Integrating Blockchain, IoT, and XBRL in Accounting Information Systems: A Systematic Literature Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Over the last few decades, remarkable technical advancements, including artificial intelligence, machine learning, big data, blockchain, cloud computing, and the Internet of Things, have emerged. These tools have the ability to change the accounting process. This study aims to conduct a systematic literature review on using the Internet of Things (IoT), blockchain, and eXtensible Business Reporting Language (XBRL) in a single accounting information system (AIS) to enhance the quality of digital financial reports. This paper employs a systematic literature review (SLR) methodology, specifically, by adopting the widely accepted PRISMA technique. The final sample of this study included 309 related studies from 2013 to 2023. Our findings highlight the lack of literature related to the integration of these three types of technologies within a unified AIS. This study is extremely significant because it proposes a new research stream that explores the possibility of integrating IoT, blockchain, and XBRL in a single accounting system, yielding a plethora of benefits to the accounting field. However, the potential benefits of such an integration are evident, including enhanced transparency, real-time reporting capabilities, and improved data security. Our paper’s main contribution is that it is the first paper, to the best of our knowledge, to explore the integration of these three technologies. We also identified important gaps in the research and pointed out ways for future research to somehow take a lead in exploring further how this integrated system is affecting accounting practices.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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