A three-phase framework for mapping barriers to blockchain adoption in sustainable supply chain
Bibliographic record
Abstract
Purpose Blockchain technology is one of the major contributors to supply chain sustainability because of its inherent features. However, its adoption rate is relatively low due to reasons such as the diverse barriers impeding blockchain adoption. The purpose of this study is to identify blockchain adoption barriers in sustainable supply chain and uncovers their interrelationships. Design/methodology/approach A three-phase framework that combines machine learning (ML) classifiers, BORUTA feature selection algorithm, and Grey-DEMATEL method. From the literature review, 26 potential barriers were identified and evaluated through the performance of ML models with accuracy and f-score. Findings The findings reveal that feature selection algorithm detected 15 prominent barriers, and random forest (RF) classifier performed with the highest accuracy and f-score. Moreover, the performance of the RF increased by 2.38% accuracy and 2.19% f-score after removing irrelevant barriers, confirming the validity of feature selection algorithm. An RF classifier ranked the prominent barriers and according to ranking, financial constraints, immaturity, security, knowledge and expertise, and cultural differences resided at the top of the list. Furthermore, a Grey-DEMATEL method is employed to expose interrelationships between prominent barriers and to provide an overview of the cause-and-effect group. Practical implications The outcome of this study can help industry practitioners develop new strategies and plans for blockchain adoption in sustainable supply chains. Originality/value The research on the adoption of blockchain technology in sustainable supply chains is still evolving. This study contributes to the ongoing debate by exploring how practitioners and decision-makers adopt blockchain technology, developing strategies and plans in the process.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".