Determinants of Security Behavior Intention in State-Owned Enterprises: Applying Protection Motivation Theory to Phishing Emails
Bibliographic record
Abstract
The increasing prevalence of cyber security threats underscores the need to understand employee behavior to prevent phishing-related risks and effectively promote a sustainable working environment.This issue is particularly critical for state-owned enterprises (SOEs), especially those operating in sensitive industries.Our study explores the factors influencing the behavior intentions of SOE employees to avoid clicking on phishing email links.The research adopts a quantitative approach, utilizing Structural Equation Modelling (SEM) for data analysis using SmartPLS 4.1.0software.A sample size of 189 respondents was determined using the G-Power Calculator and selected through purposive sampling.The results reveal that self-efficacy, perceived vulnerability, and perceived severity significantly influence security behavior intention.Furthermore, threat awareness was identified as a significant predictor of response efficacy, perceived vulnerability, and self-efficacy.Security knowledge was found to play a crucial role in shaping perceived severity, perceived vulnerability, and response efficacy.However, three hypotheses were not supported, specifically the relationships between threat awareness and perceived severity, security awareness and self-efficacy, and response efficacy and security behavior intention.These findings underscore the need for organizations to address the gaps by reinforcing practical training and targeted intervention for strengthening employee perception of severity perception, self-efficacy, and security behavior intention.The study highlights the importance of implementing robust cybersecurity awareness campaigns and policies within organizations prone to cyber threats.By fostering a culture of vigilance and improving employees understanding of the severity and vulnerability of phishing attacks, organizations can enhance their resilience against cyber threats and mitigate potential risks effectively.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".