When enough is enough: Investigating the antecedents and consequences of information security fatigue
Bibliographic record
Abstract
Abstract Despite concerns raised by practitioners, the potential downside of the information security demands imposed by organizations on their employees has received limited scholarly attention. Our research focuses on information security fatigue (hereafter security fatigue), which is defined as a socio‐emotional state experienced by an individual who is tired of and disillusioned with security policies and their associated guidelines and procedures. This research delves into the security fatigue concept, investigates its antecedents and reports how fatigue affects employee security policy compliance (and non‐compliance). Since security fatigue is not well articulated in the literature and there is limited understanding of its antecedents and consequences, we take a research approach that affords novel insight into this phenomenon. Specifically, we conduct 38 in‐depth interviews with business and IT professionals, and then use a qualitative approach to construct a model, including seven research propositions, to highlight the key aspects of security fatigue. Our results indicate that four distinct antecedents contribute to security fatigue, which result in three unique consequences. We discuss security fatigue in relation to past theoretical views and related concepts within the security policy compliance literature and identify directions for future research.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".