The critical success factors (CSF) of blockchain technology effecting excel performance of banking sector: Case of UAE Islamic Banks
Bibliographic record
Abstract
The present study aims to examine the implementation of Blockchain Technology within the financial sector, with a specific emphasis on its acceptance by Islamic banks operating in the United Arab Emirates (UAE). In the context of a technologically advanced period that necessitates expeditious and safe transactions, blockchain emerges as a viable remedy by obviating the need for intermediary entities and augmenting the velocity and security of transactions. This study uses Partial Least Squares Structural Equation Modeling (PLS-SEM) to investigate the important success aspects of technology and its influence on the performance of Islamic banks. The findings of this study are of particular importance, as they demonstrate the considerable impact of Investment Willingness on Financial Resources. Additionally, the study highlights the crucial role played by Organizational Culture and Leadership Support in fostering readiness for adoption. This study serves as an initial step towards the wider use of blockchain technology in the financial industry, with a particular focus on its application within Islamic banking institutions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".