Blockchain for Accounting and Auditing—Accounting and Auditing for Cryptocurrencies: A Systematic Literature Review and Future Research Directions
Bibliographic record
Abstract
The aim of this study is to analyze and synthesize the key challenges that are prevalent in the application of blockchain in accounting and auditing, to study the approaches to account for cryptocurrencies, to study the effect of blockchain on the accounting and auditing profession, and to identify the current direction of research of blockchain in accounting and auditing, as well as identify potential avenues of future research. The research is based on 75 peer-reviewed academic studies on the topic of blockchain in accounting and auditing, followed by a descriptive and thematic analysis of the literature. Our results indicate that there is a need for more empirical studies to be carried out, which coincides with the notion of growing digitization and blockchain adoption in accounting and auditing. Based on our thematic analysis of the literature, we recommend that future research on blockchain in accounting and auditing should concentrate on the following specific areas: skills and education, governance, auditor independence, accounting standards and regulation, and the challenges faced by the accounting and auditing professions due to the adoption of blockchain technology.
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 | 0.020 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".