Abstract
ABSTRACT Understanding the factors influencing a country's inbound tourism is crucial for shaping effective tourism strategies. This study analyzed historical data from 1964 to 2021 on tourist arrivals, duration of stay, GDP per capita, migration, heritage and attractions, COVID-19 impacts, population growth, and bilateral relationships in Nepal. Initially, regression analysis identified the statistical significance (p-value < 0.05) of these factors on Nepal's inbound tourism. Subsequently, the significant factors were modeled and visualized using neural networks. Historical arrival patterns were then identified using self-organizing maps. The results indicated that population growth, GDP, length of stay, and bilateral relationships positively influenced international arrivals, while migration, heritage, and COVID-19 had negative effects. Five distinct historical arrival patterns were identified form self organizing map comparing with the key drivers of inbound tourism. These findings are vital for tourism planners and policymakers, providing essential insights for the strategic planning and sustainable management of tourism in Nepal.
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