Economic Contribution and Infrastructure as Mediators of Tourism-Led Development in Sudurpaschim, Nepal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examined the role of the tourism industry in enhancing economic development in Sudurpaschim Province, Nepal, focusing on the mediating effects of economic contribution and infrastructure development. This study utilized a cross sectional approach, gathering 657 valid responses from individuals involved in tourism activities. Descriptive data were analyzed using SPSS 26, while inferential relationships were tested using SmartPLS 4 through structural equation modeling (SEM). The results show that tourism significantly impacted economic contributions, infrastructure development, and economic development. The mediation results of economic contribution and infrastructure development showed partial mediation, with VAF values of 28.077% for economic contribution and 35.58% for infrastructure development. The HTMT and Fornell–Larcker criteria verified the reliability and validity of the model. The findings suggest that tourism directly and indirectly contributes to economic development by strengthening infrastructure and increasing economic activity, thus offering valuable implications for regional development strategies. The parallel mediation model verified partial mediation, tourism influence entirely transmitted through its impact on economic and infrastructure improvements. The findings suggest that destination managers should implement data-driven segmentation strategies aimed at age groups and nationalities that demonstrate the highest levels of engagement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it