Quality of Service in Mobile Telecommunications in Albania – Application of Marketing Strategies
Bibliographic record
Abstract
The sector of telecommunication services in Albania has undergone enormous changes over the past two decades. The decline in fixed services in number in prices but more importantly the decline in the use and perception of customers and the amazing growth of mobile services were mostly observed in any city in Albania. This paper takes into consideration mobile services and their marketing strategies, as they reach this market and how to fulfill their objectives. The focus of this study in marketing strategies and marketing mix will be specifically the quality of service that is offered by cellular companies in Albania. Also, the strategy applied will be tested if it delivers or better to say the translation in the quality perceived by the customer and their assessment of their respective companies. The study was conducted in the Albanian market in the years 2016-2017. The study will include a representative sample for the Albanian market and measure and compare the quality of services through instruments such as Servqual. In the data analysis it shows that there are statistically significant differences in the quality offered by mobile companies in Albania and the quality perceived by the client, even though the services offered, packages and products are of a more homogeneous nature. The primary data obtained in this study indicate that companies in addition to suitable marketing strategies and benchmarking with other market players should bear in mind the integrated marketing communication and placing customer and values at the center of services offered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".