A systematic literature review of management accounting research in small businesses
Bibliographic record
Abstract
This paper aimed to systematically review the literature on management accounting research among small businesses in emerging economies. The study analysed 69 management accounting survey articles on small businesses that were published between 2013 and 2023 using a database-driven search strategy. This analysis synthesised the results into five themes: utilisation of management accounting, factors affecting its use, and its effects on firm performance. It further identified a theme in the management accounting frameworks developed for SMEs in emerging economies as well as the role of government in supporting the implementation of MAPs among SMEs. This study found that general SMEs in developing countries are low adopters of management accounting; those that are using MAPs widely apply the traditional systems. In addition, the study noted that the usage of MAPs is influenced by numerous factors that are similar in different countries. The present view indicates that there is limited research on models or frameworks that have been developed to improve the easy application of management accounting in SMEs. There is a need for more research to be conducted on providing solutions to the low adoption level and how the barriers to the application of MAPs can be overcome by SMEs in emerging economies, instead of just identifying the constraints.
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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 | 0.013 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.042 | 0.037 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".