Dutch citizens ( N = 33,702), exploring the psychological and financial impact for different groups of cybercrime victims. The results showed a higher negative impact on emotional well-being for victims of person-centered cybercrime, victims for whom the offender was an acquaintance, and victims whose financial loss was not compensated and a lower negative impact on emotional well-being for victims with a higher income. The study led to novel scientific insights and showed the applicability of the SAT for developing hypotheses about cybercrime victimization impact. In this study, most hypotheses had to be rejected, leading to the conclusion that more work has to be done to test the applicability of the SAT in the field of cybercrime. Furthermore, policy implications were identified considering the prioritization of and approach to specific cybercrimes, treatment of victims, and financial loss compensation.

This paper addresses the importance of building knowledge on the impact of cybercrime victimization. Because the topic is understudied, it is unclear whether the impact of cybercrime differs from that of traditional crime. Our understanding of potential impact differences needs to be improved, considering that society and criminality are digitizing and, consequently, more people are likely to become victims of cybercrime. From a practical perspective, knowledge about the impact of different crimes is important to develop victim policies within law enforcement and other relevant agencies, and to treat victims appropriately. In this paper, a literature review is provided, as well as future research directions to address the current knowledge gap. The future research directions are divided in three topics: (1) distinguishing between cybercrime and traditional crime, (2) classifying cybercrime and traditional crime, and (3) measuring the victimization impact of cybercrime and traditional crime.

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