Blockchain technology as an enabler for sustainable business ecosystems: A comprehensive roadmap for socioenvironmental and economic sustainability
Bibliographic record
Abstract
Abstract Blockchain technology is a core technology expected to play a highly instrumental role in competing with socioenvironmental challenges. The literature hypothesizes various blockchain functions for building a sustainable business ecosystem. This study unifies these diverse perspectives into an interpretive strategy roadmap that provides a holistic overview of how blockchain should be leveraged to deliver sustainability functions optimally. The study first identified the sustainability functions of blockchain through a content‐centric literature review. The study applied interpretive structural modeling (ISM) and drew on experts' opinions to model how and in which order blockchain delivers these sustainability functions. The study further drew on the ISM output and interpretive logic‐knowledge base to develop the promised roadmap. Results revealed that blockchain promotes a decentralized decision system that facilitates automation and real‐time information sharing (RIS) across supply chains. Blockchain introduces traceability and transparency into supply chain operations. These conditions offer monitoring of business operations and the development of trust across value‐chain stakeholders. These driver functions lead to value chain optimization and circularity integration into business and supply chain operations. When these necessary functional conditions are met, businesses can further draw on blockchain to promote economic and environmental aspects of sustainability through more complex functions enabling resource efficiency, cost reduction, pollution prevention, and higher profit margins. The order in which businesses can leverage these functions would define blockchain sustainability performance. Each function is uniquely valuable to sustainability, and none of them can be overlooked.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".