Examining employee security violations: moral disengagement and its environmental influences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 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