Measuring user acceptance of satellite broadband in the UAE
Bibliographic record
Abstract
The Information and Communications Technologies (ICT) sector provides different connectivity solutions in urban areas. Unfortunately, market players make little effort to ensure connectivity to underserved markets. The analysis of the existing ICT market revealed the possibility of a solution to this problem by utilizing the latest advances in satellite communication. It is recognized that connectivity through satellites is generally not a popular option in the United Arab Emirates (UAE), although there are still some underserved markets in the UAE that require better connectivity solutions. In this study, the Technology Acceptance Model (TAM) has been used to assess users’ acceptance in the UAE market to introduce NGSO satellite broadband connectivity. We consider the main two independent variables of the model: perceived usefulness (PU) and perceived ease of use (PEU). Additional two variables had been proposed which significantly affect UAE consumers’ intention to use satellite broadband which are: innovativeness (INN) and satisfaction with current services (SAT). The study results support that PU, PEU, and INN will positively influence the UAE population’s intention to use the service. The hypothesis that PEU will positively affect PU was also supported. The hypothesis that SAT will negatively influence the UAE population’s intention to the service was not supported. Additionally, the study also shows that UAE consumers’ intention to use satellite broadband is unrelated to gender or age group. A set of recommendations were drawn to support the smooth introduction of NGSO satellite broadband service in the UAE.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".