The Use of Management Accounting Techniques by Small and Medium-Sized Enterprises: A Field Study of Canadian and Australian Practice
Why this work is in the frame
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Bibliographic record
Abstract
Small and medium-sized enterprises (SMEs) represent a large and important part of developed economies. However, little is known about the extent to which SMEs use contemporary management accounting (MA) techniques such as costing systems, budgets, responsibility center reporting, and analysis for decision making. To address this gap in the literature, we conducted in-depth field interviews at 22 SMEs to: (1) determine the extent to which common MA techniques and tools are being used by SMEs; and (2) explore the underlying reasons why specific MA techniques are not being used. We find that of the 19 common MA techniques covered in our interviews, a very small number are moderately or highly used by our respondent companies. Moreover, we find that manufacturing companies in our study are more likely to use a broader set of techniques such as costing systems, operating budgets, and variance analysis and that smaller, early-stage SMEs are the lightest users of MA tools overall. We identify three main factors affecting the adoption and use of MA techniques: (1) the perceived decision-usefulness of the technique; (2) the complexity of the SMEs’ operating environment; and (3) the age of the SME. We discuss the contributions of our study and its potential implications for MA educators, developers of professional education programs, designers of SME control systems, and textbook authors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it