Intelligent matching: Supply chain management and financial accounting technology
Bibliographic record
Abstract
The main objective of this paper is to investigate the effect of accounting Fintech, Financial Technology Matching (FTM) and Supply Chain Management (SCM) in Jordanian manufacturing corporations. A questionnaire was created in order to meet the goal of this article to collect the data related to the financial technology and supply chain management and 850 questionnaires out of 1300 distributed questionnaires were collected from respondents, working in Jordanian manufacturing corporations. Linkways hypothesis testing indicates that financial technology and company size had a positive effect on the management of supply chain towards adoption of fintech. In addition, the results also found that integrated supply chain management strategy and environmental uncertainty are significantly affecting the adoption of fintech for supply chain management. However, supply chain optimization uses technologies and resources such as blockchain, artificial intelligence, and Internet of things in the best possible way to improve the efficiency and performance of a supply network.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".