Once Bitten, Twice Shy? Understanding Repeat and Multiple Victimization in Business Cybercrime

This study examines repeat victimization (RV) and multiple victimization (MV) in business cybercrime using data from the 2024 UK Cyber Security Breaches Survey (CSBS). Drawing on Routine Activity Theory, particularly its VIVA framework (value, inertia, visibility, accessibility) and capable guardianship, it investigates how organizational characteristics, routine activities and cybersecurity practices influence victimization risks. The study applies multinomial logit, bivariate probit, and hurdle negative binomial models. Results show that RV and MV risk is not evenly distributed among victimized businesses, and that the predictors associated with initial or single victimization differ from those predicting RV and MV. The findings highlight the limitations of one-size-fits-all prevention strategies and call for more tailored, stage-specific prevention responses.

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Research Explorer (The University of Manchester)
Published
2026-03-12
Primary Topic
Information and Cyber Security
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article
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article

Once Bitten, Twice Shy? Understanding Repeat and Multiple Victimization in Business Cybercrime

Yijie; id_orcid 0009-0003-7252-2369 He, Emma; id_orcid 0000-0003-0762-5196 Barrett, Jonathan; id_orcid 0000-0002-4561-1839 Davies, David; id_orcid 0000-0002-7549-6317 Buil-Gil
Research Explorer (The University of Manchester)
Information and Cyber Security
article

Once Bitten, Twice Shy? Understanding Repeat and Multiple Victimization in Business Cybercrime

Yijie; id_orcid 0009-0003-7252-2369 He, Emma; id_orcid 0000-0003-0762-5196 Barrett, Jonathan; id_orcid 0000-0002-4561-1839 Davies, David; id_orcid 0000-0002-7549-6317 Buil-Gil
article en

Abstract

This study examines repeat victimization (RV) and multiple victimization (MV) in business cybercrime using data from the 2024 UK Cyber Security Breaches Survey (CSBS). Drawing on Routine Activity Theory, particularly its VIVA framework (value, inertia, visibility, accessibility) and capable guardianship, it investigates how organizational characteristics, routine activities and cybersecurity practices influence victimization risks. The study applies multinomial logit, bivariate probit, and hurdle negative binomial models. Results show that RV and MV risk is not evenly distributed among victimized businesses, and that the predictors associated with initial or single victimization differ from those predicting RV and MV. The findings highlight the limitations of one-size-fits-all prevention strategies and call for more tailored, stage-specific prevention responses.

Research Explorer (The University of Manchester)
Institute of Criminology (SI)
Peace, Justice and strong institutions
Openalex Percentile: Top 31%
Information and Cyber Security
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