‘What a waste of time’: An examination of cybersecurity legitimacy
Bibliographic record
Abstract
Abstract Managers who oversee cybersecurity policies commonly rely on managerial encouragement (e.g., rewards) and employee characteristics (e.g., attitude) to drive compliant behaviour. However, whereas some cybersecurity initiatives are perceived as reasonable by employees, others are viewed as a ‘waste of time’. This research introduces employee judgements of cybersecurity legitimacy as a new angle for understanding employee compliance with cybersecurity policies over time. Drawing on theory from the organisational legitimacy and cybersecurity literature, we conduct a three‐wave survey of 529 employees and find that, for each separate wave, negative legitimacy judgements mediate the relationship between management support and compliance, as well as between cybersecurity inconvenience and compliance. Our results provide support for cybersecurity legitimacy as an important influence on employee compliance with cybersecurity initiatives. This is significant because it highlights to managers the importance of not simply expecting compliant employee behaviour to follow from the introduction of cybersecurity initiatives, but that employees need to be convinced that the initiatives are fair and reasonable. Interestingly, we did not find sufficient support for our expectation that the increased likelihood of a cybersecurity incident will moderate the legitimacy‐policy compliance relationship. This result suggests that the legitimacy perceptions of employees are unyielding to differences in the risk characteristics of the cybersecurity incidents facing organisations.
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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 | 0.019 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".