Green innovation and sustainability in Saudi hospitality and tourism industry: The mediating role of vision 203
Bibliographic record
Abstract
This research aims to investigate the upcoming challenges of green innovation and sustainability in the hospitality and tourism sectors of Saudi Arabia and the mediating role of National Vision 2030 in fostering industry growth. A quantitative design was used, and a close-ended questionnaire was used to collect data from 499 respondents in the hospitality and tourism sectors of Saudi Arabia. The analysis was performed using SmartPLS and structural equation modeling (SEM). The findings indicate several upcoming challenges, such as renewable energy consumption, energy efficiency, and the changing preferences of consumers. However, these challenges can be dealt with adequate government policies and the mediating role of the Saudi Arabia 2030 vision to achieve sustainability in the hospitality and tourism sectors. These findings have theoretical and practical implications, which are discussed in the end. The scope of the findings is constrained to the geographic context of Saudi Arabia, inviting future investigations in a broader international context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".