Overcoming technological barriers for blockchain adoption in supply chains: a diffusion of innovation (DOI)-informed framework proposal
Bibliographic record
Abstract
Purpose The integration of blockchain technology (BT) in supply chain management (SCM) is at the forefront of technological advancements, yet it faces significant barriers that hinder its widespread adoption. This study aims to delve into these challenges, employing the diffusion of innovations (DOI) theory to systematically investigate and propose a strategic framework for overcoming the technological barriers to BT adoption within SCM. Design/methodology/approach Through a comprehensive systematic literature review (SLR) of 155 publications, complemented by rigorous content analysis and expert interviews, this research identifies and categorizes 16 primary technological barriers, including scalability and privacy issues, that impede BT integration. Findings The proposed framework, informed by DOI theory, outlines tailored strategies across three critical adoption stages: initiation, where the focus is on mitigating high energy consumption and scalability issues; adoption decision, emphasizing the formulating international standards for blockchain architecture, embedding abstraction layers within software projects; and implementation, concentrating on enhancing security, interoperability and system efficiency. Originality/value This research contributes significantly to both academic literature and practical applications. Academically, it extends the DOI theory within the SCM context and enriches the blockchain literature by providing a nuanced understanding of the specific barriers to BT adoption. Practically, it offers a roadmap for industry practitioners, delineating actionable strategies to navigate the adoption process effectively. This study not only bridges the gap between theoretical insights and practical implementations but also serves as a vital resource for policymakers and standard-setting bodies in facilitating and regulating BT adoption in SCM, thereby fostering innovation and competitive advantage in the marketplace.
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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 | 0.020 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".