An Exploratory Study on the Accounting Practices among Pasar Tani Micro-Entrepreneurs in Malaysia
Bibliographic record
Abstract
Micro entrepreneurs contribute significantly to economic growth, and poverty eradication through self-employment, and job creation. However, micro-entrepreneurs are believed to wrestle with effective financial recording and planning. In the absence of proper bookkeeping and accounting, business analysis, and hence future growth planning are difficult. This exploratory study draws on the Social Entrepreneurship Bookkeeping Program initiated by the Faculty of Accountancy, Universiti Teknologi MARA Cawangan Selangor, Malaysia, in 2018. This study examines whether the micro-entrepreneurs at Pasar Tani maintain a proper record of their business transactions, and if not, what are the reasons for not doing so. Using a purposive sampling design involving sixteen micro-entrepreneurs, this study finds a striking result that half of them did not record their business transactions at all. The other half recorded their transactions but to a very limited extent. Furthermore, most of them had no accounting background and were not aware of the importance of bookkeeping for their business growth. Interestingly, 62.5% of the micro-entrepreneurs indicated that they were keen to learn more about accounting even though only 37.5% of them claimed that such a learning opportunity exists. Together, the findings of this study suggest that more accounting short courses and training programs should be organized and promoted to assist the micro-entrepreneurs with their financial recording and planning.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".