How Emerging Technologies are Transforming Financial Reporting for Small Businesses in Developed Economies?
Why this work is in the frame
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Bibliographic record
Abstract
This study examined the effect of emerging technologies on financial reporting quality among small businesses in developed economies. The specific objective was to determine how Artificial Intelligence (AI) expert systems, blockchain technology, and cloud accounting influence the accuracy, timeliness, and reliability of financial reports. The study adopted a survey research design and targeted 200 small business operators from the United States, United Kingdom, France, and Canada using a snowball sampling technique. Data were collected through structured questionnaires administered via Google Forms, capturing both the use of emerging technologies and the quality of financial reporting. Multiple regression analysis at a five percent significance level was used to test the hypotheses, while frequencies were used to analyze the research questions. The findings revealed that: AI expert systems positively affect financial reporting quality among small businesses in developed economies (β = 0.276, p = 0.000); blockchain technology positively affects financial reporting quality among small businesses in developed economies (β = 0.218, p = 0.000); cloud accounting positively affects financial reporting quality among small businesses in developed economies (β = 1.312, p = 0.000). In conclusion, adopting AI expert systems, blockchain technology, and cloud accounting enhances the reliability and effectiveness of financial reporting among small businesses in developed economies. Therefore, the study recommended that regulators and small business owners should adopt blockchain-based solutions to ensure transparency and traceability in financial transactions. Implementing secure, tamper-proof ledgers can strengthen stakeholder confidence, improve compliance with reporting standards, and reduce the risk of fraud, providing greater assurance to investors, auditors, and business partners.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it