Not all information security-related stresses are equal: the effects of challenge and hindrance stresses on employees’ compliance with information security policies
Bibliographic record
Abstract
Information security-related stresses (SRSs) are widely considered to play a negative role in the workplace, motivating employees' violation of information security policies (ISPs). However, researchers have neglected to challenge SRS and its role in promoting positive security actions. Therefore, this study aimed to explore the effect of both challenge and hindrance SRSs on employees' intention to comply with ISPs and the moderating mechanisms of regulatory focus in the relationship between SRSs and ISP compliance. Using survey data from 489 employees in Chinese enterprises, we applied a PLS-SEM method to test hypotheses. Our study found that the hindrance SRS had a negative effect on ISP compliance intention, differently, challenge SRS motivates employees to comply with ISPs. Our study also found prevention focus acted as a positive moderator in the relationship between hindrance SRS and compliance intention, while promotion focus had no effect on this relationship; Promotion focus positively moderated the relationship between challenge SRS and compliance intention, and prevention focus negatively moderated the relationship between challenge SRS and compliance intention. These findings provide new knowledge by examining the different effects and the boundary conditions to understand how two types of SRS influence employees' informaiton security dicisions.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".