Cybersecurity Compliance and Other Factors Influencing Employee Protective Behavior: A Case Study of Bank X in Indonesia
Why this work is in the frame
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Bibliographic record
Abstract
Indonesia's banking sector faces over 1 million daily cyberattacks, with human error causing 80% of security breaches, yet existing cybersecurity research predominantly focuses on technology solutions rather than employee behavior within high-regulation environments.This study addresses a critical research gap by investigating how organizational levers-policy provision and Security Education, Training, and Awareness (SETA) programs-influence employee cybersecurity compliance behavior in Indonesia's banking industry.We surveyed 360 employees from Bank X (a consortium of Indonesia's three largest state-owned banks) using PLS-SEM analysis.Our theoretical framework integrates Protection Motivation Theory and Theory of Planned Behavior to examine pathways from organizational interventions through cybersecurity awareness, compliance attitude, and ISPC intention to protective behavior.Nine of ten hypotheses were supported: policy provision and SETA programs significantly enhance cybersecurity awareness, cascading through compliance attitude and ISPC intention to drive protective behavior.Notably, protection motivation does not directly influence behavior, revealing a boundary condition for PMT in hierarchical contexts.This study delivers the first largescale evidence from Indonesia's banking industry, demonstrating that clear policies and sustained SETA investment can turn human vulnerabilities into organizational resilience.Financial institutions should prioritize clear policies and comprehensive SETA programs as primary cybersecurity culture drivers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it