Blockchain technology for corporate governance and IT governance: A financial perspectiv
Bibliographic record
Abstract
The development of information technology (IT) and the adoption of blockchain have affected the world of finance today. Research on these two matters is still lacking, especially from a financial perspective. This study aims to review the latest research regarding changes in corporate governance with the adoption of IT governance and blockchain. A computerized multi-database literature search was conducted in January–March 2022, using the ScienceDirect and Emerald search engines. The terms “corporate governance”, “IT governance”, and “blockchain” were entered in the descriptor fields, with “language” limited to English and “source” limited to peer-reviewed journal articles. The implementation of good corporate governance will reduce the company's risk and protect investors. Technological advances can be used to develop better IT governance by making information transparent and adopting technological advances to support the implementation of good corporate governance. Under a blockchain framework, corporate governance might evolve in a variety of ways. There are several advantages to issuing and trading corporate securities on blockchains, but there are also certain drawbacks connected to increased ownership transparency. Businesses would seek out board members and outside advisors with various skill sets, and crucial issues like managerial incentives would probably change to account for the shifting character of corporate securities.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".