The dynamic role of the Internet of Things (IoT) on the excel performance of Islamic banks in United Arab Emirates
Bibliographic record
Abstract
The rapid advancement of technology has substantially impacted numerous sectors, including the banking industry. It is now apparent that the banking industry is affected by the innovative role that the Internet of Things (IoT) plays, which affects a multitude of operations and services. This study uses a quantitative approach and PLS-SEM to investigate the widespread impact of Internet of Things (IoT) technology on the Excel Performance of Islamic Banks in the UAE. The study integrates the Resource-Based View (RBV), Islamic Banking Theories, and Fraud Triangle Theory to create a complete framework. The research's reliability is supported by a sample of 407 replies from 504 participants. The findings strongly support the hypotheses that IoT integration improves Islamic banking performance in the UAE, such as data analytics, customer service, automation systems, fraud detection capabilities, and asset-backed finance, while aligning with Sharia Principles and improving risk-sharing mechanisms. However, the influence of IoT on escalating fraudulent activities and hence negatively impacting performance was not proven. The study emphasizes the importance of IoT in improving operational efficiency and customer satisfaction in Islamic banks in the UAE.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".