Effects of Personal Factors and Organizational Reinforcing Tools in Decreasing Employee Engagement in Unhygienic Cyber Practices
Bibliographic record
Abstract
Employee engagement in unhygienic cyber practices (UCP) is a concern for organizations across the world. The purpose of this paper is to explore the effects of personal and environmental factors in decreasing workers’ engagement in UCP in a developing country: A personal-environment-behavior model was adapted for the study. Data was collected from working MBA students in Ethiopia. The key results show that the personal factor of self-regulation related to acceptable cyber practices decreases workers’ engagement in UCP, while self-efficacy did not. The environmental factor of computer monitoring (CM) decrease workers’ engagement in UCP, while the availability of security education and training awareness (SETA) programs did not. Both CM and SETA have positive effects in improving self-efficacy. Only SETA programs positively impact self-regulation. This study adds to the understanding of end-user security behavior by focusing on UCP with insights from a developing country.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".