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http://dx.doi.org/10.14400/JDC.2020.18.2.057

An Analysis of the Factors Affecting Technology Acceptance : Focusing on fintech in high-end technology  

Um, Sa-Rang (Dept. of Gerontology, Kyung Hee University)
Shin, Hye-Ri (Dept. of Gerontology, Kyung Hee University)
Kim, Young-Sun (Dept. of Gerontology, Kyung Hee University)
Publication Information
Journal of Digital Convergence / v.18, no.2, 2020 , pp. 57-71 More about this Journal
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.
Keywords
middle-aged and older adult; technology Acceptance Model(TAM); Accessibility; Affordability; Innovation; Uncertainty; Fintech;
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1 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.   DOI
2 R. A. Bauer. (1960). Consumer behavior as risk taking. Chicago: American Marketing Association.
3 D. Gefen, E. Karahanna & D. W. Straub. (2003). Trust and TAM in online shopping: an integrated model. MIS quarterly, 27(1), 51-90.   DOI
4 F. F. Reichheld & P. Schefter. (2000). E-loyalty: your secret weapon on the web. Harvard business review, 78(4), 105-113.
5 S. L. Jarvenpaa, N. Trictinsky & M. Vitale. (2000). Consumer trust in an Internet store. Information technology and management, 1(1-2), 45-71.   DOI
6 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.   DOI
7 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.   DOI
8 S. Y. Oh. (2017). Differences in Financial Adaptation Between Generations through Fintech. Seoul : Korea Insurance Research Institute.
9 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.   DOI
10 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.
11 J. H. Kim. (2015). Global Fintech Industry Trends and Outlook. Local Informatization Research, 91, 40-45.
12 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.
13 R. Goldsmith & I. Reinecke Flynn. (1992). Identifying innovators in consumer product markets. European Journal of Marketing, 26(12), 42-55.   DOI
14 E. M. Rogers. (2010). Diffusion of innovations. Stuttgart : Hohenheim University.
15 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.   DOI
16 C. Leavitt & J. Walton. (1975). Development of a scale for innovativeness. Advances in Consumer Research, 2, 545-554.
17 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.   DOI
18 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.
19 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.   DOI
20 I. Ajzen & M. Fishbein. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, New Jersey : Prentice Hall.
21 S. Taylor & P. A. Todd. (1995). Understanding information technology usage: A test of competing models. Information systems research, 6(2), 144-176.   DOI
22 E. Karahanna & D. W. Straub. (1999), The psychological origins of perceived usefulness and ease-of-use. Information& Management, 35(4), 237-250.   DOI
23 C. Fornell & D. F. Larcker. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.   DOI
24 Korea Health Industry Development Institute. (2012). Aging-friendly Industry Status and Outlook. Osong: KHIDI
25 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.   DOI
26 R. P. Bagozzi & Y. Yi. (1988). On the evaluation of structural equation models. Journal of the academy of marketing science, 16(1), 74-94.   DOI
27 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.
28 P. M. Bentler & D. G. Bonett. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological bulletin, 88(3), 588.   DOI
29 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.
30 G. J. Kim. (2009). A Study on Acceptance Factor of Digital Multimedia Broadcasting. Korean Journal of Journalism & Communication Studies, 53(3), 296-323.
31 P. M. Bentler. (1990). Comparative fit indexes in structural models. Psychological bulletin, 107(2), 238.   DOI
32 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.
33 M. W. Browne & R. Cudeck. (1993). Alternative ways of assessing model fit. Sage focus editions, 154, 136-136.
34 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.   DOI
35 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.
36 I. Ajzen. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211.   DOI
37 A. Bandura. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191.   DOI
38 D. F. Hoffman, T. P. Novak & M. Peralta. (1999). Building consumer trust online. Communications of the ACM, 42(4), 80-85.   DOI
39 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.   DOI
40 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.   DOI
41 D. J. Plude. (1985). Attention and performance: Identifying and localizing age deficits. iAging and Human Performance, 47-99.
42 R. A. Posner, (1995). Aging and old age. Chicago : University of Chicago Press.
43 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.   DOI
44 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.   DOI
45 R. T. Moriarty & T. J. Kosnik. (1989). High-tech marketing: concepts, continuity, and change. MIT Sloan Management Review, 30(4), 7.
46 M. Rogers Everett. (1995). Diffustion of Innovations. New York : The Free Press.
47 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.
48 C. McCreadie & A. Tinker. (2005). The acceptability of assistive technology to older people. Ageing & Society, 25(1), 91-110.   DOI
49 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.
50 T. I. Panagos. (2003). In search of the silver lining: Case analysis for mature market businesses. Doctoral dissertation. Massachussetts Institute of Technology, Massachussetts.
51 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.   DOI
52 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.   DOI
53 A. S. Kavuri & A. Milne. (2019). Fintech and the future of financial services: What are the research gaps?. CAMA Working Paper, 18, 1-86.
54 S. G. Park. (2015). A Study on a Fintech industry trends and major business models. Korea Multimedia Society, 19(1), 1-8.
55 G. J. Lee. (2016). Fintech Industry Trends. Seoul's Fintech Industry Development Forum(pp. 1-85). Seoul : The Seoul Institute.
56 Global Venture Capital Investment in Fintech Industry Set Record in 2017(2018.02.28.). Accenture. p. 1.
57 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.
58 F. D. Davis. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
59 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.
60 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.   DOI
61 K. R. Kim. (2019.08.01.). Fintech innovation to alienate only the elderly. Maeil Business Newspaper, p. 1.
62 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