Blockchain Technology in Accounting: A Paradigm Shift in Transparency and Efficiency in the UK
Bibliographic record
Abstract
Background: Traditional accounting processes face challenges like data manipulation, fraud, and inefficiencies in the processes. Blockchain in accounting provides full transparency, where auditors, accountants, and clients can have access to identical ledgers for verifying the information. Objectives: To evaluate the impact of Blockchain technology on transparency and efficiency in the accounting profession in the United Kingdom. Methodology: This research follows a quantitative research method. The survey method has been used for collecting data from accountants, auditors, CFOs, financial analysts, and others. In this study, questionnaires have been sent to the accounting professionals from the big four accounting firms in London, United Kingdom. The raw data has been gathered from a total of 156 people participants from the accounting firms. A questionnaire has been used in the study, where various independent and dependent variables are identified. Findings/conclusions: The results show that Blockchain security, cost, adoption, and implementation have a positive and significant influence on transparency. On the other hand, Blockchain security, cost, adoption and implementation, compliance, and performance have a positive and significant influence on efficiency. Lastly, it is identified that Blockchain efficiency and transparency have a positive and significant influence on stakeholder trust among accounting firms in London, UK. Policy Recommendations: The first policy recommendation is promoting the adoption of Blockchain among accounting firms. Tax incentives can be provided to accounting organizations that are heavily investing in Blockchain technology. Besides, regulatory frameworks, development programs, and comprehensive guidelines can help in guiding the adoption of Blockchain technology among accounting firms.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".