Barriers to Blockchain Adoption in the Circular Economy: A Fuzzy Delphi and Best-Worst Approach
Bibliographic record
Abstract
Blockchain can help to fundamentally alter aspects of circular economy (CE) activities and overcome pressing sustainability issues. Nevertheless, limited studies have investigated the barriers to blockchain adoption in the CE. This study aims to close the knowledge gap by providing a comprehensive review of the barriers hampering the adoption and integration of blockchain technology in the CE. An integrated approach based on fuzzy Delphi and best-worst methods has been applied to analyze and rank the barriers. Sixteen barriers to blockchain adoption in the CE were identified from the academic literature and validated by a panel of experts. The findings from the fuzzy Delphi technique identified ten significant barriers for further analysis. Then, using the best-worst method, the optimal weights were determined based on the experts’ judgment to recognize the importance of each barrier. The findings from this method showed that a lack of knowledge and management support, reluctance to change and technological immaturity are the most significant barriers. In contrast, the least significant barriers are investment costs, security risks, and scalability issues. Theoretically, this study is the first to apply an integrated approach combining fuzzy Delphi and best-worst techniques to prioritze the barriers to blockchain adoption in the CE. It also provides valuable insights for managers and decision-makers that can be used to optimize blockchain implementations in the CE.
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 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 | 0.068 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".