| 研究生: |
洪士庭 Hung, Shih-Ting |
|---|---|
| 論文名稱: |
影響使用通路限定型行動支付APP意圖之因素探討 Analyzing the Determining Factors of Intention to Use Channel-Specific Mobile Payment APP |
| 指導教授: |
盧筱涵
Lu, Hsiao-Han |
| 學位類別: |
碩士 Master |
| 系所名稱: |
管理學院 - 電信管理研究所 Institute of Telecommunications Management |
| 論文出版年: | 2021 |
| 畢業學年度: | 109 |
| 語文別: | 中文 |
| 論文頁數: | 95 |
| 中文關鍵詞: | 限定型 、行動支付 、品牌 、折價券 、科技接受模型 、使用意圖 |
| 外文關鍵詞: | Channel-Specific, Mobile Payment, Technology Acceptance Model, Brand Attachment, Coupon Proneness |
| 相關次數: | 點閱:119 下載:31 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
拜行動網路的普及和政府政策加持所賜,近年行動支付發展蔚為風潮,尤其由品牌商推出的「限定型」行動支付APP,更是有如雨後春筍般出現,帶領台灣進入行動支付的戰國時代,這些挾帶品牌優勢的限定型APP是否能站穩競爭白熱化的支付市場值得關注。而在探討影響用戶對於接受資訊科技及新技術之採用意圖的研究中,以科技接受模型(TAM)最為廣泛被應用,因其能在各大領域中合理解釋及預測個人對於採用科技之接受行為,尤其行動支付相關文獻更是常見,所以本研究選用並延伸科技接受模型,探討用戶使用限定型行動支付APP的因素,考量品牌限定之特點,以自我效能、系統品質、資訊品質、知覺易用性、知覺有用性、品牌自我一致性、品牌依戀、折價券傾向以及知覺風險變數作為討論對使用意圖之影響。
研究透過網路問卷的形式發放,共回收519份樣本,有效樣本數為473份,接著使用結構方程模型做為資料分析方法。統計分析結果顯示,用戶的自我效能和APP的系統品質將產生知覺易用性,且其中又以系統品質影響較深,而APP的資訊品質則被發現將影響知覺有用性。另外,知覺易用性和知覺有用性的關係再度被證實為顯著的。至於直接影響使用意圖的有知覺有用性、品牌依戀和折價券傾向,其中又以知覺有用性的影響效果最好,並發現知覺風險並無相關影響。
本研究的貢獻為少數專門探討台灣限定型支付APP的發展現況,而這在過去行動支付領域文獻中較少提及,此外還加入諸如品牌依戀、折價券傾向的變數作為討論,是相對新穎的地方。最後,本研究也針對分析結果進行討論,提出管理意涵和建議,供有意進入行動支付市場的品牌商作為策略參考。
Thanks to the popularization of mobile networks and government policy, the development of mobile payment has become a trend in recent years. Particularly “channel specific” mobile payment apps which are launched by retailers have sprung up. Whether this type of mobile payment app with brand advantages can stand firm in the fiercely competitive payment market is worth paying attention to. Through extending the technology acceptance model, this research aims to explore the factors of users using channel specific mobile payment apps. Variables including self-efficacy, system quality, information quality, perceived ease of use, perceived usefulness, brand-self congruity, brand attachment, coupon proneness, and perceived risk are used as the impact on intention to use. The research was distributed in the form of online questionnaires. Total 519 samples were collected, with a valid of 473 samples. Then the structural equation modeling was used as the data analysis method. The statistical analysis results show that user's self-efficacy and system quality would affect perceived ease of use, and system quality has a greater influence, while information quality was found to affect perceived usefulness instead. In addition, the relationship between perceived ease of use and perceived usefulness was once again proved to be significant. As for perceived usefulness, brand attachment and coupon proneness that directly affect intention to use, perceived usefulness has the greatest effect, and it is found that perceived risk has no significant impact. Finally, this research also discusses the results of the analysis, and puts forward managerial implications and suggestions for brand retailer who intend to enter the mobile payment market as a strategic reference.
網路文獻
iThome(2015)。資策會調查:國內行動裝置用戶已超過1600萬。2020年取自:
https://www.ithome.com.tw/news/97479
WishMobile(2020)。新冠疫情催出「非接觸新經濟」,盤點當前5大非接觸應用
場景。2020年取自: https://ppt.cc/f4NB5x
科技新報(2018)。行動支付再進化,百貨業者推會員專屬Pay。2020年取自:
https://technews.tw/2018/07/31/department-store-mobile-payment-taiwan/
經濟日報(2020)。國發會:2025年行動支付普及率90%。2020年取自:
https://money.udn.com/money/story/5613/4300286
經濟日報(2021)。疫情效應 行動支付衝上5000億。2021年取自:
https://money.udn.com/money/story/5613/5496788
數位時代(2018)。麥當勞開放支援信用卡、三大Pay!VISA推出專屬優惠。2020年取自:
https://www.bnext.com.tw/article/48601/mcdonalds-mobile-payment-kfc
數位時代(2019)。7-11加入支付戰icash Pay上線!搶攻無卡的學生族、新鮮
人,拚綁定增3成。2020年取自:
https://www.bnext.com.tw/article/55356/icash-pay-launched
數位時代(2019)。終於!吃麥當勞可嗶悠遊卡,一次看懂各家速食業者支付工
具。2020年取自: https://www.bnext.com.tw/article/52815/mcdonalds-now-
accept-east-card-payment
數位時代(2020)。憂鈔票沾病毒,行動支付取用大增!疫情下的3個消費現
象。2020年取自: https://www.bnext.com.tw/article/57193/coronavirus-triggers-
boom-in-payment
數位時代(2020)。台灣人愛用支付APP調查出爐:LINE PAY稱霸70款行動支
付、全聯PX Pay擊敗Apple Pay。2020年取自:
https://www.bnext.com.tw/article/58421/mobile-payment-line-applepay
資策會(2018)。台灣人更黏手機了!近8成民眾每天使用手機逾2小時 資策
會:掌握娛樂市場需求成下一波商機。2020年取自:
https://www.iii.org.tw/Press/NewsDtl.aspx?nsp_sqno=2081&fm_sqno=14
資策會產業情報研究所(2020)。【行動支付大調查】行動支付用戶達六成 最常
使用方案與場域大排名。2020年取自: https://mic.iii.org.tw/news.aspx?id=551
資策會產業情報研究所(2020)。【2020上半年行動支付大調查】行動支付首選偏
好度已首度超越實體卡。2020年取自: https://mic.iii.org.tw/news.aspx?id=572
中文文獻
吳萬益(2019),企業研究方法(第六版),台北:華泰文化
張紹勳(2001),研究方法,修訂版,台中:滄海書局
英文文獻
Ahn, T., Ryu, S., & Han, I. (2007). The impact of Web quality and playfulness on user acceptance of online retailing. Information & management, 44(3), 263-275.
Ainsworth, M. D. S., & Bell, S. M. (1970). Attachment, exploration, and separation: Illustrated by the behavior of one-year-olds in a strange situation. Child development, 49-67.
Akram, U., Ansari, A. R., Fu, G., & Junaid, M. (2020). Feeling hungry? let's order through mobile! examining the fast food mobile commerce in China. Journal of Retailing and Consumer Services, 56, 102142.
Akram, U., Hui, P., Khan, M. K., Tanveer, Y., Mehmood, K., & Ahmad, W. (2018). How website quality affects online impulse buying. Asia Pacific Journal of Marketing and Logistics.
Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Williams, M. D. (2016). Consumer adoption of mobile banking in Jordan. Journal of Enterprise Information Management.
Arvidsson, N. (2014). Consumer attitudes on mobile payment services–results from a proof of concept test. International Journal of Bank Marketing.
Bailey, A. A., Pentina, I., Mishra, A. S., & Mimoun, M. S. B. (2017). Mobile payments adoption by US consumers: an extended TAM. International Journal of Retail & Distribution Management.
Bandura, A. (1986). The explanatory and predictive scope of self-efficacy theory. Journal of social and clinical psychology, 4(3), 359-373.
Bandura. A. (1997). Self-efficacy. the Exercise of Control. New York: W.H. Freeman.
Bandura, A., Freeman, W., & Lightsey, R. (1999). Self-efficacy: The exercise of control. In: Springer.
Baptista, G., & Oliveira, T. (2016). A weight and a meta-analysis on mobile banking acceptance research. Computers in Human Behavior, 63, 480-489.
Bauer, R. A. (1960). Consumer behavior as risk taking. Chicago, IL, 384-398.
Bauer, Raymond A. (1967), "Consumer Behavior as Risk Taking," In Donald F. Cox (eds.), Risk Taking and Information Handling in Consumer Behavior, Boston, MA: Harvard University Press, pp. 23-33.
Bossen, C., Jensen, L. G., & Udsen, F. W. (2013). Evaluation of a comprehensive EHR based on the DeLone and McLean model for IS success: approach, results, and success factors. International journal of medical informatics, 82(10), 940-953.
Bowlby, J. (1958). The nature of the child's tie to his mother. International journal of psycho-analysis, 39, 350-373.
Cao, X., Gong, M., Yu, L., & Dai, B. (2020). Exploring the mechanism of social media addiction: an empirical study from WeChat users. Internet Research.
Carranza, R., Díaz, E., Martín-Consuegra, D., & Fernández-Ferrín, P. (2020). PLS–SEM in business promotion strategies. A multigroup analysis of mobile coupon users using MICOM. Industrial Management & Data Systems.
Chen, C. C., & Tsai, J. L. (2017). Determinants of behavioral intention to use the Personalized Location-based Mobile Tourism Application: An empirical study by integrating TAM with ISSM. Future Generation Computer Systems, 96, 628-638.
Chen, K., Chen, J. V., & Yen, D. C. (2011). Dimensions of self-efficacy in the study of smart phone acceptance. Computer Standards & Interfaces, 33(4), 422-431.
Chen, R. F., & Hsiao, J. L. (2012). An investigation on physicians’ acceptance of hospital information systems: a case study. International journal of medical informatics, 81(12), 810-820.
Cheng, Y. H., & Huang, T. Y. (2013). High speed rail passengers’ mobile ticketing adoption. Transportation Research Part C: Emerging Technologies, 30, 143-160.
Cheong, J. H., & Park, M. C. (2005). Mobile internet acceptance in Korea. Internet research.
Chi, T. (2018). Understanding Chinese consumer adoption of apparel mobile commerce: An extended TAM approach. Journal of Retailing and Consumer Services, 44, 274-284.
Constantiou, I. D., Damsgaard, J., & Knutsen, L. (2006). Exploring perceptions and use of mobile services: user differences in an advancing market. International Journal of Mobile Communications, 4(3), 231-247.
Cyr, D., Head, M., Lim, E., & Stibe, A. (2018). Using the elaboration likelihood model to examine online persuasion through website design. Information & Management, 55(7), 807-821.
Dahlberg, T., Guo, J., & Ondrus, J. (2015). A critical review of mobile payment research. Electronic Commerce Research and Applications, 14(5), 265-284.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8), 982-1003.
De Chernatony, L., Veloutsou, C., Christodoulides, G., & Cottam, S. (2009). Introduction: Special issue on advances in brand management. Journal of Business Research, 62(3), 289-290.
DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information systems research, 3(1), 60-95.
Delone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of Management Information Systems, 19(4), 9-30.
DeLone, W. H., & McLean, E. R. (2004). Measuring e-commerce success: Applying the DeLone & McLean information systems success model. International Journal of electronic commerce, 9(1), 31-47.
Demoulin, N. T., & Djelassi, S. (2016). An integrated model of self-service technology (SST) usage in a retail context. International Journal of Retail & Distribution Management.
Dutot, V. (2015). Factors influencing near field communication (NFC) adoption: An extended TAM approach. The Journal of High Technology Management Research, 26(1), 45-57.
Faisal, C. N., Gonzalez-Rodriguez, M., Fernandez-Lanvin, D., & de Andres-Suarez, J. (2016). Web design attributes in building user trust, satisfaction, and loyalty for a high uncertainty avoidance culture. IEEE Transactions on Human-Machine Systems, 47(6), 847-859.
Faqih, K. M. (2013). Exploring the influence of perceived risk and internet self-efficacy on consumer online shopping intentions: Perspective of technology acceptance model. International Management Review, 9(1), 67-77.
Faqih, K. M., & Jaradat, M. I. R. M. (2015). Assessing the moderating effect of gender differences and individualism-collectivism at individual-level on the adoption of mobile commerce technology: TAM3 perspective. Journal of Retailing and Consumer Services, 22, 37-52.
Farah, M. F., Hasni, M. J. S., & Abbas, A. K. (2018). Mobile-banking adoption: empirical evidence from the banking sector in Pakistan. International Journal of Bank Marketing.
Floropoulos, J., Spathis, C., Halvatzis, D., & Tsipouridou, M. (2010). Measuring the success of the Greek taxation information system. International Journal of Information Management, 30(1), 47-56.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Fraley, R. C., & Shaver, P. R. (2000). Adult romantic attachment: Theoretical developments, emerging controversies, and unanswered questions. Review of general psychology, 4(2), 132-154.
Fu, J.-R., Farn, C.-K., & Chao, W.-P. (2006). Acceptance of electronic tax filing: A study of taxpayer intentions. Information & Management, 43(1), 109-126.
Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: an extended technology acceptance perspective. Information & management, 39(8), 705-719.
Gorla, N., Somers, T. M., & Wong, B. (2010). Organizational impact of system quality, information quality, and service quality. The Journal of Strategic Information Systems, 19(3), 207-228.
Gupta, A., Yousaf, A., & Mishra, A. (2020). How pre-adoption expectancies shape post-adoption continuance intentions: An extended expectation-confirmation model. International Journal of Information Management, 52, 102094.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis.
Hwang, E., Baloglu, S., & Tanford, S. (2019). Building loyalty through reward programs: The influence of perceptions of fairness and brand attachment. International Journal of Hospitality Management, 76, 19-28.
Igbaria, M., & Iivari, J. (1995). The effects of self-efficacy on computer usage. Omega, 23(6), 587-605.
Im, H., & Ha, Y. (2015). Is this mobile coupon worth my private information? Consumer evaluation of acquisition and transaction utility in a mobile coupon shopping context. Journal of Research in Interactive Marketing.
Islam, J. U., Rahman, Z., & Hollebeek, L. D. (2018). Consumer engagement in online brand communities: a solicitation of congruity theory. Internet Research.
Karjaluoto, H., Laukkanen, T., & Kiviniemi, V. (2010). The role of information in mobile banking resistance. International Journal of bank marketing.
Karjaluoto, H., Riquelme, H. E., & Rios, R. E. (2010). The moderating effect of gender in the adoption of mobile banking. International Journal of bank marketing.
Kassarjian, H. H. (1971). Personality and consumer behavior: A review. Journal of marketing Research, 8(4), 409-418.
Kaufmann, H. R., Petrovici, D. A., Gonçalves Filho, C., & Ayres, A. (2016). Identifying moderators of brand attachment for driving customer purchase intention of original vs counterfeits of luxury brands. Journal of Business Research, 69(12), 5735-5747.
Khajehzadeh, S., Oppewal, H., & Tojib, D. (2014). Consumer responses to mobile coupons: The roles of shopping motivation and regulatory fit. Journal of Business Research, 67(11), 2447-2455.
Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310-322.
Kim, K. H., Yeo, I. G., & Kim, D. Y. (2004). Antecedents and consequences of trusts in on and off line in internet banking. Journal of Global Academy of Marketing Science, 13, 159-181.
Kim, M. J., Kim, W. G., Kim, J. M., & Kim, C. (2016). Does knowledge matter to seniors’ usage of mobile devices? Focusing on motivation and attachment. International Journal of Contemporary Hospitality Management.
Kwon, O., & Wen, Y. (2010). An empirical study of the factors affecting social network service use. Computers in Human Behavior, 26(2), 254-263.
Lam, S. Y., Chiang, J., & Parasuraman, A. (2008). The effects of the dimensions of technology readiness on technology acceptance: An empirical analysis. Journal of interactive marketing, 22(4), 19-39.
Lapierre, J., & Denier, A. (2005). ICT adoption and moderating effects of institutional factors on salesperson's communication effectiveness: a contingency study in high-tech industries. Technovation, 25(8), 909-927.
Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). The technology acceptance model and the World Wide Web. Decision Support Systems, 29(3), 269-282.
Lee, S., & Koubek, R. J. (2010). The effects of usability and web design attributes on user preference for e-commerce web sites. Computers in Industry, 61(4), 329-341.
Lee, Y., & Kozar, K. A. (2012). Understanding of website usability: Specifying and measuring constructs and their relationships. Decision support systems, 52(2), 450-463.
Leon, S. (2018). Service mobile apps: a millennial generation perspective. Industrial Management & Data Systems.
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.
Lewis, T. L., & Loker, S. (2014). Technology usage intent among apparel retail employees. International Journal of Retail & Distribution Management.
Li, C. Y., & Fang, Y. H. (2019). Predicting continuance intention toward mobile branded apps through satisfaction and attachment. Telematics and Informatics, 43, 101248.
Li, H., Liu, Y., & Heikkilä, J. (2014). Understanding the Factors Driving NFC-Enabled Mobile Payment Adoption: an Empirical Investigation. In PACIS (p. 231).
Liébana-Cabanillas, F., Muñoz-Leiva, F., & Sánchez-Fernández, J. (2018). A global approach to the analysis of user behavior in mobile payment systems in the new electronic environment. Service Business, 12(1), 25-64.
Liébana-Cabanillas, F., Muñoz-Leiva, F., Ibáñez-Zapata, J. A., & Rey-Pino, J. (2012). The role of mobile payment systems in Electronic Commerce. In Actas de la EMAC Conference.
Liébana-Cabanillas, F., Ramos de Luna, I., & Montoro-Ríos, F. (2017). Intention to use new mobile payment systems: a comparative analysis of SMS and NFC payments. Economic research-Ekonomska istraživanja, 30(1), 892-910.
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.
Lin, H. F. (2007). The role of online and offline features in sustaining virtual communities: an empirical study. Internet Research.
Lin, J. C. C., & Lu, H. (2000). Towards an understanding of the behavioural intention to use a web site. International journal of information management, 20(3), 197-208.
Lin, X., Featherman, M., & Sarker, S. (2017). Understanding factors affecting users’ social networking site continuance: A gender difference perspective. Information & Management, 54(3), 383-395.
Liu, B., & Karahanna, E. (2007). Emotional attachment to it brands and technology acceptance. Southern Association for Information Systems 2007 Proceedings, 7-12.
Liu, F., Zhao, X., Chau, P. Y., & Tang, Q. (2015). Roles of perceived value and individual differences in the acceptance of mobile coupon applications. Internet Research.
Liu, S. F., Huang, L. S., & Chiou, Y. H. (2012). An integrated attitude model of self-service technologies: evidence from online stock trading systems brokers. The Service Industries Journal, 32(11), 1823-1835.
Lu, Y., Deng, Z., & Wang, B. (2010). Exploring factors affecting Chinese consumers' usage of short message service for personal communication. Information systems journal, 20(2), 183-208.
Luarn, P., & Lin, H. H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in human behavior, 21(6), 873-891.
McKinney, V., Yoon, K., & Zahedi, F. M. (2002). The measurement of web-customer satisfaction: An expectation and disconfirmation approach. Information systems research, 13(3), 296-315.
Meschtscherjakov, A., Wilfinger, D., & Tscheligi, M. (2014). Mobile attachment causes and consequences for emotional bonding with mobile phones. Paper presented at the Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.
Moon, J.-W., & Kim, Y.-G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217-230.
Muñoz‐Leiva, F., Hernández‐Méndez, J., & Sánchez‐Fernández, J. (2012). Generalising user behaviour in online travel sites through the Travel 2.0 website acceptance model. Online Information Review.
Nassuora, A. B. (2013). Understanding factors affecting the adoption of m-commerce by consumers. Journal of Applied Sciences, 13(6), 913-918.
Olschewski, M., Renken, U. B., Bullinger, A. C., & Möslein, K. M. (2013). Are you ready to use? Assessing the meaning of social influence and technology readiness in collaboration technology adoption. Paper presented at the 2013 46th Hawaii International Conference on System Sciences.
Ooi, K. B., & Tan, G. W. H. (2016). Mobile technology acceptance model: An investigation using mobile users to explore smartphone credit card. Expert Systems with Applications, 59, 33-46.
Ovčjak, B., Heričko, M., & Polančič, G. (2015). Factors impacting the acceptance of mobile data services–A systematic literature review. Computers in human behavior, 53, 24-47.
Parida, V., Mostaghel, R., & Oghazi, P. (2016). Factors for elderly use of social media for health‐related activities. Psychology & Marketing, 33(12), 1134-1141.
Park, C. W., MacInnis, D. J., Priester, J., Eisingerich, A. B., & Iacobucci, D. (2010). Brand attachment and brand attitude strength: Conceptual and empirical differentiation of two critical brand equity drivers. Journal of marketing, 74(6), 1-17.
Pedeliento, G., Andreini, D., Bergamaschi, M., & Salo, J. (2016). Brand and product attachment in an industrial context: The effects on brand loyalty. Industrial Marketing Management, 53, 194-206.
Peikari, H. R., Shah, M. H., Zakaria, M. S., Yasin, N. M., & Elhissi, A. (2015). The impacts of second generation e-prescribing usability on community pharmacists outcomes. Research in Social and Administrative Pharmacy, 11(3), 339-351.
Perkowitz, M., & Etzioni, O. (1999). Towards adaptive Web sites: conceptual framework and case study. Computer networks, 31(11-16), 1245-1258.
Petter, S., DeLone, W., & McLean, E. (2008). Measuring information systems success: models, dimensions, measures, and interrelationships. European journal of information systems, 17(3), 236-263.
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.
Prayag, G., & Ryan, C. (2012). Antecedents of tourists’ loyalty to Mauritius: The role and influence of destination image, place attachment, personal involvement, and satisfaction. Journal of travel research, 51(3), 342-356.
Reid, M., & Levy, Y. (2008). Integrating trust and computer self-efficacy with technology acceptance model: An empirical assessment of customers’ acceptance of banking information systems (BIS) in Jamaica. Journal of Internet Banking and Commerce, 12(3).
Rogers, E. M. (1995). Diffusion of innovations, (4th ed.). New York: Free Press.
Rogers, E. M. (2003). Diffusion of Innovations, (5th ed.). New York, Free Press.
Sangwan, S. (2005, January). Virtual community success: A uses and gratifications perspective. In Proceedings of the 38th annual hawaii international conference on system sciences (pp. 193c-193c). IEEE.
Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & management, 44(1), 90-103.
Shin, D.-H. (2009). Towards an understanding of the consumer acceptance of mobile wallet. Computers in Human Behavior, 25(6), 1343-1354.
Shin, Y. M., Lee, S. C., Shin, B., & Lee, H. G. (2010). Examining influencing factors of post-adoption usage of mobile internet: Focus on the user perception of supplier-side attributes. Information Systems Frontiers, 12(5), 595-606.
Singh, N., Sinha, N., & Liébana-Cabanillas, F. J. (2020). Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness, stress to use and social influence. International Journal of Information Management, 50, 191-205.
Sirgy, M. J. (1982). Self-concept in consumer behavior: A critical review. Journal of consumer research, 9(3), 287-300.
Sujeet, K. S., & Srikrishna, G. (2014). Internet banking adoption in India: structural equation modelling approach. Journal of Indian Business Research, 6(2), 155-169.
Sun, J., & Chi, T. (2018). Key factors influencing the adoption of apparel mobile commerce: an empirical study of Chinese consumers. The journal of the Textile Institute, 109(6), 785-797.
Swaminathan, S., & Bawa, K. (2005). Category-specific coupon proneness: The impact of individual characteristics and category-specific variables. Journal of Retailing, 81(3), 205-214.
Tams, S., Thatcher, J. B., & Craig, K. (2018). How and why trust matters in post-adoptive usage: The mediating roles of internal and external self-efficacy. The Journal of Strategic Information Systems, 27(2), 170-190.
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information systems research, 6(2), 144-176.
Teo, A. C., Tan, G. W. H., Ooi, K. B., Hew, T. S., & Yew, K. T. (2015). The effects of convenience and speed in m-payment. Industrial Management & Data Systems.
Thakur, R., & Srivastava, M. (2014). Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India. Internet Research.
Thaler, R. (1983). Transaction utility theory. ACR North American Advances.
Thomson, M., MacInnis, D. J., & Whan Park, C. (2005). The ties that bind: Measuring the strength of consumers’ emotional attachments to brands. Journal of consumer psychology, 15(1), 77-91.
Tsao, W. C., Hsieh, M. T., & Lin, T. M. (2016). Intensifying online loyalty! The power of website quality and the perceived value of consumer/seller relationship. Industrial Management & Data Systems.
Upadhyay, P., & Jahanyan, S. (2016). Analyzing user perspective on the factors affecting use intention of mobile based transfer payment. Internet Research.
Van der Heijden, H. (2003). Factors influencing the usage of websites: the case of a generic portal in The Netherlands. Information & management, 40(6), 541-549.
Vance, A., Elie-Dit-Cosaque, C., & Straub, D. W. (2008). Examining trust in information technology artifacts: the effects of system quality and culture. Journal of management information systems, 24(4), 73-100.
VanMeter, R., Syrdal, H. A., Powell-Mantel, S., Grisaffe, D. B., & Nesson, E. T. (2018). Don't just “Like” me, promote me: How attachment and attitude influence brand related behaviors on social media. Journal of Interactive Marketing, 43, 83-97.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information systems research, 11(4), 342-365.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sciences, 39(2), 273-315.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
Venkatesh, V., & Morris, M. G. (2000). Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS quarterly, 115-139.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425-478.
Vijayasarathy, L. R. (2004). Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model. Information & management, 41(6), 747-762.
Vredeveld, A. J. (2018). Emotional intelligence, external emotional connections and brand attachment. Journal of Product & Brand Management.
Wang, K., & Lin, C. L. (2012). The adoption of mobile value‐added services. Managing Service Quality: An International Journal.
Wang, W. T., & Li, H. M. (2012). Factors influencing mobile services adoption: a brand-equity perspective. Internet Research: Electronic Networking Applications and Policy, 22(2), 142-179.
Wang, Y. S., Lin, H. H., & Luarn, P. (2006). Predicting consumer intention to use mobile service. Information Systems Journal, 16(2), 157-179.
Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information systems research, 16(1), 85-102.
Wu, J. H., Wang, S. C., & Lin, L. M. (2007). Mobile computing acceptance factors in the healthcare industry: A structural equation model. International journal of medical informatics, 76(1), 66-77.
Wu, J.-H., & Wang, S.-C. (2005). What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719-729.
Xu, H., Teo, H. H., Tan, B. C., & Agarwal, R. (2009). The role of push-pull technology in privacy calculus: the case of location-based services. Journal of management information systems, 26(3), 135-174.
Yang, K. (2010). The effects of technology self-efficacy and innovativeness on consumer mobile data service adoption between American and Korean consumers. Journal of International Consumer Marketing, 22(2), 117-127.
Yang, K. (2012). Consumer technology traits in determining mobile shopping adoption: An application of the extended theory of planned behavior. Journal of Retailing and Consumer Services, 19(5), 484-491.
Yang, K. C. (2005). Exploring factors affecting the adoption of mobile commerce in Singapore. Telematics and informatics, 22(3), 257-277.
Ye, Q., Luo, Y., Chen, G., Guo, X., Wei, Q., & Tan, S. (2019). Users Intention for Continuous Usage of Mobile News Apps: the Roles of Quality, Switching Costs, and Personalization. Journal of Systems Science and Systems Engineering, 28(1), 91-109.
Yi, M. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management, 43(3), 350-363.
Yuan, S., Liu, L., Su, B., & Zhang, H. (2020). Determining the antecedents of mobile payment loyalty: Cognitive and affective perspectives. Electronic Commerce Research and Applications, 100971.
Yuan, S., Liu, Y., Yao, R., & Liu, J. (2016). An investigation of users’ continuance intention towards mobile banking in China. Information Development, 32(1), 20-34.
Zhou, T. (2011). Examining the critical success factors of mobile website adoption. Online Information Review.