博碩士論文 103421051 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:30 、訪客IP:3.141.35.60
姓名 彭雍芬(Yung-Fen Peng)  查詢紙本館藏   畢業系所 企業管理學系
論文名稱 探討朋友關係對旅遊景點選擇影響之研究
(The influence of the friend relationship in the choices of traveling sites)
相關論文
★ 在社群網站上作互動推薦及研究使用者行為對其效果之影響★ 以AHP法探討伺服器品牌大廠的供應商遴選指標的權重決定分析
★ 以AHP法探討智慧型手機產業營運中心區位選擇考量關鍵因素之研究★ 太陽能光電產業經營績效評估-應用資料包絡分析法
★ 建構國家太陽能電池產業競爭力比較模式之研究★ 以序列採礦方法探討景氣指標與進出口值的關聯
★ ERP專案成員組合對績效影響之研究★ 推薦期刊文章至適合學科類別之研究
★ 品牌故事分析與比較-以古早味美食產業為例★ 以方法目的鏈比較Starbucks與Cama吸引消費者購買因素
★ 探討創意店家創業價值之研究- 以赤峰街、民生社區為例★ 以領先指標預測企業長短期借款變化之研究
★ 應用層級分析法遴選電競筆記型電腦鍵盤供應商之關鍵因子探討★ 以互惠及利他行為探討信任關係對知識分享之影響
★ 結合人格特質與海報主色以類神經網路推薦電影之研究★ 資料視覺化圖表與議題之關聯
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 ( 永不開放)
摘要(中) 隨著網際網路與社群網站蓬勃發展,使用者在旅遊景點的選擇中會參考相關的旅遊社群網站,若旅遊社群業者不想錯過用戶的關注,應當從龐大的資訊中提供既準確又客製化的旅遊推薦給各使用者。業者必須能提供更多有價值的旅遊建議讓客戶在最少訊息的選擇中創造出較佳的旅遊決定,藉此能達到雙贏局面,使得業者也能增進營收。
然而,面對千變萬化的旅遊業中強調速度與求新求變的特性,但以往的文獻與研究皆須透過使用者有選擇的紀錄中來產生推薦機制,以計算出要推薦的品項,此方式將無法適應其變遷。另外,過往的推薦機制上的研究較無考慮到參考口碑為人們在做決策的關鍵,因此,在本研究中,推薦使用者之旅遊景點的考量因素為使用者的人際關係程度-強連結與弱連結,進一步比較該使用者與朋友間的親疏程度對最終使用者的旅遊選擇影響力為何。
本研究結果顯示出,強連結朋友比弱連結朋友更具有旅遊資訊影響力於使用者的選擇中,換言之,使用者的旅遊選擇影響力會較易受強連結朋友的影響,原因為透過強關係社會成員間濃厚情感、高度相互回報、高度信任感、較長的互動時間與較高的交換意見意願來達成共識。透過這些共識影響個人意見。因此,強連結朋友才是成員間重要的訊息來源;另外,本研究亦證實使用者在進行選擇旅遊景點的過程中確實會受朋友推薦的影響而產生變化,代表旅遊景點的推薦系統中必須將人際關係程度成為重要的考慮因素之一。
摘要(英) With the trend of Internet and social websites, the users will take the reference in tourism social websites to have the traveling choices. If the travel agencies don’t want to miss the users’ interests and preferences, they should figure out the huge information to provide the precise and customized recommendations for users. Travel agencies need to minimize the information to create the better traveling decisions and valuable recommendations for the users. Thus, it will get the win-win situation to let tourism getting more revenues.
In addition, because the tourism face the changeable tourisms emphasize speed and fast response, the way for the previous recommendation methods are not useful in nowadays and when users want to make the recommendation, they will seek information through “world of mouth”. Therefore, this study uses friendship level- strong and weak ties as recommendation elements and compares the degree for friendships on influencing the travel decisions of end users.
The results of this study show that users will be more strongly affected by the strong ties than weak ties. In other words, because of the strong ties which will have the stronger affection and trustworthy, the longer interactive times with users, the strong ties will become the important message resources for users. By the way, friendship factor needs to be considered within the travel commendation system.
關鍵字(中) ★ 弱連結
★ 社會網絡
★ 口碑
★ 推薦
關鍵字(英) ★ Weak ties
★ Social network
★ Word of mouth
★ Recommendation
論文目次 摘要................................................................i
Abstract............................................................ii
致謝..............................................................iii
目錄...............................................................iv
圖目錄.............................................................vi
表目錄............................................................vii
第一章 緒論.........................................................1
1-1 研究背景與動機..............................................1
1-2 研究目的....................................................4
第二章 文獻探討.....................................................5
2-1 推薦系統....................................................5
2-1-1 內容基礎過濾(Content-based Filtering).................5
2-1-2 合作式基礎過濾(Collaborative Filtering)...............6
2-2 社會網絡....................................................7
2-2-1 口碑行銷(Word of Mouth, WOM)..........................7
2-2-2 連結的力量(Strength of Tie)...........................9
2-2-2 強連結運用...........................................15
2-2-3 弱連結運用...........................................16
2-3 旅遊服務業特性.............................................18
第三章 研究方法....................................................19
3-1 研究假設...................................................19
3-2 研究流程與設計.............................................20
3-2-1 問卷設計.............................................20
3-2-2 研究樣本與資料蒐集...................................21
3-2-3 資料分析方法.........................................24
第四章 資料分析與檢定..............................................27
4-1 樣本特性...................................................27
4-2 假設檢定...................................................29
4-2-1以情感強度構面來看,
強連結較弱連結朋友對旅遊推薦有較大的影響力...............29
4-2-2以親密程度構面來看,
強連結較弱連結朋友對旅遊推薦有較大的影響力...............30
4-2-3以互動時間構面來看,
強連結較弱連結朋友對旅遊推薦有較大的影響力...............30
4-2-4以互惠行動構面來看,
強連結較弱連結朋友對旅遊推薦有較大的影響力...............30
4-3 討論.......................................................32
第五章 結論與建議..................................................33
5-1 研究結論...................................................33
5-2 研究限制...................................................34
5-3 未來研究建議...............................................35
參考文獻...........................................................36
參考文獻 中文參考文獻:
1. 交通部觀光局, 中華民國103年國人旅遊狀況調查報告. 2015: 交通部觀光局旅遊服務中心.
2. 陳順宇, 多變量分析. 4 ed. 2006, 台北: 華泰文化.
3. 丁耀民, H.-G. Chen, and Y.-M. Ting, 人際關係網路對虛擬社群使用意願的影響 : The Influence of Personal Network on the Intention of Using Virtual Community. 資訊管理學報, 2005. 第五卷第一期.
4. 行政院研究發展考核協會, 我國觀光發展政策之研究. 2010-09.
5. 曹勝雄, 觀光行銷學. 2001: 楊智文化.
6. 陳昭宇, 根基於自我組織特徵映射圖為基礎之最佳化演算法之推薦系統 : A SOMO-based Recommendation System. 國立中央大學資訊工程研究, 2005-07.
7. 黃鈁媖, 運用類神經網路預測新進顧客產品喜好之個人化商品推薦技術 : Using Neural Network to Predict New Customer′′s Preference for Better Recommendation. 朝陽科技大學資訊管理學系, 2006-08.
8. 廖儷霙, 觀光行為一般模型之研究. 文化大學出版部, 1986-02.

英文參考文獻:
9. Acilar, A.M. and A. Arslan, A collaborative filtering method based on artificial immune network. Expert Syst Appl, 2009. 36(4): p. 8324-8332.
10. Ajzen, I. and L.B. Driver, Prediciton of leisure participation from behavioral, normative and control beliefs : an application of the theory of planned behavior. Leisure Sciences, 1991. 13: p. 185-204.
11. Arndt, J.A., Role of Product-Related Conversations in the Diffusion of a New Product Journal of Marketing Researching, 1967. 4-3: p. 291-295.
12. Arsal, I., S. Backman, and E. Baldwin, Influence of an online travel community on travel decisions. In P. O′Connor, W. Hopken, & U. Gretzel (Eds.), € Information and communication technologies in tourism 2008 2008: p. 82-93.
13. Bansal, H.S. and P.A. Voyer, Word-of-Mouth Processes within a Services Purchase Decision Context. Joural Of Service Research, 2000.11. 3(2): p. 166-177.
14. Barnes, J.A., Class and Committees in a Norwegian Island Parish. Human Relations, 1954.02: p. 39-58.
15. Benassi, M., A. Greve, and J. Harkola, Looking For a Network Organization: The Case of GESTO. Journal of Market-Focused Management, 1999-10. 4(3): p. 205-229.
16. Bendapudi, N. and L.L. Berry, Customers′ motivations for maintaining relationships with service providers. Journal of Retailing, 1997: p. 15-37.
17. Bian, Y., Bringing strong ties back in: Indirect ties, network bridges, and job searches in China. American sociological review, 1997.06. 62(3): p. 366~385.
18. Bieger, T. and C. Laesser, Information Sources for Travel Decisions: Toward a Source Process Model. Institute for Public Services and Tourism, 2004.
19. Blackwell, R.D., Miniard, P. W., and Engel J. F., Consumer Behavior. 10th ed. 2001: South-Western College.
20. Blumstein, P. and P. Kollock, Personal Relationships. Annual Review of Sociology, 1988-08. 14: p. 467-490.
21. Bobadilla, J., Ortega, F, Hernando, and AGutiérrez, A., Recommender systems survey. Knowledge-Based Systems, 2013-07. 46: p. 109-132.
22. Breese, J.S., D. Heckerman, and C. Kadie, Empirical analysis of predictive algorithms for collaborative filtering. in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, 1998: p. 43-52.
23. Bristor, J.M., Enhanced Explanations of Word of Mouth Communications: The Power of Relationships. Research in Consumer Behavior 1990. 4: p. 51-83.
24. Brown, J.J. and H.R. Peter, Social Ties and Word-of-Mouth Referral Behavior. Journal of Consumer Research, 1987-12. 14(3): p. 350-362.
25. Burt, R.S., Structural Holes : The Social Structure of Competition. Presented at the Annual Meeting of the American Sociological Association, 1992-08: p. 18-34.
26. Capella, L.M. and A.J. Greco, Information sources of elderly for vacation decisions. Annals of Tourism Research, 1987. 14(1): p. 148~151.
27. Chatterjee, P., Online reviews—do consumers use them? In M. C. Gilly & J. Myers-Levy (Eds.), ACR2001 Proceedings, 2001: p. 129-134.
28. Chen, L.S., Hsu, F. H, Chen, M. C and Hsu, Y. C, Developing recommender systems with the consideration of product profitability for sellers. Inform Sci 2008. 178(4): p. 1032-1048.
29. Cheng, J., Romero, D.M. , Meeder, B. and Kleinberg, J., Predicting Reciprocity in Social Networks. 2011 IEEE Third Int′l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int′l Conference on Social Computing, IEEE, 2011: p. 49-56.
30. Cheung, C.M.K. and M.K.O. Lee, What drives consumers to spread electronic word of mouth in online consumer-opinion platforms. Decision Support Systems, 2012: p. 218-225.
31. Chevlier, J.A. and D. Mayzlin, The effect of word-of-mouth on sales: online book reviews. Journal of Marketing Research, 2006. 43(3): p. 345-354.
32. Cui, G., H.-K. Lui, and X. Guo, The effect of online consumer reviews on new product sales. International Journal of Electronic Commerce, 2012. 17(1): p. 39-58.
33. Dae-Young, K., Y. Lehto Xinran, and A.M. Morrisonc, Gender differences in online travel information search: Implications for marketing communications on the internet. Tourism Management, 2007-04. 28: p. 423-433.
34. Dellarocas, C., The digitization of word-of-mouth: Promise and challenges of online feedback mechanisms. . Management Science, 2003. 49(10): p. 1407-1424.
35. Dellarocas, C., X.M. Zhang, and N.F. Awad, Exploring the value of online product reviews in forecasting sales: the case of motion pictures. Journal of Interactive Marketing,, 2007. 21(4): p. 23-45.
36. Deshpande, M. and G. Karypis, Item-based Top-n recommendation algorithms. ACM Trans. Information System, 2004. 22: p. 143-177.
37. Dickinger, A., J. Mazanec, and Y. Springer-Verlag., Consumers’ preferred criteria for hotel online booking. In P. Peter O’Connor, W. Wolfram Höpken, U. Ulrike Gretzel (Eds.), Information and communication technologies in tourism 2008, 2008(244-254).
38. East, R. and M.D. Uncles, In praise of retrospective surveys. Journal of Marketing Management, 2008. 24: p. 929-944.
39. Eric, G. and K. Karrie, Predicting Tie Strength With Social Media In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.ACM, 2009-04: p. 211-220.
40. Bonnie H. Erickson, T. A. Nosanchuk, Liviana Mostacci and Christina Ford Dalrymple, The Flow of Crisis Information as a Probe of Work Relations. The Canadian Journal of Sociology 1978. 3(1): p. 71-87.
41. Filieri, R. and F. McLeay, E-WOM and accommodation: an analysis of the factors that influence travelers′ adoption of information from online reviews. Journal of Travel Research, 2014. 53(1): p. 44-57.
42. Fodness, D. and B. Murray, A Typology of Tourist Information Search Strategies. Joural Of Travel Research, 1998-11. 37(2): p. 108-119.
43. Forum, W.E., Travel and Tourism Competitiveness Report. 2015.
44. Friedkin, N., A test of structural features of granovetter′s strength of weak ties theory. Social Networks, 1980. 2(4): p. 411-422.
45. Fritz, H., The Psychology of Interpersonal Relations. Psychology Press, 1958: p. 1-322.
46. Gelb, B. and J. M, Word-of-Mouth Communication : Causes and Consequences Journal of Health Care Marketing, 1995. 15-3: p. 54-58.
47. Gelba, B.D. and S. Sundaramb, Adapting to“word of mouse”. Business Horizons, 2002-07. 45: p. 21-25.
48. Geroge, A.M., The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information. Psychological review, 1956. 63(2): p. 81.
49. Ghose, A. and I. P., Towards an Understanding of the Impact of Customer Sentiment on Product Sales and Review Quality. In Proceedings of the workshop on information technology and systems, 2006: p. 1-6.
50. Godes, D. and D. Mayzlin, Using online conversations to study word of mouth communication. Marketing Science, 2004. 23(4): p. 545-560.
51. Goldenberg, J., B. Libai, and E. Muller, Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth. Marketing Letters 2001.08. 12(3): p. 211-223.
52. Goldsmith, R.E. and D. Horowitz, Measuring motivations for online opinion seeking. Journal of Interactive Advertising, 2006. 6(2): p. 1-16.
53. Govers, R. and F. Go, Projected destination image online: Website content analysis of pictures and text. Information Technology & Tourism, 2005. 7: p. 73-89.
54. Granovetter, M., The Strength of Weak Ties. The American Journal of Sociology, 1973-05. 78(6): p. 1360-1380.
55. Granovetter, M., Getting a job : A study of contacts and careers. Harvard University Press, 1974.
56. Granovetter, M., The Strength of Weak Ties: A Network Theory Revisited. Sociological Theory, 1982. 1: p. 201-233.
57. Granovetter, M., The Strength of Weak Ties: A Network Theory Revisited. Sociological Theory, 1982. 1: p. 113~117.
58. Gretzel, U. and K.H. Yoo, Use and impact of online travel reviews. In P. O′Connor, W. HÃpken & U. Gretzel (Eds.). Information and Communication Technologies in Tourism, 2008: p. 35-46.
59. H Kautz, B Selman, and M. Shah, Referral web: combining social networks and collaborative filtering. Communications of the ACM, 1997. 40(3): p. 63-65.
60. Hannes, W. and R. Francesco, E-COMMERCE AND TOURISM. COMMUNICATIONS OF THE ACM, 2004-12. 47(12).
61. Hansen, M.T., The search-transfer problem: the role of weak ties in sharing knowledge across organisation subunits Administrative Science Quarterly, 1999. 44(1): p. 82-111.
62. Herlocker, J.L.K., Joseph. A, L.G. Terveen, and J.T. Riedl, Evaluating collaborative filtering recommender systems. ACM Transaction on Informaiton Systems, 2004. 22(1): p. 5-53.
63. Herr, P.M., F.R. Kardes, and J. Kim, Effects of Word-of-Mouth and Product-Attribute Information on Persuasion: An Accessibility-Diagnosticity Perspective. Journal of Consumer Research, 1991. 17(4): p. 454-462.
64. Hu, R. and P.P. Pearl, Potential Acceptance Issues of Personality-based Recommender Systems. In proceedings of the 3rd ACM Conference on Recommender Systems . ACM, 2009-10: p. 221-224.
65. Inversini, A., L. Cantoni, and D. Buhalis, Destinations′ information competition and web reputation. Information Technology & Tourism, 2009. 11(3): p. 221-234.
66. Ithiel, P., Comment on Mark Granovetter′s ′The Strength of Weak Ties: A Network Theory Revisited. Presented at the Annual Meeting of the International Communications Association, Acapulco, 1980-05.
67. Jöreskog, K.G. and D. Sörbom, Lisrel 8: Structural Equation Modeling with the Simplis Command Language. Scientific Software International, 1993.
68. Mehrdad Jalali, Norwati Mustapha, Nasir Sulaiman Md and Ali Mamat, WebPUM: a web-based recommendation system to predict user future movements. Expert Systems with Applications, 2010-02. 37(9): p. 6201-6212.
69. Jenkins, R.L., Family vacation decision-making. Journal of Travel Research, 1978. 16(4): p. 2~7.
70. Jin, R., J.Y. Chai, and L. Si, An automatic weighting scheme for collaborative filtering. in Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR. ACM, 2004: p. 337-344.
71. Johnson, B.J. and R.P. H, Social Ties and Word-of-Mouth Referral Behavior. Joural Of Consumer Research, 1987-12: p. 350-362.
72. Konstan, A. Joseph, and RiedlJohn, Recommender systems: from algorithms to user experience. User Model User-Adapt Interact, 2012. 22: p. 101-123.
73. Kotler, P., Reconceptualizing marketing: An interview with Philip Kotler. European Management Journal, 1994-12. 12: p. 353-361.
74. Krackhardt, D. and R.N. Stern, Informal Networks and Organizational Crises: An Experimental Simulation. Social Psychology Quarterly, 1988.06. 51(2): p. 123-140.
75. Kraut, R., Patterson, M., Lundmark, V., Kiesler, S., Mukhopadhyay T and Scherlis, W, Internet paradox: A social technology that reducessocial involvement and psychological well-being? American Psychologist, 1998. 53(9): p. 1017-1031.
76. Levin, D.Z. and R. Cross, The Strength of Weak Ties You Can Trust:The Mediating Role of Trust in Effective Knowledge Transfer. Management Science, 2004. 50(11): p. 1477-1490.
77. Lia, D., Lvb, Qin, Xiea, Xing , Shangb, Li, Xiaa, Huanhuan, Lua, Tun and Gua, Ning, Interest-based real-time content recommendation in online social communities. Knowledge-Based Systems, 2012. 28: p. 1-12.
78. Likert, R.A., Technique For The Measurement of Attitudes. Archives of Psychology, 1932. 140.
79. Lin, N., J.C. Vaughn, and W.M. Ensel, Social Resources and Occupational Status Attainment. Social Forces, 1981-06. 59(4): p. 1163-1181.
80. Liu, W. and R.W. Duff, The strength in weak ties. Public Opinion Quarterly, 1972. 36: p. 361-366.
81. Luarn, P. and Y.-P. Chiu, Key variables to predict tie strength on social network sites. Internet Research, 2015. 25(2): p. 218-238.
82. Massey, D.S., L. Goldring, and J. Durand, Continuities in transnational migration: an analysis of nineteen Mexican communities. American Journal of Sociology 99, 1994. 99: p. 1492-1533.
83. Mathews, K.M., White, M. C, Long, R. G, Soper, B and Von Bergen, C. W, Association of indicators and predictors of tie strength. Psychological Reports, 1998-12. 83: p. p. 1459-1469.
84. Mitchell, J.C., The components of strong ties among homeless women. Social Networks, 1987-03. 9(1): p. 37-47.
85. Murray, S., J. Rankin, and D. Magill, Strong Ties and Job Information. Sociology of Work and Occupations, 1981-02. 8(1): p. 119-136.
86. N, F., A test of structural features of Granovetter′s strength of weak ties theory. Social Networks, 1980: p. 411-442.
87. Palmore, J.A., The Chicago snowball: a study of the flow and diffusion of family planning information. Sociological Contributions to Family Planning Research, 1967: p. 272-363.
88. Pan, C. and W. Li, Research paper recommendation with topic analysis. In Computer Design and Applications IEEE, 2010-06. 4: p. 264-268.
89. Park, D.H., Kim, H. K, Choi, I. Y and Kim, J. K, A literature review and classification of recommender systems research. Expert System Application, 2012. 39(11).
90. Pearl, P., C.L. Chen, and H. Rong, A user-centric evaluation framework for recommender systems. Proceedings of the fifth ACM conference on Recommender systems. ACM, 2011-10: p. 157-164.
91. Perlman, D. and B. Fehr, The Development of Intimate Relationship. 1987: SAGE Publications.
92. Petróczi, A., T. Nepusz, and F. Bazsó, Measuring tie-strength in virtual social networks. International Network for Social Network Analysis, 2006. 27(2): p. 49-57.
93. Dale, F. Duhan, Scott, D. Johnson, James, B. Wilcox and Gilbert, D. Harrell, Influences on consumer use of word-of-mouth recommendation source. Joural Of the Academy of Marketing Science, 1997. 25(4): p. 283-295.
94. Rennie, J.D.M. and N. Srebro, Fast maximum margin matrix factorization for collaborative prediction. in Proceedings of the 22nd International Conference on Machine Learning, ICML, 2005: p. 713-719.
95. Research, F., How Branded Content Will Unlock The Key to Consumer Trust. 2013-03.
96. Riley, P.J., Road culture of international long-term budget travellers. Annals of Tourism Research, 1988. 15(3): p. 313-328.
97. Rindfleisch, A. and C. Moorman, The Acquisition and Utilization of Information in New Product Alliances: A Strength-of-Ties Perspective. Journal of Marketing 2001-04. 65(2): p. 1-18.
98. Robin, B., Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction, 2002-11. 12(4): p. 331-370.
99. S, M., Influentials advising their friends to sell lots of high tech gadgetry. Wall Street Journal, 1983.
100. Salakhutdinov, R. and A. Mnih, Probabilistic matrix factorization. in: J. Platt, D. Koller, Y. Singer, S. Roweis (Eds.), Advances in Neural Information Processing Systems, 2008. 20: p. 1257-1264.
101. Sarwar, B., Karypis, G, Konstan, J and Riedl, J, Item-based collaborative filtering recommendation algorithms. in Proceedings of the 10th International Conference on World Wide Web, ACM, 2001: p. 285-295.
102. Schluchter, W., Ferdinand Tönnies: comunidad y sociedad. Signos Filosoficos 2011-12. 13(26): p. 43-62.
103. Senecal, S. and J. Nantel, The influence of online product recommendations on consumers′ online choices. Journal of Retailing, 2004. 80(2): p. 159-169.
104. Seth, A., A Social Network Based Approach to Personalized Recommendation of Participatory Media Content. School of Computer Science, 2008-05.
105. Park Seung-taek, Pennock David, Madani Omid, Good Nathan and Decoste Dennis, Naïve filterbots for robust cold-start recommendations. In proceedings of the 12th ACM SIGKDD internaitonal conference on Knowledge Discovery and Data Mining. ACM, 2006: p. 699-705.
106. Shardanand, U. and P. Maes, Social Information Filtering: Algorithms for Automating "Word of Mouth". in Proceesings of the Conference on Human Factors in Computing Systems(CHI95), 1995-02: p. 210-217.
107. Shyong, K., Tony Lam, Dan Frankowski and John Riedl, Do You Trust Your Recommendations? An Exploration of Security and Privacy Issues in Recommender Systems. In Proceedings of the 2006 International Conference on Emerging Trends in Information and Communication Security (ETRICS), 2006. 3995: p. p. 14-29.
108. Smith, D., S. Menon, and K. Sivakumar, Online peer and editorial recommendations, trust, and choice in virtual markets. Interactive Marketing, 2005. 19(3): p. 15-37.
109. Souvik, D., G. Niloy, and M. Pabitra, Feature Weighting in Content Based Recommendation System Using Social Network Analysis. In proceedings of the 17th internaitonal conference on World Wide Web(pp. 1041-1042). ACM, 2008-04.
110. Sparks, B.A. and V. Browning, The impact of online reviews on hotel booking intentions and perception of trust. Tourism Management, 2011. 32(6): p. 1310-1323.
111. Stats/Strategy/Gadgets, D.M. By the Numbers : 200+ Amazing Facebook User Statistics. 2015-10; Available from: http://expandedramblings.com/index.php/by-the-numbers-17-amazing-facebook-stats/2/.
112. Stelzner, M., 2015 Social Media Marketing Industry Report-How Marketers Are Using Social Media to Grow Their Business. Socail Media Examiner. 2015-5.
113. Su, X. and T.M. Khoshgoftaar, A survey of collaborative filtering techniques. Adv Artif Intell, 2009.
114. T, H., Latent semantic models for collaborative filtering. ACM Trans. Information Systwm, 2004: p. 89-115.
115. Thurau, H., Thorsten, Kevin P. Gwinner, Gianfranco Walsh and Dwayne Gremler, Electronic Word-of-Mouth Via Consumer-Opinion Platforms: What Motivates Consumers to Articulate Themselves on the Internet. Journal of Interactive Marketing, , 2004. 18(1): p. 38-52.
116. Tucker, T., Online word of mouth : Characteristics of Yelp.com reviews. The Elon Joural of Undergraduate Research in Communications, 2011. 2(1): p. 37-42.
117. V, M.P. and C.K. E., Measuring Tie Strength. Social Forces, 1984-12. 63(2): p. 482-501.
118. Vermeulen, I.E. and D. Seegers, Tried and tested: The impact of online hotel reviews on consumer consideration. Tourism Management, 2009. 30(1): p. 123-127.
119. W, L.S., G.R. E, and P. Bing, Electronic word-of-mouth in hospitality and tourism management Tourism Management, 2008.
120. Walker, M.E., S. Wsdderman, and B. Wellman, Statistical Models for Social Support Networks. Sociological Methods Research, 1993-08. 22(1): p. 71-98.
121. Bonnie H. Erickson, T. A. Nosanchuk, Liviana Mostacci and Christina Ford Dalrymple, Electronic Word-Of-Mouth Via Consumer-Opinion Platforms : What Motivates Consumers To Articulate Themselves On The Internet ? Journal Of Interactive Marketing, 2004. 18.
122. Wellman, B., Studying personal communities. Social structure and network analysis 1982: p. 61-80.
123. Wellman, B. and S. Wortley, Different Strokes from Different Folks: Community Ties and Social Support. American Journal of Sociology, 1990-11. 96(3): p. 558-588.
124. Wilson, T.D., Weak Ties, Strong Ties: Network Principles in Mexican Migration. Human Organization, 1998. 57(4): p. 394-403.
125. Yap, K.B., B. Soetarto, and J.C. Sweeney, The relationship between electronic word-of-mouth motivations and message characteristics: The sender’s perspective. Australasian Marketing Journal 2013-02. 21: p. 66-74.
126. Ye, Q., Law, R. and B. Gu, The impact of online user reviews on hotel room sales. International Journal of Hospitality Management, 2009a. 28(1): p. 180-182.
127. Ye, Q., Zhang, Z and R. Law, Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Systems with Applications, 2009b. 36(3): p. 6527-6535.
128. Zhu, F. and X. Zhang, Impact of online consumer reviews on sales: the moderating role of product and consumer characteristics. Journal of Marketing, 2010. 74(2): p. 133-148.
指導教授 許秉瑜(Ping-Yu Hsu) 審核日期 2016-6-13
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明