Examining employee security violations: moral disengagement and its environmental influences
Bibliographic record
Abstract
Purpose Employee security behaviors are the cornerstone for achieving holistic organizational information security. Recent studies in the information systems (IS) security literature have used neutralization and moral disengagement (MD) perspectives to examine employee rationalizations of noncompliant security behaviors. Extending this prior work, the purpose of this paper is to identify mechanisms of security education, training, and awareness (SETA) programs and deterrence as well as employees’ organizational commitment in influencing MD of security policy violations and develop a theoretical model to test the proposed relationships. Design/methodology/approach The authors validate and test the model using the data collected from six large multinational organizations in Korea using survey-based methodology. The model was empirically analyzed by structural equation modeling. Findings The results suggest that security policy awareness (PA) plays a central role in reducing MD of security policy violations and that the certainty of punishment and immediacy of enforcing penalties are instrumental toward reducing such MD; however, the higher severity of penalties does not have an influence. The findings also suggest that SETA programs are an important mechanism in creating security PA. Originality/value The paper expands the literature in IS security that has examined the role of moral evaluations. Drawing upon MD theory and social cognitive theory, the paper points to the central role of SETA and security PA in reducing MD of security policy violations, and ultimately the likelihood of this behavior. The paper not only contributes to theory but also provides important insights for practice.
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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 | 0.004 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".