Identifying the Facilitating Factors for Web-based Trading: A Case Study of Blockchain & Cryptocurrency

Authors

  • Sang Hoon Lee School of Computer and Information Engineering, Daegu University, Republic of Korea
  • Hyun-Seok Hwang Dept. of Business Administration, Hallym University, Republic of Korea
  • Su-Yeon Kim School of Computer and Information Engineering, Daegu University, Republic of Korea

DOI:

https://doi.org/10.13052/jwe1540-9589.2162

Keywords:

Web Trading, Blockchain, Crytocurrency, Technology Acceptance

Abstract

Blockchain, which is spotlighted as one of the core technologies in the Web 3.0 era, is being used as a tool for high security and decentralization. In addition, blockchain has been positioned as a core technology for services such as cryptocurrency, NFT, De-Fi, and metaverse, and has already provided high-quality services. In particular, cryptocurrency has shown rapid growth and has been receiving worldwide attention. Cryptocurrency is a web technology and has the property that it can be an investment target, and it is expected to develop further in the future. In this research, we analyzed factors influencing the intention to use cryptocurrency and structural causalities among the factors. We considered personal characteristics, characteristics of cryptocurrency itself, and social characteristic, and a research model has been established for an empirical study. In addition, a multi-group analysis was performed to identify differences between users and non-users. As a result of the analysis, it was found that some of the personal characteristics and cryptocurrency characteristics affect the intention to use. And in the case of non-users, it was found that not only personal and cryptocurrency characteristics, but also social characteristic influence their intention to use. The results of this research are expected to provide implications for cryptocurrency service providers and users, as well as institutions that establish related policies.

Downloads

Download data is not yet available.

Author Biographies

Sang Hoon Lee, School of Computer and Information Engineering, Daegu University, Republic of Korea

Sang Hoon Lee received his Bachelor of Information Engineering from Daegu University in 2013, Master of Computer and Information Engineering from Daegu University in 2015, and PhD in Information Engineering from Daegu University in 2018. He is currently a researcher and lecturer in the School of Computer and Information Engineering, Daegu University, Republic of Korea. His main research fields are recommendation systems, intellectual property rights and smart systems. Recently, he is working as an advisor to several projects in the blockchain and NFT fields.

Hyun-Seok Hwang, Dept. of Business Administration, Hallym University, Republic of Korea

Hyun-Seok Hwang is a Professor of Business Administration and a research fellow of Hallym Business Research Institute at Hallym University, Chuncheon, Republic of Korea. He received his PhD, Master, Bachelor in Industrial and Management Engineering from the Pohang University of Science and Technology (POSTECH), South Korea. His research interests cover Fintech, Big Data Analytics, and Digital Contents. He is also a director of fintech start-up and a deliberation committee member of the government’s information institution.

Su-Yeon Kim, School of Computer and Information Engineering, Daegu University, Republic of Korea

Su-Yeon Kim received her Bachelor of Science in Mathematics from Pohang University of Science and Technology (POSTECH) in 1991, her Master of Science in Information Industry from Soongsil University in 1997, and her PhD in Industrial Engineering from POSTECH in 2003. She has worked for many years in IT consulting, including information strategy planning and data modeling for financial institutions, and is currently a professor in the Department of Computer and Information Engineering, Daegu University, Republic of Korea. Her research interests includes technology management, recommendation systems, and intellectual property management. She works as an informatization advisor for a local government in Korea, and recently founded a technology-based startup and serves as the CEO.

References

Abbas Borhani, S., Babajani, J., Raeesi Vanani, I., Sheri Anaqiz, S., and Jamaliyanpour, M. (2021). Adopting Blockchain Technology to Improve Financial Reporting by Using the Technology Acceptance Model (TAM). International Journal of Finance and Managerial Accounting, 6(22), 155–171. Retrieved from https://ijfma.srbiau.ac.ir/article_17481.html

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

Agarwal, R. and Prasad, J. (1998) A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology. Information Systems Research, 9, 204–224.

Agudo-Peregrina, Á. F., Hernández-García, Á., 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

Agustina, D. (2019). Extension of Technology Acceptance Model (Etam): Adoption of Cryptocurrency Online Trading Technology. Jurnal Ekonomi, 24(2), 272. https://doi.org/10.24912/je.v24i2.591

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

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

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

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.

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

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

Chen, K., Chen, J. V., and Yen, D. C. (2011). Dimensions of self-efficacy in the study of smart phone acceptance. Computer Standards and Interfaces, 33(4), 422–431. https://doi.org/10.1016/j.csi.2011.01.003

Chong, K. W., Kim, Y. S., and Choi, J. (2021). A study of factors affecting intention to adopt a cloud-based digital signature service. Information (Switzerland), 12(2), 1–15. https://doi.org/10.3390/info12020060

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

Cofta, P. L. (2019). Trust and the Web – A decline or a revival? Journal of Web Engineering, 17(8), 591–616. https://doi.org/10.13052/jwe1540-9589.1781

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

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.

Davis, F.D., A Technology Acceptance Model for EmpiricallyT esting New End-User Information Systems: Theory and Results, Doctoral Dissertation, MIT Sloan School of Management, Cambridge, MA, 1986.

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

Dickinger, A., Arami, M., and Meyer, D. (2008). The role of perceived enjoyment and social norm in the adoption of technology with network externalities. European Journal of Information Systems, 17, 4–11. https://doi.org/10.1057/palgrave.ejis.3000726

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

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

Everett M Rogers, Diffusion of innovations, Free Press, NY, 2003.

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 and Applied Sciences, 1(2), 19e28. Retrieved from http://rrpjournals.org/wjepas/en_wjepas_vol_1_iss_2_pg_19_28

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

França, A. S. L., Amato Neto, J., Gonçalves, R. F., and Almeida, C. M. V. B. (2020). Proposing the use of blockchain to improve the solid waste management in small municipalities. Journal of Cleaner Production, 244, 118529. https://doi.org/10.1016/j.jclepro.2019.118529

Ghaebi Panah, P., Bornapour, M., Cui, X., and Guerrero, J. M. (2022). Investment opportunities: Hydrogen production or BTC mining? International Journal of Hydrogen Energy, 47(9), 5733–5744. https://doi.org/10.1016/j.ijhydene.2021.11.206

González Bravo, L., Nistor, N., Castro Ramírez, B., Gutiérrez Soto, I., Varas Contreras, M., Núñez Vives, M., and Maldonado Robles, P. (2022). Higher education managers’ perspectives on quality management and technology acceptance: A tale of elders, mediators, and working bees in times of Covid-19. Computers in Human Behavior, 131(February). https://doi.org/10.1016/j.chb.2022.107236

He, Y., Xiong, W., Yang, B., Zhang, R., Cui, M., Feng, T., and Sun, Y. (2021). Distributed Energy Transaction Model Based on the Alliance Blockchain in Case of China. Journal of Web Engineering, 20(March), 359–386. https://doi.org/10.13052/jwe1540-9589.2026

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

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

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

Hwang, Y. (2014). User experience and personal innovativeness: An empirical study on the Enterprise Resource Planning systems. Computers in Human Behavior, 34, 227–234. https://doi.org/10.1016/j.chb.2014.02.002

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

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

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

Lee, W. J., Hong, S. T., and Min, T. (2019). Bitcoin distribution in the age of digital transformation: Dual-path approach. Journal of Distribution Science, 16(12), 47–56. https://doi.org/10.15722/jds.16.12.201812.47

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

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

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

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

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

Muñoz-Leiva, F., Climent-Climent, S., and Liébana-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

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

Nasri, W., and Charfeddine, L., (2012). An Exploration of Facebook.Com Adoption in Tunisia Using Technology Acceptance Model (TAM) and Theory of Reasoned Action (TRA). Interdisciplinary Journal of Contemporary Research in Business, 4(5), 948–968.

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

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

Raffaghelli, J. E., Rodríguez, M. E., Guerrero-Roldán, A. E., and Bañeres, D. (2022). Applying the UTAUT model to explain the students’ acceptance of an early warning system in Higher Education. Computers and Education, 182(February). https://doi.org/10.1016/j.compedu.2022.104468

Satoshi Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System”, bitcoin.org

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

Scovell, M. D. (2022). Explaining hydrogen energy technology acceptance: A critical review. International Journal of Hydrogen Energy, in Press, 1–19. https://doi.org/10.1016/j.ijhydene.2022.01.099

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

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

Xu, Q., Hwang, B. G. (BG), and 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

Yang, H., and Zhou, L., (2011). Extending TPB and TAM to mobile viral marketing: An exploratory study on American young consumers’ mobile viral marketing attitude, intent and behavior. Journal of Targeting, Measurement and Analysis for Marketing, 19, 85–98. https://doi.org/10.1057/jt.2011.11

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

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

Yoon, C., (2018). Extending the TAM for Green IT: A normative perspective. Computers in Human Behavior, 83, 129–139. https://doi.org/10.1016/j.chb.2018.01.032

Zhang, J., Yang, A., and Shuaishuai, F. (2022). Data Protection of Internet Enterprise Platforms in the Era of Big Data. Journal of Web Engineering, 21, 861–878. https://doi.org/10.13052/jwe1540-9589.21314

Downloads

Published

2022-11-09

How to Cite

Lee, S. H. ., Hwang, H.-S. ., & Kim, S.-Y. . (2022). Identifying the Facilitating Factors for Web-based Trading: A Case Study of Blockchain & Cryptocurrency. Journal of Web Engineering, 21(06), 1767–1792. https://doi.org/10.13052/jwe1540-9589.2162

Issue

Section

Articles