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A Study on Acceptance Intentions to Use the Mobile Payment Service Based on Biometric Authentication: Focusing on ApplePay

생체 인증 기반 모바일 결제 서비스 수용의도 분석: 애플페이를 중심으로

  • Kim, Kwanmo (Graduate School of Information Security at Sejong Cyber University) ;
  • Park, Yongsuk (Graduate School of Information Security at Sejong Cyber University)
  • 김관모 (세종사이버대학교 정보보호대학원) ;
  • 박용석 (세종사이버대학교 정보보호대학원)
  • Received : 2020.03.25
  • Accepted : 2020.07.20
  • Published : 2020.07.28

Abstract

The aim of this study is to scrutinize acceptance intentions of Korean users and influences of information security related factors on mobile payment services based on biometric authentication methods, like finger print authentication or face recognition authentication, by focusing on ApplePay. Unlike previous studies on user acceptance of mobile payment which lack considerations on information security related factors, this study employs the UTAUT with detailed information security factors to create a research model and PLS(Partial Least Squares) method to analyze the model. Based on the analysis, gaining trust on service through company's efforts on information protection, personal characteristics and trust on applied security technologies are important factors to Korean users along with social awareness and service infrastructures. The result of this study would be helpful to companies or organizations, which provide biometric-based mobile payment services, to understand needs of Korean consumers. Based on this study, further analysis is expected to find impacts of user experiences on same company's or competitors' products to acceptance intentions.

본 연구에서는 애플페이를 중심으로 하여 지문인식, 안면인식과 같은 생체 인증 기반 기술들을 이용한 모바일 결제 서비스에 대한 국내 사용자의 수용의도와 정보보호 관련 요인들이 미치는 영향을 분석하고자 하였다. 정보보호와 관련된 요인들에 대한 연구가 부족하였던 기존 모바일 결제 수용의도 연구들과 달리, 본 연구에서는 정보보호 관련 요인들을 추가한 모형을 통합기술수용이론(UTAUT)을 기반으로 만들고, 이를 PLS(Partial Least Squares) 기법을 이용하여 분석하였다. 분석을 통하여 기업의 정보보호 노력을 통한 제공 서비스 신뢰도 확보, 개인의 성향, 그리고 적용 보안기술에 대한 신뢰도는 사회적 인식 및 서비스 인프라와 함께 국내 사용자들에게 중요한 요인임을 알 수 있었다. 본 연구 결과는 생체인증 기반 모바일 결제 서비스를 제공하는 기업 또는 조직에게 국내 소비자들을 파악하는데 도움이 될 것으로 판단된다. 향후 본 연구를 바탕으로 동일 기업 또는 경쟁 기업 제품 사용 경험이 수용의도에 미치는 영향을 분석할 수 있을 것으로 기대한다.

Keywords

References

  1. A. He. (2019). Apple pay dominance drives mobile payment transaction volume. Emarketer(Online). https://www.emarketer.com/content/apple-pay-dominance-drives-mobile-payment-transaction-volume
  2. B. T. Kim. (2019). Emerging easy payment market, from barcode-based payment to SamsungPy. Newdaily(Online). http://biz.newdaily.co.kr/site/data/html/2019/11/12/2019111200154.html
  3. J. Porter. (2019). Apple could be forced to let Apple Pay competitors access NFC under German law. Verge(Online). https://www.theverge.com/2019/11/15/20966785/apple-pay-nfc-antitrust-german-law-parliament-competitors
  4. H. C. Kang. (2019). Will ApplePay be available?. Business Watch(Online). http://news.bizwatch.co.kr/article/finance/2019/09/10/0029
  5. S. H. Lee. (2019). Apple starts to lift up the bar for Korean market. MK(Online). https://mk.co.kr/news/it/view/2019/09/750856/
  6. J. S. Park & H. I. Kwon. (2018). A study on the factors influencing innovation resistance and intention of using on the biometrics technology, The Journal of Information Systems, 27(2), 53-75. https://doi.org/10.5859/KAIS.2018.27.2.53
  7. S. H. Kim. (2016). FIDO-based fintech authentication techonologies. The Journal of The Korean Institute of Communication Sciences, 33(2), 59-65.
  8. V. L. Johnson, A. Kiser, R. Washington & R. Torres. (2018). Limitations to the rapid adoption of M-payment services: understanding the impact of privacy risk on M-payment services. Computers in Human Behavior, 79, 111-122. DOI: 10.1016/j.chb.2017.10.035
  9. S. H. Noh & T. K. Kwon. (2014). A comparison study on domestic mobile easy payment services. Fall Conference of The Korea Society of Management Information Systems (pp. 695-698).
  10. S. W. Kim, B. J. Park, & S. Lee. (2015). Recent trends of fintech and comparative analysis of SamsungPay and ApplePay. Conference of Korean Institute of Information Scientists and Engineers (pp. 2054-2056).
  11. Apple. (2019). Make contactless payments using Apple Pay on iPhone. Apple(Online). https://support.apple.com/guide/iphone/make-contactless-payments-iphbd4cf42b4/ios
  12. J. H. Jeon. (2016). A study on security risk according to the activation of bio-authentication technology. Journal of Information and Security, 16(5), 57-63.
  13. V. Venkatesh, M. G. Morris, G. B. Davis & F. D. Davies. (2003). User acceptance of information technology: toward a unified view. MIS quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
  14. O. J. Kwon. (2010). An empirical study on potential smartphone users, Internet and Information Security, 1(1), 55-83.
  15. S. H. Jeon, N. R. Park & C. C. Lee. (2011). Study on the factors affecting the intention to adopt public cloud computing service, Entrue Journal of Information Technology, 10(2), 97-112.
  16. S. H. Yang, Y. S. Hwang & J. K. Park. (2016). A study on the use of fintech payment services based on the UTAUT model. Journal of Vocational Rehabilitation, 38(1), 183-209.
  17. M. Fiedler. (2015). ApplePay, towards the acceptance of German customers, Asian Social Science, 11(22), 124. DOI: 10.5539/ass.v11n22p124
  18. Y. J. Choi & H. Choi. (2018). The impact of system factors in mobile payment systems on cognitive trust and emotional responses. Journal of the Korea Institute of Information and Communication Engineering, 22(6), 881-887. DOI:10.6109/jkiice.2018.22.6.881
  19. C. K. Kim, J. G. Kim & S. J. Choi. (2017). A study on the acceptance decision factors for mobile easy payment services in digital convergence media ara: focusing SamsungPay. Journal of Digital Convergence, 15(4), 213-221. https://doi.org/10.14400/JDC.2017.15.4.213
  20. K. Y. Chen & M. L. Chang. (2011). User acceptance of near field communication mobile phone service: an investigation based on the unified theory of acceptance and use of technology model. The Service Industries Journal, 33(6), 609-623. DOI:10.1808/02642069.2011.622369
  21. S. W. Park, S. H. Kim & W. J. Choi. (2020). The effect of consumers' uncertainty avoidance on their acceptance to use mobile payments. Korean Journal of Marketing, 35(1), 53-68. https://doi.org/10.15830/kjm.2020.35.1.53
  22. S. H. No. (2014). A study of factors affecting the intention to use a mobile easy payments system : focused on the moderating effects of consumer's innovativeness. Masters dissertation, Yonsei University, Seoul.
  23. D. Pietro, R. G. Mugion, G. Mattia, M.F. Renzi & M. Toni. (2015). The integrated model on mobile payment acceptance(IMMPA) : an empirical application to public transport. Transportation Reasearch Parc C : Emerging Technologies, 56, 463-479. https://doi.org/10.1016/j.trc.2015.05.001
  24. N. R. Kim & J. Y. Yun. (2020). The effect of easiness and security on preference of mobile easy payment service. Journal of the HCI Society of Korea, 15(1), 29-37. https://doi.org/10.17210/jhsk.2020.03.15.1.29
  25. H. S. Lee, N. Y. Kwak. & C. C. Lee. (2015). Exploring determinants affecting mobile application use and recommendation, Journal of the Korea Contents Association, 15(8), 481-494. DOI: 10.5392/JKCA2015.15.08.481
  26. E. Slade, M. Williams & Y. Dwivdei. (2013, March). Extending UTAUT2 to explore consumer adoption of mobile payments, UKAIS, 36.
  27. R. Agrawal & E. Karahanna. (2000). Time flies when you're having fun: cognitive absorption and beliefs about information technology usage, MIS Quarterly, 24(4), 665-694. https://doi.org/10.2307/3250951
  28. I. S. Park & H. C. Ahn. (2012). A study on the user acceptance model of mobile credit card service based on UTAUT. The e-Business Studies, 13(3), 551-574. https://doi.org/10.15719/geba.13.3.201209.551
  29. O. J. Kwon. (2010). An empirical study on potential smartphone users, Internet and Information Security, 1(1), 55-83.
  30. M. Tan & T. S. Teo. (2000). Factors influencing the adoption of Internet banking. Journal of the AIS, 1(5), 1-44.
  31. S. H. Jeon, N. R. Park & C. C. Lee. (2011). Study on the factors affecting the intention to adopt public cloud computing service, Entrue Journal of Information Technology, 10(2), 97-112.
  32. T. Zhou. (2011). An empirical examination of initial trust in mobile banking, Internet Research, 21(5), 527-540. https://doi.org/10.1108/10662241111176353
  33. J. Lu, C. Liu, C. S. Yu & K. Wang. (2008). Determinants of accepting wireless mobile data services in China. Information & Management, 45(1), 52-64. DOI: 10.1016/j.im.2007.11.002
  34. D. H. Shin. (2009). Towards an understanding of the consumer acceptance of mobile wallet. Computers in Human Behavior, 25(6), 1343-1354. DOI: 10.1016/j.chb.2009.06.001
  35. A. Powell, C. K. Williams, D. B. Bock, T. Doellman & J. Allen. (2012). e-Voting intent: A comparison of young and elderly voters. Government Information Quarterly, 29(3), 361-372. DOI: 10.1016/j.giq.2012.01.003
  36. P. G. Schierz, O. Schilke & B. W. Wirtz. (2010). Understanding consumer acceptance of mobile payment services: an empirical analysis. Electronic Commerce Research and Applications, 9(3), 209-216. DOI: 10.1016/j.elerap.2009.07.005
  37. J. F. Hair, W. C. Black, B. J. Babin, R. E. Anderson & R. L. Tatham. (1998). Multivariate Data Analysis. Upper Saddle River, NJ: Prentice Hall.
  38. C. Fornell & D. F. Larcker. (1981). Structural equation models with unobservable variables and measurement error: algebra and statistics, Journal of Marketing Research, 18(3), 382-388. https://doi.org/10.1177/002224378101800313
  39. D. Barclay, C. Higgins & R. Thompson. (1995). The partial least squared(PLS) approach to casual modeling: personal computer adoption and use as an illustration. Technology Studies, 2(2), 285-324.
  40. W. W. Chin. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336.
  41. B. Efron. (1979). Bootstrap method: another look at the jackknife. The Annals of Statistics, 7(1), 1-26. https://doi.org/10.1214/aos/1176344552
  42. T. Zhou, Y. Lu & B. Wang. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760-767. DOI: 10.1016/j.chb.2010.01.013