Social Networks and Customer Relationship Development at the Saudi Telecommunication Service Providers
Bibliographic record
Abstract
This paper is aimed at examining the relationship between the use of social networks and customer relationship development at the three telecommunication service providers in Saudi Arabia. It is also aimed at identifying some factors that may influence the companies’ relationships with their customers. Drawing on previous research, the researcher has selected three factors related to customers on social networks: trust and loyalty, service assessment, and information engagement. The study revealed a strong association of the use of social networks with the company’s customer relationship development, trust and loyalty, and service assessment, but not with information engagement. The findings also demonstrated significant positive associations of trust and loyalty, and service assessment with customer relationship development. However, an insignificant positive relationship was found between information engagement and customer relationship development. To leverage the effectiveness of customer relationship management at telecommunication companies in Saudi Arabia, the study recommended these companies to adopt the concept of Social Customer Relationship Management (Social CRM), and to develop customer service skills of their staff in charge of social networks. The study also pointed out the importance of encouraging customers to use social networks to connect with companies, rather than just using the traditional methods. Furthermore, the study recommended the companies to pay more attention to the customers’ assessment of their social networks, and to ensure security and privacy of their data. Ultimately, the companies need to focus on providing customers with the needed information, and benefiting from their feedbacks on social networking sites.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".