Blockchain technology and corporate governance: A bibliometric and systematic literature review
Bibliographic record
Abstract
Corporate governance is a dynamic and complex research area that has gained significant attention for decades. The increasing complexity of the business terrain, advocacy for transparency, and stakeholder focus have prompted the exploration of technologies that may support robust corporate governance systems. Blockchain technology supports a decentralized network of transactions and thus acts as a highly immutable database of transactions and records. These features have guided a burgeoning interest in leveraging blockchain technology in corporate governance systems. To contribute to this field of knowledge, we conduct a bibliometric and systematic review of Blockchain Technology and Corporate Governance literature. We use the Biblioshiny and VOSViewer Software to perform bibliometric analyses on forty-three papers published between 2016-2022, which were extracted from Scopus following the PRISMA protocol. We also conduct a detailed thematic analysis. The study identifies three knowledge clusters, maps social patterns, and clarifies nomological networks of research exploring the role and significance of blockchain technology in corporate governance. The review demonstrates the extent to which blockchain technology encourages transparency, reduces agency costs, and increases shareholder engagement. The review draws out important strategies that may be adopted to ensure the effective leveraging of blockchain technology within firms for various purposes as well as day-to-day operations, and shareholder activities. Finally, the study presents twenty-three research questions with a focus on knowledge gaps that may guide 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 | 0.022 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.175 | 0.168 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".