Interactions among Factors Affecting Micro Entrepreneur Performance in Sarawak, Malaysia
Bibliographic record
Abstract
Micro enterprise plays important role in achieving Malaysia’s Vision 2020. Micro enterprises constitute about 76.5% of all businesses in Malaysia and employ over 1.2 million employees. In this regard, out of the total 907,065 Malaysian SMEs, 693,670 (78.7%) business foundations have been identified as micro enterprises (MEs) (Department of Statistics Malaysia, 2016). Hence, this paper analyses the factors affect the micro-entrepreneur’s firm performance in Sarawak Malaysia. The \nresearch is based on 373 sample respondents from all over Sarawak with non-probability sampling and structured questionnaire had been used to collect response from the respondents. In terms of gender, female (67.8%) respondents clearly outnumbered the male (32.2%) respondents. AIM only offered loans to female micro entrepreneur while TEKUN and SEDC offered loans to both genders. The highest group of the micro entrepreneurs sampled in the present study, namely, more than onethird (39.8%), fell into the age group of 26 to 36. The lowest age group was 59 to 69 (5%). The respondents in this research were mostly micro entrepreneurs with more than two-thirds were educated up to secondary level MCE/SPM/SPMV (38%) and lower secondary level LCE/SRP/PMR \n(27%). Not even a quarter of the respondents were educated up to post-secondary and tertiary level \neducation. Nevertheless, there were also a small number of them who had postgraduate qualifications, namely, Master’s degree (0.5%) and Ph.D. (0.3%). The survey findings further revealed that the two identified factors (age and financial management knowledge) are significantly associated with micro entrepreneur compare to education level. The outcomes of this research can benefit the decision makers such as governments, microfinance institutions and other related institutions to support micro entrepreneur not only for poverty provision but also successful in firm performance.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".