Transforming Customer Service: AI’s Role in Boosting UAE Business Performance: The Case of the UAE
Bibliographic record
Abstract
This study investigates the impact of artificial intelligence (AI) implementation in call centers on organizational performance across various sectors in the United Arab Emirates (UAE), with a specific focus on how AI enhances operational efficiency and customer satisfaction. Data was collected from 210 UAE-based companies, and a conceptual model was developed to analyze the technological, organizational, and environmental factors influencing AI adoption. Utilizing structural equation modeling, the study reveals that AI-enabled call centers significantly enhance company performance by improving customer service response times and issue resolution rates. Interestingly, smaller companies experienced greater benefits from AI adoption compared to larger firms, indicating that smaller organizations may be more agile in leveraging AI for competitive advantage. However, the study is limited to firms within the UAE, which may restrict the generalizability of the findings to other regions with different business contexts. Despite this limitation, the research provides robust evidence of AI’s value in enhancing customer service operations within the Middle Eastern market. A noteworthy finding is that AI adoption also positively influences employee satisfaction, suggesting broader implications beyond operational efficiency. This insight paves the way for further discussion on AI’s role in workforce dynamics and organizational transformation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".