Comprehensive risk assessment and analysis of blockchain technology implementation using fuzzy cognitive mapping
Bibliographic record
Abstract
Identifying and assessing potential risks of implementing new technologies is critical for organizations to respond to them efficiently during the technology life cycle. Blockchain has been introduced as one of the emerging and disruptive technology in the field of information technology in recent years, which system developers have noted. In this study, a comprehensive set of risks have been identified and categorized based on the literature findings to identify the risks of blockchain implementation. Critical risks are defined by performing a two-stage fuzzy Delphi method based on the experts' opinions. Then, possible causal relationships between considered risks are identified and analyzed using the fuzzy cognitive mapping method. Finally, the most important risks are ranked based on the degree of prominence and the relationships between them. Industry enterprise resource planning system based on blockchain technology has been studied as a case study. The obtained results indicate that the technology's immaturity has the most impact, the high investment cost is the most impressive risk, and privacy has a critical role in risks relationships. In addition, the high investment cost has the highest priority among other risks and the privacy and issues with contract law are ranked second and third, respectively.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".