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dc.contributor.authorYu-Shan Linen_US
dc.identifier.citation中文 卓瑩鎗. (2017). 萬物聯網行動生活-行動支付在台灣的現行發展. 新社會政策, (54), 27-31. 陳靜怡. (2005). 何時買? 買多少? 整合潛藏消耗率之層級貝氏預測模型. 行銷科學學報, 1(1), 5-21. 張昭容. (2014). 行動支付使用者採用意圖之研究. 臺灣大學資訊管理學研究所學位論文, 1-53. 楊雅雯. (2018). 消費者使用行動支付因素之研究. 淡江大學管理科學學系企業經營碩士在職專班學位論文, 1-83 金融研究發展基金管理委員會 (2016),各國行動支付發展趨勢及相關個案研究。取自 金融研究發展基金管理委員會 (2017),金融科技發展策略白皮書。取自 國家通訊傳播委員會 (2018),106 年通訊市場調查結果報告。取自 MIC產業情報研究所 (2015),環境條件改善將帶動行動支付成長。取自 MIC產業情報研究所 (2017),【行動支付消費者調查】80%消費者有意願使用行動支付。取自 MIC產業情報研究所 (2017),【行動支付消費者調查】用戶消費仍「低頻小額」 業者不妨採合作策略。取自 MIC產業情報研究所 (2018),【行動支付大調查一】近四成手機用戶曾使用行動支付 LINE Pay、Apple Pay認知度最高。取自 MIC產業情報研究所 (2018),【行動支付大調查二】用戶最常用Line Pay Apple Pay 街口支付 2020年第一波收斂。取自 遠見雜誌 (2017),22家業者強攻行動支付 搶吃1.5兆市場大餅。取自 經濟日報 (2018),行動支付交易 三大Pay打敗所有的Pay。取自 Ettoday新聞雲 (2017),行動支付落後國!日本要靠東京奧運翻身 台灣卡這4點?取自 英文 Au, Y. A., & Kauffman, R. J. (2008). The economics of mobile payments: Understanding stakeholder issues for an emerging financial technology application. Electronic Commerce Research and Applications, 7(2), 141-164. Bourreau, M., & Verdier, M. (2010). Cooperation for innovation in payment systems: The case of mobile payments, 79(3), 1-25 Calantone, R. J., C. A. Di Benedetto and J. B. Schmidt (1999). Using the analytic hierarchy process in new product screening. Journal of Product Innovation Management, 16(1), 65-76. Cao, X., Yu, L., Liu, Z., Gong, M., & Adeel, L. (2018). Understanding mobile payment users' continuance intention: a trust transfer perspective. Internet Research, 28(2), 456-476. Carbonell, M., Sierra, J. M., & Lopez, J. (2009). Secure multiparty payment with an intermediary entity. Computers & Security, 28(5), 289-300. Cocosila, M., & Trabelsi, H. (2016). An integrated value-risk investigation of contactless mobile payments adoption. Electronic Commerce Research and Applications, 20, 159-170. de Albuquerque, J. P., Diniz, E. H., & Cernev, A. K. (2016). Mobile payments: A scoping study of the literature and issues for future research. Information Development, 32(3), 527-553. Dahlberg, T., Mallat, N., Ondrus, J., & Zmijewska, A. (2008). Past, present and future of mobile payments research: A literature review. Electronic Commerce Research and Applications, 7(2), 165-181. Dahlberg, T., Guo, J., & Ondrus, J. (2015). A critical review of mobile payment research. Electronic Commerce Research and Applications, 14(5), 265-284. Edelman, B. (2015). How to launch your digital platform. Harvard Business Review, 93(4), 21. Evans, D. S., & Schmalensee, R. (2010). Failure to launch: Critical mass in platform businesses. Review of Network Economics, 9(4), 1-26 Fan, J., Shao, M., Li, Y., & Huang, X. (2018). Understanding users' attitude toward mobile payment use: A comparative study between China and the USA. Industrial Management & Data Systems, 118(3), 524-540. Gawer, A., & Henderson, R. (2007). Platform owner entry and innovation in complementary markets: Evidence from Intel. Journal of Economics & Management Strategy, 16(1), 1-34. Ghezzi, A., Renga, F., Balocco, R., & Pescetto, P. (2010). Mobile Payment Applications, 12(5), 3-22. Green, P. E., & Srinivasan, V. (1978). Conjoint analysis in consumer research: issues and outlook. Journal of Consumer Research, 5(2), 103-123. Guo, J., & Bouwman, H. (2016). An analytical framework for an m-payment ecosystem: A merchants׳ perspective. Telecommunications Policy, 40(2-3), 147-167. Hedman, J., & Henningsson, S. (2015). The new normal: Market cooperation in the mobile payments ecosystem. Electronic Commerce Research and Applications, 14(5), 305-318. Hal Dean, D. (2004). Evaluating potential brand associations through conjoint analysis and market simulation. Journal of Product & Brand Management, 13(7), 506-513. Hassan, S. S., & Craft, S. H. (2005). Linking global market segmentation decisions with strategic positioning options. Journal of Consumer Marketing, 22(2), 81-89. Huertas-García, R., Nuñez-Carballosa, A., & Miravitlles, P. (2016). Statistical and cognitive optimization of experimental designs in conjoint analysis. European Journal of Management and Business Economics, 25(3), 142-149. Halme, M., & Kallio, M. (2014). Likelihood estimation of consumer preferences in choice-based conjoint analysis. European Journal of Operational Research, 239(2), 556-564. Iman, N. (2018). Is mobile payment still relevant in the fintech era?. Electronic Commerce Research and Applications, 30, 72-82. Katz, M. L., & Shapiro, C. (1986). Technology adoption in the presence of network externalities. Journal of Political Economy, 94(4), 822-841. Kanninen, B. J. (2002). Optimal design for multinomial choice experiments. Journal of Marketing Research, 39(2), 214-227. Kannisto, P. (2016). 'I'm not a target market': power asymmetries in market segmentation. Tourism Management Perspectives, 20, 174-180. Keramati, A., Taeb, R., Larijani, A. M., & Mojir, N. (2012). A combinative model of behavioural and technical factors affecting 'Mobile'-payment services adoption: an empirical study. The Service Industries Journal, 32(9), 1489-1504. Kievit, W., Van Hulst, L., Van Riel, P., & Fraenkel, L. (2010). Factors that influence rheumatologists' decisions to escalate care in rheumatoid arthritis: Results from a choice‐based conjoint analysis. Arthritis Care & Research, 62(6), 842-847. Kim, Y., Park, Y. J., & Choi, J. (2016). The adoption of mobile payment services for 'Fintech'. International Journal of Applied Engineering Research, 11(2), 1058-1061. Koenig-Lewis, N., Marquet, M., Palmer, A., & Zhao, A. L. (2015). Enjoyment and social influence: predicting mobile payment adoption. The Service Industries Journal, 35(10), 537-554. Lai, P. M., & Chuah, K. B. (2010). Developing an analytical framework for mobile payments adoption in retailing: a supply-side perspective. In Management of e-Commerce and e-Government, 356-361 Lee, I., & Shin, Y. J. (2018). Fintech: Ecosystem, business models, investment decisions, and challenges. Business Horizons, 61(1), 35-46. Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2014). Antecedents of the adoption of the new mobile payment systems: The moderating effect of age. Computers in Human Behavior, 35, 464-478. Liébana-Cabanillas, F., Muñoz-Leiva, F., & Sánchez-Fernández, J. (2015). Behavioral model of younger users in m-payment systems. Journal of Organizational Computing and Electronic Commerce, 25(2), 169-190. Liébana-Cabanillas, F., & Lara-Rubio, J. (2017). Predictive and explanatory modeling regarding adoption of mobile payment systems. Technological Forecasting and Social Change, 120, 32-40. Liu, J., Kauffman, R. J., & Ma, D. (2015). Competition, cooperation, and regulation: Understanding the evolution of the mobile payments technology ecosystem. Electronic Commerce Research and Applications, 14(5), 372-391. McCabe, S. (2009). Positioning Marketing Communications for Tourism and Hospitality. Routledge, 1-17 Meyll, T., & Walter, A. (2018). Tapping and Waving to Debt: Mobile Payments and Credit Card Behavior, Finance Research Letters, 1-7 Miao, M., & Jayakar, K. (2016). Mobile payments in Japan, South Korea and China: Cross-border convergence or divergence of business models? Telecommunications Policy, 40(2-3), 182-196. Moskowitz, H. R., & Silcher, M. (2006). The applications of conjoint analysis and their possible uses in Sensometrics. Food Quality and Preference, 17(3-4), 145-165. Moore, W. L. (2004). A cross-validity comparison of ratings-based and choice-based conjoint analysis models. International Journal of Research in Marketing, 21(3), 299-312 Natter, M., & Feurstein, M. (2002). Real world performance of choice-based conjoint models. European Journal of Operational Research, 137(2), 448-458 Norbäck, P. J., Persson, L., & Svensson, R. (2016). Creative destruction and productive preemptive acquisitions. Journal of Business Venturing, 31(3), 326-343 Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404-414 Ondrus, J., & Lyytinen, K. (2011). Mobile payments market: Towards another clash of the Titans? In Mobile Business, 166-172 Orme, B. (2002). Formulating attributes and levels in conjoint analysis. Sawtooth Software Research Paper, 1-4 Pham, T. T. T., & Ho, J. C. (2015). The effects of product-related, personal-related factors and attractiveness of alternatives on consumer adoption of NFC-based mobile payments. Technology in Society, 43, 159-172 Rochet, J. C., & Tirole, J. (2003). Platform competition in two‐sided markets. Journal of the European Economic Association, 1(4), 990-1029 Rodríguez-Pinto, J., Rodríguez-Escudero, A. I., & Gutiérrez-Cillán, J. (2008). Order, positioning, scope and outcomes of market entry. Industrial Marketing Management, 37(2), 154-166 Ruutu, S., Casey, T., & Kotovirta, V. (2017). Development and competition of digital service platforms: A system dynamics approach. Technological Forecasting and Social Change, 117, 119-130 Slade, E. L., Williams, M. D., & Dwivedi, Y. K. (2013). Mobile payment adoption: Classification and review of the extant literature. The Marketing Review, 13(2), 167-190 Schierz, P. G., Schilke, O., & Wirtz, B. W. (2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research and Applications, 9(3), 209-216 Staykova, K. S., & Damsgaard, J. (2015). The race to dominate the mobile payments platform: Entry and expansion strategies. Electronic Commerce Research and Applications, 14(5), 319-330 Suarez, F. F., & Cusumano, M. (2008). The role of services in platform markets. Platforms, Markets and Innovation, 77-98 Tsalgatidou, A., & Pitoura, E. (2001). Business models and transactions in mobile electronic commerce: requirements and properties. Computer Networks, 37(2), 221-236 Voleti, S., Srinivasan, V., & Ghosh, P. (2017). An approach to improve the predictive power of choice-based conjoint analysis. International Journal of Research in Marketing, 34(2), 325-335 Wellman, G. S., & Vidican, C. (2008). Pilot study of a hierarchical Bayes method for utility estimation in a choice-based conjoint analysis of prescription benefit plans including medication therapy management services. Research in Social and Administrative Pharmacy, 4(3), 218-230 Yang, Y., Liu, Y., Li, H., & Yu, B. (2015). Understanding perceived risks in mobile payment acceptance. Industrial Management & Data Systems, 115(2), 253-269 Yu, J., Goos, P., & Vandebroek, M. (2011). Individually adapted sequential Bayesian conjoint-choice designs in the presence of consumer heterogeneity. International Journal of Research in Marketing, 28(4), 378-388 Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems, 54(2), 1085-1091 Zhou, T. (2014). Understanding the determinants of mobile payment continuance usage. Industrial Management & Data Systems, 114(6), 936-948 Bank for International Settlements (2012), Innovations in retail payments. Retrieved from Business Insider Intelligence (2017), The mobile payments report: Key strategies that wallet providers can implement to break from disappointing growth. Retrieved from World Pay (2017), Global Payments Report 2017. Retrieved from port-2017. Zenith the ROI agency (2017), Smartphone penetration to reach 66% in 2018. Retrieved from
dc.description.abstract面臨國際三大行動支付的登台,讓台灣市場競爭日趨激烈,台灣廠商該如何找到行動支付服務的目標市場,發展適當產品策略以提升競爭優勢,成為相關業者未來經營挑戰。行動支付是具有雙邊市場 (Two-sided market) 特性的產業,消費者與商家分別處於平台兩端,因此瞭解兩端使用者對行動支付的需求偏好就顯得相當重要。本研究以層級貝氏-選擇式聯合分析法 (Hierarchical Bayes Choice-Based conjoint analysis) 探討行動支付平台兩端的消費者與商家如何在不同的行動支付方案間取捨而作出採用決策,以找出消費者與商家的行動支付屬性偏好,接著再利用集群分析 (Cluster analysis) 依個別偏好資訊分群了解消費者與商家的市場區隔,以及運用判別分析 (Discriminant analysis) 以人口變數、消費行為檢視分群特性,讓台灣廠商可以針對其目標族群以平台觀點擬定差異化產品策略、市場定位。本研究以某台灣廠商為例,並且建議個案廠商針對小額支付市場以學生族群及小型餐飲攤販族群作為目標族群,提供適當的行動支付服務來滿足消費者、商家需求。另外將台灣廠商定位為簡易、方便的行動支付品牌,以增加行動支付使用人數及達到臨界量形成群聚效應,提升台灣廠商行動支付採用率。zh_TW
dc.description.abstractIn recent years, mobile payment services have been widely used in our daily life. It not only changes payment methods to consumers, but also influences merchants' business model. With the arrival of Apple Pay, Samsung Pay, and Google Pay in Taiwan have increased the competition in mobile payment industry. How to catch up with this trend under sheer competition from global mobile payment players becomes an important issue for Taiwanese mobile payment service providers. This study uses Hierarchical Bayes Choice-Based conjoint (HB-CBC) analysis to determine the preferences of consumers and merchants on mobile payments. This method allows respondents to experience simulated consumption situation and capture their trade-offs between products attributes. Based on individual preference, market segments of Taiwanese mobile payments could be identified and mobile payment service providers could determine appropriate services to their target segments. With a clearly defined target audience, Taiwanese mobile payment providers could develop their product strategies to keep up with competitors and sustain continuous market growth.en_US
dc.description.tableofcontents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 - 3 - 第二章 文獻探討 - 5 - 第一節 行動支付介紹 - 5 - 第二節 行動支付採用因素 - 7 - 第三節 行動支付策略分析 - 10 - 第三章 研究方法 - 12 - 第一節 確認重要產品屬性與水準 - 13 - 第二節 問卷設計及樣本收集 - 14 - 第三節 評估屬性偏好 - 16 - 第四節 市場區隔與產品策略分析 - 16 - 第四章 資料分析與策略建議 - 19 - 第一節 台灣行動支付市場案例背景 - 19 - 第二節 聯合分析法結果 - 19 - 第三節 市場區隔分析結果 - 22 - 第四節 策略建議與管理意涵 - 24 - 第五章 結論 - 28 - 參考文獻 - 30 - 附錄一 - 35 - 附錄二 - 43 -zh_TW
dc.subjectMobile Paymenten_US
dc.subjectProduct Attributesen_US
dc.subjectTwo-Sided Marketen_US
dc.subjectConjoint Analysisen_US
dc.subjectHierarchical Bayesen_US
dc.titleUsing Hierarchical Bayes Choice-Based Conjoint Analysis for Taiwanese Mobile Payment Market Analysis: The Theory of Two-Sided Marketen_US
dc.typethesis and dissertationen_US
item.openairetypethesis and dissertation-
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