Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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An Analysis of the Factors Affecting Technology Acceptance : Focusing on fintech in high-end technology

중·고령자의 기술수용도(Technology Acceptance) 영향요인 분석 : 최신기술 핀테크(Fintech)를 중심으로

  • Received : 2019.12.03
  • Accepted : 2020.02.20
  • Published : 2020.02.28
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Abstract

The purpose of this study is to extend Davis's Technology Acceptance Model(TAM) to verify the intention of use fintech factors in which usefulness, easiness, accessibility, affordability, innovation, and uncertainty for middle-aged and older adult. Data was derived from the 2017 Driving and Mobility Survey of Older Adult Korean, which was collected from 457 middle-aged and older adult aged 55 and over in Seoul and Gyeonggi-do Province. Then, structural equation was used to verify the fintech technology acceptance factors of the middle-aged and older adult. The results showed that fintech technology acceptance factors of middle-aged and older adult were verified as usefulness, easiness, innovation, and uncertainty. Namely, the higher usefulness, easiness and innovation resulted in higher the intention to use fintech. Also, the lower the uncertainty resulted in higher the intention to use fintech. This study has implication for fintech, a representative technology of the Aging-Friendly Finance Industry, to identify the technology acceptance factors by expanding the Technology Acceptance Model(TAM) for middle-aged and older adult.

본 연구는 Davis의 기술수용모델(TAM)을 확장하여 유용성 및 편리성과 접근성, 가격, 혁신성, 불확실성이 중·고령자의 핀테크 사용의도에 미치는 요인을 규명하고자 한다. 이를 위해 서울과 경기권에 거주하는 만 55세 이상의 중·고령자 457명을 대상으로 수집한, 2017년 한국 고령자 운전 및 이동 실태조사 자료를 활용하였다. 이후 구조방정식을 통해 중·고령자의 핀테크 기술수용요인을 검증하였다. 연구결과, 중·고령자의 핀테크 기술수용요인은 유용성, 편리성, 혁신성, 불확실성인 것으로 확인하였다. 즉, 중·고령자의 핀테크에 대한 유용성 및 편리성이 높아짐에 따라 핀테크 사용의도가 높아질 뿐 아니라, 혁신성이 높을수록, 불확실성이 낮을수록 핀테크 사용의도를 높이는 것으로 나타났다. 따라서 본 연구는 고령친화금융산업의 대표적인 기술인 핀테크에 대하여, 기술수용모델에서 주류로서 다루지 않았던 중·고령자를 대상으로 일반적인 기술수용모델 확장하여 기술수용요인을 규명하였다는 함의를 가진다.

Keywords

References

  1. S. G. Park. (2015). A Study on a Fintech industry trends and major business models. Korea Multimedia Society, 19(1), 1-8.
  2. G. J. Lee. (2016). Fintech Industry Trends. Seoul's Fintech Industry Development Forum(pp. 1-85). Seoul : The Seoul Institute.
  3. Global Venture Capital Investment in Fintech Industry Set Record in 2017(2018.02.28.). Accenture. p. 1.
  4. A. S. Kavuri & A. Milne. (2019). Fintech and the future of financial services: What are the research gaps?. CAMA Working Paper, 18, 1-86.
  5. M. G. S. D. N. Jayaratne & H. Cripps. (2017). Mobile banking adoption by senior citizens in Perth. In The proceedings of 2nd Business Doctoral and Emerging Scholars Conference(pp. 104-110). Australia : Edith Cowan University.
  6. F. D. Davis. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
  7. C. Lee. (2014). The role of trust in older adults' adoption and use of technology. GSA 2014 Annual Scientific Meeting(pp. 1-7). Washington : GSA.
  8. C. Lee & J. F. Coughlin. (2015). PERSPECTIVE: Older adults' adoption of technology: an integrated approach to identifying determinants and barriers. Journal of Product Innovation Management, 32(5), 747-759. https://doi.org/10.1111/jpim.12176
  9. C. Munteanu, B. Axtell, H. Rafih, A. Liaqat & Y. Aly. (2018). Designing for Older Adults: Overcoming Barriers toward a Supportive, Safe, and Healthy Retirement. Pennsylvania : University of Pennsylvania
  10. K. R. Kim. (2019.08.01.). Fintech innovation to alienate only the elderly. Maeil Business Newspaper, p. 1.
  11. S. Y. Oh. (2017). Differences in Financial Adaptation Between Generations through Fintech. Seoul : Korea Insurance Research Institute.
  12. J. K. Lee, J. M. Kim, K. E. Lee, S. R. Yoon & H. Jo. (2017). A Study on Factors Influencing Acceptance Intention of Fintech -Focusing on Mobile Payment Service-. Knowledge Management Research, 18(3), 181-199. https://doi.org/10.15813/kmr.2017.18.3.009
  13. 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.
  14. J. H. Kim. (2015). Global Fintech Industry Trends and Outlook. Local Informatization Research, 91, 40-45.
  15. L. M. Chuang, C. C. Liu & H. K. Kao H. (2016). The adoption of fintech service: TAM perspective. International Journal of Management and Administrative Sciences, 3(7), 1-15.
  16. C. Tun-Pin, W. C. Keng-Soon, Y. Yen-San, C. Pui-Yee, J. T. Hong-Leong & N. Shwu-Shing. (2019). An adoption of fintech service in Malaysia. South East Asia Journal of Contemporary Business, 18(5). 134-147.
  17. S. H. Joo, E. H. Koh & M. S. Yoo. (2018). Exploring Factors Related to FinTech Acceptance in Financial Transaction. Journal of Consumption Culture, 21, 175-202. https://doi.org/10.17053/jcc.2018.21.2.008
  18. I. Ajzen & M. Fishbein. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, New Jersey : Prentice Hall.
  19. F. D. Davis, R. P. Bagozzi & P. P. Warshaw. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982
  20. S. Taylor & P. A. Todd. (1995). Understanding information technology usage: A test of competing models. Information systems research, 6(2), 144-176. https://doi.org/10.1287/isre.6.2.144
  21. E. Karahanna & D. W. Straub. (1999), The psychological origins of perceived usefulness and ease-of-use. Information& Management, 35(4), 237-250. https://doi.org/10.1016/S0378-7206(98)00096-2
  22. J. J. Park. (2004). The impact of the consumer's innovativeness on online shopping behavior - Based on the Technology Acceptance. Advertising Research, 63, 79-101.
  23. G. J. Kim. (2009). A Study on Acceptance Factor of Digital Multimedia Broadcasting. Korean Journal of Journalism & Communication Studies, 53(3), 296-323.
  24. Y. A. Park & Y. H. Hyun. (2013). A Verification of Predictive Factors of Offline Behavior by Adopting of a Smartphone Application: A Focus on Applying a TAM-TRA Mixed Model. Korean Corporation Management Review, 50(0), 114-132.
  25. I. Ajzen. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211. https://doi.org/10.1016/0749-5978(91)90020-t
  26. A. Bandura. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191. https://doi.org/10.1037/0033-295X.84.2.191
  27. C. J. Armitage & M. Conner. (2001). Efficacy of the theory of planned behavior: A meta- analytic review. British Journal of Social Psychology, 40, 471-499. https://doi.org/10.1348/014466601164939
  28. D. F. Hoffman, T. P. Novak & M. Peralta. (1999). Building consumer trust online. Communications of the ACM, 42(4), 80-85. https://doi.org/10.1145/299157.299175
  29. R. Agarwal & 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
  30. M. G. Morris, V. Venkatesh & P. L. Ackerman. (2005). Gender and age differences in employee decisions about new technology: An extension to the theory of planned behavior. IEEE transactions on engineering management, 52(1), 69-84. https://doi.org/10.1109/TEM.2004.839967
  31. D. J. Plude. (1985). Attention and performance: Identifying and localizing age deficits. iAging and Human Performance, 47-99.
  32. R. A. Posner, (1995). Aging and old age. Chicago : University of Chicago Press.
  33. M. Heinz, P. Martin, J. A. Margrett, M. Yearns, W. Franke, H. I. Yang & C. K. Chang. (2013). Perceptions of technology among older adults. Journal of Gerontological Nursing, 39(1), 42-51. https://doi.org/10.3928/00989134-20121204-04
  34. S. T. Peek, E. J. Wouters, J. Van Hoof, K. G. Luijkx, H. R. Boeije & H. J. Vrijhoef. (2014). Factors influencing acceptance of technology for aging in place: a systematic review. International journal of medical informatics, 83(4), 235-248. https://doi.org/10.1016/j.ijmedinf.2014.01.004
  35. R. T. Moriarty & T. J. Kosnik. (1989). High-tech marketing: concepts, continuity, and change. MIT Sloan Management Review, 30(4), 7.
  36. M. Rogers Everett. (1995). Diffustion of Innovations. New York : The Free Press.
  37. S. L. Wood & J. Swait. (2002). Psychological Indicators of Innovation Adoption: Cross‐Classification Based on Need for Cognition and Need for Change. Journal of Consumer Psychology, 12(1), 1-13. https://doi.org/10.1207/s15327663jcp1201_01
  38. S. D. Cho & K. E. Kim. (2007). A Study on the Factors Influencing the Use Diffusion of Technological Products. Korean Journal of Marketin, 22(2), 67-86.
  39. C. McCreadie & A. Tinker. (2005). The acceptability of assistive technology to older people. Ageing & Society, 25(1), 91-110. https://doi.org/10.1017/S0144686X0400248X
  40. H. Tanriverdi & C. S. Iacono. (1999). Toy or Useful Technology?: The Challenge of Diffusing Telemedicine in Three Boston Hospitals. In Success and pitfalls of information technology management, 1-13.
  41. T. I. Panagos. (2003). In search of the silver lining: Case analysis for mature market businesses. Doctoral dissertation. Massachussetts Institute of Technology, Massachussetts.
  42. A. Wang, L. Redington, V. Steinmetz & D. Lindeman. (2011). The ADOPT model: Accelerating diffusion of proven technologies for older adults. Ageing International, 36(1), 29-45. https://doi.org/10.1007/s12126-010-9072-1
  43. R. Steele, A. Lo, C. Secombe & Y. K. Wong. (2009). Elderly persons' perception and acceptance of using wireless sensor networks to assist healthcare. International journal of medical informatics, 78(12), 788-801. https://doi.org/10.1016/j.ijmedinf.2009.08.001
  44. R. A. Bauer. (1960). Consumer behavior as risk taking. Chicago: American Marketing Association.
  45. D. Gefen, E. Karahanna & D. W. Straub. (2003). Trust and TAM in online shopping: an integrated model. MIS quarterly, 27(1), 51-90. https://doi.org/10.2307/30036519
  46. F. F. Reichheld & P. Schefter. (2000). E-loyalty: your secret weapon on the web. Harvard business review, 78(4), 105-113.
  47. S. L. Jarvenpaa, N. Trictinsky & M. Vitale. (2000). Consumer trust in an Internet store. Information technology and management, 1(1-2), 45-71. https://doi.org/10.1023/A:1019104520776
  48. P. A. Pavlou. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International journal of electronic commerce, 7(3), 101-134. https://doi.org/10.1080/10864415.2003.11044275
  49. R. Agarwal & J. Prasad. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information systems research, 9(2), 204-215. https://doi.org/10.1287/isre.9.2.204
  50. E. M. Rogers. (2010). Diffusion of innovations. Stuttgart : Hohenheim University.
  51. B. Joseph & S. J. Vyas. (1984). Concurrent validity of a measure of innovative cognitive style. Journal of the Academy of Marketing Science, 12(1-2), 159-175. https://doi.org/10.1007/BF02729494
  52. C. Leavitt & J. Walton. (1975). Development of a scale for innovativeness. Advances in Consumer Research, 2, 545-554.
  53. R. Goldsmith & I. Reinecke Flynn. (1992). Identifying innovators in consumer product markets. European Journal of Marketing, 26(12), 42-55. https://doi.org/10.1108/03090569210022498
  54. Korea Health Industry Development Institute. (2012). Aging-friendly Industry Status and Outlook. Osong: KHIDI
  55. I. Im, Y. Kim & H. J. Han. (2008). The effects of perceived risk and technology type on users' acceptance of technologies. Information & Management, 45(1), 1-9. https://doi.org/10.1016/j.im.2007.03.005
  56. R. P. Bagozzi & Y. Yi. (1988). On the evaluation of structural equation models. Journal of the academy of marketing science, 16(1), 74-94. https://doi.org/10.1007/BF02723327
  57. C. Fornell & D. F. Larcker. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
  58. S. H. Hong. (2000). The Criteria for Selecting Appropriate Fit Indices in Structural Equation Modeling and Their Rationales. Korean Journal of Clinical Psychology, 19(1), 161-177.
  59. P. M. Bentler & D. G. Bonett. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological bulletin, 88(3), 588. https://doi.org/10.1037/0033-2909.88.3.588
  60. P. M. Bentler. (1990). Comparative fit indexes in structural models. Psychological bulletin, 107(2), 238. https://doi.org/10.1037/0033-2909.107.2.238
  61. J. H. Steiger & J. Lind. (1980). Statistically based tests for the number of common factors. In the annual meeting of the Psychometric Society, Iowa City, IA.
  62. M. W. Browne & R. Cudeck. (1993). Alternative ways of assessing model fit. Sage focus editions, 154, 136-136.