The Essentials and Challenges of Online Business Among Bumiputera SME Entrepreneurs in Malaysia
Bibliographic record
Abstract
The e-commerce trend has increasingly grown in Malaysia. The revenue for e-commerce platforms in 2019 is USD3.7 billion with more than 20 million users. There were various calls from the ministries for Malaysian to take up e-commerce as it is progressively contributing to the country’s GDP. Bumiputera entrepreneurs have been demanded to play active roles in e-commerce, however their participation and performance are in an alarming state. Therefore, this paper aims to identify the challenges faced, examine the awareness of the existing business support and identify support and assistance which can be implemented to address the issues faced by Bumiputera SME online entrepreneurs. The research was done using survey format. Questionnaire was distributed via online survey. The population is the online entrepreneurs from Malaysian Internet Entrepreneurs Association (PUIM). 493 respondents participated in this study. Findings show that the main challenges were the lack of access to financial assistance and lack of knowledge to conduct market study. While the training needed are Facebook Ads and Business Coaching. Research also shows that the higher the level of education, the lesser the constraints experienced and the greater the level of confidence in business success. Finally, several improvements and suggestions have been proposed.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".