1. NCCU Academic Hub
  2. Publications
  3. College of Commerce
  4. Theses
  5. Item

Publications-Theses

Article View/Open

Publication Export

Google ScholarTM

NCCU Library

Citation Infomation

Related Publications in TAIR

題名 探討三種分類方法來提升混合方式用在兩階段決策模式的準確率：以旅遊決策為例
Improving the precision rate of the Two-stage Decision Model in the context of tourism decision-making via exploring Decision Tree, Multi-staged Binary Tree and Back Propagation of Error Neural Network
作者 陳怡倩
Chen, Yi Chien
貢獻者 傅豐玲
Fu, Fong Lin
陳怡倩
Chen, Yi Chien
關鍵詞 Classification
Data-mining
Two-stage Decision Model
Multi-staged Binary Tree
K-nearest Neighbour
Back Propagation of Error Neural Network
日期 2013
上傳時間 29-Jul-2014 16:04:20 (UTC+8)
摘要 The two-stage data mining technique for classifications in tourism recommendation system is necessary to connect user perception, decision criteria and decision purpose. In existed literature, hybrid data mining method combining Decision Tree and K-nearest neighbour approaches (DTKNN) were proposed. It has a high precision rate of approximately 80% in K-nearest Neighbour (KNN) but a much lower rate in the first stage using Decision Tree (Fu & Tu, 2011). It included two potential improvements on two-stage technique. To improve the first stage of DTKNN in precision rate and the efficiency, the amount of questions is decreased when users search for the desired recommendation on the system. In this paper, the researcher investigates the way to improve the first stage of DTKNN for full questionnaires and also determines the suitability of dynamic questionnaire based on its precision rate in future tourism recommendation system. Firstly, this study compared and chose the highest precision rate among Decision Tree, Multi-staged Binary Tree and Back Propagation of Error Neural Network (BPNN). The chosen method is then combined with KNN to propose a new methodology. Secondly, the study compared and deter¬mined the suitability of dynamic questionnaires for all three classification methods by decreasing the number of attributes. The suitable dynamic questionnaire is based on the least amount of attributes used with an appropriate precision rate. Tourism recommendation system is selected as the target to apply and analyse the usefulness of the algorithm as tourism selection is a two-stage example. Tourism selection is to determine expected goal and experience before going on a tour at the first stage and to choose the tour that best matches stage one. The result indicates that Multi-staged Bi¬nary Tree has the highest precision rate of 74.167% comparing to Decision Tree with 73.33% then BPNN with 65.47% for full questionnaire. This new approach will improve the effectiveness of the system by improving the precision rate of first stage under the current DTKNN method. For dynamic questionnaire, the result has shown that Decision Tree is the most suitable method given that it resulted in the least difference of 1.33% in precision rate comparing to full questionnaire, as opposed to 1.48% for BPNN and 4% for Multi-staged Binary Tree. Thus, dynamic questionnaire will also improve the efficiency by decreasing the amount of questions which users are required to fill in when searching for the desired recommendation on the system. It provides users with the option to not answer some questions. It also increases the practicality of non-dynamic questionnaire and, therefore, affects the ultimate precision rate.
參考文獻 1. Alippi, C., Piuri, V. & Sami, M. (1995). Sensitivity to errors in artificial neural networks: A behavioral approach. Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on, 42(6), 358-361.
2. Baloglu, S. & AcCleary, K.W. (1999). A Model of Destination Image Formation. Annals of Tourism Research, 26(4), 868-897.
3. Black, T.R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement, and statistics. Thousand Oaks, CA: SAGE Publications, Inc. (p. 118).
4. Chattamvelli R. (2009). Data Mining Methods. Alpha Science.
5. Chesnut, T.J., Woodward, J. & Wilson, E. (2007). A Comparison of Closed- and Open-Ended Question Formats for Select Housing Characteristics in the 2006 American Community Survey Content Test. U.S. Census Bureau Washington, D.C.20233. Retrieved February 21, 2014, from http://www.fcsm.gov/07papers/Chesnut.VI-A.pdf
6. Chi, C.G. & Qu, H. (2008). Examining the Structural Relationships of destination Image, Tourist Satisfaction and Destination Loyalty: An integrated Approach. Tourism Management, 29, 624-636.
7. Chuvakin, A.A., Schmidt, K.J. & Phillips, C. (2012). Logging and log management: The authoritative Guide to Understanding the concepts surrounding logging and log management. Syngeress. Available form Google Books.
8. Cooper, D.R. & Schindler, P.S. (2011). Business Research Method (Eleventh Edition). McGraw-Hill Education.
9. Dennett, D.C. (1981). Brainstorms: Philosophical Essays on Mind and Psychology. United States of America: MIT Press edition.
10. Doyle, B. (2011). Free Will: The Scandal in Philosophy. Cambridge, MA, USA: I-Phi Press. Available from Google Books.
11. Ekonde, C.N. (2010). Tourism destination marketing: A comparative study, between Gotland Island, Sweden and Limbe city, Cameroon. Retrieved February 13, 2014, from http://www.diva-portal.org/smash/get/diva2:322381/FULLTEXT01.pdf
12. Explorable.com (2009). Convenience Sampling. Retrieved March 5, 2014, from http://explorable.com/convenience-sampling
13. Fayyad, U. M. & Irani, K. B. (1990). What Should Be Minimized in a Decision Tree? Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), Boston, Massachusetts, 749-754.
14. Fu, F. & Tu, Y. (2011). Intelligence on Gift Giving Website Based on Hybrid Approach of Decision Tree and Similarity. WASE International Conference on Information Engineering (ICIE), Aug 13-14, Xi`an, China.
15. Gaur, P. (2012). Neural Networks in Data Mining. International Journal of Electronics and Computer Science Engineering, 1(3), 1449. Retrieved from The University of Auckland database.
16. Gong, A. & Liu, Y. (2011). Improved KNN Classification Algorithm by Dynamic Obtaining K. In G. Shen & X. Huang (Eds.), Advanced Research on Electronic Commerce, Web Application, and Communication: International Conference, ECWAC 2011, Guangzhou, China, April 16-17, 2011, Proceedings, Part I (pp. 320-324). Retrieved from The University of Auckland database.
17. Joseph P. & Gilmore, G.H. (1998). Welcome to the Experience Economy. Harvard Business Review, 76(4), 97-105.
18. Jönsson, C. & Devonish, D. (2008). Does nationality, gender, and age affect travel motivation? A case of visitors to the Caribbean island of Barbados. Journal of Travel & Tourism Marketing, 25(3-4), 398-408.
19. Lehto, X.Y., O’Leary, J.T. & Morrison, A.M. (2004). The Effect of Prior Experience on Vacation Behaviour. Annals of Tourism Research, 31(4), 801-818.
20. Martino, B.D., Kumaran, D., Seymour, B. & Dolan, R.J. (2006). Frames, Biases, and Rational Decision-Making in the Human Brain. Science, 313(5787), 684-687.
21. Meng, F. & Uysal, M. (2008). Effects of gender differences on perceptions of destination attributes, motivations, and travel values: An examination of a nature-based resort destination. Journal of Sustainable Tourism, 16(4), 445-466.
22. Mitchel T.M. (1997). Machine Learning. McGraw-Hill.
23. Myers, I.B. (1985). A Guide to the Development and Use of the Myers-Briggs Type Indicator: Manual. Palo Alto, CA: Consulting Psychologists Press.
24. Peng, L.C., Kao, Y.H., Hung, S.Y., Yeh, Y.Z., Hsiao, H.C., (2011). "Find Fun." Report for National Cheng-Chi University Seminar Presentation.
25. Quenk, N.L. (2009). Essentials of Myers-Briggs Type Indicator Assessment. (Second Edition). John Wiley & Sons.
26. QuestionPro. (2007). Developing Dynamic Surveys. Retrieved March 3, 2014, from http://www.questionpro.com/images/bookshelf/dynamicsurveys.pdf
27. Reja, U., Manfreda, K.L., Hlebec, V. & Vehovar, V. (2003). Open-ended vs. close-ended questions in web questionnaires. Advances in Methodology and Statistics (Metodološki zvezki), 19, 159-77.
28. Rojas, C. & Camarero, C. (2008). Visitor’s Experience, Mood and Satisfaction in Heritage Context: Evidence from an Interpretation Center. Tourism Management, 29,525-537.
29. Rust, R.T. & Oliver, R.L. (1994). Service quality: insights and managerial implication from the frontier. In R.T. Rust & R.L. Oliver (Eds.), Service quality: New directions in theory and practice (pp. 1-19). Thousand Oaks, CA: Sage.
30. Safavin, S.R. & Landgrebe, D. (1991). A survey of decision tree classier methodology. IEEE Transactions on Systems, Man and Cybernetics, 21(3), 660-674.
31. Seng, C. & Chen, M. (2012). A Study of Experience Expectations of Museum Visitors. Tourism Management, 33, 53-60.
32. Shmueli G., Patel N.R. & Bruce P.C. (2010). Data Mining For Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner (Second Edition). Wiley.
33. Snap Surveys. (2014). Dynamic Questionnaires. Retrieved March 3, 2014, from http://www.snapsurveys.com/survey-software/dynamic-questionnaires/
34. Stackoverflow (2014). Whats is the difference between train, validation and test set, in neural networks? Retrieved March 3, 2014, from http://stackoverflow.com/questions/2976452/whats-is-the-difference-between-train-validation-and-test-set-in-neural-networ
35. Wang, N. (1999). Rethink Authenticity in Tourism Experience. Annals of Tourism Research, 26(2), 349-370.
36. Wu, J. & Coggeshall, S. (2012). Foundations of Predictive Analytics. CRC Press. Retrieved from The University of Auckland database.
37. Yeh, Y.L., Hou, T.H. & Low, C.Y. (2012). The Classification of Children’s Occupational Therapy Problems Using Neural Network. In Qian, Z., Cao, L., Su, W., Wang, T. & Yang, H. (Eds.), Recent Advances in Computer Science and Information Engineering, 1 (pp. 687-692). Retrieved from The University of Auckland database.
38. Yuksel, A., Yuksel, F. & Bilim, Y. (2010). Destination Attachment: Effects on Customer Satisfaction and Cognitive. Affective and Conative Loyalty. Tourism Management. 31, 274-284.
39. United Nation World Tourism Organisation (2013). UNWTO Tourism Highlights, 2013 Edition. Retrieved January 7, 2014, from http://dtxtq4w60xqpw.cloudfront.net/sites/all/files/pdf/unwto_highlights13_en_hr.pdf
描述 碩士
國立政治大學
資訊管理研究所
101356043
102
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0101356043
資料類型 thesis
dc.contributor.advisor 傅豐玲zh_TW
dc.contributor.advisor Fu, Fong Linen_US
dc.contributor.author (Authors) 陳怡倩zh_TW
dc.contributor.author (Authors) Chen, Yi Chienen_US
dc.creator (作者) 陳怡倩zh_TW
dc.creator (作者) Chen, Yi Chienen_US
dc.date (日期) 2013en_US
dc.date.accessioned 29-Jul-2014 16:04:20 (UTC+8)-
dc.date.available 29-Jul-2014 16:04:20 (UTC+8)-
dc.date.issued (上傳時間) 29-Jul-2014 16:04:20 (UTC+8)-
dc.identifier (Other Identifiers) G0101356043en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/67866-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理研究所zh_TW
dc.description (描述) 101356043zh_TW
dc.description (描述) 102zh_TW
dc.description.abstract (摘要) The two-stage data mining technique for classifications in tourism recommendation system is necessary to connect user perception, decision criteria and decision purpose. In existed literature, hybrid data mining method combining Decision Tree and K-nearest neighbour approaches (DTKNN) were proposed. It has a high precision rate of approximately 80% in K-nearest Neighbour (KNN) but a much lower rate in the first stage using Decision Tree (Fu & Tu, 2011). It included two potential improvements on two-stage technique. To improve the first stage of DTKNN in precision rate and the efficiency, the amount of questions is decreased when users search for the desired recommendation on the system. In this paper, the researcher investigates the way to improve the first stage of DTKNN for full questionnaires and also determines the suitability of dynamic questionnaire based on its precision rate in future tourism recommendation system. Firstly, this study compared and chose the highest precision rate among Decision Tree, Multi-staged Binary Tree and Back Propagation of Error Neural Network (BPNN). The chosen method is then combined with KNN to propose a new methodology. Secondly, the study compared and deter¬mined the suitability of dynamic questionnaires for all three classification methods by decreasing the number of attributes. The suitable dynamic questionnaire is based on the least amount of attributes used with an appropriate precision rate. Tourism recommendation system is selected as the target to apply and analyse the usefulness of the algorithm as tourism selection is a two-stage example. Tourism selection is to determine expected goal and experience before going on a tour at the first stage and to choose the tour that best matches stage one. The result indicates that Multi-staged Bi¬nary Tree has the highest precision rate of 74.167% comparing to Decision Tree with 73.33% then BPNN with 65.47% for full questionnaire. This new approach will improve the effectiveness of the system by improving the precision rate of first stage under the current DTKNN method. For dynamic questionnaire, the result has shown that Decision Tree is the most suitable method given that it resulted in the least difference of 1.33% in precision rate comparing to full questionnaire, as opposed to 1.48% for BPNN and 4% for Multi-staged Binary Tree. Thus, dynamic questionnaire will also improve the efficiency by decreasing the amount of questions which users are required to fill in when searching for the desired recommendation on the system. It provides users with the option to not answer some questions. It also increases the practicality of non-dynamic questionnaire and, therefore, affects the ultimate precision rate.en_US
dc.description.tableofcontents I. Introduction 1
1.1. Background 1
1.2. Problem Statement 1
1.3. Goals and Objectives 4
1.4. Significance of this Research 5
1.5. A Brief Overview of Research Methodology 5
II. Literature Review 6
2.1. Research Definition and Framework of Decision Tree 6
2.2. Research Definition and Framework of Multi-staged Binary Tree 10
2.3. Research Definition and Framework of Back Propagation of Error Neural Network 13
2.4. Research Definition and Framework of K Nearest Neighbour 16
2.5. Target Application: Tourists’ Decision on Destination Selection 17
III. Research Methodology 21
3.1. Research Conceptual Framework 21
3.2. Target Application 25
3.3. Research Operational Definition 26
3.4. Research Hypotheses 31
3.5. Data Collection 33
3.6. Planned Evaluation Method 34
3.7. Questionnaire Development and Evaluation 36
IV. Result Analysis and Evaluations 41
4.1 Decision Tree Analysis 41
4.1.1. Analysis of “excited, usable and warm” for Decision Tree 42
4.2 Multi-staged Binary Tree Analysis 61
4.2.1. Analysis of “excited and unexcited” for Multi-staged Binary Tree 62
4.2.2. Analysis of “usable and non-usable” 81
4.2.3. Analysis of “warm and non-warm” 102
4.3 Back Propagation of Error Neural Network Analysis 115
V. Conclusions and Suggestions 139
5.1. Conclusion of the Research 140
5.2. Implication and Suggestion of the Research 142
5.3. Limitation of the Research 143
Appendices 145
Appendix A. Decision Tree for Full Questionnaire 145
Appendix B. Multi-staged Binary Tree for Dynamic Questionnaire 146
Appendix C. Multi-staged Binary Tree for Full Questionnaire 148
Appendix D. First Set for Back Propagation of Error Neural Network in the hidden nodes from 10-45. 150
Appendix E. The Precision Rate Plot for Full Questionnaire in Back Propagation of Error Neural Network 153
Appendix F. The Precision Rate Plot for Dynamic Questionnaire One in Back Propagation of Error Neural Network 154
Appendix G. The Precision Rate Plot for Dynamic Questionnaire Two in Back Propagation of Error Neural Network 155
Appendix H. The Precision Rate Plot for Dynamic Questionnaire Three in Back Propagation of Error Neural Network 156
Appendix I. The Precision Rate Plot for Dynamic Questionnaire Four in Back Propagation of Error Neural Network 157
Appendix J. The calculation of the unused attributes values for Dynamic questionnaire three 158
Appendix K. The calculation of the unused attributes values for Dynamic questionnaire four 159
References 160		zh_TW
dc.format.extent 4052287 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0101356043en_US
dc.subject (關鍵詞) Classificationen_US
dc.subject (關鍵詞) Data-miningen_US
dc.subject (關鍵詞) Two-stage Decision Modelen_US
dc.subject (關鍵詞) Multi-staged Binary Treeen_US
dc.subject (關鍵詞) K-nearest Neighbouren_US
dc.subject (關鍵詞) Back Propagation of Error Neural Networken_US
dc.title (題名) 探討三種分類方法來提升混合方式用在兩階段決策模式的準確率：以旅遊決策為例zh_TW
dc.title (題名) Improving the precision rate of the Two-stage Decision Model in the context of tourism decision-making via exploring Decision Tree, Multi-staged Binary Tree and Back Propagation of Error Neural Networken_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 1. Alippi, C., Piuri, V. & Sami, M. (1995). Sensitivity to errors in artificial neural networks: A behavioral approach. Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on, 42(6), 358-361.
2. Baloglu, S. & AcCleary, K.W. (1999). A Model of Destination Image Formation. Annals of Tourism Research, 26(4), 868-897.
3. Black, T.R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement, and statistics. Thousand Oaks, CA: SAGE Publications, Inc. (p. 118).
4. Chattamvelli R. (2009). Data Mining Methods. Alpha Science.
5. Chesnut, T.J., Woodward, J. & Wilson, E. (2007). A Comparison of Closed- and Open-Ended Question Formats for Select Housing Characteristics in the 2006 American Community Survey Content Test. U.S. Census Bureau Washington, D.C.20233. Retrieved February 21, 2014, from http://www.fcsm.gov/07papers/Chesnut.VI-A.pdf
6. Chi, C.G. & Qu, H. (2008). Examining the Structural Relationships of destination Image, Tourist Satisfaction and Destination Loyalty: An integrated Approach. Tourism Management, 29, 624-636.
7. Chuvakin, A.A., Schmidt, K.J. & Phillips, C. (2012). Logging and log management: The authoritative Guide to Understanding the concepts surrounding logging and log management. Syngeress. Available form Google Books.
8. Cooper, D.R. & Schindler, P.S. (2011). Business Research Method (Eleventh Edition). McGraw-Hill Education.
9. Dennett, D.C. (1981). Brainstorms: Philosophical Essays on Mind and Psychology. United States of America: MIT Press edition.
10. Doyle, B. (2011). Free Will: The Scandal in Philosophy. Cambridge, MA, USA: I-Phi Press. Available from Google Books.
11. Ekonde, C.N. (2010). Tourism destination marketing: A comparative study, between Gotland Island, Sweden and Limbe city, Cameroon. Retrieved February 13, 2014, from http://www.diva-portal.org/smash/get/diva2:322381/FULLTEXT01.pdf
12. Explorable.com (2009). Convenience Sampling. Retrieved March 5, 2014, from http://explorable.com/convenience-sampling
13. Fayyad, U. M. & Irani, K. B. (1990). What Should Be Minimized in a Decision Tree? Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), Boston, Massachusetts, 749-754.
14. Fu, F. & Tu, Y. (2011). Intelligence on Gift Giving Website Based on Hybrid Approach of Decision Tree and Similarity. WASE International Conference on Information Engineering (ICIE), Aug 13-14, Xi`an, China.
15. Gaur, P. (2012). Neural Networks in Data Mining. International Journal of Electronics and Computer Science Engineering, 1(3), 1449. Retrieved from The University of Auckland database.
16. Gong, A. & Liu, Y. (2011). Improved KNN Classification Algorithm by Dynamic Obtaining K. In G. Shen & X. Huang (Eds.), Advanced Research on Electronic Commerce, Web Application, and Communication: International Conference, ECWAC 2011, Guangzhou, China, April 16-17, 2011, Proceedings, Part I (pp. 320-324). Retrieved from The University of Auckland database.
17. Joseph P. & Gilmore, G.H. (1998). Welcome to the Experience Economy. Harvard Business Review, 76(4), 97-105.
18. Jönsson, C. & Devonish, D. (2008). Does nationality, gender, and age affect travel motivation? A case of visitors to the Caribbean island of Barbados. Journal of Travel & Tourism Marketing, 25(3-4), 398-408.
19. Lehto, X.Y., O’Leary, J.T. & Morrison, A.M. (2004). The Effect of Prior Experience on Vacation Behaviour. Annals of Tourism Research, 31(4), 801-818.
20. Martino, B.D., Kumaran, D., Seymour, B. & Dolan, R.J. (2006). Frames, Biases, and Rational Decision-Making in the Human Brain. Science, 313(5787), 684-687.
21. Meng, F. & Uysal, M. (2008). Effects of gender differences on perceptions of destination attributes, motivations, and travel values: An examination of a nature-based resort destination. Journal of Sustainable Tourism, 16(4), 445-466.
22. Mitchel T.M. (1997). Machine Learning. McGraw-Hill.
23. Myers, I.B. (1985). A Guide to the Development and Use of the Myers-Briggs Type Indicator: Manual. Palo Alto, CA: Consulting Psychologists Press.
24. Peng, L.C., Kao, Y.H., Hung, S.Y., Yeh, Y.Z., Hsiao, H.C., (2011). "Find Fun." Report for National Cheng-Chi University Seminar Presentation.
25. Quenk, N.L. (2009). Essentials of Myers-Briggs Type Indicator Assessment. (Second Edition). John Wiley & Sons.
26. QuestionPro. (2007). Developing Dynamic Surveys. Retrieved March 3, 2014, from http://www.questionpro.com/images/bookshelf/dynamicsurveys.pdf
27. Reja, U., Manfreda, K.L., Hlebec, V. & Vehovar, V. (2003). Open-ended vs. close-ended questions in web questionnaires. Advances in Methodology and Statistics (Metodološki zvezki), 19, 159-77.
28. Rojas, C. & Camarero, C. (2008). Visitor’s Experience, Mood and Satisfaction in Heritage Context: Evidence from an Interpretation Center. Tourism Management, 29,525-537.
29. Rust, R.T. & Oliver, R.L. (1994). Service quality: insights and managerial implication from the frontier. In R.T. Rust & R.L. Oliver (Eds.), Service quality: New directions in theory and practice (pp. 1-19). Thousand Oaks, CA: Sage.
30. Safavin, S.R. & Landgrebe, D. (1991). A survey of decision tree classier methodology. IEEE Transactions on Systems, Man and Cybernetics, 21(3), 660-674.
31. Seng, C. & Chen, M. (2012). A Study of Experience Expectations of Museum Visitors. Tourism Management, 33, 53-60.
32. Shmueli G., Patel N.R. & Bruce P.C. (2010). Data Mining For Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner (Second Edition). Wiley.
33. Snap Surveys. (2014). Dynamic Questionnaires. Retrieved March 3, 2014, from http://www.snapsurveys.com/survey-software/dynamic-questionnaires/
34. Stackoverflow (2014). Whats is the difference between train, validation and test set, in neural networks? Retrieved March 3, 2014, from http://stackoverflow.com/questions/2976452/whats-is-the-difference-between-train-validation-and-test-set-in-neural-networ
35. Wang, N. (1999). Rethink Authenticity in Tourism Experience. Annals of Tourism Research, 26(2), 349-370.
36. Wu, J. & Coggeshall, S. (2012). Foundations of Predictive Analytics. CRC Press. Retrieved from The University of Auckland database.
37. Yeh, Y.L., Hou, T.H. & Low, C.Y. (2012). The Classification of Children’s Occupational Therapy Problems Using Neural Network. In Qian, Z., Cao, L., Su, W., Wang, T. & Yang, H. (Eds.), Recent Advances in Computer Science and Information Engineering, 1 (pp. 687-692). Retrieved from The University of Auckland database.
38. Yuksel, A., Yuksel, F. & Bilim, Y. (2010). Destination Attachment: Effects on Customer Satisfaction and Cognitive. Affective and Conative Loyalty. Tourism Management. 31, 274-284.
39. United Nation World Tourism Organisation (2013). UNWTO Tourism Highlights, 2013 Edition. Retrieved January 7, 2014, from http://dtxtq4w60xqpw.cloudfront.net/sites/all/files/pdf/unwto_highlights13_en_hr.pdf		zh_TW