Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/97993
標題: 台灣行動支付服務創新擴散之研究:系統動態觀點
Innovation Diffusion of Taiwanese Mobile Payment Services: A System Dynamics Perspective
作者: 卓佐勲
Tso-Hsun Cho
關鍵字: 行動支付;系統動態學;選擇式聯合分析;雙邊市場;創新擴散;Mobile payment;System dynamics;Conjoint analysis;Two-sided market;Diffusion of innovation
引用: 中文 中華系統動力學學會(2009),系統動力學簡介。取自 http://www.csds.org.tw/aboutsd 曾子容(2017),行動支付將在未來扮演舉足輕重的角色 以Apple Pay為例。臺灣經濟研究月刊,40(6),130-136。 胡自立(2017),洞悉行動支付產業動態與未來趨勢,財金資訊季刊,No.89 科技新報(2015),行動支付大補帖:一次就讓你看懂。取自 http://technews.tw/2015/05/06/nfc-smart-phone/ 資策會FIND(2017),臺灣已有八成上網民眾採智慧手機進行連網。取自 https://www.find.org.tw/market_info.aspx?k=2&n_ID=9053 IDC國際數據資訊(2014),2015年台灣ICT市場十大趨勢預測。取自 https://www.idc.com.tw/about/448.html 數位時代(2017),Samsung Pay要贏Apple Pay,決戰點在超商。取自 https://www.bnext.com.tw/article/44625/samsung-pay-apple-pay-mobile-payment 自由時報(2017),ApplePay綁卡量 金管會說了不能說的秘密。取自 http://news.ltn.com.tw/news/business/breakingnews/2026683 新唐人亞太台(2017),秒結帳! Apple Pay登台衝擊台灣業者。取自 http://www.ntdtv.com.tw/b5/20170420/video/194839.html 工商時報(2017),台灣pay 強打QR code付款。取自 http://www.chinatimes.com/newspapers/20171205000171-260205 創市際市場研究顧問(2017),創市際『電子票證使用行為』調查。取自 http://www.ixresearch.com/news/news_12_27_17 MIC產業情報研究所(2017),國人認知度已突破八成 NFC功能漸普及。取自 https://mic.iii.org.tw/IndustryObservations_PressRelease02.aspx?sqno=456 MIC產業情報研究所(2017),80%消費者有意願使用行動支付。取自 https://mic.iii.org.tw/IndustryObservations_PressRelease02.aspx?sqno=457 MIC產業情報研究所(2018),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 https://mic.iii.org.tw/IndustryObservations_PressRelease02.aspx?sqno=486 中華民國財政部(2018),小規模營業人導入行動支付經申請核准可享有適用1%營業稅稅率及免用統一發票之租稅優惠。取自 https://www.mof.gov.tw/Detail/Index?nodeid=137&pid=78321 ePrice(2018),台灣手機品牌最新排名 (2017 年 12 月銷售市占)。取自 http://www.eprice.com.tw/mobile/talk/102/5048886/1/ 英文 Clemen, R. T. (1996). Making Hard Decisions: An Introduction to Decision Analysis. Belmont, CA, Duxbury Press. Bass, F. M. (1969). A new product growth for model consumer durables. Management Science, 15(5), 215-227. Burns, R. P., & Burns, R. (2008). Business Research Methods and Statistics Using SPSS. Sage. Chrzan, K., & Orme, B. (2000). An overview and comparison of design strategies for choice-based conjoint analysis. Sawtooth Software Research Paper Series , 98382. Campbell-Kelly, M., Garcia-Swartz, D., Lam, R., & Yang, Y. (2015). Economic and business perspectives on smartphones as multi-sided platforms. Telecommunications Policy, 39(8), 717-734. DeSarbo, W. S., Ramaswamy, V., & Cohen, S. H. (1995). Market segmentation with choice-based conjoint analysis. Marketing Letters, 6(2), 137 Devlin, G., & Bleackley, M. (1988). Strategic alliances—guidelines for success. Long Range Planning, 21(5), 18-23. 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. Forrester, J. W. (1958). Industrial dynamics: a major breakthrough for decision makers. Harvard Business Review, 36(4), 37-66. Eisenmann, T., Parker, G., & Van Alstyne, M. W. (2006). Strategies for two-sided markets. Harvard Business Review, 84(10), 92. Forrester, J. W. (1969). Urban dynamics. MIT Press, Cambridge, Mass. Green, P. E., & Srinivasan, V. (1990). Conjoint analysis in marketing: New developments with implications for research and practice. The Journal of Marketing, 3-19. Gawer, A., & Cusumano, M. A. (2014). Industry platforms and ecosystem innovation. Journal of Product Innovation Management, 31(3), 417-433. Guo, J., & Bouwman, H. (2016). An analytical framework for an m-payment ecosystem: A merchants׳ perspective. Telecommunications Policy, 40(2-3), 147-167. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis (Vol. 5, No. 3, pp. 207-219). Upper Saddle River, NJ: Prentice Hall. Hedman, J., & Henningsson, S. (2015). The new normal: Market cooperation in the mobile payments ecosystem. Electronic Commerce Research and Applications, 14(5), 305-318. Iansiti, M., & Levien, R. (2004). The Keystone Advantage: What the New Dynamics of Business Ecosystems Mean for Strategy, Innovation, And Sustainability. Harvard Business Press. Islam, M. A., Ahmad, T. S. B., Khan, M. A., & Ali, M. H. (2010). Adoption of M-commerce services: The case of Bangladesh. World Journal of Management, 2(1), 37-54. Iman, N. (2018). Is mobile payment still relevant in the fintech era? Electronic Commerce Research and Applications 30, 72-82. Kanninen, B. J. (2002). Optimal design for multinomial choice experiments. Journal of Marketing Research, 39(2), 214-227. Karnouskos, S. (2004). Mobile payment: a journey through existing procedures and standardization initiatives. IEEE Communications Surveys & Tutorials, 6(4). Kshetri, N., & Acharya, S. (2012). Mobile payments in emerging markets. IT Professional, 14(4), 9-13. Khiaonarong, T. (2014). Oversight issues in mobile payments (No. 14-123). International Monetary Fund. Kilinc, M. S., & Bennett Milburn, A. (2016). A study of home telehealth diffusion among US home healthcare agencies using system dynamics. IIE Transactions on Healthcare Systems Engineering, 6(3), 140-161. Kreng, V. B., & Wang, B. J. (2013). An innovation diffusion of successive generations by system dynamics—An empirical study of Nike Golf Company. Technological Forecasting and Social Change, 80(1), 77-87. Louviere, J. J., & Woodworth, G. (1983). Design and analysis of simulated consumer choice or allocation experiments: an approach based on aggregate data. Journal of Marketing Research, 350-367. Lyneis, J. M. (2000). System dynamics for market forecasting and structural analysis. System Dynamics Review, 16(1), 3. Lee, J., Kim, Y., Lee, J. D., & Park, Y. (2006). Estimating the extent of potential competition in the Korean mobile telecommunications market: Switching costs and number portability. International Journal of Industrial Organization, 24(1), 107-124. Lyneis, J. M., & Ford, D. N. (2007). System dynamics applied to project management: a survey, assessment, and directions for future research. System Dynamics Review, 23(2‐3), 157-189. Lee, S., Han, W., & Park, Y. (2015). Measuring the functional dynamics of product-service system: A system dynamics approach. Computers & Industrial Engineering, 80, 159-170. Lee, D. H., Park, S. Y., Kim, J. W., & Lee, S. K. (2013). Analysis on the feedback effect for the diffusion of innovative technologies focusing on the green car. Technological Forecasting and Social Change, 80(3), 498-509. Lu, Y., Yang, S., Chau, P. Y., & Cao, Y. (2011). Dynamics between the trust transfer process and intention to use mobile payment services: A cross-environment perspective. Information & Management, 48(8), 393-403. Leong, L. Y., Hew, T. S., Tan, G. W. H., & Ooi, K. B. (2013). Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach. Expert Systems with Applications, 40(14), 5604-5620. 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. 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. 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. Liébana-Cabanillas, F., Marinkovic, V., de Luna, I. R., & Kalinic, Z. (2017). Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach. Technological Forecasting and Social Change, 129: 117-130. Lai, P. M., & Chuah, K. B. (2010, October). Developing an analytical framework for mobile payments adoption in retailing: a supply-side perspective. In Management of e-Commerce and e-Government (ICMeCG), 2010 Fourth International Conference on (pp. 356-361). IEEE. Moore, J. F. (1993). Predators and prey: A new ecology of competition. Harvard Business Review, 71(3), 75-86. Moore, J. F. (1996). The Death of Competition: Leadership And Strategy In The Age of Business Ecosystems. Harper Paperbacks. Magnier-Watanabe, R. (2014, January). An institutional perspective of mobile payment adoption: The case of Japan. In System Sciences (HICSS), 2014 47th Hawaii International Conference on (pp. 1043-1052). IEEE. Ondrus, J., Gannamaneni, A., & Lyytinen, K. (2015). The impact of openness on the market potential of multi-sided platforms: a case study of mobile payment platforms. Journal of Information Technology, 30(3), 260-275. Orme, B., & Johnson, R. (2015). Including holdout choice tasks in conjoint studies. Washington: Sawtooth Software Inc. 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. Peltoniemi, M. (2006). Preliminary theoretical framework for the study of business ecosystems. Emergence: Complexity and Organization, 8(1), 10. Pal, D., Vanijja, V., & Papasratorn, B. (2015). An empirical analysis towards the adoption of NFC mobile payment system by the end user. Procedia Computer Science, 69, 13-25. Polydoropoulou, A., & Lambrou, M. A. (2012). Development of an e-Learning Recommender System Using Discrete Choice Models and Bayesian Theory: A Pilot Case in the Shipping Industry.Retrieved from https://www.intechopen.com/books/security-enhanced-applications-for-information-systems/development-of-an-e-learning-recommender-system-using-discrete-choice-models-and-bayesian-theory Rhee, H. T., Yang, S. B., & Kim, K. (2016). Exploring the comparative salience of restaurant attributes: A conjoint analysis approach. International Journal of Information Management, 36(6), 1360-1370. 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. Forrester, J. W. and P. M. Senge (1980). Tests for building confidence in system dynamics models. System Dynamics. A. A. Legasto, J. W. Forrester and J. M. Lyneis. Amsterdam, North-Holland. Sterman, J. D. (2001). System Dynamics Modeling: Tools for Learning in a Complex World. California Management Review, 43(4), 8-25. Sterman, J. D. (2000). Business dynamics: Systems thinking and modeling for a complex world. Shin, D. H. (2009). Towards an understanding of the consumer acceptance of mobile wallet. Computers in Human Behavior, 25(6), 1343-1354. Slade, E., Williams, M., Dwivedi, Y., & Piercy, N. (2015). Exploring consumer adoption of proximity mobile payments. Journal of Strategic Marketing, 23(3), 209-223. Van Alstyne, M. W., Parker, G. G., & Choudary, S. P. (2016). Pipelines, platforms, and the new rules of strategy. Harvard Business Review, 94(4), 54-62. Wang, J., Lai, J. Y., & Chang, C. H. (2016). Modeling and analysis for mobile application services: The perspective of mobile network operators. Technological Forecasting and Social Change, 111, 146-163. Walsh, S., Flannery, D., & Cullinan, J. (2018). Analysing the preferences of prospective students for higher education institution attributes. Education Economics, 26(2), 161-178. Zmijewska, A., & Lawrence, E., (2005). Reshaping the framework for analysing success of mobile payment solutions. In Proceedings of the IADIS International Conference on E-Commerce. Porto, Portugal Zarmpou, T., Saprikis, V., Markos, A., & Vlachopoulou, M. (2012). Modeling users' acceptance of mobile services. Electronic Commerce Research, 12(2), 225-248. eMarket(2016), Mobile Taiwan: A Look at a Highly Mobile Market. Retrieved from https://www.emarketer.com/Article/Mobile-Taiwan-Look-Highly-Mobile-Market/1014877?ecid=NL1007
摘要: 
隨著數位時代來臨,行動裝置相關技術不斷的進步,台灣在2015年時放寬了行動支付相關法規的標準,2017年在Apple Pay的導入後,掀起台灣一波新的行動支付熱潮,隨後Google Pay和Samsung Pay的導入,進而加快台灣整體行動支付的發展。在具有國際大品牌的行動支付競爭壓力下,該如何提升消費者與店家對台灣本土行動支付的採用將是一項重大挑戰。
本研究目的在於了解台灣消費者與商家對於不同行動支付的偏好與分析台灣行動支付的創新擴散行為。將行動支付視為雙邊市場,並藉由選擇式聯合分析法收集消費者以及商家的多屬性偏好數據,來探討哪些因素會影響台灣消費者與商家採用行動支付,接著開發出行動支付的系統動態模擬 (SD simulation) 模型,透過模型了解行動支付市場的擴散行為以及預測台灣行動支付的未來成長情境並建立發展策略。
研究結果看出,台灣本土行動支付如要與國際行動支付競爭,必須改善所提供的支付服務,藉由SD模擬策略發現改善可綁卡類型、支付操作步驟、可支付方式與補貼政策有效提升台灣Pay市佔率。最後,本研究的貢獻有四個。第一,選擇式聯合分析法幫助行動支付服務供應商了解影響消費者和商家採用行動支付的服務屬性,以改善提供的服務水準。第二,本研究將行動支付視為雙邊市場開發出系統動態模型,透過模型模擬顯示在有無跨邊網路效應的影響之下,消費者與商家採用行動支付的變化。第三,可藉由本研究開發出的系統動態模型來了解行動支付採用者的擴散行為。最後,建構出的系統動態模擬模型能夠模擬行動支付不同方案的實驗,以協助台灣行動支付業者面臨急遽增加的市場競爭壓力,制定適當產品策略以擴展行動支付市佔率。

With the internet age coming,mobile device related technology is improving constantly.In 2015, Taiwan relaxed the standards for mobile payment related regulations.After Apple pay was introduced in 2017,it caused trends of new mobile payment in Taiwan.Then,Google Pay and Samaung Pay introduced,which accelerated the development of Taiwan's overall mobile payment.Under the great competition pressure from global brands, how to enhance the adoption of consumers and merchants is becoming a major challenge for Taiwanese m-payment service providers.
The purpose of the research is to realize consumers and merchants in Taiwan for the preference of different mobile payment, and find out the behavior of diffusion of innovation in Taiwan.This research treats the mobile payment as a two-sided market and uses choice-based conjoint analysis to collect multi-attribute preference data of consumers and merchants in order to understand what factors will influence the adoption of mobile payment. A system dynamics (SD) model is developed to understand the diffusion behavior of mobile payment and predict the future growth of Taiwan's mobile payment market, establishing development strategies for Taiwanese m-payment service providers.
In the reslut of research.If taiwanese mobile payment want to catch up with the world , must improve the service of payment. The way are improve 'the kind of Binding card' , 'the Progress of payment steps' , 'the kind of payment method' and 'Subsidy policy' that discovered by SD simulation strategy.The contributions of this research are four-fold.First, choice-based conjoint analysis to collect multi-attribute preference data of consumers and merchants in order to understand what factors will influence the adoption of mobile payment.Second, this research treats the mobile payment as a two-sided market and a system dynamics (SD) model is developed to understand the influence of cross-side network effects, consumers and merchants adopt changes in mobile payments.Third, the SD model developed in this study can be used to understand the dynamic diffusion behavior of mobile payment market.Finally, the SD simulation model can help Taiwan's mobile payment service providers develop appropriate strategies to expand their mobile payment market shares.
URI: http://hdl.handle.net/11455/97993
Rights: 同意授權瀏覽/列印電子全文服務,2021-08-30起公開。
Appears in Collections:科技管理研究所

Files in This Item:
File SizeFormat Existing users please Login
nchu-107-7103026034-1.pdf2.85 MBAdobe PDFThis file is only available in the university internal network    Request a copy
Show full item record
 

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.