The Conceptual, Social, and Intellectual Structure of the Financial Information/Accounting Manipulation Literature: A Bibliometric Analysis
Bibliographic record
Abstract
This study presents a comprehensive bibliometric analysis of studies on financial information/accounting manipulation. The dataset of research includes 1.266 studies from the Web of Science database for the period 1991–2023. All studies included in the research contain either the term ‘financial information manipulation’ or ‘accounting manipulation’ in the topic (title, abstract, or keywords). The bibliometric network mapping technique was used for the analysis of the data. The analysis was conducted utilizing the Biblioshiny interface of the R package programs Bibliometrix and Vosviewer. The results pointed out a notable upward trend in the publication and citation rates of financial information/accounting manipulation studies over the last two decades. Several key findings were identified. Firstly, a substantial rise in research output on financial information/accounting manipulation was observed, particularly after 2000, driven by global financial scandals. Secondly, prolific contributors to this field include authors such as Valaskova and Durana. Thirdly, the United States leads in research output, with significant contributions from institutions like the State University System of Florida and the State University System of Ohio. Lastly, The Accounting Review was identified as the most prolific journal in this domain, with the Journal of Accounting Economics being the most impactful based on citations. The most frequently used keywords indicate that the research topics focus on earnings management as a method of manipulation, fraudulent financial reporting, and the relationship with corporate governance. The comprehensiveness of the bibliometric data lends itself to a further examination of how financial information/accounting manipulation has progressed as a subject in the literature since the 2000s. In addition, this study reveals the social and intellectual structures of the issue, the key research streams, and potential research directions for future research.
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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 | 0.015 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.210 | 0.211 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".