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arxiv: 2605.24551 · v1 · pith:LRXQ2W24new · submitted 2026-05-23 · 💻 cs.CR · cs.HC

Routing Cybersecurity Awareness Training by FFM Personality Trait: A Quasi-Experimental Evaluation

Glory Okwata , Mohammad A. Razzaque This is my paper

Pith reviewed 2026-06-30 13:11 UTC · model grok-4.3

classification 💻 cs.CR cs.HC
keywords cybersecurity awareness trainingpersonality traitsFive-Factor ModelBFI-10tailored trainingquasi-experimental evaluationlearning outcomesmobile application
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The pith

Personality-conditional cybersecurity training raises post-assessment scores and pass rates over standard video sessions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper evaluates whether routing cybersecurity awareness training content to match a user's dominant Five-Factor Model personality trait produces better learning outcomes than a one-size-fits-all approach. It describes the design of the TailoredSec mobile app that uses the BFI-10 inventory to assign one of four trait-specific modules. In a quasi-experiment with 74 adults, the routed group outperformed the control on the post-assessment with a medium effect size and achieved a 100 percent pass rate versus 77.5 percent. These findings indicate that personality-based tailoring can improve retention of security information in practical training settings.

Core claim

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).

What carries the argument

The TailoredSec mobile application, which administers the BFI-10 to determine a dominant FFM trait and routes the user to one of four training modules (with Conscientiousness and Neuroticism sharing a module).

Load-bearing premise

The BFI-10 accurately identifies a dominant personality trait that can be used to route training content in a way that produces better learning outcomes, and the non-random quasi-experimental allocation did not introduce selection bias between groups.

What would settle it

A fully randomized trial with a larger sample that finds no significant difference in post-assessment scores or pass rates between the personality-routed condition and the standard video condition.

Figures

Figures reproduced from arXiv: 2605.24551 by Glory Okwata, Mohammad A. Razzaque.

Figure 1
Figure 1. Figure 1: Content routing structure. BFI-10 dominant trait [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: CONSORT-style participant flow diagram. Of 112 individuals approached, 74 completed the study. Non-completers (𝑛 = 38) did not install the application, most likely due to reluctance to enable sideloading of an unsigned APK. One participant in Sample 2 passed the pre-assessment and proceeded directly to post-assessment without receiving personality-routed training content. personality-conditional training c… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of TailoredSec system architecture. The Android client manages application state in-memory (FlutterFlow AppState cache); scores and session data are persisted to Firebase Firestore on completion of each quiz segment. homogeneity was examined via the variance ratio 𝑠 2 Trad∕𝑠 2 PC; a ratio substantially greater than 1 was interpreted as evi￾dence of a ceiling effect in the personality-conditional g… view at source ↗
Figure 4
Figure 4. Figure 4: Application logical flow. Participants who pass the pre-assessment (≥ 30) may exit or proceed directly to post￾assessment. Participants who fail are routed through the BFI-10 (personality-conditional group) or directly to standard training (traditional group), and then to post-assessment. screen with routing instructions. Representative screenshots of the pre-assessment interface are shown in [PITH_FULL_I… view at source ↗
Figure 5
Figure 5. Figure 5: User interface for the scenario-based pre-assessment quiz. Radio buttons present four response options per scenario; scoring logic is embedded in the navigation action of the “Next” button [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: User interface for the scenario-based pre-assessment quiz. Radio buttons present four response options per scenario; scoring logic is embedded in the navigation action of the “Next” button. 5 in the persTestOpt1 state variable, yielding the correct reverse-scored contribution to the Extraversion subscale. The dominant trait was determined by comparing the five computed trait scores, and in the event of a t… view at source ↗
Figure 8
Figure 8. Figure 8: Trait-based Personalised Training Modules. 4.5. Training Modules Four personality-specific training modules were imple￾mented, informed by the coping mechanism recommen￾dations of Uebelacker and Quiel [21]. The specific im￾plementations represent pragmatic approximations to the ideal formats given the scope and resources of the project [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Post-assessment score distributions. The personality￾conditional group showed a highly concentrated distribution at the upper end (30–40), indicative of a ceiling effect in the four-item post-assessment instrument. difference between groups at baseline (𝑡(69.1) = 0.43, 𝑝 = .67, Cohen’s 𝑑 = 0.10), supporting the assumption of baseline equivalence and providing a valid basis for between group comparison on t… view at source ↗
Figure 10
Figure 10. Figure 10: Pass-rate comparison at the 30-mark threshold. All 33 personality-conditional participants who received trait￾routed training passed the post-assessment (100%), compared with 31/40 (77.5%) in the traditional group (Fisher’s exact 𝑝 < .01, one-tailed) [PITH_FULL_IMAGE:figures/full_fig_p010_10.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript reports the design and quasi-experimental evaluation of TailoredSec, a mobile application for cybersecurity awareness training that routes content based on a user's dominant Five-Factor Model (FFM) personality trait as assessed by the BFI-10. In a study with 74 UK adults, participants were allocated to either a traditional video-based training condition (n=40) or a personality-conditional condition (n=34) where they completed the BFI-10 and received one of four tailored modules. Both groups showed equivalent pre-assessment scores, but the personality-conditional group demonstrated significantly higher post-assessment scores (M=35.88, SD=5.00 vs. M=30.75, SD=10.23; Welch's t(58.5)=2.81, p=.007, d=0.62) and a higher pass rate (100% vs 77.5%, Fisher's exact p<.01), providing preliminary evidence for the efficacy of personality-based routing in cybersecurity training.

Significance. If replicated under a design that isolates the routing mechanism, this would be the first end-to-end empirical evaluation of personality-conditional cybersecurity awareness training and could inform more effective, individualized security education programs. The moderate effect size (d=0.62) and use of standard statistical tests provide a concrete starting point, though the quasi-experimental nature limits causal strength.

major comments (2)
  1. [Abstract / Design] The quasi-experimental allocation (n=40 traditional vs n=34 personality-conditional) is non-random, and only the personality arm completes the BFI-10 before training. This introduces potential confounds from selection bias or procedural effects (e.g., demand characteristics from the extra questionnaire) that are not controlled, so the post-assessment difference cannot be attributed specifically to trait-based content routing (abstract).
  2. [Abstract] The BFI-10 is used to identify a dominant trait for routing to one of four modules (Conscientiousness and Neuroticism share a module), but the manuscript provides no validation of this mapping's reliability or appropriateness for isolating a single dominant trait within the sample, leaving the routing mechanism's internal validity untested.
minor comments (2)
  1. [Abstract] The four-item scenario-based pre- and post-assessments (scored 0-40) are described only at a high level; including example items or full scoring rubric would improve reproducibility.
  2. [Abstract] The abstract reports baseline equivalence via t(69.1)=0.43, p=.67 but does not specify whether the full methods section includes power analysis or handling of the small total sample (n=74) for the reported effect sizes and CIs.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful and constructive comments on the manuscript. We address each major comment below, acknowledging the inherent limitations of the quasi-experimental design and outlining specific revisions to the abstract and methods sections.

read point-by-point responses

  1. Referee: [Abstract / Design] The quasi-experimental allocation (n=40 traditional vs n=34 personality-conditional) is non-random, and only the personality arm completes the BFI-10 before training. This introduces potential confounds from selection bias or procedural effects (e.g., demand characteristics from the extra questionnaire) that are not controlled, so the post-assessment difference cannot be attributed specifically to trait-based content routing (abstract).

    Authors: We agree that the non-random allocation and the procedural difference (BFI-10 completion only in one arm) introduce potential confounds, including possible demand characteristics, that prevent strong causal attribution to the routing mechanism alone. Although pre-assessment equivalence supports baseline comparability, we cannot fully isolate the effect. We will revise the abstract to more explicitly state these limitations and describe the results as preliminary evidence. revision: yes

  2. Referee: [Abstract] The BFI-10 is used to identify a dominant trait for routing to one of four modules (Conscientiousness and Neuroticism share a module), but the manuscript provides no validation of this mapping's reliability or appropriateness for isolating a single dominant trait within the sample, leaving the routing mechanism's internal validity untested.

    Authors: The dominant trait was identified via the highest BFI-10 subscale score, with Conscientiousness and Neuroticism sharing a module based on their overlapping implications for security-related caution and compliance. We did not perform additional validation of this mapping or its reliability for single-trait isolation in the sample. We will revise the methods section to clarify the rationale for the mapping and explicitly note the lack of validation as a study limitation. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical quasi-experimental evaluation with direct statistical comparisons

full rationale

This paper reports a quasi-experimental study comparing traditional video training against personality-conditional routing using BFI-10 scores and scenario-based assessments. Outcomes rest on measured pre/post test scores, Welch's t-tests, Cohen's d, and Fisher's exact tests with no mathematical derivations, fitted parameters renamed as predictions, or self-referential equations. The central claim (higher post-assessment scores in the personality arm) is supported by direct empirical data collection rather than any derivation chain that reduces to its own inputs by construction. No load-bearing self-citations, ansatzes, or uniqueness theorems are invoked. The design is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the validity of the BFI-10 as a personality measure and on standard statistical assumptions for the reported tests; no free parameters or new entities are introduced.

axioms (2)
  • domain assumption The BFI-10 validly and reliably measures dominant Five-Factor Model traits for routing purposes
    Used to assign participants to training modules.
  • standard math Standard assumptions for Welch's t-test and Fisher's exact test hold for the group comparisons
    Invoked to interpret the reported p-values and effect sizes.

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