The Perceived Effectiveness of Blockchain for Digital Operational Risk Resilience in the European Union Insurance Market Sector
Bibliographic record
Abstract
Due to the rise in the demand for information communication technologies (ICT), the need for operational risk resilience within the European insurance market sector has grown exponentially. This study aims to use the case of blockchain to evaluate whether the five characteristics determined from the literature to be required for effective digital risk resilience (specifically, integration, flexibility, reliability, relevance, and timeliness) have an impact on effectiveness in addressing the requirements of the European Union’s proposed Digital Operational Resilience Act (DORA). To achieve this, we developed a survey with 29 statements, which participants were required to answer using a five-point Likert scale. In total, 513 valid responses were received from participants. These were analyzed using exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM). Results show that in the case of blockchain, reliability, flexibility, and relevance were found to significantly relate to its effectiveness in addressing DORA’s requirements, but relationships of effectiveness with integration and timeliness were found to be insignificant. However, when the experience variable was added to the model as the moderator variable, we found that timeliness and relevance have a significant relationship with blockchain effectiveness, while integration, reliability, and flexibility do not.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".