Factors Affecting the Adoption of Solar Energy Technology to Promote Sustainable Tourism: An Exploratory Study in the Makkah Region, Saudi Arabia
Bibliographic record
Abstract
Saudi Arabia has implemented substantial reforms to diversify its economy as part of its Vision 2030 strategic plan.Tourism is one of sectors the country seeks to promote.However, tourism relies significantly on fossil fuels for various applications, including cooling, lighting, and cleaning.The objective of this research is to analyze the factors influencing the adoption of solar energy technology within the tourism sector in the Makkah region of Saudi Arabia.This study provides the first empirical examination of barriers to solar energy technology adoption within the tourism sector in Saudi Arabia.Moreover, it considers a wide range of factors, including environmental concerns, awareness, cost, ease of use, government support, financial incentives, and the perceived image of solar energy utilization.The empirical analysis is based on primary data obtained from 332 owners/managers of hotels.The structural equation modelling was implemented to analyze the study hypotheses.The findings indicate that awareness, ease of use, government support, and financial incentives promote the adoption of solar energy.In contrast, the cost of solar energy has a negative impact.Finally, environmental concerns and the image of utilizing solar energy have no significant impacts on the willingness of hotel owners/managers to adopt solar energy technology.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".