Vol. 4 No. 2 (2025): April
RESEARCH ARTICLES

A Structural Equation Modelling Approach to Understanding Data Breach Factors Through Modern Crime Theory

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  • B. Sravani,
  • G Bhumika,
  • J Prashanth,
  • B Pavan,
  • S Asha Haseena
B. Sravani
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
G Bhumika
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
J Prashanth
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
B Pavan
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
S Asha Haseena
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
DOI: https://doi.org/10.5281/zenodo.15184367

Published 2025-04-09

Keywords

  • Data breach,
  • cybersecurity,
  • Modern Crime Theory,
  • structural equation modelling,
  • organizational risk assessment

How to Cite

B. Sravani, G Bhumika, J Prashanth, B Pavan, & S Asha Haseena. (2025). A Structural Equation Modelling Approach to Understanding Data Breach Factors Through Modern Crime Theory. International Journal of Computational Learning & Intelligence, 4(2), 473–479. https://doi.org/10.5281/zenodo.15184367

Copyright (c) 2025 B. Sravani, G Bhumika, J Prashanth, B Pavan, S Asha Haseena

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

With the increasing reliance on digital infrastructure, data breaches have become a critical concern for organizations worldwide. Despite extensive research in cybersecurity, limited studies have examined the external factors influencing an organization’s vulnerability to data breaches. This study applies Modern Crime Theory (MCT) to analyze the external conditions contributing to data breaches, incorporating elements such as attractiveness, visibility, and guardianship as key predictors. A Covariance-Based Structural Equation Modeling (CB-SEM) framework was employed to investigate how these factors influence the likelihood of organizations experiencing data breaches. The study utilized a dataset comprising 4,868 organizations, assessing both victimized and non-victimized entities to determine the relationships between the proposed variables. The results confirm that organizations with high data value (attractiveness), increased public exposure (visibility), and weak security measures (guardianship deficits) face a significantly higher risk of data breaches. The findings validate the application of MCT in cybersecurity research, providing actionable insights into how organizations can proactively reduce breach risks by strengthening protective measures.

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