Nudges and Cybersecurity: Harnessing Choice Architecture for Safer Work-From-Home Cybersecurity Behaviour
Bibliographic record
Abstract
The number of security breaches and the cost of damages hit a record high in 2021, with an average cost of $5.4 million per incident in Canada, according to the IBM Security Report (2021).Despite safety measures and risk management policies, workfrom-home arrangements and employee behaviour are reported as major factors affecting cybersecurity.This research examines the choice architecture of work-from-home cybersecurity behaviour through a multiple case study research design and cross-case analysis of employees from eight small and medium businesses.Contributions include an inventory of individual and organizational factors that influence cybersecurity behaviour, a framework to specify a nudge, three nudges specified using the framework, and a sixstep method to design a nudge to influence the cybersecurity behaviour of work-fromhome employees.These results can assist mangers, researchers, and entrepreneurs to understand and improve their work-from-home cybersecurity posture.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".