Bibliographic record
Abstract
This study investigates the impact of the Internet revolution on travel agencies on Saudi Arabia’s travel agency market. A reliable and valid three-part questionnaire was developed: the first part collects the basic information on the travel agencies; the second part examines the extent to which travel agencies use the benefits of the Internet for their operations; the last part measures the real impact of the internet on travel agencies with four dimensions. A sample of 50 travel agencies fully participated in this study. The descriptive data of the sample indicates that the travel-agency industry in Saudi Arabia is still very small; more than 50% of the agencies operate with less than five employees in one or two branches only. More than 55% of the agencies have less than four years of experience and relatively small capital. In addition, the descriptive data reveals that 72% of the agencies in the sample do not have their own websites, and only 4% of the agencies have websites with features that complete customers’ transactions without human involvement. The main results assure the importance and the benefit of using the Internet for Saudi Arabia travel agencies; however, they have not yet used most of its advantages. Moreover, they do not see any threat or negative impact to their business from the Internet. A number of recommendations have been provided to this industry, such as using the power of the Internet as a global competition tool, and the opportunity of a major emergence among travel agencies in this market.
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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 | 0.006 |
| 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 | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".