Enhancing estate governance using blockchain technology through risk management in estate governance of business sustainability
Bibliographic record
Abstract
The integration of blockchain technology into estate governance has the potential to revolutionize transparency, efficiency, and security in estate management. Traditional governance structures often grapple with inefficiencies, lack of transparency, and security issues in estate management. This paper comprehensively explores the impact of blockchain on estate governance, and then risk management and business sustainability. This research centers on the role of risk management on business sustainability to mediate and moderate the effect of estate governance on business sustainability. The results indicate that effective real estate governance positively affects risk management practices in real estate. However, both real estate governance and risk management contribute to business sustainability. Moreover, there are still gaps in the literature that require further investigation. Where policymakers and practitioners can develop informed strategies to strengthen governance structures, mitigate risks, and promote sustainable practices in real estate; Thus, promoting long-term success and resilience in the real estate industry. It is worth noting that future research should focus on empirical testing of the proposed hypotheses to provide a better understanding of these dynamics and their implications for risk management that can affect business sustainability.
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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| 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".