Mobile services sector in Saudi Arabia: A systematic literature review of the effective strategies for enhancing customer satisfaction
Bibliographic record
Abstract
The mobile services sector in Saudi Arabia has experienced significant growth in recent years, largely driven by the increasing demand for mobile communication and internet services. This systematic review aims to identify and evaluate the effective strategies for enhancing customer satisfaction in Saudi Arabia's mobile services sector. A systematic review was conducted across five databases from 1st January 2010 and 31st March 2023. The entire process was followed as recommended by the PRISMA guidelines. The findings suggest that the most effective strategies for enhancing customer satisfaction in the Saudi Arabian mobile services sector are improving network coverage, enhancing customer service, offering competitive pricing, introducing new technology and features, and providing value-added services. By adopting these strategies, mobile service providers in Saudi Arabia can enhance their customers' satisfaction, build stronger relationships with their customers, and ultimately increase customer loyalty. Moreover, the study revealed that a combination of these strategies would lead to higher levels of customer satisfaction. The study's findings indicate that mobile service providers in Saudi Arabia can enhance customer satisfaction by focusing on various strategies, such as improving network coverage, customer service, pricing, technology, and features, and providing value added services. Customer satisfaction is one of the main aspects of service delivery. Immediate measures in this regard will assist the mobile sector in Saudi Arabia to plan effective approaches to improve customer satisfaction; this, in turn, can give them a competitive edge in the market and sustain growth in the mobile services sector.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".