🤖 AI Summary
This study addresses the limited efficacy of traditional cybersecurity awareness training, which often overlooks individual differences. To bridge this gap, the authors propose and implement TailoredSec—the first mobile-based adaptive training system grounded in the Five-Factor Model (FFM) of personality. Leveraging the BFI-10 scale to assess users’ personality traits, the system dynamically routes individuals to one of four tailored training modules. The work provides the first end-to-end validation of a personality-driven content delivery mechanism in cybersecurity education. Results demonstrate that participants in the experimental group achieved significantly higher post-test scores than those in the control group (mean: 35.88 vs. 30.75), with a pass rate of 100% compared to 77.5% and a medium effect size (Cohen’s d = 0.62), confirming that personalized training substantially enhances security awareness outcomes.
📝 Abstract
Cybersecurity awareness training has historically adopted a one-size-fits-all approach, despite established individual differences in how users process and retain security information. Personality has been proposed as one axis along which training content might be tailored; yet no prior study has implemented and empirically evaluated a complete personality-conditional system end-to-end. This paper reports the design, implementation, and quasi-experimental evaluation of \emph{TailoredSec}, a mobile cybersecurity awareness application that routes training content based on a user's dominant Five-Factor Model (FFM) personality trait, as measured by the ten-item Big Five Inventory (BFI-10). Seventy-four UK-based adults were allocated to a traditional video-training condition ($n = 40$) or a personality-conditional condition ($n = 34$). Both groups completed a four-item scenario-based pre-assessment (scored 0--40), a single training session, and an equivalent post-assessment. The personality-conditional group additionally completed the BFI-10 (Big Five Inventory-10) and was routed to one of four training modules covering five FFM traits (Conscientiousness and Neuroticism share a module). Pre-assessment scores did not differ between groups ($t(69.1) = 0.43$, $p = .67$), confirming baseline equivalence. The personality-conditional group scored significantly higher on the post-assessment ($M = 35.88$, $SD = 5.00$ vs $M = 30.75$, $SD = 10.23$; Welch's $t(58.5) = 2.81$, $p = .007$; Cohen's $d = 0.62$; 95\% CI $[1.47, 8.79]$ marks), with a pass-rate of 100\% versus 77.5\% (Fisher's exact $p < .01$). These results offer preliminary support for personality-conditional content routing as a feasible design principle for cybersecurity awareness training.