ISSN : 1738-3110
Purpose - The potential use of cryptocurrencies in a retail environment proposes a rapid shift from the traditional financial system. Nakamoto(2008) defines Bitcoin as an open source alt-coin based on the blockchain technology. Luther(2016) insists that the new technology will be widely adopted for the digital payment processes. However, the use of Bitcoin is in the real world is still sparse. Despite the growing attention and purported benefits, it is doubtful whether the Bitcoin will be eagerly accepted by ordinary consumers in the mainstream market. To answer this question, this paper develops a causal model that has a dual path to explain the motivation to adopt Bitcoin. According to Glaser, Zimmermann, Haferkorn, Weber, and Siering(2014), Bitcoin is both an asset and a currency at the same time. In summary, the attitude towards Bitcoin may vary depending on whether the fin-tech product is viewed as an asset or as a currency. Based on the arguments, we propose that asset attitude and currency attitude will give influence to consumers' intention to adopt Bitcoin. Research design, data, and methodology - Quantitative data collection is conducted from a Bitcoin SIG(special interest group) working in an internet community. As a result, 192 respondents who know Bitcoin completed the survey. To analyze the causal relations in the research model, PLS-SEM(partial least squares structural equation modeling) method is used. Also, reliability and validity of measures are tested by performing Cronbach's alpha test, Fornell-Larcker test and confirmatory factor test. Results - Our test results show that every hypothesis is supported except the influence of perceived ease of use. In addition, we find that the relationships between constructs are different between the high innovative group and low innovative group. Conclusions - We provide evidence that asset attitude and currency attitude are key antecedents of Bitcoin adoption.
Adrian, P. (2015). Money for nothing and bits for free:the geographies of Bitcoin. University of Tronto.
Athey, S., Parashkevov, I., Sarukkai V., & Xia, J. (2017). Bitcoin pricing, adoption, and usage: Theory and evidence. Stanford Institute for Economic Policy Research, 1-70.
Bacon, L. D. (1999). Using LISREL and PLS to measure customer satisfaction, In Proceedings of Sawtooth Software Conference, 2(5), 305-306.
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
Barber, S., Boyen, X., Shi, W., & Uzun, E. (2012). Bitter to better – How to make Bitcoin a better currency. Lecture Notes in Computer Science, 7397, 399-414.
Baur, A. W., Bühler, J., Bick, M., & Bonorden, C. S. (2015). Cryptocurrencies as a disruption? Empirical findings on user adoption and future potential Bitcoin and co. In Proceedings of Conference on e-Business, e-Service and e-Society, 9373, 63-80.
Bettencourt, L. A. (1997). Customer voluntary performance: Consumers as partners in service delivery. Journal of Retailing, 73(3), 383-406.
Bhattacherjee, A., & Premkumar, G. (2004). Understanding changes in belief and attitude toward information technology use: A theoretical model and longitudinal test. MIS Quarterly, 28(2), 229-254.
Bohme, R., Edelman, B., & Moore, T. (2015). Bitcoin:Economics, technology, and governance. Journal of Economic Perspectives, 29(2), 213-238.
Bonett, D. G., & Wright, T. A. (2015). Cronbach’s alpha reliability: Internal estimation, hypothesis testing, and sample size planning. Journal of Organizational Behavior, 36(1), 3-15.
Cho, K.., & Kim C. (2017). The study on the influence of technology acceptance in the traditional markets for consumer purchasing intention: based on augmented reality technology. Journal of Distribution Science, 14(12), 119-127.
European Central Bank (2012). Virtual currency schemes. Bank Report.
Fishbein, M. (1980). A theory of reasoned action: some applications and implications. University of Nebraska Press.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Glaser, F., Zimmermann, K., Haferkorn, M., Weber M. C., & Siering, M. (2014). Bitcoin –asset or currency? Revealing user’s hidden intention. In proceedings of Twenty Second European Conference on Information Systems, Tel Aviv.
Grabner-Krauter, S., & Faullant, R. (2008). Consumer acceptance of internet banking: The influence of internet trust. International Journal of Bank Marketing, 26(7), 483-504.
Hess, Z. T. J., McNab, A. L., & Basoglu, K. A. (2014). Reliability generalization of perceived ease of use, perceived usefulness, and behavioral intentions. MIS Quarterly, 38(1), 1-28.
Hong, I. B., & Cha, H. S. (2013). The mediating role of consumer trust in an online merchant in predicting purchase intention. International Journal of Information Management, 33(6), 927-939.
Hulland, J. (1999). Use of partial least square (PLS) in strategic management research: a review of four recent studies. Strategic Management Journal, 20(2), 195-204.
Katsikeas, C. S., Morgan, N. A., Leonidou, L. C., & Hult, G. T. M. (2016). Assessing performance outcomes in marketing. Journal of Marketing, 80(March), 1-20.
Kim, H., & Cho, H. (2013), The effects of consumption value of smartphone users on relational factors and repurchase intention. Journal of Distribution Science, 11(4), 73-80.
Krugman, P. (2014, October 4). The long cryptocoin: The conscience of a liberal. New York Times.
Luther, W. J. (2016). Bitcoin and the future of digital payments. The Independent Review, 20(3), 397-404.
Lee, T. (2013, August 21). Five surprising facts about Bitcoin, The Washington Post.
Liu, S. (2013). The demographics of Bitcoin. Retrieved September 1, 2017 from http://simulacrum.cc/2013/03/04/the-demographics-of-bitcoin-part-1-updated/
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192-222.
Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system, 1-9.
Oliveira, T., Alhinho, M., Rita, P., & Dhillon, G. (2017). Modelling and testing consumer trust dimensions in e-commerce. Computers in Human Behavior, 71(June), 153-164.
Rahi, S., Ghani, M. A., & Alnaser, F. M. (2017). Predicting customer’s intentions to use internet banking:the role of technology acceptance model (TAM) in e-banking. Management Science Letters, 7, 513-524.
Shin, S. J., & Lee, W-J. (2014). The effects of technology readiness and technology acceptance on NFC mobile payment services in Korea. Journal of Applied Business Research, 30(6), 1615-1626.
Simon, B., Xavier, B., Elaine, S., & Ersin, U. (2012). Bitter to better – how to make Bitcoin a better currency. Lecture Notes in Computer Science, 7397, 399-414.
Statsoft (2013). Structural equation modeling, Statsoft Electronic Statistics Textbook, Retrieved November 2, 2017 from http://www.statsoft.com/Textbook/Structural-Equation-Modeling
Tarasov, A. (2017, June 26). Bitcoin price, explained. The Cointelegraph.
Todorov, T. (2017). Bitcoin – an innovative payment method with a new type of independent currency. Trakia Journal of Science, 15(1), 163-166.
Tsukerman, M. (2015). The block is hot: A survey of the state of Bitcoin regulation and suggestions for the future. Berkely Tech, 30(4), 1127-1168.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology:Toward a unified view. MIS Quarterly, 27(3), 425-478.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the united theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178.
Verma, P., & Sinha, N. (2017). Role of attitude as mediator of the perceived ease of use and behavioral intention, relationship. International Journal of Management Concept and Philosophy, 10(3), 227-245.
Wang, Y. S., Wang, Y. M. M., Lin, H. H., & Tang, T. I. (2003). Determinants of user acceptance of internet banking: An empirical study. International Journal of Service Industry Management, 14(5), 501-519.
Wolfson, S. N. (2015). Bitcoin: The early market. Journal of Business & Economics Research, 13(4), 201-214.
Wong, K. K. (2013). Partial least squares structural equation modeling (PL-SEM) techniques using SmartPLS. Marketing Bulletin, 24(1), 1-32.
Woo, M., Park, J., & Jung, B. Y. (2014). An empirical study on the impact of quality oriented corporate culture on sustainability management performances, Journal of Distribution Science, 12(6), 31-39.
Wu, S. I. (2006). A comparison of the behavior of different customer clusters towards internet bookstores. Information & Management, 43(8), 986-1001.
Yelowitz, A., & Wilson, M. (2015). Characteristics of Bitcoin users: An analysis of Google search data. Applied Economics Letters, 22(13), 1030-1036.