Analysis of Behavioral and Information Security Awareness among Users of Zoom Application in COVID-19 Era
Bibliographic record
Abstract
Behavior in using inappropriate technology and low level of individual awareness causes a high risk of cybercrime. This study aims to see the behavior and awareness of information security among users of the Zoom application, especially in the COVID-19 era in Indonesia. The measurement scales used in this study were RBS (Risky Behavior Scale), CBS (Conservative Behavior Scale), EOS (Exposure Offence Scale), and RPS (Risk Perception Scale). This research used an online questionnaire to collect data with 400 respondents. Data analysis techniques used the Independent Sample T-Test, Mann Whitney, One Way Anova, Krusskal Wallis, Tukey, Pairwise Comparison, and Spearman Rank, which were processed using the SPSS program. The results showed significant differences between demographics on behavior and information security awareness. Daily Internet usage time influenced behavior and awareness of information security. Security behavior and awareness of information security are essential to determine the risks due to behavior and lack of awareness of information security. The most important findings in this study can be used as a reference in designing information security training. Further studies are expected to add other variables such as psychography, geography, or other users of different video conferencing applications such as Google Meet, Microsoft Team, and Webex.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".