Economic Contribution and Infrastructure as Mediators of Tourism-Led Development in Sudurpaschim, Nepal
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".