Examining marketing cyber-security in the digital age: Evidence from marketing platforms
Bibliographic record
Abstract
The current study aimed to examine marketing cyber-security (DDoS Attacks, Cross-Site Scripting, SQL Attacks, and passwords attacks) in the digital age by presenting evidence from digital marketing platforms. Depending on the quantitative approach and utilizing a questionnaire as a tool, (133) marketing managers in digital marketing companies in Jordan responded to an online questionnaire. SPSS was used to screen and analyze the gathered data. Results of the study accepted the main hypothesis, and it appeared that marketing cyber-security has a statistically positive influence on marketing platforms, in addition to that, it appeared that the highest influence of sub-variables was for the benefit of Structured Query Language (SQL) Attacks explaining 35.8% of the variation. This result meant that SQL attacks-security does have a statistically positive influence on marketing platforms. This hypothesis could be tested through various methodologies, for example, surveys, interviews, focus groups, and/or experiments. The study recommended that marketers should use role-based access to limit the data employees can access and regularly review their permissions. Further recommendations were presented in the study.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".