Time Will Tell: The Case for an Idiographic Approach to Behavioral Cybersecurity Research
Why this work is in the frame
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Bibliographic record
Abstract
Many of the theories used in behavioral cybersecurity research have been applied with a nomothetic approach, which is characterized by cross-sectional data (e.g., one-time surveys) that identify patterns across a population of individuals. Although this can provide valuable between-person, point-in-time insights (e.g., employees who use neutralization techniques, such as denying responsibility for cybersecurity policy violations, tend to comply less), it is unable to reveal within-person patterns that account for varying experiences and situations over time. This paper articulates why an idiographic approach, which undertakes a within-person analysis of longitudinal data, can: (1) help validate widely used theories in behavioral cybersecurity research that imply patterns of behavior within a given person over time and (2) provide distinct theoretical insights on behavioral cybersecurity phenomena by accounting for such within-person patterns. To these ends, we apply an idiographic approach to an established theory in behavioral cybersecurity research—neutralization theory—and empirically test a within-person variant of this theory using a four-week experience sampling study. Our results support a more granular application of neutralization theory in the cybersecurity context that considers the behavior of a given person over time. We conclude the paper by highlighting the contexts and theories that provide the most promising opportunities for future behavioral cybersecurity research using an idiographic approach.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it