We<i>must</i>overcome the controversial relationship between management accounting research and practice
Bibliographic record
Abstract
Purpose The purpose of this paper is to expand on some of the points made in Ken Merchant's paper (this issue) in connection with the research‐practice gap. Design/methodology/approach Aiming to be provocative for the purpose of evoking further discussion, this commentary adopts the perspective that some deeply rooted misconceptions about the nature and production of scientific knowledge underpin the research‐practice gap. Findings There are three key findings. First, contrary to popular belief, practical knowledge does not simply derive from basic (“scientific”) knowledge “trickling down” to practice; instead, basic knowledge needs to betransformedinto a theory or phronesis of management accounting in a manner that reflects the context and purpose of organizations. Practical knowledge therefore becomes a distinct and rigorous mode of knowing in its own right, no less important than basic knowledge. Second, the adoption of field research or the case study method may be the only way to overcome all of the dimensions associated with the “data problem” existing in management accounting. Finally, a strong argument can be made to suggest that the research‐practice gap and its epistemological underpinnings not only impede the discipline's ability to carve out its own unique intellectual identity (Malmi and Granlund), but they also explain the discipline's inability to produce a cumulative body of knowledge. Originality/value The paper suggests that a key step, among others, in addressing the researcher‐practitioner gap is the need to overcome philosophical misconceptions about the nature and production of scientific knowledge. This perspective has not received significant coverage in accounting.
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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 | 0.060 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.055 |
| Scholarly communication | 0.030 | 0.046 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.028 | 0.028 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".