DOI QR코드

DOI QR Code

The Study on the Factors Affecting Discontinuance Intention of FinTech Payment Service: Focusing on Y University Students

핀테크 지급결제 서비스 사용중단의도 영향요인 연구: Y대학 재학생을 중심으로

  • Received : 2021.08.15
  • Accepted : 2022.03.20
  • Published : 2022.03.28

Abstract

In the perspective of value-based adoption mode, this study empirically examined the factors that affect the intention of users of Fintech payment services to stop using them. A survey of college students who are familiar with digital devices, have no objection to payment and settlement services, and have high service access. A total of 148 questionnaires were analyzed using SPSS and SmartPLS. The study results show that perceived benefits, complexity, and security concerns are significant factors influencing the discontinue intention of Fintech payment services. Among them, the perceived benefit showed the most significant influence. Based on the results of this study, Fintech providers will be able to build a service environment to provide continuous benefits for maintaining long-term relationships with users, improve systems to secure various uses, and reduce users' negative perceptions of security. Recently, the use of services by the elderly has increased, so it is necessary to expand the scope of this study to target various age groups in future research.

본 연구는 가치기반수용모형을 기반으로 핀테크 지급결제 서비스 사용자의 사용중단의도에 영향을 미치는 요인을 실증 검증하였다. 디지털 기기에 익숙하고 지급결제 서비스에 대한 거부감이 없고 서비스 접근성이 높은 20대 대학생을 대상으로 설문을 진행하였다. SPSS와 SmartPLS를 이용하여 총 148부의 설문지를 분석한 결과, 핀테크 지급결제 서비스 사용자의 사용중단에 영향을 미치는 요인으로 지각된 혜택, 복잡성, 보안에 대한 우려가 유의한 영향을 보였다. 이 중 지각된 혜택이 가장 큰 영향을 보였다. 본 연구결과를 바탕으로 핀테크 제공 기업들은 사용자와의 장기적인 관계 유지를 위한 노력으로 지속적인 혜택 제공, 다양한 사용 가능성 확보를 위한 시스템 개선, 보안에 대한 사용자의 부정적 인식 감소를 위한 서비스 환경을 구축할 수 있을 것이다. 최근 고령층의 서비스 사용이 증가하면서 향후 연구에서는 다양한 연령층을 대상으로 확대할 필요성이 있다.

Keywords

References

  1. Technology and Information Promotion Agency. (2019). Technology Roadmap for SME 2019-2021 Fintech.
  2. Y. J. John. (2021). Security and Trust on Non-Contact Financial Transaction. Journal of Digital Convergence, 19(7), 147-154. DOI : 10.14400/jdc.2021.19.7.147
  3. J. H. Hong. (2020). A Study on the Institutional Plan on Financial Consumer Protection in the Case of Unfair Settlement due to Information Leak: Focusing on the Contents of the Comprehensive Digital Finance Innovation Plan. Korean Journal of Industrial Security, 10(3), 89-110. DOI : 10.33388/kais.2020.10.3.089
  4. The Bank of Korea. (2021). Domestic payment trends in the first half of 2021.
  5. The Bank of Korea(2021). Status of electronic payment service use during the first half of 2021.
  6. Samjong KPMG ERI. (2021). The financial industry became a battleground for dinosaurs: Big Tech's full-fledged financial advance.
  7. Bank of Korea(2020). Results of a survey on payment service and mobile financial service usage in 2019.
  8. Korea Investment & Securities. (2019). Fintech Industry Analysis.
  9. DCMREPORT, Survey on usage behavior of simple payment service in 2020.
  10. H. J. Hwang & J. K. Kim. (2018). The Study on the User Resistance Intention of Mobile Easy Money Transfer Service. The e-Business Studies, 19(1), 135-153. DOI : 10.20462/TeBS.2018.2.19.1.135
  11. H. S. Park & S. H. Kim. (2017). Impacts of Perceived Risks and Technical Traits of Mobile Easy Payment Service on Use Conflict and Acceptance Resistance. The Journal of Internet Electronic Commerce Resarch, 17(4), 119-138.
  12. K. K. Lee. (2020). Improvement of Domestic Mobile Payment Security Problem. The Journal of Korean Institute of Communications and Information Sciences, 45(10), 1720-1727. DOI : 7840/kics.2020.45.10.1720 https://doi.org/10.7840/kics.2020.45.10.1720
  13. Bank of Korea(2020). 2020 Payment Report.
  14. J. Hwang & H. S. Yu. (2016). A Study of Factors Affecting the Intention to use a Mobile Easy Payment Service: An Integrated Extension of TAM with Perceived Risk. Journal of Information Technology and Architecture, 13(2), 291-306.
  15. Bank of Korea(2019). Electronic payment service usage during the first half of 2019.
  16. Korea Consumer Agency. (2016). Survey on mobile simple payment service(fintech1).
  17. S. J. Chung & S. I. Kim. (2020). A study on the user experience of mobile Fintech service in Z generation:Focused on KakaoPay and Toss. Journal of Digital Convergence, 18(1), 315-320. DOI : 10.14400/jdc.2020.18.1.315
  18. 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. DOI : 10.17210/jhsk.2020.03.15.1.29
  19. D. W. Heo & W. J. Sung. (2021). The Effect of Privacy Concerns on Using Mobile Payment Services: Moderating Effect of Multidimensional Consumer Innovativeness. Informatization Policy, 28(1), 22-42. DOI : 10.22693/niaip.2021.28.1.022
  20. D. H. Lee. (2021). A Study on the Intention to Use Mobile Payment Derived from FinTech during the Fourth Industrial Revolution. The e-Business Studies, 22(4), 3-17.
  21. S. H. Hwang & J. K. Kim. (2018). The Study of User Resistance to Fintech Payment Service: In the Perspective of Innovation Diffusion And Status Quo Bias Theory. The Journal of Information Systems, 27(1), 133-151. DOI : 10.5859/kais.2018.27.1.133
  22. S. H. Hwang & J. H. Kim. (2018). An Analysis of Factors Affecting Fintech Payment Service Acceptance Using Logistic Regression. Journal of the Korea Society for Simulation, 27(1), 51-60. DOI : 10.9709/jkss.2018.27.1.051
  23. Y. Kim, Y. J. Park & J. Choi. (2016). The adoption of mobile payment services for "fintech". International Journal of Applied Engineering Research, 11(2), 1058-1061.
  24. D. S. Ravindran. (2015). An empirical study on service quality perceptions and continuance intention in mobile banking context in india. The Journal of Internet Banking and Commerce, 17(1), 1-21.
  25. F. Liebana-Cabanillas, J. Sanchez-Fernandez & F. Munoz-Leiva. (2014). Role of gender on acceptance of mobile payment. Industrial Management & Data Systems, 114(2), 220-240. DOI : 10.1108/imds-03-2013-0137
  26. T. Zhou. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems, 54(2), 1085-1091. DOI : 10.1016/j.dss.2012.10.034
  27. S. Yang, Y. Lu, S. Gupta, Y. Cao & R. Zhang. (2012). Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits. Computers in Human Behavior, 28(1), 129-142. DOI : 10.1016/j.chb.2011.08.019
  28. H. W. Kim, H. C. Chan & S. Gupta. (2007). Value-based adoption of mobile internet: an empirical investigation. Decision support systems, 43(1), 111-126. DOI : 10.1016/j.dss.2005.05.009
  29. J. H. You, J. C. Park & K. H. Kim. (2018). A Study on the Factors Affecting the Diffusion Intention of Fin-Tech Services : Focused on Mobile Simple Payment Services. Journal of Industrial Economics and Business, 31(1), 1-21. DOI : 10.22558/jieb.2018.02.31.1.1
  30. D. H. Son. (2020). The Effect of the Reliability and the Perceived Value on the Continuous Use of Fintech Service. The Journal of Internet Electronic Commerce Resarch, 20(4), 1-11. DOI : 10.37272/jiecr.2020.08.20.4.1
  31. S. B. Lee, J. Y. Lee & J. Y. Moon. (2018). What is the Switching Intention from Existing Payment to Mobile Payment Service?. Journal of the Korea society of computer and information, 23(6), 59-66. DOI : 10.9708/jksci.2018.23.06.059
  32. M. J. Kim & S. B. Lee. (2018). The effect of the perceived benefit and sacrifice of delivery application service users in the food industry on perceived value and behavioral intention : Using the value-based adoption model(VAM). International Journal of Tourism and Hospitality Research, 32(2), 217-233. DOI : 10.21298/ijthr.2018.02.32.2.217
  33. J. C. Oh. (2017). An Empirical Study on Use-Diffusion of AR Technology based on VAM : The Moderating Effects of Postive TRI. The e-Business Studies, 18(5), 225-244. DOI :10.20462/tebs.2017.10.18.5.225
  34. Y. G. Jo, J. E. Lee, M. S. Suh, J. G. Jung & K. H. Kim. (2016). A study on the formation factors of Continuance Intention of Real Estate Mobile App by Expectation-Confirmation Model and Value based Adoption Model, Korea Science & Art Forum, 25, 389-407. DOI : 10.17548/ksaf.2016.09.25.389
  35. J. H. Han, S. B. Kang & T. S. Moon. (2013). An empirical study on perceived value and continuous intention to use of smart phone, and the moderating effect of personal innovativeness. Asia Pacific Journal of Information Systems, 23(4), 53-84. DOI : 10.14329/apjis.2013.23.4.053
  36. D. H. Kim , J. H. Lee & Y. P. Park. (2012). A Study of Factors Affecting the Adoption of Cloud Computing. The Journal of Society for e-Business Studies, 17(1), 111-136. DOI : 10.7838/jsebs.2012.17.1.111
  37. H. Y. Wang, & S. H. Wan. (2010). Predicting mobile hotel reservation adoption: Insight from a perceived value standpoint. International Journal of Hospitality Management, 29(4), 598-608. DOI : 10.1016/j.ijhm.2009.11.001
  38. S. B. Im, B. L. Bayus & C. H. Mason. (2003). An empirical study of innate consumer innovativeness, personal characteristics, and new-product adoption behavior. Journal of the Academy of Marketing Science, 31, 61-73. DOI : 10.1177/0092070302238602
  39. Y. Lu, Y. Cao, B. Wang & S. Yang. (2011). A study on factors that affect users' behavioral intention to transfer usage from the offline to the online channel. Computers in Human Behavior, 27(1), 355-364. DOI : 10.1016/j.chb.2010.08.013
  40. R. Agarwal & J. Prasad. (1998). A Conceptual and Operational Definition of Personal Innovativeness in the Information Technology. Information Systems Research, 9(2), 204-215. DOI : 10.1287/isre.9.2.204
  41. K. Y. Lee. (2017). A Study of Structural Relationship between Technostress and Mobile Application Discontinuance Intention. Korean Jouranl of Business Administration, 30(10), 1835-1855. DOI : 10.18032/kaaba.2017.30.10.1835
  42. Q. Su, L. Li & Y. W. Cui. (2009). Analysing relational benefits in e-business environment from behavioural perspective. Systems Research and Behavioral Science, 26(2), 129-142. DOI : 10.1002/sres.965
  43. R. A. Ping. (1993). The effects of satisfaction and structural constraints on retailer exiting, voice, loyalty, opportunism, and neglect. Journal of Retailing, 69(3), 320-352. DOI : 10.1016/0022-4359(93)90010-g
  44. C. Kim, M. Mirusmonov & I. Lee. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310-322. DOI : 10.1016/j.chb.2009.10.013
  45. D. G. Mick & S. Fournier. (1998). Paradoxes of technology: Consumer cognizance, emotions, and coping strategies. Journal of Consumer Research, 25(2), 123-143. DOI : 10.1086/209531
  46. N. K. Malhotra, S. S. Kim & J. Agarwal. (2004). Internet users' information privacy concerns(IUIPC): the construct, the scale, and a causal model. Information Systems Research, 15(4), 336-355. DOI : 10.1287/isre.1040.0032
  47. T. Zhou & H. Li. (2014), Understanding mobile SNS continuance usage in China from the perspectives of social influence and privacy concern. Computers in Human Behavior, 37, 283-289. DOI : 10.1016/j.chb.2014.05.008
  48. Y. C. Ku, R. Chen & H. Zhang. (2013). Why do users continue using social networking sites? An exploratory study of members in the United States and Taiwan. Information & Management, 50(7), 571-581. DOI : 10.1016/j.im.2013.07.011
  49. D. H. McKnight, V. Choudhury & C. Kacmar. (2002). Developing and Validating Trust Measures for E-commerce: An Integrative Typology. Information Systems Research, 13(3), 334-359. DOI : 10.1287/isre.13.3.334.81
  50. K. M. Kim & Y. S. Park. (2020). A Study on Acceptance Intentions to Use the Mobile Payment Service Based on Biometric Authentication: Focusing on ApplePay. Journal of Digital Convergence, 18(7), 123-133. DOI : 10.14400/jdc.2020.18.7.123
  51. H. W. Kim & S. I. Kim. (2020). A study on User experience of Fintech Application Service : Focused on Toss and Kakaobank. Journal of Digital Convergence, 18(1), 287-293. DOI : 10.14400/jdc.2020.18.1.287
  52. S. J. Lee(2019). An Analysis of Factors Influencing Switching Intention toward Online Platform-based Easy Payment Service with Moderating Effects of Policy Expectations: Focusing on Kakao Pay. The Journal of the Korea Contents Association, 19(5), 426-442. DOI : 10.5392/jkca.2019.19.05.426
  53. T. Dinev & P. Hart. (2006). An extended privacy calculus model for e-commerce transactions. Information Systems Research, 17(1), 61-80. DOI : 10.1287/isre.1060.0080
  54. J. F. Hair, W. C. Black, B. J. Babin & R. E. Anderson. (2009) Multivariate Data Analysis, 7th Edition. London: Prentice Hall.
  55. W. W. Chin. (2010). How to Write Up and Report PLS Analysis. Handbook of Partial Least Squares: Concepts, Methods and Applications, New York: Springer.
  56. C. M. Ringle, M. Sarstedt & D. Straub. (2012). A critical look at the use of PLS-SEM in MIS quarterly. MIS Quarterly, 36(1), 3-14. DOI : 10.2307/41410402
  57. M. Tenenhaus, E. V. Vincenzo, Y. M. Chatelin & C. Lauro. (2005). PLS Path Modeling. Computational Statistics and Data Analysis, 48(1), 159-205. DOI : 10.1016/j.csda.2004.03.005
  58. B. S. Kim. (2017). Effects of Brand Loyalty of Consumer Loyalty toward Loyalty Programs and Consumer Satisfaction:Focused on Coffee Chains. Journal of Korea Service Management Society, 18(1), 135-157. DOI : 10.15706/jksms.2017.18.1.00
  59. H. G. Kim. (2020). A Study on the Factors Influencing on the Intention to Continuously Use a Smart Factory. Journal of the Korea Industrial Information Systems Research, 25(2), 73-85. DOI : 10.9723/jksiis.2020.25.2.073
  60. J. H. Oh, J. H. Seo, J. D. Kim. (2019). The Effect of Both Employees' Attitude toward Technology Acceptance and Ease of Technology Use on Smart Factory Technology Introduction level and Manufacturing Performance. Journal of Information Technology Applications & Management, 26(2), 13-26. DOI : 10.21219/jitam.2019.26.2.013