Cyber-Security Culture towards Digital Marketing Communications among Small and Medium-Sized (SME) Entrepreneurs
Bibliographic record
Abstract
Cybersecurity is a multidisciplinary field of study that focuses on preserving and protecting data and information from a wide range of threats and dangers. This study presents a cyber-security culture for assessing the knowledge, attitude and practice towards digital marketing communications among small and medium-sized entrepreneurs. The objectives of this study were to identify the knowledge, attitudes, and practices of cyber-security culture toward digital marketing communications among small and medium-sized entrepreneurs in Selangor, as well as to look into the relationship between knowledge and practice in this area. This study utilized a quantitative methodology in the form of a survey, with respondents being selected at random from a list of numbers and from a box of random numbers. Several lists were generated using Instagram business account listings, telegram entrepreneur groups, the National Entrepreneurs Institute, and the Kuala Selangor District Council webpage for recruiting respondents. From the findings, this study found that there is a strong relationship between the level of knowledge and practices towards cybersecurity in digital marketing communications among small and medium-sized entrepreneurs. The study concluded that good knowledge of cybersecurity is crucial among entrepreneurs for them to establish good practices in managing their business.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".