Estimating cyber attack risk from healthcare employee behaviour using a HEXACO machine learning model

Kenneth David Strang

International Journal of Business Continuity and Risk Management, 2025, vol. 15, issue 3, 234-262

Abstract: Cyber attack risk is examined by collecting a sample from healthcare business employees using the previously validated six-factor HEXACO personality theory construct from the psychology discipline. Cybercrime theories and studies are reviewed from sociology, criminology and computer science. The research design involved developing a predictive logistic regression model using machine learning. Control variables were added to capture fixed participant demographics. The result was a significant model with 95% classification accuracy, and a 60% McFadden effect size. Two of the six HEXCACO factors predicted cyber attack risk: humility and openness, while none of the control variables had any impact.

Keywords: HEXACO personality theory; cyber attack; cybersecurity; machine learning; employee attributes; healthcare business; psychology. (search for similar items in EconPapers)
Date: 2025
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