Utilizing blockchain technology in enhancing supply chain efficiency and export performance, and its implications on the financial performance of SMEs
Bibliographic record
Abstract
This study examines the intricate relationships among Blockchain Technology utilization, Supply Chain Efficiency, Export Performance, and the Financial Performance of Small and Medium-sized Enterprises (SMEs). The research aims to elucidate the impact of technology adoption on various operational and financial aspects within the SME context. Employing a quantitative research design, data was collected from a diverse sample of SMEs across industries. The relationships were analyzed using statistical techniques, and the hypotheses were tested to uncover the implications of Blockchain Technology integration on SMEs' performance dimensions. The findings reveal that the adoption of Blockchain Technology significantly enhances Supply Chain Efficiency, underscoring its potential for optimizing operational workflows. However, the direct impact of technology on SME Financial Performance is not established, suggesting the importance of a holistic approach to financial growth. Moreover, the positive association between Blockchain Technology and Export Performance highlights the pivotal role of technology in fostering international trade success. Theoretical implications underscore the intricate interplay between technology adoption, operational efficiencies, and financial outcomes in SMEs. Managerially, the study advocates for SMEs to strategically integrate technology within their supply chain management practices to achieve enhanced efficiency and market competitiveness. Limitations include the potential for contextual variations and measurement biases. Future research can delve deeper into the moderating factors that influence the relationship between technology and financial performance in SMEs. The novelty of this study lies in its comprehensive examination of the interrelationships between these factors within the SME context.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".