Theoretical Framework of Digital Marketing and the Future Availability in Malaysia
Bibliographic record
Abstract
Digital marketing is well known in the foreign countries (America, Canada, Dubai and India) rather than in Malaysia. This is due to their advance technology, high idealistic marketing plan, population and high internet usage. Currently there are many concerns being raised by individuals in the marking sector and also the marketing experts, on the future growth of digital marketing. Nevertheless, in Malaysia the business idealistic marketing plans are very broad and mostly using traditional approach towards promoting and selling their product. Thus, the technology advancement in the Malaysia is currently in a moderate stage and not as advance as the Europe or foreign countries. Besides, the technology advancement in Malaysia, the knowledge on following the right process flow in digital marketing is very low. Furthermore, a developing country usually follow the trends in other developed foreign countries rather than establishing new trends. The suggestion of new technology with better marketing strategy other than digital marketing in the Europe or foreign countries, eventually reduce the use of digital marketing strategies. However, the aim is to ensure that the digital marketing strategies are used in Malaysia for a longer term without any extinction. Therefore, this paper proposes a strategy to deliver a correct and resourceful training with the precise process flow or steps in using digital marketing. The strategy enables users to adapt the current technology of digital marketing for betterment and innovation especially implementing Artificial Intelligence (AI) into Digital Marketing for more security and technology advancement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".