隨著行動科技時代的演進及無線通訊技術的成熟，人們購買產品和服務的形式越來越多元，其中行動支付正逐漸改變使用者消費習慣與購買行為。過去行動支付相關研究諸多以產品系統面或是風險安全性因素探討。然而，在現今資訊媒體蓬勃發展及行動支付服務技術逐步發展下。本研究認為使用者從傳統付款模式到使用行動支付是一個行為轉換的過程。因此，本研究以自我效能理論為基礎並整合創新擴散理論中的相對優勢，探討個人能力、對於不同資訊媒體之依賴與社會資本中的強弱連結，藉以瞭解發展行動支付時，影響消費者使用行動支付意願的主要因素。
本研究透過網路問卷方式進行調查，總共回收313份有效樣本進行研究分析，透過實證研究，結構化方程式模型(SEM)分析研究問卷。我們發現，使用者期待行動支付之相對優勢和個人自我效能對於提升採用行動支付的意願均有顯著影響，其中，大眾媒體影響(弱連結)為相對優勢的重要因素，而影響自我效能的兩因子為電腦素養和人際媒體影響(強連結)。除此，本研究進一步分析發現，使用者本身因科技創新接受程度之差異性（早期採用者、主要採用者、晚期採用者），對於本研究提出之行動支付模式觀點也有所不同。無論政府或企業在發展行動支付時，可依據本研究探討消費者自我效能、期待的行動支付優勢與對於不同資訊媒體的依賴影響其採用意圖，規劃適合的媒體管道及行銷策略以刺激消費者使用行動支付的意願。最後，學術與管理意涵，研究限制與未來研究等討論也提供後續研究者與管理者做為參考。

With the evolution of mobile and wireless communication technology, the way of people purchasing products and services become more diversified. Moreover, mobile payment service has been gradually changing the habit of consumer purchasing behavior. Many studies have been devoted on the analysis of mobile payment service from the perspective of the usefulness and the usability of the system or the system of risk and trust. However, in the context of prosperous communication media and the progressively development of the mobile payment technology. In this study, we considered the user paying behavior from the traditional way to mobile payment is a process of switching behavior. To analyze the adoption intention of consumers, we proposed a mobile payment research model, which based on the theory of self-efficacy, integrating the construct of relative advantage from the concept of diffusion of innovations (DOI). To explore the influence on consumer computer literacy and the dependence of communication media (i.e. mass media, interpersonal media) to identify the main determinants of mobile payment adoption and to expand the development of mobile payment service.
This study questionnaire was surveyed online with a valid sample of 313 respondents. Through empirical study, this proposed model being tested with structural equation modeling (SEM) approach. Results indicate that the expected relative advantage of mobile payment service and the consumer self-efficacy are both strong antecedents to consumer adoption of mobile payment service. Additionally, the influence of mass media (weak ties) has direct impact on knowing the relative advantage. Both the influence of interpersonal (strong ties) and computer literacy has direct impact on the consumer self-efficacy. This study also finds out different groups of technological innovation (early adopters, major adopters, and late adopters) will vary in the conceptions of the proposed model. The study findings may serve as a guide for government and entrepreneur on developing the suitable communication media and marketing strategy to stimulate the intention toward mobile payment. Finally, implications, limitations and future researched of the research are discussed.

英文文獻
[1]. Abrahão, R. d. S., Moriguchi, S. N., & Andrade, D. F. (2016). Intention of adoption of mobile payment: An analysis in the light of the Unified Theory of Acceptance and Use of Technology (UTAUT). RAI Revista de Administração e Inovação, 13(3), 221-230. doi:http://dx.doi.org/10.1016/j.rai.2016.06.003
[2]. Adler, P. S., & Kwon, S.-W. (2002). Social capital: Prospects for a new concept. Academy of Management Review, 27(1), 17-40.
[3]. Agrawal, A. K., Catalini, C., & Goldfarb, A. (2011). The geography of crowdfunding. Retrieved from
[4]. Ajzen, I., & Fishbein, M. (2005). The influence of attitudes on behavior. The handbook of attitudes, 173, 221.
[5]. Al-Jabri, I. M., & Sohail, M. S. (2012). Mobile banking adoption: Application of diffusion of innovation theory. Journal of Electronic Commerce Research, 13(4), 379-391.
[6]. Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Williams, M. D. (2016). Consumer adoption of mobile banking in Jordan: examining the role of usefulness, ease of use, perceived risk and self-efficacy. Journal of Enterprise Information Management, 29(1), 118-139.
[7]. Alliance, S. C. (2011). The mobile payments and NFC landscape: A US perspective. Smart Card Alliance, 1-53.
[8]. Aydin, G., & Burnaz, S. (2016). ADOPTION OF MOBILE PAYMENT SYSTEMS: A STUDY ON MOBILE WALLETS. Journal of Business, Economics & Finance, 5(1), 73.
[9]. Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012). The role of social networks in information diffusion. Paper presented at the Proceedings of the 21st international conference on World Wide Web.
[10]. Bagozzi, R., & Phillips, L. (1982). Representing and Testing Organizational Theories: A Holistic Construal. Administrative Science Quarterly, 27(3), 459-489.
[11]. Ball-Rokeach, S. J., Rokeach, M., & Grube, J. W. (1984). The great American values test: Influencing behavior and belief through television.
[12]. Ball-Rokeach, S. J. (1985). The origins of individual media-system dependency a sociological framework. Communication Research, 12(4), 485-510.
[13]. Ball-Rokeach, S. J., & DeFleur, M. L. (1976). A dependency model of mass-media effects. Communication Research, 3(1), 3-21.
[14]. Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191.
[15]. Bandura, A. (2006). On integrating social cognitive and social diffusion theories. Communication of innovations: A journey with Ev Rogers, 111-135.
[16]. Bandura, A. (2012). On the functional properties of perceived self-efficacy revisited. Journal of management, 38(1), 9-44.
[17]. Balaji, M. S., Khong, K. W., & Chong, A. Y. L. (2016). Determinants of negative word-of-mouth communication using social networking sites. Information & Management, 53(4), 528-540.
[18]. Bhattacherjee, A. (2000). Acceptance of e-commerce services: the case of electronic brokerages. IEEE Transactions on systems, man, and cybernetics-Part A: Systems and humans, 30(4), 411-420.
[19]. Bourdieu, P. (2011). The forms of capital.(1986). Cultural theory: An anthology, 81-93.
[20]. Brass, D. J., Butterfield, K. D., & Skaggs, B. C. (1998). RELATIONSHIPS AND UNETHICAL BEHAVIOR: A SOCIAL NETWORK PERSPECTIVE. Academy of Management Review, 23(1), 14-31.
[21]. Carillo, K., Scornavacca, E., & Za, S. (2016). The role of media dependency in predicting continuance intention to use ubiquitous media systems. Information & Management.
[22]. Cassidy, S., & Eachus, P. (2002). Developing the Computer User Self-Efficacy (CUSE) Scale: Investigating the Relationship between Computer Self-Efficacy, Gender and Experience with Computers. Journal of Educational Computing Research, 26(2), 133-153.
[23]. Chao, C.-W., Reid, M., & Mavondo, F. T. (2012). Consumer innovativeness influence on really new product adoption. Australasian Marketing Journal (AMJ), 20(3), 211-217.
[24]. Chen, C.-Y., & Chang, S.-L. (2016). User-orientated perspective of social media used by campaigns. Telematics and Informatics.
[25]. Chiu, C.-J., Hu, Y.-H., Lin, D.-C., Chang, F.-Y., Chang, C.-S., & Lai, C.-F. (2016). The attitudes, impact, and learning needs of older adults using apps on touchscreen mobile devices: Results from a pilot study. Computers in Human Behavior, 63, 189-197.
[26]. Chun-Mei, C., Hsiao, H.-C., Chien-Hua, S., & Su-Chang, C. (2010). Analysis of factors in technological and vocational school teachers' perceived organizational innovative climate and continuous use of E-teaching: Using computer self-efficacy as an intervening variable. TOJET: The Turkish Online Journal of Educational Technology, 9(4).
[27]. Coleman, J. S. (1988). Social capital in the creation of human capital. American journal of sociology, S95-S120.
[28]. Colombelli, A., Krafft, J., & Vivarelli, M. (2016). Entrepreneurship and Innovation: New Entries, Survival, Growth.
[29]. Colombo, M. G., Franzoni, C., & Rossi-Lamastra, C. (2015). Internal Social Capital and the Attraction of Early Contributions in Crowdfunding. Entrepreneurship Theory and Practice, 39(1), 75-100.
[30]. Compeau, D., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS quarterly, 145-158.
[31]. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS quarterly, 189-211.
[32]. 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. doi:http://dx.doi.org/10.1016/j.elerap.2007.02.001
[33]. Daştan, İ., & Gürler, C. (2016). Factors Affecting the Adoption of Mobile Payment Systems: An Empirical Analysis. EMAJ: Emerging Markets Journal, 6(1), 16-24. doi:10.5195/emaj.2016.95
[34]. Di Pietro, L., Guglielmetti Mugion, R., Mattia, G., Renzi, M. F., & Toni, M. (2015). The Integrated Model on Mobile Payment Acceptance (IMMPA): An empirical application to public transport. Transportation Research Part C: Emerging Technologies, 56, 463-479. doi:http://dx.doi.org/10.1016/j.trc.2015.05.001
[35]. Eze, U. C., Gan, G. G. G., Ademu, J., & Tella, S. A. (2008). Modelling user trust and mobile payment adoption: a conceptual Framework. Communications of the IBIMA, 3(29), 224-231.
[36]. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 39-50.
[37]. Fraillon, J., Schulz, W., & Ainley, J. (2013). International computer and information literacy study: Assessment framework.
[38]. Ghezzi, A., Renga, F., Balocco, R., & Pescetto, P. (2010). Mobile Payment Applications: offer state of the art in the Italian market. info, 12(5), 3-22.
[39]. Govender, I., & Sihlali, W. (2014). A study of mobile banking adoption among university students using an extended TAM. Mediterranean Journal of Social Sciences, 5(7), 451.
[40]. Granovetter, M. S. (1973). The strength of weak ties. American journal of sociology, 1360-1380 0002-9602.
[41]. Gupta, R., & Jain, K. (2014). Adoption of Mobile Telephony in Rural India: An Empirical Study. Decision Sciences, 45(2), 281.
[42]. Grant, A. E., Guthrie, K. K., & Ball-Rokeach, S. J. (1991). Television shopping a media system dependency perspective. Communication Research, 18(6), 773-798.
[43]. Holden, H., & Rada, R. (2011). Understanding the influence of perceived usability and technology self-efficacy on teachers’ technology acceptance. Journal of Research on Technology in Education, 43(4), 343-367.
[44]. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate data analysis (7th Eds.). NY: Pearson.
[45]. Hassani, H., & Silva, E. (2015). A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts. Econometrics, 3(3), 590. Retrieved from http://www.mdpi.com/2225-1146/3/3/590
[46]. Hsi-Peng, L., & Kuo-Lun, H. (2007). Understanding intention to continuously share information on weblogs. Internet Research, 17(4), 345-361.
[47]. Hwang, H., & Kim, K.-O. (2015). Social media as a tool for social movements: the effect of social media use and social capital on intention to participate in social movements. International Journal of Consumer Studies, 39(5), 478-488.
[48]. Ji Kyung, P., & Roedder John, D. (2014). I Think I Can, I Think I Can: Brand Use, Self-Efficacy, and Performance. Journal of Marketing Research (JMR), 51(2), 233-247.
[49]. Knutsson, O., Blåsjö, M., Hållsten, S., & Karlström, P. (2012). Identifying different registers of digital literacy in virtual learning environments. The Internet and Higher Education, 15(4), 237-246.
[50]. Kujala, S., Mugge, R., & Miron-Shatz, T. (2016). The role of expectations in service evaluation: A longitudinal study of a proximity mobile payment service. International Journal of Human-Computer Studies.
[51]. Kuttschreuter, M., Rutsaert, P., Hilverda, F., Regan, Á., Barnett, J., & Verbeke, W. (2014). Seeking information about food-related risks: The contribution of social media. Food Quality and Preference, 37, 10-18.
[52]. Lam, S. Y., & Shankar, V. (2014). Asymmetries in the Effects of Drivers of Brand Loyalty Between Early and Late Adopters and Across Technology Generations. Journal of Interactive Marketing, 28(1), 26-42. doi:http://dx.doi.org/10.1016/j.intmar.2013.06.004
[53]. Lee, C.-L., & Huang, M.-K. (2014a). The influence of computer literacy and computer anxiety on computer self-efficacy: the moderating effect of gender. Cyberpsychology, Behavior And Social Networking, 17(3), 172-180. doi:10.1089/cyber.2012.0029
[54]. Lee, C.-L., & Huang, M.-K. (2014b). The Influence of Computer Literacy and Computer Anxiety on Computer Self-Efficacy: The Moderating Effect of Gender. CyberPsychology, Behavior & Social Networking, 17(3), 172-180. doi:10.1089/cyber.2012.0029
[55]. Lee, Y. C. (2016). Effects of branded e-stickers on purchase intentions: The perspective of social capital theory. Telematics and Informatics, 34(1), 397-411.
[56]. Leung, L. (2010). Effects of Internet Connectedness and Information Literacy on Quality of Life. Social Indicators Research, 98(2), 273-290. doi:10.1007/s11205-009-9539-1
[57]. 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. doi:http://dx.doi.org/10.1016/j.chb.2014.03.022
[58]. Li, S., & Jin, Z. (2013). Global dynamics analysis of homogeneous new products diffusion model. Discrete Dynamics in Nature and Society, 2013.
[59]. Li, X. (2014). Perceived channel efficiency and motivation and orientation of information seeking as predictors of media dependency. Telematics and Informatics, 31(4), 628-639.
[60]. Limberg, L., Sundin, O., & Talja, S. (2013). Three theoretical perspectives on information literacy. Human IT: Journal for Information Technology Studies as a Human Science, 11(2).
[61]. Lin, G.-Y. (2016). Self-Efficacy Beliefs and Their Sources in Undergraduate Computing Disciplines. Journal of Educational Computing Research, 53(4), 540-561.
[62]. Lin, T., Chiang, Y., & Jiang, C. (2013). Examining mobile dependency and sociability among young users in Singapore.
[63]. Lin, M., Prabhala, N. R., & Viswanathan, S. (2013). Judging borrowers by the company they keep: Friendship networks and information asymmetry in online peer-to-peer lending. Management Science, 59(1), 17-35.
[64]. Linck, K., Pousttchi, K., & Wiedemann, D. G. (2006). Security issues in mobile payment from the customer viewpoint.
[65]. 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.
[66]. Loges, W. E. (1994). Canaries in the coal mine perceptions of threat and media system dependency relations. Communication Research, 21(1), 5-23.
[67]. Lu, J., Yao, J. E., & Yu, C. S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. Journal of Strategic Information Systems, 14(3), 245-268. doi:10.1016/j.jsis.2005.07.003
[68]. Lu, Y., Yang, S., Chau, P. Y. K., & 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.
[69]. Lule, I. (2012). Application of technology acceptance model (TAM) in m-banking adoption in Kenya.
[70]. Magsamen-Conrad, K., Dowd, J., Abuljadail, M., Alsulaiman, S., & Shareefi, A. (2015). Life-span differences in the uses and gratifications of tablets: Implications for older adults. Computers in Human Behavior, 52, 96-106.
[71]. Mallat, N. (2007). Exploring consumer adoption of mobile payments–A qualitative study. The Journal of Strategic Information Systems, 16(4), 413-432.
[72]. McClure, C. R. (1994). Network literacy: A role for libraries? Information Technology and libraries, 13(2), 115.
[73]. Mehrad, D., & Mohammadi, S. (2016). Word of Mouth impact on the adoption of mobile banking in Iran. Telematics and Informatics. doi:10.1016/j.tele.2016.08.009
[74]. Mills, A., Tennant, V., & Chevers, D. A. (2011). Understanding mobile internet diffusion: the case of Jamaica.
[75]. Ming‐Yen Teoh, W., Choy Chong, S., Lin, B., & Wei Chua, J. (2013). Factors affecting consumers’ perception of electronic payment: an empirical analysis. Internet Research, 23(4), 465-485. doi:10.1108/IntR-09-2012-0199
[76]. Mollick, E. R., & Kuppuswamy, V. (2014). After the campaign: Outcomes of crowdfunding. UNC Kenan-Flagler Research Paper(2376997).
[77]. Nejad, M. G., Sherrell, D. L., & Babakus, E. (2014). Influentials and Influence Mechanisms in New Product Diffusion: An Integrative Review. Journal of Marketing Theory & Practice, 22(2), 185-208. doi:10.2753/MTP1069-6679220212
[78]. Oechslein, O., & Hess, T. (2014). The Value of a Recommendation: The Role of Social Ties in Social Recommender Systems. 1864-1873. doi:10.1109/hicss.2014.235
[79]. Oliveira, T., Faria, M., Thomas, M. A., & Popovič, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International Journal of Information Management, 34(5), 689-703.
[80]. 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.
[81]. 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.
[82]. Pandy, S., Crowe, M., 2014. Mobile Payments Industry Workgroup meeting discussion on tokenization landscape in the U.S. Federal Reserve Bank of Boston, Boston, MA, September 23.
[83]. Patwardhan, P., & Yang, J. (2003). Internet Dependency Relations and Online Consumer Behavior. Journal of Interactive Advertising, 3(2), 57-69.
[84]. 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.
[85]. Prior, D. D., Mazanov, J., Meacheam, D., Heaslip, G., & Hanson, J. (2016). Attitude, digital literacy and self efficacy: Flow-on effects for online learning behavior. The Internet and Higher Education, 29, 91-97.
[86]. Quasim, T., Brown, J., & Kinsella, J. (2015). Employment, social dependency and return to work after intensive care. Journal of the Intensive Care Society, 16(1), 31-36.
[87]. Reychav, I., Ndicu, M., & Wu, D. (2016). Leveraging social networks in the adoption of mobile technologies for collaboration. Computers in Human Behavior, 58, 443-453.
[88]. Rogers, E. M. (1995). Diffusion of Innovations.
[89]. Rogers, E. M. (2010). Diffusion of innovations: Simon and Schuster.
[90]. Shaw, N. (2014). The mediating influence of trust in the adoption of the mobile wallet. Journal of Retailing and Consumer Services, 21(4), 449-459.
[91]. Li, S., & Jin, Z. (2013). Global Dynamics Analysis of Homogeneous New Products Diffusion Model. Discrete Dynamics in Nature and Society, 2013, 6.
[92]. Skumanich, S. A., & Kintsfather, D. P. (1998). Individual media dependency relations within television shopping programming a causal model reviewed and revised. Communication Research, 25(2), 200-219.
[93]. Straub, D. W. (1989). Validating instruments in MIS research. MIS quarterly, 147-169.
[94]. Susanto, A., Chang, Y., & Ha, Y. (2016). Determinants of continuance intention to use the smartphone banking services. Industrial Management & Data Systems, 116(3), 508-525. doi:10.1108/imds-05-2015-0195
[95]. Tan, G. W.-H., Ooi, K.-B., Chong, S.-C., & Hew, T.-S. (2014). NFC mobile credit card: The next frontier of mobile payment? Telematics and Informatics, 31(2), 292-307.
[96]. Ting, Y.-L. (2015). Tapping into students' digital literacy and designing negotiated learning to promote learner autonomy. The Internet and Higher Education, 26, 25-32.
[97]. Ting, H., Yacob, Y., Liew, L., & Lau, W. M. (2016). Intention to Use Mobile Payment System: A Case of Developing Market by Ethnicity. Procedia - Social and Behavioral Sciences, 224, 368-375. doi:http://dx.doi.org/10.1016/j.sbspro.2016.05.390
[98]. Tzou, R.-C., & Lu, H.-P. (2009). Exploring the emotional, aesthetic, and ergonomic facets of innovative product on fashion technology acceptance model. Behaviour & Information Technology, 28(4), 311-322.
[99]. Usher, E. L., & Pajares, F. (2008). Sources of self-efficacy in school: Critical review of the literature and future directions. Review of educational research, 78(4), 751-796.
[100]. Valente, T. W. (1996). Social network thresholds in the diffusion of innovations. Social Networks, 18(1), 69-89. doi:http://dx.doi.org/10.1016/0378-8733(95)00256-1
[101]. van Dinther, M., Dochy, F., & Segers, M. (2011). Factors affecting students’ self-efficacy in higher education. Educational research review, 6(2), 95-108.
[102]. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 36(1), 157-178.
[103]. Wang, Y., Min, Q., & Han, S. (2016). Understanding the effects of trust and risk on individual behavior toward social media platforms: A meta-analysis of the empirical evidence. Computers in Human Behavior, 56, 34-44.
[104]. Wendy Ming‐Yen, T., Siong Choy, C., Binshan, L., & Jiat Wei, C. (2013). Factors affecting consumers’ perception of electronic payment: an empirical analysis. Internet Research, 23(4), 465-485.
[105]. Weng, F., Cheong, F., & Cheong, C. (2010). Modelling IS Student Retention in Taiwan: Extending Tinto and Bean’s Model with Self-Efficacy. Innovation in Teaching and Learning in Information and Computer Sciences, 9(2), 1-12.
[106]. Xipeng, L. (2015). The Ubiquinomics: The New Strategic Thinking After the Sharing Economy. Frontiers, 22, 005.
[107]. Yeşilyurt, E., Ulaş, A. H., & Akan, D. (2016). Teacher self-efficacy, academic self-efficacy, and computer self-efficacy as predictors of attitude toward applying computer-supported education. Computers in Human Behavior, 64, 591-601.
[108]. Yu, X., & Xie, J. (2014). Modeling Mutual Influence Between Social Actions and Social Ties.
[109]. Zhang, C.-B., Li, Y.-N., Wu, B., & Li, D.-J. (2016). How WeChat can retain users: Roles of network externalities, social interaction ties, and perceived values in building continuance intention. Computers in Human Behavior. doi:10.1016/j.chb.2016.11.069
[110]. Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760-767.
[111]. Zolkepli, I. A., & Kamarulzaman, Y. (2015). Social media adoption: The role of media needs and innovation characteristics. Computers in Human Behavior, 43, 189-209.
[112]. A study of user switching intention towards mobile payment: a Push-Pull-Mooring framework approach. Retrieved from http://nccur.lib.nccu.edu.tw/handle/140.119/98852
中文文獻
[1]. 葉建亨、黃文楨(2011)。整合社會資本與社會交換理論探討虛擬社群知識分享意願。中華民國資訊管理學報。第十八卷，第三期，頁 76-100。
[2]. 高君琳(2007)。"台灣地區檔案素養評估指標之研究"。
[3]. 臺灣金融研訓院編輯委員會編著(2016)。金融數位力：金融數位力知識檢定2016年版。臺北市：臺灣金融研訓院。
[4]. 衛瀟(2016)。以PPM模型探討移動支付用戶之轉移意圖。取自：http://nccur.lib.nccu.edu.tw/handle/140.119/98852