The Whence and Whither of Interpretive Management Accounting Research: A Structured Literature Review
Bibliographic record
Abstract
This paper analyses and synthesizes the interpretive management accounting research (IMAR) literature to understand how the field has developed and identify areas for future research. A structured literature review methodology was adopted to analyse the top 100 IMAR literatures based on citation per year and citation index as of July 2022. The findings of the study suggest that it is important to understand the practical implementation of management accounting at an organizational level. Performative research can provide valuable insights into the application of management accounting in specific contexts. The authors recommend adopting a critical localist approach to explore both emic and etic insights and conducting comparative studies through international collaboration. The lack of evidence regarding the use of management accounting in the public sector and the possibilities of management control systems in a changing environment present research gaps and opportunities. Finally, the authors call for innovation in research methodologies, with an increased emphasis on the role of theorizing in the IMAR domain. The significance of this research lies in providing a systematic analysis of literature to explore a developing area of study and assess the current state-of-the-art in the field of IMAR.
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 | 0.040 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.038 | 0.020 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".