Compliance with accounting practices of SMEs in transitional economies: Evidence from Bangladesh
Bibliographic record
Abstract
Purpose: The purpose of the study is to examine the accounting statement prepared and maintained by the SME firms in Bangladesh and to find out financing difficulties of SMEs due to Information asymmetry. Methodology/Design: A conceptual model was molded and a random sample survey of 385 SMEs’ owner or manager was conducted from entire SME population. Findings: 47.79 percent, 33.25 percent and 18.96 percent SME firms are working in trade, manufacturing and service sector respectively. Among the 34.26 percent SME firms maintained all financial record, 58.44, 13.51 and 31.17 percent firms maintain their accounting record through manual, excel and software package respectively. Only 4.68 percent SME firms’ financial record maintains by professional accountant. Financial information has positive influence on accessibility to bank credit. Practical implications: The research will be helpful in filling the research gap and will give a great contribution to the policy makers to find out the limited access to bank loan by the SME firm. Originality/Value: The research paper uses detached approach from the previous studies with large sample and Ramsey’s Tests for Model Specification Error to rummage the SME in different ways.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".