Management accounting practices adoption and determinants: a review of worldwide empirical evidences
Bibliographic record
Abstract
There is a common belief that the management accounting practices adoption offers relevant and timely information to mangers for making economic decisions and thereby improving an organization's performance. However, there are notable differences in the adoption of different MAPs, its extent, and determinants across different nations. Directed by this fact, the search for knowledge of the context that determines the adoption of MAPs is growing. To explore the intuition in understanding the MAPs’ adoption across different countries worldwide, the objectives of this article are to review the prior empirical literature on the level of adoption and benefits of adopting MAPs, to identify the factors which determine MAPs’ adoption across different countries, and to develop a conceptual framework that could be used for future research. There are forty-three research articles reviewed both from developed and developing countries, including evidence from the USA, UK, Europe, Japan, Australia, New Zealand, Taiwan, Malaysia, Singapore, China, Finland, Turkey, Vietnam, Barbados, Jamaica, Egypt, Libya, Iran, Bali, India, Estonia, Canada, Thailand, Jordan, Tehran, Kenya, Tunisian, the Czech Republic and Nigeria. The review reveals that the adoption of MAPs significantly differs across countries; the adoption of traditional MAPs is widely available worldwide, and the adoption of advanced MAPs such as activity-based costing and balanced scorecards is higher in developed countries. Further, the review identified national culture, size, competition, perceived environmental uncertainty, advanced manufacturing technology, organizational structure, organizational strategy, customer power, total quality management, the complexity of processing system, product perishable, organizational capacity to learn, industry type, the origin of organization, owner-manager commitment, Life cycle stage of the firm, interactive use, diagnostic use, dynamic tension, organizational DNA, rational capital with the supplier and interactive control as the determinants of MAPs. However, the review reports that these factors have mixed evidence concerning MAPs’ adoption. Finally, a conceptual framework has been developed based on the contingency theory of management accounting related to the determinants of MAPs that could be empirically tested for future research.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".