Examining human individual differences in cyber security and possible implications for human-machine interface design

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Abstract

With society now heavily invested in cyber-technology and most cyber-attacks due to human error, it has never been more vital to focus research on human-centric interventions. Whilst some studies have previously investigated the importance of end-user individual differences (gender, age, education, risk-taking preferences, decision-making style, personality and impulsivity) the current study extended the research to also include acceptance of the internet and the constructs used to explain behavior within the Theory of Planned Behavior (TPB) and Protection Motivation Theory (PMT). Seventy-one participants completed a battery of questionnaires on personality, risk-taking preferences, decision-making style, personality, impulsivity, acceptance of the internet, the combined PMT and TPB questionnaire, as well as an online cyber-security behaviors questionnaire. Gender, age and education did not relate to any cyber-security behaviors, however a number of individual differences were associated. These behaviors include financial risk-taking, avoidant decision-making plus ease of use, facilitating conditions, and trust in the internet. It was also found that safer cyber-security behaviors are seen in those who appraise threat as high, perceive themselves to have the required skills to protect themselves, see value in this protection and understand their place in the cyber-security chain. These findings emphasize the importance of understanding how individual differences relate to cyber-security behaviors in order to create more tailored human-centric interventions such as computer-based decision support systems and other human-machine interface solutions.

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Bishop, L. M., Morgan, P. L., Asquith, P. M., Raywood-Burke, G., Wedgbury, A., & Jones, K. (2020). Examining human individual differences in cyber security and possible implications for human-machine interface design. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12210 LNCS, pp. 51–66). Springer. https://doi.org/10.1007/978-3-030-50309-3_4

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