How different rewards tend to influence employee non-compliance with information security policies
Bibliographic record
Abstract
Purpose To help reduce the increasing number of information security breaches that are caused by insiders, past research has examined employee non-compliance with information security policy. However, existent studies have observed mixed results, which suggest that an interaction is likely to exist among the variables that explain employee non-compliance. In an effort to provide evidence for this possibility, this paper aims to better explain why employees routinely engage in non-compliant behaviors by examining the direct and interactive effects of employees’ perceived costs and rewards of compliance and non-compliance on their routinized non-compliant behaviors. Design/methodology/approach Based on rational choice theory, this study used 16 hypothetical scenarios in an experimental survey, collecting data from 326 respondents and analyzing them via structural equation modeling and a four-way factorial experiment. Findings The results suggest that routinized non-compliance of employees is more strongly influenced by the rewards than the costs they perceive in their non-compliance. Further, employees’ routinized non-compliance behavior was found to be positively influenced by an interactive effect of perceived rewards of compliance when their perceptions of their non-compliance costs and rewards were both high and low. Originality/value This paper’s key contribution is to suggest that non-compliance behavior is influenced by direct and interactive effects of perceived rewards of compliance and non-compliance.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".