Examining the Factors Affecting the Use of Crypto Assets as Foreign Payment and Investment Instruments: A Quantitative Study

Abstract

The aim of the study is to examine the factors affecting individuals' intentions to use crypto assets as foreign payment and investment instruments. A research model based on the UTAUT-2, a widely accepted model that examines individuals' attitudes toward information technologies, was formed, and the factors affecting two dependent variables, "intention to use in foreign payments" and "intention to invest," were analyzed with PLS-SEM. Path coefficients (β) demonstrated that the variables significantly affecting the intention to use in foreign payments were "performance expectancy," "social influence," and "perceived risk," respectively. Moreover, in order of importance, "performance expectancy," "social influence," "awareness," and "perceived risk" were determined as the variables significantly affecting the intention to invest. Along with path coefficients, f² and q² effect sizes were also analyzed to examine the interaction between the variables. In the context of empirical findings, it was evaluated that the most significant factors in the participants' tendency to use crypto assets in foreign payments and investment transactions were "performance expectancy" and "social influence”. Contrary to the widespread approach in the literature, the study has revealed crucial results for the literature and future studies by addressing the two main financial functions of crypto assets and the factors significantly affecting these functions.

Keywords

• Crypto Asset
• Cryptocurrency
• UTAUT-2
• Fintech

Yurtdışı Ödeme ve Yatırım Aracı Olarak Kripto Varlıkların Kullanımını Etkileyen Faktörlerin İncelenmesi: Nicel Bir Araştırma

Abstract

Çalışmanın amacı, bireylerin yurtdışı ödeme ve yatırım aracı olarak kripto varlıkları kullanma niyetlerini etkileyen faktörleri incelemektir. Bilgi teknolojilerine yönelik bireysel tutumları irdeleyen ve yaygın bir biçimde kabul görmüş olan UTAUT-2'ye dayalı bir araştırma modeli oluşturularak, "yurt dışı ödemelerde kullanım niyeti" ve "yatırımda bulunma niyeti" olmak üzere iki bağımlı değişkeni etkileyen faktörler PLS-SEM ile analiz edilmiştir. Yol katsayıları (β), yurt dışı ödemelerde kullanım niyetini anlamlı düzeyde etkileyen değişkenlerin sırasıyla "performans beklentisi", "sosyal etki" ve "algılanan risk" olduğunu göstermiştir. Ayrıca önem sırasına göre "performans beklentisi", "sosyal etki", "farkındalık" ve "algılanan risk", yatırımda bulunma niyetini anlamlı ölçüde etkileyen değişkenler olarak belirlenmiştir. Yol katsayılarının yanı sıra değişkenler arasında etkileşimi incelemek amacıyla f² ve q² etki büyüklükleri de analiz edilmiştir. Elde edilen bulgular bağlamında katılımcıların yurt dışı ödeme ve yatırım işlemlerinde kripto varlıkları kullanma eğilimlerini etkileyen en önemli faktörlerin "performans beklentisi" ve "sosyal etki" olduğu yönünde değerlendirme yapılmıştır. Çalışma, literatürdeki yaygın yaklaşımın aksine kripto varlıkların iki temel finansal işlevini ve bu işlevleri anlamlı ölçüde etkileyen faktörleri ele alarak, literatür ve gelecek çalışmalar için önemli sonuçlar ortaya koymuştur.

Keywords

• Kripto Varlık
• Kripto-Para
• UTAUT-2
• Fintek

References

1. Angelo, M. D. & Salzer, G. (2020) Tokens, Types, and Standards: Identification and Utilization in Ethereum. 2020 IEEE International Conference on Decentralized Applications and Infrastructures, pp. 1-10.
2. Arias-Oliva, M., Pelegrín-Borondo, J. & Matías-Clavero, G. (2019). Variables Influencing Cryptocurrency Use: A Technology Acceptance Model in Spain. Frontiers in Psychology, 10 ,1–13.
3. Baur, D. G., Hong, K. H. & Lee, A. D. (2017). Bitcoin: Medium of Exchange or Speculative Assets? Journal of International Financial Markets, Institutions and Money, 54, 177–189. https://doi.org/10.1016/j.intfin.2017.12.004
4. Berensten, A. & Schär, F. (2019). Stablecoins: The Quest for a Low-Volatility Cryptocurrency. In A. Fatas (Ed.), The Economics of Fintech and Digital Currencies, CEPR Press, London.
5. Bolotaeva, O.S, Stepanova, A.A. & Alekseeva, S.S. (2019) The Legal Nature of Cryptocurrency. IOP Conference Series: Earth and Environmental Science, Volume 272, Issue 3, https://doi.org/10.1088/1755-1315/272/3/032166
6. Chiu, C. M. & Wang, E. T. G. (2008). Understanding Web-based Learning Continuance Intention: The Role of Subjective Task Value. Information and Management, 45(3), 194–201.
7. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–339. https://doi.org/10.2307/249008
8. EBA. (2019). Report with Advice for the European Commission on Crypto-Assets. (https://www.eba.europa.eu/sites/default/documents/files/documents/10180/2545547/67493daa-85a8-4429-aa91-e9a5ed880684/EBA Report on crypto assets.pdf), Access Date: 27 February 2023.

9. Fornell, C. & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312
10. Gillies, F. I., Lye, C. T., & Tay, L. Y. (2020). “Determinants of Behavioral Intention to Use Bitcoin in Malaysia”. Journal of Information System and Technology Management, 5 (19), 25- 38.
11. Hair, J. E., Hult, G. T., Ringle, C. M. & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). (2nd Edition), SAGE Publications Inc.
12. Henseler, J., Ringle, C. M. & Sarstedt, M. (2015). A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.
13. Henson, R. K. (2001) Understanding Internal Consistency Reliability Estimates: A Conceptual Primer on Coefficient Alpha. Measurement and Evaluation in Counseling and Development, 34(3), 177-189, Doi: 10.1080/07481756.2002.12069034
14. Howard, M. (2014) Creation of a Computer Self-Efficacy Measure: Analysis of Internal Consistency, Psychometric Properties, and Validity. Cyberpsychology, Behavior, and Social Networking, 17(10), 677-681.
15. Hubley, A. M. (2014). Discriminant Validity. In A. C. Michalos (Ed.), Encyclopedia of Quality of Life and Well-Being Research (pp. 1664–1667). Springer Reference.
16. Hulland, J. (1999). Use of Partial Least Squares (PLS) in Strategic Management Research: A Review of Four Recent Studies. Strategic Management Journal, 20(2), 195–204.
17. ING, (2018). Cracking the Code on Cryptocurrency: Bitcoin Buy-in Across Europe, the USA and Australia.(https://think.ing.com/uploads/reports/ING_International_Survey_Mobile_Banking_2018.pdf), Access Date: 12 March 2024.
18. Jariyapan P., Mattayaphutron S., Gillani S. N. & Shafique, O. (2022). Factors Influencing the Behavioral Intention to Use Cryptocurrency in Emerging Economies During the COVID-19 Pandemic: Based on Technology Acceptance Model 3, Perceived Risk, and Financial Literacy. Front. Psychol., 12, 1-20, doi:10.3389/fpsyg.2021.814087
19. Kock, N. (2015). Common Method Bias in PLS-SEM. International Journal of e-Collaboration, 11(4), 1–10.
20. Kock, N. (2017). Structural Equation Modeling with Factors and Composites: A Comparison of Four Methods. International Journal of e-Collaboration, 13(1), 1–9.
21. Kwong, K. & Wong, K. (2013). Partial Least Squares Structural Equation Modeling (PLS-SEM) Techniques Using SmartPLS. Marketing Bulletin, 24(1), 1–32.
22. Lee, W.J., Hong, S.T., & Min, T. (2018). Bitcoin Distribution in the Age of Digital Transformation: Dual-Path Approach. Journal of Distribution Science, 16(12), 47–56.
23. Li, C., Khaliq, N., Chinove, L., Khaliq, U., Popp, J., & Oláh, J. (2023). Cryptocurrency Acceptance Model to Analyze Consumers’ Usage Intention: Evidence from Pakistan. Sage Open, 13(1), 1-19, https://doi.org/10.1177/21582440231156360
24. Mishkin, F. S. (1992). The Economics of Money, Banking and Financial Markets (Third Edition). HarperCollins Publishers Inc., New York.
25. Namahoot, K.S. & Rattanawiboonsom, V. (2022). Integration of TAM Model of Consumers’ Intention to Adopt Cryptocurrency Platform in Thailand: The Mediating Role of Attitude and Perceived Risk. Human Behavior and Emerging Technologies, 2022, 1-12, https://doi.org/10.1155/2022/9642998
26. OECD (2020). Taxing Virtual Currencies: An Overview Of Tax Treatments And Emerging Tax Policy Issues. (https://www.oecd.org/tax/tax-policy/taxing-virtual-currencies-an-overview-of-tax-treatments-and-emerging-tax-policy-issues.pdf), Access Date: 16 February 2023.
27. Pernice, I. G. A. & Scott, B. (2021). Cryptocurrency. Internet Policy Review, 10(2). https://doi.org/10.14763/2021.2.1561
28. Shahzad, F., Xiu, G. Y., Wang, J. & Shahbaz, M. (2018). An Empirical Investigation on the Adoption of Cryptocurrencies Among the People of Mainland China. Technology in Society, 55, 33–40. https://doi.org/10.1016/j.techsoc.2018.05.006
29. Streukens, S. & Leroi-Werelds, S. (2016). Bootstrapping and PLS-SEM: A Step-by-Step Guide to Get More Out of Your Bootstrap Results. European Management Journal, 34(6), 618–632. https://doi.org/10.1016/j.emj.2016.06.003
30. Tamilmani, K., Rana, N. P., Prakasam, N. & Dwivedi, Y. K. (2019). The Battle of Brain vs. Heart: A Literature Review and Meta-Analysis of “Hedonic Motivation” Use in UTAUT2. International Journal of Information Management, 46, 222–235, https://doi.org/10.1016/j.ijinfomgt.2019.01.008.
31. Ter Ji-Xi, J., Salamzadeh, Y. and Teoh, A.P. (2021). Behavioral Intention to Use Cryptocurrency in Malaysia: An Empirical Study. The Bottom Line, 34(2), 170-197. https://doi.org/10.1108/BL-08-2020-0053
32. Ullman, J. B., & Bentler, P. M. (2013). Structural Equation Modeling. In I. B. Weiner (Ed.), Handbook of Psychology Volume 2: Research Methods in Psychology (pp. 661–690).
33. Vaske, J.J., Beaman, J. & Sponarski, C.C. (2017). Rethinking Internal Consistency in Cronbach's Alpha. Leisure Sciences, 39(2), 163-173, Doi: 10.1080/01490400.2015.1127189
34. 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.
35. Venkatesh, V., Thong, J. Y. L. & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending The Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157–178.
36. Venkatesh, V., Thong, J. Y. L. & Xu, X. (2016). Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead. Journal of the Association for Information Systems, 17(5), 328–376.
37. Yeong, Y.C., Kalid, K.S., Savita, K.S., Ahmad, M.N. & Zaffar, M. (2022). Sustainable Cryptocurrency Adoption Assessment Among IT Enthusiasts and Cryptocurrency Social Communities. Sustainable Energy Technologies and Assessments, 52, 1-5, https://doi.org/10.1016/j.seta.2022.102085
38. Yuen, Y. Y., Yeow, P. H. P., Lim, N. & Saylani, N. (2010). Internet Banking Adoption: Comparing Developed and Developing Countries. Journal of Computer Information Systems, 51(1), 52–61.
39. (https://coinmarketcap.com/) Access Date: 17.10.2023
40. (https://etherscan.io/gastracker) Access Date: 14.11.2023
41. (https://fast.tcmb.gov.tr/) Access Date: 30.09.2023
42. (https://www.ziraatbank.com.tr/tr/urun-ve-hizmet-ucretleri). Access Date: 15.11.2023
43. (https://ycharts.com/indicators/ethereum_average_transaction_fee) Access Date: 14.11.2023