Blockchain Application in the Accounting Information System: Advantages, Challenges, and Future Research Directions
Bibliographic record
Abstract
Abstract As it is known, the accounting information system (AIS) plays a significant role in the business ecosystem by recording and processing financial and accounting data and reporting the produced information to all relevant parties for decision-making. However, its used methods and systems, including double-entry bookkeeping and enterprise resource planning (ERP) systems, have limitations, especially in terms of trust and reliability concerns for stakeholders and the possible scope for records manipulation and fraud. The application of blockchain technology is believed to enhance the reliability of the AIS and addresses many of its current limitations. Blockchain can offer numerous benefits if used to manage AIS functions through enhanced trust, reliability, and transparency, increased efficiency, reduced costs and fraud, improved accounting information quality and real-time accounting. Nevertheless, the adoption and implementation of blockchain in the AIS are associated with several technical and nontechnical challenges which are not easy to address and could limit the wide technology adoption in the immediate future. Considering that a full understanding of the benefits and challenges of adopting blockchain in the AIS still needs more clarification, this chapter examines blockchain technology and its implications for the AIS. It reviews blockchain characteristics and its benefits to the AIS, discusses its possible integration into the AIS, outlines adoption and implementation challenges, and suggests critical avenues 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".