對大部分國家而言，旅遊產業日益重要，也關係著一國的經濟命脈，根據調查，旅遊產業自2006年至2019年間對於GDP的貢獻連年攀升，但受制2019年COVID-19疫情的爆發，旅遊產業受到極大的影響。若能更進一步地透過資料科學的力量，了解對於不同國家而言相關的旅遊決策建議，則能夠為當地觀光局指引旅遊產業復甦的參考方向。
為此，本研究主要根據過往經常出現於商業併購中的Hofstede文化維度分數，了解在這些不同文化的國家中，文化距離與旅遊喜好間有著什麼樣的關係樣態，並且透過大數據分析以及從世界旅遊組織 （World Tourism Organization）蒐集而來220個國家、20幾年的長期資料，分析每一年國對國的旅遊人數資料，並結合其他相關資料合併之變數，以混合效果模型樹進行研究與探索。此種新方法是結合機器學習中的決策樹與傳統廣義線性混合效果模型樹GLMM Tree（Generalized Linear Mixed Model Tree）找出文化屬性，了解各種不同文化屬性分類的國家在旅遊喜好與文化距離的樣態上的差異之處。
因此，與其他過往文獻不同之處有主要四點：
一、 資料來源：過往以問卷為主，本篇研究以二手資料（大數據）為主
二、 研究單位：過往研究以一個國家為主，本篇研究以全世界國家為主
三、 樣本數目：過往研究以抽樣樣本為主，本研究以長期性世界級資料為主
四、 研究方法：以混合效果模型樹，依據文化維度對文化距離與旅遊喜好間的關係做分群，方便看出「文化屬性組合」和「旅遊喜好樣態」之間的關係

For most countries, the tourism industry is increasingly important, and it is also related to the economic lifeline of a country. According to the research, the tourism industry's contribution to GDP has increased year by year from 2006 to 2019. However, due to the outbreak of the COVID-19 after 2019, The tourism industry has been greatly affected. If we can further use the power of data science to understand the relevant tourism decision-making suggestions for different countries, it can guide local tourism bureaus as a reference for the recovery of the tourism industry.
To this end, this study, based on the Hofstede cultural dimension scores that have often been adopted in business mergers and acquisitions in the past, try to discovers the relationship between cultural distance and travel preferences across the countries with different cultures. The World Tourism Organization collects long-term data from 220 countries and more than 20 years. We analyzed the annual number of tourists from country to country, used machine learning decision trees and traditional generalized linear model methods - Glmmtree to find out cultural attributes and understand the differences in travel preferences and cultural distances between countries with different cultural attribute classifications.
Therefore, there are four main differences from other past literatures:
1. Data source: past researched by questionnaires, and this research mainly used second-hand data (big data).
2. Research unit: past research is mainly based on one country, and this research was mainly based on countries all over the world.
3. Number of samples: Past research is mainly based on sampling samples, this research is mainly based on long-term world-class data.
4. Research methods: Used a mixed-effects model tree to group the relationship between cultural distance and tourism preferences according to cultural dimensions, it is clear to see that the relationship between "cultural attribute combination" and "tourism preference pattern".

論文審定書 i
公開授權書 ii
致謝辭 iii
摘要 iv
Abstract v
目錄 vii
圖目錄 viii
表目錄 ix
第一章　緒論 1
第一節　研究背景 1
第二節　研究動機 2
第三節　研究目的 2
第二章　文獻探討 3
第一節　文化維度與文化距離的應用 3
第二節　文化距離對於旅遊喜好的重要性 6
第三章　研究方法 8
第一節　研究架構 8
第二節　資料來源與處理 9
第四章　資料分析與研究結果 19
第一節　資料處理 19
第二節　資料探索性分析 19
第三節　模型處理與結果 26
第五章　結論與建議 37
第一節　研究結果 37
第二節　研究限制與未來研究方法 38
參考文獻 39
附錄：國家代碼對照表 43

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https://geerthofstede.com/research-and-vsm/dimension-data-matrix/
20. 台灣與美國文化維度分數：
https://www.hofstede-insights.com/country-comparison/taiwan,the-usa/

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26. 國家代碼對照表：
https://zh.wikipedia.org/zh-tw/%E5%9B%BD%E5%AE%B6%E4%BB%A3%E7%A0%81%E5%AF%B9%E7%85%A7%E8%A1%A8