Using Cognitive Dissonance Theory to Explain Information Security Policy Violations
Bibliographic record
Abstract
The use of sanctions has long been advocated to enforce information security policy (ISP) compliance to control malicious and non-malicious insider threat. The ISP literature is largely based on deterrence theory. However, findings are inconsistent and deterrence has not been a strong predictor especially when non-compliant behavior is the focus of the study. To better explain this phenomenon, scholars have integrated theories and introduced additional constructs. By integrating cognitive dissonance and extended deterrence theory, this study seeks to examine the moderating effect of the personality trait of inertia on the relationship between formal and informal sanctions with ISP non-compliance. More specifically, the focus of this study is on investigating the factors that impact non-compliance of insiders (employees). \\ \\ This paper revisits the role of formal and informal sanctions in ISP compliance literature through the lens of cognitive dissonance theory. Although certainty of getting caught as well as the severity and swiftness of penalties for engage in policy violating behavior along with subjective norm and their peer behavior have a negative effect on employees’ intention to show non-compliance behavior, inertia will strengthen this relationship. \\ \\ To test the research hypotheses, we plan to use a scenario-based survey instrument for data collection following by Partial Least Squares (PLS) method using Smart PLS 3.0 for data analysis. The survey instrument will be created with items extracted from extant literature and the scenarios will be chosen from unauthorized access to computerized data. \\ \\ This study has a number of theoretical and practical implications. It contributes to ISP compliance body of knowledge by its novel theoretical approach such that individuals utilize a cognitive process to justify the inconsistency between their prior thought and the subsequent action (inertia). To the authors’ knowledge, cognitive dissonance theory has never been used in ISP compliance literature. Moreover, examining the moderating effect of inertia can shed light on the long standing debate regarding the effectiveness of deterrence on compliance. From a practical standpoint, the result of our study can help in designing training and information intervention programs for employees who are in inertia state and driving them toward attitude and behavior change. \\
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".