Blockchain in Accounting Research and Practice: Current Trends and Future Opportunities*
Bibliographic record
Abstract
ABSTRACT This article provides a review of the accounting blockchain literature, with a focus on current trends and recommendations for future research opportunities. Our review identifies seven main areas: (i) future of blockchain technology, (ii) impact on the accounting function, (iii) auditing considerations, (iv) financial reporting for cryptoassets, (v) case studies, (vi) governance, and (vii) taxation. The article aims to bridge the gap between practitioners and academics by providing a review of both areas of literature and highlighting common ground between the two arenas. While academics have begun to explore how the accounting profession might change in response to blockchain, this research is limited primarily to the auditing field. Practitioners, for their part, have expanded their scope to also devote significant attention to the financial reporting and taxation of cryptoassets. Expanding the discussion of accounting and blockchains beyond their current concentrations in auditing and accounting information systems, we call for more research on the impact of blockchain technology in other areas such as corporate governance or the intersection of accounting and society.
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 | 0.027 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".