Estimating the Translog Cost Function for the Telecommunications Sector in Saudi Arabia: The Case of Saudi Telecom Company: An Empirical Study
Bibliographic record
Abstract
The objective of this research paper is to estimate a translog cost function for the Saudi Arabian telecommunications sector. Telecommunications sector is one of the rapidly growing sectors during the past decade nationwide. A Saudi Telecom Company (STC) data set has been used, as STC has the largest share of the telecommunications sector in Saudi Arabia. Three input cost factors have been considered: marketing and sales, capital and other expenditures. The outputs were total revenue and number of subscribers. The estimation results show that marketing and sales costs are the most important productive components and that capital costs come next. The demand price elasticity values suggest that capital is the most important factor in terms of price sensitivity, which underlines the importance of capital and technology supplies as a necessary component, primarily for the telecommunications sector. From the findings, the telecom sector today relies on advanced technology that incurs high cost. Searching for appropriate funding mechanisms is logical and even necessary for sophisticated technologies and increasing costs. Upgrading marketing techniques, supporting customer services programmes and developing training programmes would yield excellent outcomes and enhance performance. The estimation results end with checking the existence economics of scale, and it has been found that the industry has increasing returns to scale. Therefore, it would be highly recommended to expand the services offered by the telecommunications sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".