Binary logistic regression analysis of the impact of social stratification on accommodation, dining, and experiential activity choices: Mongolian tourists to Khuvsgul National Park area

Looking at Mongolian tourists to the Khuvsgul National Park area, this study examines the impacts of tourists’ social stratification on their accommodation, dining, and experiential activity choices. The survey was conducted both online and offline at five different accommodations in the Khuvsgul National Park area between January 2024 and March 2024, and 288 valid questionnaires were used for the binary logistic regression analysis. The results showed that tourists’ occupation, average monthly household income, and subjective social class significantly influenced their choice of either luxury (e.g., hotels and resorts) or inexpensive accommodation (e.g., traditional Mongolian ger camps). Specifically, office workers were about 4.24 times more likely to stay in hotels or resorts than unskilled laborers, and the probability of staying at a hotel or resort increased by 1.45 times with each increment in subjective social stratification. In regard to tourists' dining choices (i.e., eating at hotel or resort restaurants vs. bringing their own food or eating at regular restaurants), average monthly household income and subjective social class had significant effects. However, education level influenced neither accommodation nor dining choices. Finally, tourists’ choices of some experiential activities (e.g., Tsaatan experience and Sukhbaatar ship travels) were influenced by their occupation and other control variables such as gender and age. The study findings and their implications were further discussed at the conclusion of this article.

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Publication Details

Published
2024-10-31
DOI
https://doi.org/10.17086/jts.2024.48.7.29.50
Primary Topic
Diverse Aspects of Tourism Research
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article
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article

Binary logistic regression analysis of the impact of social stratification on accommodation, dining, and experiential activity choices: Mongolian tourists to Khuvsgul National Park area

Gab-Hee Ryu, Jin‐Hyung Lee, Byambasuren Enkhbayar Enkhbayar
Diverse Aspects of Tourism Research
article

Binary logistic regression analysis of the impact of social stratification on accommodation, dining, and experiential activity choices: Mongolian tourists to Khuvsgul National Park area

Gab-Hee Ryu, Jin‐Hyung Lee, Byambasuren Enkhbayar Enkhbayar
article en

Abstract

Looking at Mongolian tourists to the Khuvsgul National Park area, this study examines the impacts of tourists’ social stratification on their accommodation, dining, and experiential activity choices. The survey was conducted both online and offline at five different accommodations in the Khuvsgul National Park area between January 2024 and March 2024, and 288 valid questionnaires were used for the binary logistic regression analysis. The results showed that tourists’ occupation, average monthly household income, and subjective social class significantly influenced their choice of either luxury (e.g., hotels and resorts) or inexpensive accommodation (e.g., traditional Mongolian ger camps). Specifically, office workers were about 4.24 times more likely to stay in hotels or resorts than unskilled laborers, and the probability of staying at a hotel or resort increased by 1.45 times with each increment in subjective social stratification. In regard to tourists' dining choices (i.e., eating at hotel or resort restaurants vs. bringing their own food or eating at regular restaurants), average monthly household income and subjective social class had significant effects. However, education level influenced neither accommodation nor dining choices. Finally, tourists’ choices of some experiential activities (e.g., Tsaatan experience and Sukhbaatar ship travels) were influenced by their occupation and other control variables such as gender and age. The study findings and their implications were further discussed at the conclusion of this article.

Vol. 48(7)
Openalex Percentile: Top 69%
Diverse Aspects of Tourism Research
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