Information Security Policy Compliance: An Empirical Study of Rationality-Based Beliefs and Information Security Awareness1
Bibliographic record
Abstract
Many organizations recognize that their employees, who are often considered the weakest link in information security, can also be great assets in the effort to reduce risk related to information security. Since employees who comply with the information security rules and regulations of the organization are the key to strengthening information security, understanding compliance behavior is crucial for organizations that want to leverage their human capital. This research identifies the antecedents of employee compliance with the information security policy (ISP) of an organization. Specifically, we investigate the rationality-based factors that drive an employee to comply with requirements of the ISP with regard to protecting the organization’s information and technology resources. Drawing on the theory of planned behavior, we posit that, along with normative belief and self-efficacy, an employee’s attitude toward compliance determines intention to comply with the ISP. As a key contribution, we posit that an employee’s attitude is influenced by benefit of compliance, cost of compliance, and cost of noncompliance, which are beliefs about the overall assessment of consequences of compliance or noncompliance. We then postulate that these beliefs are shaped by the employee’s outcome beliefs concerning the events that follow compliance or noncompliance: benefit of compliance is shaped by intrinsic benefit, safety of resources, and rewards, while cost of compliance is shaped by work impediment; and cost of noncompliance is shaped by intrinsic cost, vulnerability of resources, and sanctions. We also investigate the impact of information security awareness (ISA) on outcome beliefs and an employee’s attitude toward compliance with the ISP. Our results show that an employee’s intention to comply with the ISP is significantly influenced by attitude, normative beliefs, and self-efficacy to comply. Outcome beliefs significantly affect beliefs about overall assessment of consequences, and they, in turn, significantly affect an employee’s attitude. Furthermore, ISA positively affects both attitude and outcome beliefs. As the importance of employees’ following their organizations’ information security rules and regulations increases, our study sheds light on the role of ISA and compliance-related beliefs in an organization’s efforts to encourage 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 | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".