바로가기메뉴

본문 바로가기 주메뉴 바로가기

logo

Use Intention of Mobile Fingerprint Payment between UTAUT and DOI in China

The Journal of Distribution Science / The Journal of Distribution Science, (P)1738-3110; (E)2093-7717
2017, v.15 no.10, pp.15-28
https://doi.org/https://doi.org/10.15722/jds.15.10.201710.15
Wu, Runze
Lee, Jong-Ho
  • Downloaded
  • Viewed

Abstract

Purpose - With the rapid growth of Chinese mobile pay market, it's necessary to run a study of the aims why users prefer to intention of use for mobile fingerprint payment. To reach this goal, UTAUT added Perceived Security and DOI. Research design, data, and methodology - The researchers conducted this study by using collected 3126 responses and the collected data was analyzed by applying statistical techniques factor analysis, AMOS, and Cronbach's Alpha and SPSS 22.0. Results - The result shows that compatibility and relative advantage of mobile fingerprint payment have positive effect on performance expectancy and effort expectancy separately, and the performance expectancy and effort expectancy have positive effect on people's use intention of mobile fingerprint payment. Social influence has a positive effect on the users' use intention of mobile fingerprint payment, Facilitating conditions has a slight effect on the users' use intention of mobile fingerprint payment, Perceived security has the most significant effect on he users' use intention of mobile fingerprint payment. Conclusions - The research showed that compatibility is one of the most important elements that make users continue to use the product. The mobile fingerprint payment must own clearer advantages than other ones that it can reach the biggest market. The Social Influence has a positive influence on the intention of use.

keywords
UTAUT, DOI, Mobile Fingerprint Payment

Reference

1.

Aladwani, A. M. (2002). The development of two tools for measuring the easiness and usefulness of transactional Web sites. European Journal of Information Systems, 11(3), 223-234.

2.

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.

3.

Badra, M., & Badra, R. B. (2016). A Lightweight Security Protocol for NFC-based Mobile Payments. Procedia Computer Science, 83, 705-711.

4.

Baghdadi, Y. (2013). Enterprise social interaction patterns for enterprise transformation. J. Enterp. Transform, 3(4), 307–329.

5.

Chakchai, S. I., Songyut, P., & Kanokmon, R. (2016). Soft computing-based localizations in wireless sensor networks. Pervasive and Mobile Computing, 29, 17-37.

6.

Chang, I. C., & Hwang, H. G. (2007). Physicians’acceptance of pharmacokinetics based clinical decision support systems. Expert Systems with Applications, 33(2), 296-303.

7.

Chang, Y. H., & Park, J. G. (2010). Adoption Model of Microblog: An Integrated Approach to Media Adoption Studies. Korean Journal of Journalism &Communication Studies, 54(5), 32-58.

8.

Chuang, M. C., Chen, M. C. (2014). An anonymous multi-server authenticated key agreement scheme based on trust computing using smart cards and biometrics. Expert Systems with Applications, 41(4), 1411-1418.

9.

Chun, T. Y., & Park, N. H. (2015). The Effect of Augmented Reality Traits on Presence, Flow, and Relational Continuance Behavior with Smart-Phones. Journal of Distribution Science, 13(5), 45-52.

10.

Dahlberg, T., & Mallat, N. (2002). Mobile payment service development-managerial implications of consumer value perceptions. European Conference on Information Systems, 2002(6), 6-8.

11.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.

12.

DCCI (2016). Retrieved May 21, 2017. from http://www.dcci.com.cn/

13.

Derawi, M. O. (2011). Biometric options for mobile phone authentication. Biometric Technology Today, 2011(9), 5-7.

14.

Eom, W. S., & Jeon, I. O. (2011). Development for Reliability Quality and Performance Evaluate Model of Fingerprint Recognition System. The Journal of the Korea Contents Association, 11(2), 79-87.

15.

Hosseini, S. S., & Shahriar, M. (2012). Review Banking on Biometric in the World’s Banks and Introducing a Biometric Model for Iran’s Banking System. Journal of Basic and Applied Scientific Research, 2(9), 9152-9160.

16.

Im, I., Hong, S. T., & Kang, M. S. (2011). An international comparison of technology adoption:Testing the utaut model. Information &Management, 48(1), 1-8.

17.

iResearch (2017). Retrieved May 21, 2017. from http://www.iresearch.com.cn/

18.

Jung, B. H., & Kim, H. K. (2016). The Effects of Belongingness and Loneliness on Self-Disclosure in MIM: The Moderating Role of System Quality. Journal of Distribution Science, 14(9), 85-94.

19.

Karnouskos, S., & Vilmos, A. (2004). Mobile payment: A journey through existing procedures and standardization initiatives. IEEE Communications Surveys and Tutorials, 6(4), 44–66.

20.

Kerviler, G., Demoulin, T. M., & Zidda, P. (2016). Adoption of in-store mobile payment: Are perceived risk and convenience the only drivers?. Journal of Retailing and Consumer Services, 31, 334-344.

21.

Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand’s community health centers:Applying the UTAUT model. International Journal of Medical Informatics, 78(6), 404-416.

22.

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.

23.

Kim, H. K., & Kim, W. K. (2017). An Exploratory Study for Artificial Intelligence Shopping Information Service. Journal of Distribution Science, 15(4), 69-78.

24.

Kim, J. B. (2011). Research Issues in Mobile Banking in the Smart Phone Era: Korean Case and Literature Survey. Entrue Journal of Information Technology, 10(2), 223-238.

25.

Kim, T. H. (2014). Threat of Mobile Payment Security in Chaina. Retrieved May 21, 2017. from http://www.boannews.com/media/view.asp?idx=40843&kind=0

26.

Kim, Y. O., Byun, C. G., & Ryu, T. C. (2011). A Study on the Current Fire Insurance Subscription and Solutions for Ensuring the Safety of the Traditional Market. Journal of Distribution Science, 9(4), 43-50.

27.

Lee, J. G., & Lee, M. Y. (2006). Examining Factors Affecting the Intention to Use IP-TV with the Extended Technology Acceptance Model(TAM). Broadcasting & Communication, 7(1), 100-131.

28.

Lee, J. Y. (2012). A Study on a Fingerprint Identification System Complemented with Additional Three-Dimensional Information. Journal of the Korea Academia-Information cooperation Society, 13(3), 1318.

29.

Lee, K. J., Choi, M. H., & Kwon, S. H. (2011). Current Status and Future of Mobile Payment Business Models. Korea Payment & Settlement Association, 5(2), 63-83.

30.

Lee, S. C., & Ahn, S. H. (2009). Business Model and Floral Distribution Service Strategy for Creating New Value on Internet Environment: ROSE Web Solution Case. Journal of Distribution Science, 7(1), 5-34.

31.

Lee, S. C., & Suh, E. K. (2012). User Satisfaction of Mobile Convergence Device: The Expectation and Disconfirmation Approach. Journal of Distribution Science, 10(11), 89-99.

32.

Lee, S. J., & Dae. J. (2014). The Effect of Mobile Tourism Information Service Features on Perceived Value, Satisfaction, and Using Intentions. Journal of Distribution Science, 12(12), 75-82.

33.

Lee, S. J., & Dai, J. (2015). Use Intentions of Mobile Tour Apps through Expansion of the Technology Acceptance Model. Journal of Distribution Science, 13(10), 135-142.

34.

Liu, C. L., Tsai, C. J., Chang, T. Y., Tsai, W. J., & Zhong, P. K. (2015). Implementing multiple biometric features for a recall-based graphical keystroke dynamics authentication system on a smart phone. Journal of Network and Computer Applications, 53, 128-139.

35.

Liu, Y. (2015). Consumer protection in mobile payments in China: A critical analysis of Alipay’s service agreement. Computer Law & Security Review, 31(5), 679-688.

36.

Manvi, S. S., Bhajantri, L. B., & Vijayakumar, M. A. (2009). Secure Mobile Payment System in Wireless Environment. ICFCC 09 Proceedings of the 2009International Conference on Future Computer and Communication, 2009(4), 31-35.

37.

Martins, C., Oliveira, T., & Popovič, A. (2014). Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34(1), 1-13.

38.

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.

39.

Miltaen, C. L., Popovic, A., & Oliveria, T. (2013). Determinants of end-user acceptance of biometrics:Integrating the “Big 3” of technology acceptance with privacy context. Decision Support Systems, 56, 103-114.

40.

Min, C. H., & Ko, W. S. (2005). The Empirical Research on Mobile Payment Commonplace Characteristics from the Recognition of Mobile Security Services. Korea Academy Society of e-Business, 6(2), 43-53.

41.

Morosan, C., & DeFranco, A. (2016). It’s about time:Revisiting UTAUT2 to examine consumers'intentions to use NFC mobile payments in hotels. International Journal of Hospitality Management, 53, 17-29.

42.

Oh, Y. S., & Lee, Y. C. (2012). An Exploratory Study of the Utilitarian and Hedonic Values on Buying Intention in Mobile Service. Journal of Distribution Science, 10(9), 23-29.

43.

Ohana, D. J., Phillips, L., & Chen, L. (2013). Preventing Cell Phone Intrusion and Theft using Biometrics:Fingerprint Biometric Security utilizing Dongle and Solid State Relay Technology. 2013 IEEE Security and Privacy Workshops, 24(23), 173-180

44.

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.

45.

Oliveira, Y., 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.

46.

Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). ES-QUAL a Multiple-item Scale for Assessing Electronic Service Quality. Journal of Service Research, 7(3), 213-233.

47.

Park, A., & Lee, K. J. (2014). Critical Success Factor of Noble Payment System: Multiple Case Studies. Korea Intelligent Information Systems Society, 20(4), 59-87.

48.

Rivera, G. G., Garrido, J., Ribalda, R., & Castro, A. (2010). A Mobile Biometric System-on-Token System for Signing Digital Transactions. IEEE Security & Privacy, 8(2), 13-19.

49.

Rogers, E. M. (1995). Difusion of innovations(4th ed.). New York: Free Press.

50.

Rogers, E. M. (2003). Difusion of innovations(5th ed.). New York: Free Press.

51.

Rouibah, K., Lowry, P. B., & Hwang, Y. (2016). The effects of perceived enjoyment and perceived risks on trust formation and intentions to use online payment systems: New perspectives from an Arab country. Electronic Commerce Research and Applications, 19, 33-43.

52.

Sadhya, D., & Singh, S. K. (2016). Privacy preservation for soft biometrics based multimodal recognition system. Computers & Security, 58, 160-179.

53.

Salisbury, R. P., Pearson, A., & Miller, D. W. (2001). Identifying barriers that keep shoppers off the World Wide Web: developing a scale of perceived web security. Industrial Management & Data Systems, 101(4), 165-176.

54.

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 .

55.

Seo, D. G., Park, Y., Kim, M. K., & Park, J. (2016). Mobile phone dependency and its impacts on adolescents’ social and academic behaviors. Computers in Human Behavior, 63, 282-292.

56.

Shen, Y. C., Huang, C. Y., Chuand, C. H., Hsu, C. T. (2010). A Benefit-Cost Perspective of The Consumer Adoption of The Mobile Banking System. Behaviour & Information Technology, 29(5), 497-511.

57.

Sohn, K. W., & Liu, W. S. (2015). The Price of Risk in the Korean Stock Distribution Market after the Global Financial Crisis. Journal of Distribution Science, 13(5), 71-82.

58.

Suh, E. K. (2015). A Study of Factors Affecting Group Polarization in Online Communication: Based on Anonymity. Journal of Distribution Science, 13(2), 75-83.

59.

Tan, W. H., Tan, B. I., & Ooi, K. B. (2011). Cash, credit card or mobile phone? Exploring the intention to adopt mobile credit card: A conceptual model. International Research Symposium in Service Management Yogyakarta, 22(8), 26-30.

60.

Touray, A., Salminen, A., & Murse, A. (2014). Internet adoption at the User Level: Empirical Evidence from The Gambia. Information Technology for Development, 21(2), 281-296.

61.

Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315.

62.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology:Toward a unified view. MIS Quarterly, 27(3), 425-478.

63.

Wang Y. S., & Shih, Y. W. (2009). Why do people use information kiosks: A Validation of the Unified Theory of Acceptance and Use of Technology. Government Information Quarterly, 26(1), 158-165.

64.

Wei, J., Lowry, P. B., & Seedorf, S. (2016). The assimilation of RFID technology by Chinese companies: A technology diffusion perspective. Information & Management, 52(6), 625-642.

65.

Wu, F., Xu, L. L., Kumari, S., & Li, X. (2015). A novel and provably secure biometrics-based three-factor remote authentication scheme for mobile client–server networks. Computers & Electrical Engineering, 45, 274-285.

66.

Wu, I. L., & Chen, J. L. (2014). A stage-based diffusion of IT innovation and the BSC performance impact:A moderator of technology organization environment. Technological Forecasting and Social Change, 88, 76-90.

67.

Wu, R. Z., & Lee, J. H. (2017). The Use Intention of Mobile Travel Apps by Korea-Visiting Chinese Tourists. Journal of Distribution Science, 15(5), 53-64.

68.

Wu, R. Z., & Lee, J. H. (2016). The Effects of Repurchase Intention by Social Commerce Traits and Consumer’s Traits in China. Journal of Distribution Science, 14(5), 97-106.

69.

Wua, J. H., & Wang, S. H. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information &Management, 42(5), 719–729.

70.

Yang, H. C., & Kim, Y. E. (2015). Intermittent Addiction and Double Sidedness of Thought Suppression:Effects of Student Smart Phone Behavior. Journal of Distribution Science, 13(9), 13-18.

71.

Yang, Q., Pang, C., Liu, L., Yen, D. C., & Tarn, J. M. (2015). Exploring consumer perceived risk and trust for online payments: An empirical study in China’s younger generation. Computers in Human Behavior, 50, 9–24.

72.

Yoon, C. (2010). Antecedents of Customer Satisfaction with Online Banking in China: The Effects of Experience. Computers in Human Behavior, 26(6), 1296-1304.

73.

Zhu, K., Kraemer, K. L., & Xu, S. (2006). The Process of Innovation Assimilation by Firms in Different Countries: A Technology Diffusion Perspective on E-Business. Management Science, 52(10), 1557-1576.

The Journal of Distribution Science