The moderating role of organizational readiness on the blockchain adoption in supply chain among Saudi SMEs
Bibliographic record
Abstract
The study aims to investigate the moderating role of Organizational Readiness (OR) in the relationship between Supply Chain Agility (SCA) and Blockchain Adoption Intention (BAI) among these SMEs, drawing on the Resource-Based View (RBV) theory. The study adopts a positivist approach and a cross-sectional design to explore how organizational readiness moderates blockchain adoption in Saudi SMEs. It uses a quantitative methodology, surveying decision-makers from SMEs engaged in supply chain activities to gather data. Data analysis is performed using Structural Equation Modeling (SEM). The study's findings indicate that Operational Supply Chain Transparency (OSCT) impacts Supply Chain Alignment (SCL), Supply Chain Adaptability (SCD), SCA, and BAI. Moreover, there is a positive influence of SCL on SCD and SCA. SCA also significantly influences BAI. Furthermore, the moderation analysis shows that OR significantly affects the SCA-BAI relationship, indicating that higher OR amplifies the positive impact of supply chain agility on blockchain adoption intention, underscoring the critical roles of supply chain factors and OR in blockchain adoption among SMEs. Based on the RBV theory, the study delves into how constructs like SCL, OSCT, SCD, and SCA are influenced by blockchain technology. It assesses the extent to which these factors would impact the propensity of SMEs to implement blockchain yet explores the moderating influence of OR on this dynamic.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".