The Use of Management Accounting Techniques by Small and Medium-Sized Enterprises: A Field Study of Canadian and Australian Practice
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".