Employee Moral Disengagement in Response to Stressful Information Security Requirements: A Methodological Replication of a Coping-Based Model
Bibliographic record
Abstract
The purpose of this study is to methodologically replicate the model presented by D’Arcy et al. (2014) using a new sampling frame that consists of employees in a single organization – a large academic institution in Canada (N = 150). This is in contrast to the original study, which used a large, demographically diverse sample of online panel respondents that spanned multiple organizations and industries. Our replication results confirm the results of the original study, and in doing so, support the theoretical position that security-related stress induces moral disengagement of information security policy (ISP) violations, which in turn increases ISP violation intention. The findings also indirectly support the viability of online panel respondents for studies of employees’ security-related intentions. Having established the robustness of the D’Arcy et al. (2014) model across two sampling frames, we recommend future conceptual replications that employ alternate measures of security-related stress and more rigorous research designs that capture the relationships between security-related stress, moral disengagement, and ISP violations.
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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 | 0.049 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 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".