Trends in scientific and public interest in managerial accounting: Bibliometric analysis
Bibliographic record
Abstract
In conditions of fierce market competition, changing business environment, digitalization and remote workplaces, managerial accounting plays an increasingly important role in facilitating effective monitoring of a company’s activities, making optimal decisions, achieving its goals and development. The paper deals with identifying main trends of scientific and public interest in managerial accounting. Bibliometric analysis is made to determine interconnected clusters of multidisciplinary research in connection with which managerial accounting is considered, and to investigate dynamics in scientific interest on this issue based on tools of Scopus and WoS databases and VosViewer software (the sample of 10,495 articles, including 3,586 in Scopus for 1941–2022 and 6,909 in WoS for 1953–2022). These identified interconnections proved the significance of the managerial accounting component in the system of a company’s management, showed dominate place and described its multidimensionality. In particular, six defined clusters reflect key scientific and practical fields, goals, tasks, potential effects, and risks of managerial accounting. Based on analytical analysis using Google Trends (from 2004 to present) and Google Books Ngram Viewer (for 1800–2019), dynamics and trends of public interest on the issue of managerial accounting were also explored and visualized. The highest level of public interest in managerial accounting was indicated in Jordan, Bangladesh, Pakistan, Egypt, Thailand, Korea, the USA, and Canada.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.175 | 0.207 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".