臺灣博碩士論文加值系統

近年來，旅遊業的快速成長已經成為各國重要的經濟發展之一。因此，管理者開始注重目的地發展以便於提升競爭力。目的地發展是針對目的地全方面的管理，它可以將目的地分為多個面向，方便用於找出不足的面向來加以改善。但在過去幾年中，沒有任何研究在討論遊客對於目的地發展的關係。由於遊客是根據目的地特徵來選擇目的地，因此我們利用遊客的分類，進而討論目的地特徵與遊客類型的關聯分析。關聯分析在過去的研究中，大多採用統計作為方法，為了避免複雜的統計過程，本篇論文提出一個矩陣方法，並且協助管理者了解有價值的資訊與比較目的地之間是否相似。本篇論文也利用一則案例來顯示如何協助管理者提升他們的目的地發展，同時給予一個結果做為決策參考。

In recent years, the rapid growth of tourism industry has become the most important economic development of countries. For the purpose, the manager has focus on the destination development (DD) to enhance the competitive. DD is all concepts for tourism destination of management. It can be dividing into multiple factors, and to identify shortcomings to be improved. But in recent years, there are no studies about the relation of DD with tourist. Due to tourists are based on destination features to select the destination, we classifying tourists, and discuss the correlation between destination features and tourist types. Most correlation analysis method is adopted on statistic, but this thesis proposes a matrix method to avoid the complex processes, and help manager to understand the valuable information and to compare similarity between destinations. This thesis also shows a cased study about how manager to enhance DD and gives a result as refer of decision.

List of Contents
1. Introduction 1
1.1 Motivation and Background 1
1.2 Objectives 2
1.3 Research Rationale 2
1.4 Assumption and Limitation 3
1.5 Research Organization 4
2. Literature Review 5
2.1 Destination Features and Tourist Types 5
2.2 Correlation between Destination Features and Tourist types 7
3. Solution approach 13
3.1 Modeling relationship for destination between destination features and tourist types 13
3.2 Design for partition problem 17
3.3 Solution to identify Clustering 18
4. Case Study 24
5. Conclusion and Future Work 43
References 45
Appendix A 49
Appendix B 54
Appendix C 63
Appendix D 66
Appendix E 69

 
List of Figure
Figure 1. 1 The research process 3

List of Table
Table 2. 1 The drawbacks of statistic methods 11
Table 3. 1 The individual destination values of destination features and tourist types 15
Table 3. 2 The interaction matrix for destination feature: Minter-f 15
Table 3. 3 The interaction matrix for tourist type: Minter-c 16
Table 3. 4 The interaction with quantity matrix for destination feature: Minter-f-q 16
Table 3. 5 The interaction with quantity matrix for tourism type: Minter-c-q 16
Table 4. 1 The code of destination feature. 25
Table 4. 2 The code of tourist type. 26
Table 4. 3 The individual destination values of destination features and tourist types 27
Table 4. 4 The interaction matrix for destination feature: Minter-f 28
Table 4. 5 The interaction matrix for tourist type: Minter-c 29
Table 4. 6 The interaction with quantity matrix for destination feature: Minter-f-q 30
Table 4. 7 The interaction with quantity matrix for tourism type: Minter-c-q 30
Table 4. 8 Rearranged interaction with quantity matrix for destination feature: Marrange-f-q 31
Table 4. 9 Rearranged interaction with quantity matrix for tourism type: Marrange-c-q ……………………32
Table 4. 10 The mixed of rearrange the interaction matrix: Mmix 33
Table 4. 11 The clustering matrix: Mclu 35
Table 4. 12 The value table 37
Table 4. 13 The correlation between destination features and tourist types 38
Table 4. 14 The individual tourism type values for destinations 39
Table 4. 15 The tourism types’ interaction matrix for destination 40
Table 4. 16 The tourism types’ interaction with quantity matrix for destination 40
Table 4. 17 Rearranged Tourist types’ interaction matrix for destination 41
Table 4. 18 The order of popular destinations 42

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