The effect of blockchain and smart inventory system on supply chain performance: Empirical evidence from retail industry
Bibliographic record
Abstract
This research aims to fill the research gap with empirical evidence that exists about the impact of blockchain and smart inventory systems on supply chain performance in the retail industry in the UAE. The proposed model is uniquely researched as no prior research explores the link between supply chain performances, blockchain, and smart inventory in prestigious academic journals. A quantitative technique with convenient cluster sampling is used. A descriptive, exploratory, causal and analytical design was applied—a sample size of 303 respondents was used for data analysis through regression and hypothesis with ANOVA. The findings revealed a significant positive impact of blockchain and smart inventory systems on SC performance. Limited construct-based research can be focused on more industries and constructs for future studies. There are numerous chances for businesses to leverage blockchain technology to their advantage over the competition, giving them the chance to strengthen their market position. Managers must carefully consider the qualities of their goods, services, and supply chains to ascertain whether they require or would sufficiently benefit from blockchain.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".