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題名 線上訂房網站優惠方案偏好選擇之研究—聯合分析法之應用
Study on Preference for Online Travel Agency Discount Program-Application of Conjoint Analysis作者 劉文婷
Law, Boon-Theng貢獻者 樓永堅
劉文婷
Law, Boon-Theng關鍵詞 聯合分析法
優惠方案
線上訂房平台
Conjoint analysis
Discount program
Online travel agency日期 2018 上傳時間 10-Jul-2018 15:45:52 (UTC+8) 摘要 網路訂房已成為消費者選擇旅遊及商務住宿的趨勢，訂房平台經營者若能瞭解消費者對訂房促銷方案的偏好及行為模式，將能有助於提升網路訂房平台的行銷價值及經營品質。本研究目的是針對線上訂房平台優惠方案進行調查研究，探討消費者對各方案屬性之相對重要性，以及各變數區隔間消費者對於優惠方案組成要素偏好排序之差異，以提供線上訂房網站未來在設計行銷策略上的建議。本研究採用聯合分析法，透過直交排列法（Orthogonal Arrays）來建立受測體，正式問卷採整體輪廓法呈現。本共發出320份問卷，實際回收307份，其中無效問卷為23份，因此有效問卷為284份，回收率為88.75%。經由統計分析，主要研究發現如下：一、 整體受測者屬性偏好依序為： 「折扣方式」、「付款方式」、「附加待遇買貴退差價」、「回饋金方式」、「兌換方式」、「積點方式」。二、 以性別、年齡、行業、教育程度、月收入或零用金、國籍、使用線上訂房網站的頻率、網路預訂每晚每個房間的平均消費金額、較常使用網路訂房的目的等作為事前區隔變數，發現每一區隔變數消費者皆最重視「折扣方式」。另外，除了男性消費者、馬來西亞消費者、訂房頻率三個月約一次消費者之外，其他區隔變數消費者都將「積點方式」視為最不重視的屬性。三、 以受測者在每個屬性水準上的成分效用值進行事後區隔分群，將整體受測者分為兩群。集群一最重視「付款方式」是否多元化以及是否有買貴退差價之附加待遇，以女性，年齡20-25歲、大學生，零用錢10000元以下，平均每晚每房消費金額約1501元到2500元，訂房頻率一年約一次的族群佔多數。集群二最重視「折扣方式」，在意產品的價格，屬於價格敏感度極高的消費者客群，以女性，年齡21-30歲，大學生，零用金10000元以下，平均每晚每房消費金額約1501元到2500元，訂房頻率一年至少一次以上佔多數。最後，本研究根據研究結果進行討論及提出建議，希望能作為網路訂房平台經營者的重要行銷管理參考。關鍵字：聯合分析法、優惠方案、線上訂房平台
Online booking has become a trend for consumers to choose for their travel accommodation. If the operators of online travel agency are able to understand the consumer preferences and behavioural patterns on accommodation booking promotions, it will help to increase the marketing value and operating quality of the online booking platform. The purpose of this research is to investigate the third-party online booking platform discount programs. On top of that, this study also going to explore the relative importance of consumers` attributes for each program, and the differences in the preferences of consumers in different variable compartments.In this study, a conjoint analysis method was applied and established a test subject by using Orthogonal Arrays. The formal questionnaire was presented by using the Full-Profile Approach. A total of 300 questionnaires were sent out in this study, and 291 responses were received, which 7 were invalid questionnaires. Therefore, the number of valid questionnaires was 284, and the effective response rate was 94.66%. The main research findings through statistical analysis were listed below:(1) The overall subject’s attribute preferences are: "Discount Method", "Payment Method", "Price Match Method","Gold Refund Method","Redeem Method" and "Point Collect Method".(2) The pre-segmented variables can be separated into gender, age, industry, education level, monthly income, nationality, the frequency of using third-party booking sites, the average of spending amount for one room and the purpose of using third-party booking sites. It was found that consumers in each segment were most concerned about the "Discount method.". In addition, apart from male consumers, Malaysian consumers, and booking frequencies at least one bookings per three months, consumers in other segments consider the “Point Collection Method” as the least preferences attribute.(3) Grouping the subjects into two groups based on the part-worth utility at each attribute level. The most important preferences attribute of the first cluster is "Payment Method" and the second cluster is "Discount Method". The first cluster contains mostly women who age between 20 to 25, college students with monthly income less than NTD 10,000, the average amount spent for one room was between NTD1500 to NTD2500 and booking frequency was about once a year. The second cluster contains mostly women, ages who age between 21 to 30, college students with monthly income less than NTD 10,000, the average amount spent for one room was between NTD1500 to NTD2500, booking frequency at least once a year or more. Finally, this study will make recommendations based on the research results, hoping to serve as an important marketing strategy reference for the online travel agency.Keyword: Conjoint Analysis, Discount Program, Online travel agency參考文獻 中文部分1. 林震岩.(2012).多變量分析:SPSS 的操作與應用(p.695)，台北:智勝文化事業有限公司.2. 劉景賢.(2001).聯合分析法在廠商決策之應用─以海外進入模式的選擇為例.國立臺灣大學碩士論文.3. 張勤昌.(2003).聯合分析應用於金控後銀行顧客知覺與偏好之研究.國立成功大學統計學系碩士班碩士論文.4. 黃俊英.(2000).多變量分析 Multivariate analysis an introduction eng (第七版 ed.). 台北市: 中國經濟企業研究所.5. 吳兆益. (1982).「聯合分析法在果汁消費者知覺與偏好之應用研究」.國立政治大學企業管理研究所碩士論文.6. 王昭傑.(2000).消費者對水果醋屬性偏好之研究─聯合分析法之應用.屏東科技大學農企業管理研究所碩士論文.7. 吳宗軒.(2008). 聯合分析法在不同市場區隔下之產品屬性組合—以運動鞋產品為例.國立成功大學國際企業研究所碩博士論文.8. 曾信融.(2012).以聯合分析法探討量販商自有品牌產品屬性之最佳化組合.國立成功大學企業管理學系學位論文.9. 楊佳和.(2006).旅遊產品線上購買動機與購買涉入之研究.靜宜大學觀光事業研究所碩士論文.10. 郭蕙心.(2013).網站信任與旅館線上和離線品牌形象對顧客透過第三方訂位網站線上訂房意願影響之研究.國立高雄餐旅大學餐旅管理研究所碩士論文.11. 陳勁甫，呂明純.(2004).網路線上訂房顧客滿意度關係模式之研究.觀光研究學報，10(3)，89-107.12. 陳彥芳.(2004).價格促銷、認知價值與商店形象對購買意願影響之研究-以 大台北地區 3C 連鎖家電為例.真理大學管理科學研究所碩士論文.13. 劉文良.(2007).《網際網路行銷》(二版)，台北碁峯資訊.14. 榮泰生.(2007).《網際行銷:電子商務實務》(三版)，台北:五南圖書.英文部分1. Horng and Tsai, 2010 J.S. Horng, C.T. TsaiGovernment websites for promoting East Asian culinary tourism: a cross-national analysis.Tourism Management, 31 (1) (2010), pp. 74-85.2. Standing and Vasudavan, 2000 .C. Standing, T. VasudavanThe marketing of regional tourism via the internet: lessons from Australian and South.Marketing Intelligence & Planning, 18 (1) (2000), pp. 45-48.3. Stepchenkovaetal.,2010S. Stepchenkova, L. Tang, S.C. Jang, A.P. Kirilenko, A.M. MorrisonBenchmarking CVB website performance: spatial and structural patterns.Tourism Management, 31 (5) (2010), pp. 611-620.4. Heung, V.(2003). Internet usage by international travellers: reasons and barriers. International Journal of Contemporary Hospitality Management. 15(7), 370-378.5. Vrana, V., & Zafiropoulos, C. (2004). Tourism agents` attitudes on internet adoption: an analysis from Greece. International Journal of Contemporary Hospitality Management. 18 (7), 601-608.6. Cole, J. I., Suman, M., Schramm, P., Lunn, R., & Aquino, J. S. (2003). The UCLA Internet report surveying the digital future year three.7. Madden, M., & Rainie, L. (2003, December 23). America’s online pursuits: The changing picture of who’s online and what they do.8. Horner, Susan., & Swarbrooke, John. (2008). International cases in tourism management. 4th edition. Butterworth-Heinemann Publications. Elsevier.9. Vermeulen, I., & Seegers, D. (2009). Tried and Tested: the Impact of Online Hotel Reviews on Consumer Consideration. Elsevier Tourism Management .10. Lin, C., Lee, S., & Horng, D. (2011). The Effects of Online Reviews on Purchasing Intention: the Moderating Role of Need for Cognition . Social Behaviour and Personality (39), 69-82.11. Park, D., Lee, J., & Han, I. (2007). The effects of on-line consumer reviews on consumer purchasing intention: the moderating role of involvement. International Journal of Electronic Commerce , 4 (11), 120-138.12. Morosan, C., & Jeong, M. (2008). Users’ perceptions of two types of hotel reservation web sites. International Journal of Hospitality Management, 27(2), 284-292. 13. Werthner, H., & Ricci, F. (2004). E-Commerce and Tourism. Communications of the ACM, 47(12), 101-105.14. Marcussen, C. H. (2008). Trends in European internet distribution - of travel and tourism services. Centre for Regional and Tourism Research. Denamark.15. Inversini, A., & Buhalis, D. (2009). Information convergence in the long tail: the case of tourism destination information. In W. Hopken, U. Gretzel & R. Law (Eds.), Information and Communication Technologies in Tourism (pp. 381-392). Vienna: Springer.16. Chandon, Pierre, Brian Wansink, and Gilles Laurent .(2000). "A Benefit Congruency Framework of Sales Promotion Effectiveness," Journal of marketing, 64 (October), 65-81.17. American Marketing Association, (1960).Marketing Definition: A Glossary of Marketing Terms, American Marketing Association, 15.18. Dommermuth, W. P. (1989). Promotion: Analysis, Creativity, Strategy (2nd ed.). Boston, MA: PWS-Kent Publishing Company.19. Shimp, T. (1997). Advertising, Promotion, and Supplemental Aspects Integrated Marketing Communications (4th ed.). Chicago, IL: Dryden Press.20. Davidson, J. H. (1987). Offensive Marketing: How to Make Your Competitors Follow (2nd ed.). England: Gower Publishing Company Limited.21. Kotler, P. (2000).Marketing Management: The Millennium Edition, Prentice Hall. 22. Campbell, L. & Diamond, W. D. (1990). Framing and sales promotion: The characteristics of a “good Deal”. Journal of Consumer Marketing, 7(4), 25-31.23. Davis, S., J. J. Inman, and L. McAlister .(1992). Promotion Has a Negative Effect on Brand Evaluations- Or Does It? Additional Disconfirming Evidence. Journal of Marking Research, 29, 143-48.24. Green, P. E. (1974). “On the design of choice experiments involving multifactor alternatives,” Journal of Consumer Research, 1(2), 61-68.25. Green, P. E., & Srinivasan, V. (1978). Conjoint analysis in consumer research: issues and outlook. Journal of consumer research, 103-123.26. Roger J.Best.(2009).Market-Based Management: Strategies for Growing Customer Value and Profitability(5th ed.), Pearson International Edition,pp.161-168.27. Green, P. E., & Wind, Y. (1973). Multiattribute decisions in marketing : a measurement approach: Dryden Press, 39-4628. Segal, M. N. (1982),“Reliability of conjoint analysis: Contrasting data collection procedures”, Journal of Marketing Research, 19(1), pp. 139-143.29. Hoffman, D. L., Novak, T. P., & Chatteriee, P. (1996). Commercial scenarios for the web: opportunities and challenges. Journal of Computer Mediated, 1(3), 1-21.30. Carroll, B., & Siguaw, J.(2003). The Evolution of Electronic Distribution: Effects on Hotels & Intermediaries. Cornell Hotel and Restaurant Administration Quarterly, 44(4),38-50.網路部分1. 觀光局行政資訊系統，Retrieved January 27, 2018 from http://admin.taiwan.net.tw/public/public.aspx?no=3152. 創世紀雙週刊，旅遊篇與飯店訂房類別網站使用概況. Retrieved January 27, 2018 from https://www.scribd.com/document/361711279/InsightXplorer-Biweekly-Report-20171016#fullscreen&from_embed3. 旅遊研究所，五張圖表看懂台灣旅遊產業. Retrieved January 27, 2018 from https://buzzorange.com/techorange/2017/08/22/about-taiwan-travel/4. StatisticBrain. (2017). Internet Travel Hotel Booking Statistics. Retrieved January 27, 2018 from https://www.statisticbrain.com/internet-travel-hotel-booking-statistics/ 描述 碩士
國立政治大學
企業管理研究所(MBA學位學程)
105363118資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105363118 資料類型 thesis dc.contributor.advisor 樓永堅 zh_TW dc.contributor.author (Authors) 劉文婷 zh_TW dc.contributor.author (Authors) Law, Boon-Theng en_US dc.creator (作者) 劉文婷 zh_TW dc.creator (作者) Law, Boon-Theng en_US dc.date (日期) 2018 en_US dc.date.accessioned 10-Jul-2018 15:45:52 (UTC+8) - dc.date.available 10-Jul-2018 15:45:52 (UTC+8) - dc.date.issued (上傳時間) 10-Jul-2018 15:45:52 (UTC+8) - dc.identifier (Other Identifiers) G0105363118 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/118566 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 企業管理研究所(MBA學位學程) zh_TW dc.description (描述) 105363118 zh_TW dc.description.abstract (摘要) 網路訂房已成為消費者選擇旅遊及商務住宿的趨勢，訂房平台經營者若能瞭解消費者對訂房促銷方案的偏好及行為模式，將能有助於提升網路訂房平台的行銷價值及經營品質。本研究目的是針對線上訂房平台優惠方案進行調查研究，探討消費者對各方案屬性之相對重要性，以及各變數區隔間消費者對於優惠方案組成要素偏好排序之差異，以提供線上訂房網站未來在設計行銷策略上的建議。本研究採用聯合分析法，透過直交排列法（Orthogonal Arrays）來建立受測體，正式問卷採整體輪廓法呈現。本共發出320份問卷，實際回收307份，其中無效問卷為23份，因此有效問卷為284份，回收率為88.75%。經由統計分析，主要研究發現如下：一、 整體受測者屬性偏好依序為： 「折扣方式」、「付款方式」、「附加待遇買貴退差價」、「回饋金方式」、「兌換方式」、「積點方式」。二、 以性別、年齡、行業、教育程度、月收入或零用金、國籍、使用線上訂房網站的頻率、網路預訂每晚每個房間的平均消費金額、較常使用網路訂房的目的等作為事前區隔變數，發現每一區隔變數消費者皆最重視「折扣方式」。另外，除了男性消費者、馬來西亞消費者、訂房頻率三個月約一次消費者之外，其他區隔變數消費者都將「積點方式」視為最不重視的屬性。三、 以受測者在每個屬性水準上的成分效用值進行事後區隔分群，將整體受測者分為兩群。集群一最重視「付款方式」是否多元化以及是否有買貴退差價之附加待遇，以女性，年齡20-25歲、大學生，零用錢10000元以下，平均每晚每房消費金額約1501元到2500元，訂房頻率一年約一次的族群佔多數。集群二最重視「折扣方式」，在意產品的價格，屬於價格敏感度極高的消費者客群，以女性，年齡21-30歲，大學生，零用金10000元以下，平均每晚每房消費金額約1501元到2500元，訂房頻率一年至少一次以上佔多數。最後，本研究根據研究結果進行討論及提出建議，希望能作為網路訂房平台經營者的重要行銷管理參考。關鍵字：聯合分析法、優惠方案、線上訂房平台 zh_TW dc.description.abstract (摘要) Online booking has become a trend for consumers to choose for their travel accommodation. If the operators of online travel agency are able to understand the consumer preferences and behavioural patterns on accommodation booking promotions, it will help to increase the marketing value and operating quality of the online booking platform. The purpose of this research is to investigate the third-party online booking platform discount programs. On top of that, this study also going to explore the relative importance of consumers` attributes for each program, and the differences in the preferences of consumers in different variable compartments.In this study, a conjoint analysis method was applied and established a test subject by using Orthogonal Arrays. The formal questionnaire was presented by using the Full-Profile Approach. A total of 300 questionnaires were sent out in this study, and 291 responses were received, which 7 were invalid questionnaires. Therefore, the number of valid questionnaires was 284, and the effective response rate was 94.66%. The main research findings through statistical analysis were listed below:(1) The overall subject’s attribute preferences are: "Discount Method", "Payment Method", "Price Match Method","Gold Refund Method","Redeem Method" and "Point Collect Method".(2) The pre-segmented variables can be separated into gender, age, industry, education level, monthly income, nationality, the frequency of using third-party booking sites, the average of spending amount for one room and the purpose of using third-party booking sites. It was found that consumers in each segment were most concerned about the "Discount method.". In addition, apart from male consumers, Malaysian consumers, and booking frequencies at least one bookings per three months, consumers in other segments consider the “Point Collection Method” as the least preferences attribute.(3) Grouping the subjects into two groups based on the part-worth utility at each attribute level. The most important preferences attribute of the first cluster is "Payment Method" and the second cluster is "Discount Method". The first cluster contains mostly women who age between 20 to 25, college students with monthly income less than NTD 10,000, the average amount spent for one room was between NTD1500 to NTD2500 and booking frequency was about once a year. The second cluster contains mostly women, ages who age between 21 to 30, college students with monthly income less than NTD 10,000, the average amount spent for one room was between NTD1500 to NTD2500, booking frequency at least once a year or more. Finally, this study will make recommendations based on the research results, hoping to serve as an important marketing strategy reference for the online travel agency.Keyword: Conjoint Analysis, Discount Program, Online travel agency en_US dc.description.tableofcontents 第一章 緒論 5一、研究背景與動機 10二、研究目的 11三、研究對象 11四、研究流程 12五、章節架構 13第二章 文獻探討 14一、線上訂房網站 14二、優惠方案 16三、 聯合分析法 18第三章 研究方法 25一、研究架構 25二、屬性與水準確認 26三、聯合分析法 28第四章 資料分析與研究結果 33一、樣本背景分析 33二、整體樣本對產品屬性重視程度之聯合分析 35三、不同區隔變數對產品屬性重視程度之聯合分析-事前區隔 45四、不同類型消費者對產品屬性重視程度之聯合分析-事後區隔 102第五章 研究結論與建議 115一、研究結論 115二、研究限制 121三、後續研究建議 122參考文獻 123中文部分 123英文部分 124網路部分 126附錄一 研究問卷 127 zh_TW dc.format.extent 4889520 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105363118 en_US dc.subject (關鍵詞) 聯合分析法 zh_TW dc.subject (關鍵詞) 優惠方案 zh_TW dc.subject (關鍵詞) 線上訂房平台 zh_TW dc.subject (關鍵詞) Conjoint analysis en_US dc.subject (關鍵詞) Discount program en_US dc.subject (關鍵詞) Online travel agency en_US dc.title (題名) 線上訂房網站優惠方案偏好選擇之研究—聯合分析法之應用 zh_TW dc.title (題名) Study on Preference for Online Travel Agency Discount Program-Application of Conjoint Analysis en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 中文部分1. 林震岩.(2012).多變量分析:SPSS 的操作與應用(p.695)，台北:智勝文化事業有限公司.2. 劉景賢.(2001).聯合分析法在廠商決策之應用─以海外進入模式的選擇為例.國立臺灣大學碩士論文.3. 張勤昌.(2003).聯合分析應用於金控後銀行顧客知覺與偏好之研究.國立成功大學統計學系碩士班碩士論文.4. 黃俊英.(2000).多變量分析 Multivariate analysis an introduction eng (第七版 ed.). 台北市: 中國經濟企業研究所.5. 吳兆益. 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