Management accounting research trends: Bibliometrics and content analysis
Bibliographic record
Abstract
This study aims to examine an overview of management accounting research in the last decade. In addition, it also describes the latest research trends, namely in the last five years, both in terms of the topics discussed, the use of theory, and the methods used. The researchers employ the bibliometric analysis method and use VOSviewer to describe management accounting research for the period 2013-2022. We retrieve data on articles from two leading journals in this field, namely Management Accounting Research (MAR) and Journal of Management Accounting Research (JMAR), using the Scopus database. The findings of this study indicate that there were six research clusters over the past decade, namely (1) governance and performance management, (2) management control, (3) performance evaluation, (4) risk-based performance management and CSR, (5) budgeting and ethics, and lastly (6) the risk and interfirm relationships. Research trends in the last five years show that the theories explicitly used in research in this field include agency, economic, social identity, social comparison, and contingency theory. The research methods used during the last five years have been dominated by archives, experiments, and surveys. Papers published by JMAR are dominated by those using archival methods, while the papers in MAR are dominated by those using experiments and field research. This research provides an overview, for students and scholars interested in management accounting research, of the subjects, ideas, and research methodologies that have been employed in the recent past and today.
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 | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.211 | 0.291 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".