The effect of the implementation of financial accounting standards on MSMEs
Bibliographic record
Abstract
Over the last two decades, the global financial landscape has changed dramatically, including the corporate and political climates, the creation of more market-based economies, and rapid technological advancements. Micro, Small, and Medium Enterprises (MSMEs) is one form of market-based economies created with the most significant business opportunities and very important socially and economically in developing countries for many reasons. However, MSMEs face significant difficulty related to financial reporting standards to evaluate the achievement of business activities. Most MSMEs use a simple form for financial reporting, such as by calculating the difference between inflow and outflow from their business activities. Also, numerous previous studies that focus on Financial Accounting Standards in Indonesia are still limited. Thus, the present study investigates the factors that influence the implementation of Financial Accounting Standards (FAS) of Micro, Small, and Medium Enterprises (MSMEs) in Aceh Province, Indonesia. This quantitative study uses cross-sectional data collected by distributing 200 questionnaires to MSMEs actors that spread North Aceh Regency, namely Lhokseumawe city and Bireuen regency. The data are analyzed using descriptive statistics (e.g., frequency, percentage, mean, standard deviation) and inferential statistic (multiple linear regression) analyses. The study indicated that Perception, Education, Socialization and Incentive factors had significant positive effects on implementing Financial Accounting Standards of Micro, Small, and Medium Enterprises (MSMEs) in Aceh Province, Indonesia. In conclusion, this study has successfully investigated the factors that influence the implementation of Financial Accounting Standards of Micro, Small, and Medium Enterprises (MSMEs) in Aceh Province.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| 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".