Management Accounting Theory and Practice: Measuring the Gap in North American Businesses
Bibliographic record
Abstract
The purpose of this research project was to determine the adoption rate of a number of management accounting practices by North American businesses and to ask accounting practitioners working in that business if the degree of use of each management accounting practice was sufficient for efficient operations of that business. We surveyed members of the Institute of Management Accountants (IMA), all Certified Management Accountants (CMA) working in the United States and Canada to determine 1) the adoption rate of forty-one management accounting practices by their organisations, 2) the degree of importance for the efficient operation of their business that they would place on each management accounting practice and 3) to measure the variance between the respondent's answers to the two questions. We then measured the size of the “gaps” between the use and importance of each management accounting tool and highlighted those management accounting (MA) tools that CMA's think could possibly improve business operations. Findings indicate that for a large number of MA practices a discrepancy existed between what accountants consider to be efficient management accounting tools and the use of those tools by their respective companies. Evidence also suggest that businesses rely more on the traditional management accounting practices rather than the recently developed “strategic” practices such as activity based management and the use of the balanced scorecards. This study updates the management accounting literature on the adoption rate of forty-one MA practices by businesses in North America, establishes that a gap does exist between the theory and practice of MA tools and attempts to measure the variance for each MA tool.
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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 | 0.018 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".