Browse > Article
http://dx.doi.org/10.9723/jksiis.2022.27.4.093

An Empirical Study on Factors Affecting NFT Purchase Intention  

Lee, Sang Hoon (대구대학교 컴퓨터정보공학부)
Kim, Su-Yeon (대구대학교 컴퓨터정보공학부)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.27, no.4, 2022 , pp. 93-104 More about this Journal
Abstract
Recently, NFT, which is growing at a rapid pace, is gradually entering our lives. NFT is an acronym for Non-Fungible Token, a technology that allows you to claim ownership of digital data. As ownership of digital data takes over, it is showing characteristics as an investment value along with the characteristics of new technology, and it is expected to develop further in the future. In this study, we tried to analyze the intentions of users who own NFTs to purchase. Factors that can influence purchase intentions were selected and a research model was established using personal characteristics, NFT characteristics, and social characteristics. As a result of conducting an empirical study, it was found that individual innovativeness, profitability and reliability of NFT, and FOMO factors significantly influence purchase intention.
Keywords
Non-Fungible Token(NFT); Technology Acceptance Model; Intention to Purchase;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Ding, Y. (2019). Looking forward: The role of hope in information system continuance, Computers in Human Behavior, 91(September 2018), 127-137. https://doi.org/10.1016/j.chb.2018.09.002.   DOI
2 Fox, G., Clohessy, T., van der Werff, L., Rosati, P. and Lynn, T. (2021). Exploring the competing influences of privacy concerns and positive beliefs on citizen acceptance of contact tracing mobile applications, Computers in Human Behavior, 121(April), 106806. https://doi.org/10.1016/j.chb.2021.106806.   DOI
3 Hsu, M.-H. and Chiu, C.-M. (2004). Internet self-efficacy and electronic service acceptance, Decision Support Systems, 38(3), 369-381. https://doi.org/10.1016/j.dss.2003.08.001.   DOI
4 Hwang, I. (2021). The Study on Factors to Improve the Intention to Share Knowledge Using KMS: Focusing on Technology Acceptance Model, Task Stress, Knowledge Share Climate, Journal of the Korea Industrial Information Systems Research, 26(6), 17-34.   DOI
5 Islam, N., Marinakis, Y., Olson, S., White, R. and Walsh, S. (2022). Is BlockChain Mining Profitable in the Long Run?, IEEE Transactions on Engineering Management, 1-14. https://doi.org/10.1109/TEM.2020.3045774.   DOI
6 Jung, T., Chung, N. and Leue, M. C. (2015). The determinants of recommendations to use augmented reality technologies: The case of a Korean theme park, Tourism Management, 49, 75-86. https://doi.org/10.1016/j.tourman.2015.02.013.   DOI
7 De Angelis, P., De Marchis, R., Marino, M., Martire, A. L. and Oliva, I. (2021). Betting on bitcoin: a profitable trading between directional and shielding strategies, Decisions in Economics and Finance, 44(2), 883-903. https://doi.org/10.1007/s10203-021-00324-z.   DOI
8 Mutambara, D. and Bayaga, A. (2021). Determinants of mobile learning acceptance for STEM education in rural areas, Computers and Education, 160(September 2020), 104010. https://doi.org/10.1016/j.compedu.2020.104010.   DOI
9 Cheung, H., Baumber, A. and Brown, P. J. (2022). Barriers and enablers to sustainable finance: A case study of home loans in an Australian retail bank, Journal of Cleaner Production, 334(December 2021), 130211. https://doi.org/10.1016/j.jclepro.2021.130211.   DOI
10 Lee, J. Y. and Jo, G. S. (2021). Understanding and Utilizing the Latest NFT Technology, Korea Institute of Information Technology Magazine, 19(1), 7-11.   DOI
11 Lee, W. S., Choi, D. H. and Kim, J. S. (2021). A Study on the Acceptance of Users in Mobile Transportation Management System: Focusing on Technology Acceptance Models, Journal of the Korea Industrial Information Systems Research, 26(3), 59-69.   DOI
12 Liu, K. and Tao, D. (2022). The roles of trust, personalization, loss of privacy, and anthropomorphism in public acceptance of smart healthcare services, Computers in Human Behavior, 127(August 2021), 107026. https://doi.org/10.1016/j.chb.2021.107026.   DOI
13 Luarn, P. and Lin, H. (2005). Toward an understanding of the behavioral intention to use mobile banking, Computers in Human Behavior, 21(6), 873-891. https://doi.org/10.1016/j.chb.2004.03.003.   DOI
14 Mohammadi, H. (2015). A study of mobile banking loyalty in Iran, Computers in Human Behavior, 44, 35-47. https://doi.org/10.1016/j.chb.2014.11.015.   DOI
15 Munoz-Leiva, F., Climent-Climent, S. and Liebana-Cabanillas, F. (2017). Determinants of intention to use the mobile banking apps: An extension of the classic TAM model, Spanish Journal of Marketing - ESIC, 21(1), 25-38. https://doi.org/10.1016/j.sjme.2016.12.001.   DOI
16 Oyman, M., Bal, D. and Ozer, S. (2022). Extending the technology acceptance model to explain how perceived augmented reality affects consumers' perceptions, Computers in Human Behavior, 128(August 2021), 107127. https://doi.org/10.1016/j.chb.2021.107127.   DOI
17 Jang, Y. and Park, E. (2020). Social acceptance of nuclear power plants in Korea: The role of public perceptions following the Fukushima accident, Renewable and Sustainable Energy Reviews, 128(May), 109894. https://doi.org/10.1016/j.rser.2020.109894.   DOI
18 Kim, B. C. (2021). Why is everyone paying attention to NFTs?, KISO JOURNAL, 44, 1-6.
19 Park, E. (2019). Social acceptance of green electricity: Evidence from the structural equation modeling method, Journal of Cleaner Production, 215, 796-805. https://doi.org/10.1016/j.jclepro.2019.01.075.   DOI
20 Fadare, O. G., Babatunde, O. H., Akomolafe, D. T. and Lawal, O. O. (2011). Behavioral intention for mobile learning on 3G mobile internet technology in south-west part of Nigeria, World Journal of Engineering and Pure & Applied Sciences, 1(2), 19e28. Retrieved from http://rrpjournals.org/wjepas/en_wjepas_vol_1_iss_2_ pg_19_28.
21 Sciarelli, M., Prisco, A., Gheith, M. H. and Muto, V. (2021). Factors affecting the adoption of blockchain technology in innovative Italian companies: an extended TAM approach, Journal of Strategy and Management, 15(3), 495-507. https://doi.org/10.1108/JSMA-02-2021-0054.   DOI
22 Wang, X., Chao, F., Yu, G. and Zhang, K. (2022). Factors influencing fake news rebuttal acceptance during the COVID-19 pandemic and the moderating effect of cognitive ability, Computers in Human Behavior, 130(June 2021), 107174. https://doi.org/10.1016/j.chb.2021.107174.   DOI
23 Seok. Y. N. and Yoon, B. K. (2022). The effect of smart tourism technology and memorable tourism experience (MTE) on continuous use intention : Focusing on the technology acceptance model (TAM), International Journal of Tourism and Hospitality Research, 36(3), 73-89.   DOI
24 Song, W. Y. (2022). Comparative Legal Review on NFT Regulation ? Focusing on Security-type NFTs and Virtual Asset-type NFTs?, The Korean Journal of Securities Law, 23(1), 251-286.   DOI
25 Venkatesh, V. and Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies, Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926.   DOI
26 Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008.   DOI
27 Alenezi, A. R., Karim, A. M. A. and Veloo, A. (2010). An empirical investigation into the role of enjoyment, computer anxiety, computer self-efficacy and internet experience in influencing the students' intention to use e learning: A case study from saudi arabian governmental universities, Turkish Online Journal of Educational Technology, 9(4), 22-34.
28 Xu, Q. and Hwang, B. G. (BG), & Lu, Y. (2021). Households' acceptance analysis of a marketized behavioral intervention - Household energy-saving option, Journal of Cleaner Production, 318(July), 128493. https://doi.org/10.1016/j.jclepro.2021.128493.   DOI
29 Yeong, Y.-C. (2019). What drives cryptocurrency acceptance in Malaysia?, Science Proceedings Series, 1(2), 47-50. https://doi.org/10.31580/sps.v1i2.625.   DOI
30 Cigdem, H., Ozturk, M. and Topcu, A. (2016). Vocational college students' acceptance of web-based summative listening comprehension test in an EFL course, Computers in Human Behavior, 61, 522-531. https://doi.org/10.1016/j.chb.2016.03.070.   DOI
31 Hong, W., Liu, R. De, Ding, Y., Jiang, R., Sun, Y. and Jiang, S. (2021). A time-lagged study of two possible routes from personal innovativeness to life satisfaction in adolescents: Learning and social interaction on mobile phones, Personality and Individual Differences, 182(19), 111075. https://doi.org/10.1016/j.paid.2021.111075.   DOI
32 Abdullah, F. and Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors, Computers in Human Behavior, 56, 238-256. https://doi.org/10.1016/j.chb.2015.11.036.   DOI
33 Agudo-Peregrina, A. F., Hernandez-Garcia, A. and Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning, Computers in Human Behavior, 34, 301-314. https://doi.org/10.1016/j.chb.2013.10.035.   DOI
34 Ahmad, I., Ahmad, M. O., Ahmad, M. O., Almazroi, A. A., Khan Khalil, M. I. and Alqarni, M. A. (2021). Using algorithmic trading to analyze short term profitability of Bitcoin, PeerJ Computer Science, 7, 1-19. https://doi.org/10.7717/peerj-cs.337.   DOI
35 Estriegana, R., Medina-Merodio, J. A. and Barchino, R. (2019). Student acceptance of virtual laboratory and practical work: An extension of the technology acceptance model, Computers and Education, 135(February), 1-14. https://doi.org/10.1016/j.compedu.2019.02.010.   DOI
36 Martin, B. A. S., Chrysochou, P., Strong, C., Wang, D. and Yao, J. (2022). Dark personalities and Bitcoin®: The influence of the Dark Tetrad on cryptocurrency attitude and buying intention, Personality and Individual Differences, 188(November 2021), 111453. https://doi.org/10.1016/j.paid.2021.111453.   DOI
37 Jeon, E. K., Oh, S. H., Son, D. H., Lee, S. H., Yoo, H. Y. and Im, K. S. (2022). How Game Changer NFTs Affect the Metaverse, The Journal of The Korean Institute of Communication Sciences, 39(2), 57-63.
38 Lee, H. and Kim, H. (2019). A Study on Cryptocurrency Market in China, Journal of Digital Contents Society, 20(3), 537-545.   DOI
39 Lee, W.-J. (2018). Understanding Consumer Acceptance of Fintech Service : An Extension of the TAM Model to Understand Bitcoin, Journal of Business and Management, 20(7), 34-37. https://doi.org/10.9790/487X-2007023437.   DOI
40 Park, S. H. and Lee, J. E. (2021). Effects of Information System Quality on the Technology Acceptance Model and User Intention, Journal of the Korea Industrial Information Systems Research, 26(5), 21-35.   DOI
41 Choi, S. W., Lee, S. M., Koh, J. E., Kim, H. J. and Kim, J. S. (2021). A Study on the elements of business model innovation of non-fungible token blockchain game : based on 'PlayDapp' case, an in-game digital asset distribution platform, Journal of Korea Game Society, 21(2), 123-137.   DOI
42 Al-Emran, M., Mezhuyev, V. and Kamaludin, A. (2018). Technology Acceptance Model in M-learning context: A systematic review, Computers and Education, 125(August 2017), 389-412. https://doi.org/10.1016/j.compedu.2018.06.008.   DOI
43 Bandura, A. (1982). Self-efficacy mechanism in human agency, American Psychologist, 37(2), 122-147. https://doi.org/10.1037/0003-066X.37.2.122.   DOI
44 Chen, K., Chen, J. V. and Yen, D. C. (2011). Dimensions of self-efficacy in the study of smart phone acceptance, Computer Standards & Interfaces, 33(4), 422-431. https://doi.org/10.1016/j.csi.2011.01.003.   DOI
45 Chou, S. F., Horng, J. S., Liu, C. H., Yu, T. Y. and Kuo, Y. T. (2022). Identifying the critical factors for sustainable marketing in the catering: The influence of big data applications, marketing innovation, and technology acceptance model factors, Journal of Hospitality and Tourism Management, 51(1), 11-21. https://doi.org/10.1016/j.jhtm.2022.02.010.   DOI
46 Hwang, J., Kim, H. and Kim, W. (2019). Investigating motivated consumer innovativeness in the context of drone food delivery services, Journal of Hospitality and Tourism Management, 38(September 2018), 102-110. https://doi.org/10.1016/j.jhtm.2019.01.004.   DOI
47 Shin, J., Moon, S., Cho, B. ho, Hwang, S. and Choi, B. (2022). Extended technology acceptance model to explain the mechanism of modular construction adoption, Journal of Cleaner Production, 342(February), 130963. https://doi.org/10.1016/j.jclepro.2022.130963.   DOI
48 Wang, R., Zhao, X., Wang, W. and Jiang, L. (2021). What factors affect the public acceptance of new energy vehicles in underdeveloped regions? A case study of Gansu Province, China, Journal of Cleaner Production, 318(967), 128432. https://doi.org/10.1016/j.jclepro.2021.128432.   DOI
49 Yang, Y. and Wang, X. (2019). Modeling the intention to use machine translation for student translators: An extension of Technology Acceptance Model, Computers and Education, 133(January), 116-126. https://doi.org/10.1016/j.compedu.2019.01.015.   DOI
50 Compeau, D. R. and Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test, MIS Quarterly: Management Information Systems, 19(2), 189-210. https://doi.org/10.2307/249688.   DOI
51 Davidson, M. and Diamond, T. (2020). On the Profitability of Selfish Mining Against Multiple Difficulty Adjustment Algorithms, IACR Cryptology EPrint Archive, (2020/094), 1-22.
52 Al-Gahtani, S. S. (2016). Empirical investigation of e-learning acceptance and assimilation: A structural equation model, Applied Computing and Informatics, 12(1), 27-50. https://doi.org/10.1016/j.aci.2014.09.001.   DOI
53 Agarwal, R. and Prasad, J. (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.   DOI
54 Al-Ammary, J. H., Al-Sherooqi, A. K. and Al-Sherooqi, H. K. (2014). The Acceptance of Social Networking as a Learning Tools at University of Bahrain, International Journal of Information and Education Technology, 4(2), 208-214. https://doi.org/10.7763/ijiet.2014.v4.400.   DOI