You are entitled to access the full text of this documentThe inflected of fintech solutions on financial performance in SMEs: An analysis of the IT industry in the UAE
Bibliographic record
Abstract
This study investigates the impact of introducing financial technology (Fintech) on increasing the financial viability of small and medium enterprises (SMEs) operating in the IT sector in the United Arab Emirates (UAE). By identifying the obstacles that SMEs experience while dealing digital transformation, this study aims to show how the emerging financial technologies, including digital banking, peer-to-peer lending, and AI applications, can drive enhancements in financial utilization and the innovation of banking products, cost-cuts, increased organizational productivity, and better client experiences. A quantitative research approach was implemented, and the research tool used was a structured questionnaire developed to capture respondents’ data from 250 respondents from SMEs in the UAE. Descriptive and inferential statistics of correlation and regression analysis were done using the stratified random sampling technique. Results indicated that fintech adoption enhances financial performance in SMEs. Specifically, increased accessibility shows a strong positive correlation with financial performance (r = 0.998, p < 0.01), while enhanced efficiency and improved customer experience also show strong correlations (r = 0.635, p < 0.01). The variable "Increased Accessibility" showed the most significant effect on the financial performance of SMEs, with a highly significant positive correlation and the strongest predictive value in the regression analysis. This study concludes that the implementation of fintech solutions offers significant opportunities for improving the financial performance of SMEs and recommends increased investment in fintech and appropriate regulation for that purpose.
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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 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".