Prospect of Tourism Development in Khaptad National Park: A Local Perspective
Bibliographic record
Abstract
Tourism in Nepal has flourished over the past two decades, making it one of the top global destinations for visit. Its abundant wildlife, stunning trekking routes, snow-fed rivers, picturesque lakes, and warm-hearted locals have been contributing perpetually to appeal to the global and local tourists. With the rise of the digital economy, tourism is experiencing modern advancements. This study aims to analyze the perceptions of local residents regarding tourism development. Structural Equation Modeling (SEM) was employed to validate the research results, utilizing both descriptive and inferential statistics. A sample size of 219 respondents was conveniently selected; indicating a higher interest among males and a predominance of economically active individuals aged 21-40 years. The study shows ICT development emerging as a common challenge for visitors to Khaptad National Park. The SEM results confirmed the fit and validity of five constructs. To establish KNP as a premier tourist destination, the Tourism Board, Government, and Municipality should prioritize its development and manage the influx of tourists. Notably, motivation and subjective norms significantly influence visitors’ behavior, while perceived behavioral control and behavioral intention do not. Local communities should actively engage in promoting tourism through effective marketing strategies, and the improvement of ICT facilities will enhance travel convenience. Hence, efforts should be made to promote local business activities and products. The findings of this analysis will benefit various stakeholders, including the Ministry of Culture, Tourism and Civil Aviation, Nepal Tourism Board, Department of Tourism, hotel associations, Travel agencies, local governments, researchers, professionals, and prospective students. This article is an original work with no potential conflict of interest regarding its research and publication.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".