Exploring Tourism's Contribution to Saudi Arabia's Vision 2030: Aligning with UN SDG 8 for Sustainable Growth
Bibliographic record
Abstract
This research explores how tourism contributes to successfully implementing the Saudi Arabia Vision 2030, mainly in terms of resilience and correlation with the sustainable development goal (SDG) 8.This paper adopts a Systematic Literature Review (SLR) methodology to examine efforts to promote tourism in support of KSA Vision 2030 and its compliance with SDG 8. Scopus, Web of Science, and Science Direct were the primary databases from which relevant articles were retrieved.Ten studies were finally considered eligible for the review.It is evident that the tourism industry accounts for a large percentage of the GDP reports, which highlight that tourism accounted for approximately 9% of Saudi Arabia's GDP in 2018, equating to $65 billion.Nevertheless, there are regulatory and ecological constraints.Outcomes reveal the importance of sustainable tourism as implemented in the Al-Ula and The Red Sea projects.In addition, community engagement and digital touchpoints, such as interactive screens, digital signage, and kiosks that enhance visitor interaction, stand out as successful strategies for the future of tourism experiences.It compares findings with extant research, providing practical strategies to ensure sustainable tourism development supports Vision 2030 and global SDGs.However, it is essential to note the limitations of this study, including the small sample size of reviewed articles, which may affect the generalizability of the findings.Therefore, this research has suggested the need to adopt strategic leadership, incorporate all stakeholders into the practices, and realize the value of technology in enhancing Saudi Arabia's tourism destination status.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".