The role of Islamic destination image in the development of KSA tourism attractivenes
Bibliographic record
Abstract
This research aims to analyze the importance of Islamic Moderation (IM), which is the most prominent factor in the tourism attractiveness of the Kingdom of Saudi Arabia. This has made it an integrated and sustainable tourism destination throughout the year. The study applies a quantitative survey method and selects a probability sample of 470 questionnaires to collect data in the Jeddah region of the Kingdom of Saudi Arabia, using a probability sampling method. After employing purposive sampling to select the participants, a structural equation model (SEM) is employed to analyze the data. The proposed theoretical framework is utilized to examine the mediating role of Islamic Destination Image (IDI), in the correlation between Islamic Moderation (IM) and Destination Attractiveness (DA). Upon revisiting previous research, the study discovers several studies that concentrate on the concepts, mechanisms, and characteristics of moderation, the significance of disseminating moderate thought, addressing extremist thought, and its impact on the economy and international and regional relations. An examination of the total effect yields the conclusion that Islamic Moderation (IM) has a positive impact on Destination Attractiveness (DA); this effect is both positive and strong, both directly and indirectly. The Islamic Moderation (IM) has a significant, positive, and significant impact on the Islamic Destination Image (IDI).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".