The accessibility of FinTech services is increasing, and their convenience is making them more popular than traditional banks, particularly among Generation Z. The objective of this research is to identify and compare the factors influencing the conscious use of FinTech services among Generation Z members, who are the most active participants in this field of financial technology. The questionnaire based purposive sample consisted of Generation Z students who demonstrated adequate financial literacy and utilized FinTech, and who were learning in a university environment in Hungary and Romania. A sample of 600 respondents was selected for analysis after cleaning the data online. The methodological approach entailed the utilization of covariance-based structural equation modeling (CB-SEM). The results indicate that social influence (β = 0.18), consumer attitude (β = 0.53) and facilitating conditions intention (β = 0.11) all have a significant effect on the behavior intention, explaining 49% of the variance. In the context of performance expectation, the effect of facilitating conditions intention is not significant (p = 0.491). The motivation of Generation Z towards fintech solutions is evident in their preference for speed and ease of use. However, in order to reinforce consumer expectations and transfer the necessary experience and attitudes, it may be beneficial for service providers to adopt a partially different strategy in different countries. Generation Z can thus serve as a crucial reference point for the even more discerning expectations of subsequent generations. The findings may inform the formulation of strategies for fintech service providers to better understand customer behavior.

1. Akinwale, Y. O., & Kyari, A. K. (2020). Factors influencing attitudes and intention to adopt financial technology services among the end-users in Lagos State, Nigeria. African Journal of Science, Technology, Innovation and Development, 14(1), 272–279. https://doi.org/10.1080/20421338.2020.1835177
2. Akinwande, M. O., Dikko, H. G., & Gulumbe, S. U. (2015). Identifying the Limitation of Stepwise Selection for Variable Selection in Regression Analysis. American Journal of Theoretical and Applied Statistics, 4(5), 414. https://doi.org/10.11648/j.ajtas.20150405.22
3. Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99–110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002
4. Algina, J., & Moulder, B. C. (2001). A note on estimating the Jöreskog-Yang model for latent variable interaction using LISREL 8.3. Struct. Equ. Model. Multidiscip. J., 8 (1), 40-52. https://www.doi.org/10.1207/S15328007SEM0801_3
5. Alkhwaldi, A. F., Alharasis, E. E., Shehadeh, M., et al. (2022). Towards an Understanding of FinTech Users’ Adoption: Intention and e-Loyalty Post-COVID-19 from a Developing Country Perspective. Sustainability, 14(19), 12616. https://doi.org/10.3390/su141912616
6. Allport, F. (1924). Social psychology. New York: Houghton Mifflin, 4(5), 242–52.
7. Amnas, M. B., Selvam, M., Raja, M., et al. (2023). Understanding the Determinants of FinTech Adoption: Integrating UTAUT2 with Trust Theoretic Model. Journal of Risk and Financial Management, 16(12), 505. https://doi.org/10.3390/jrfm16120505
8. Antwi-Boampong, A., Boison, D., Doumbia, M., et al. (2022). Factors Affecting Port Users’ Behavioral Intentions to Adopt Financial Technology (Fintech) in Ports in Sub-Saharan Africa: A Case of Ports in Ghana. FinTech, 1(4), 362–375. https://doi.org/10.3390/fintech1040027
9. Aseng, A. C. (2020). Factors Influencing Generation Z Intention in Using FinTech Digital Payment Services. CogITo Smart Journal, 6(2), 155–166. https://doi.org/10.31154/cogito.v6i2.260.155-166
10. Asif, M., Khan, M. N., Tiwari, S., et al. (2023). The Impact of Fintech and Digital Financial Services on Financial Inclusion in India. Journal of Risk and Financial Management, 16(2), 122. https://doi.org/10.3390/jrfm16020122
11. Assensoh-Kodua, A. (2023). Fintech: Controversies and Complexities around Device Size for Mobile Banking. In: Proceedings of the 7th International Conference on Applied Research in Management, Economics and Accounting. https://doi.org/10.33422/7th.iarmea.2023.07.126
12. Ateş, S., & Altuner Çoban, G. Ş. (2022). A Validity and Reliability Study on Developing a Scale for Assessing Classroom Teachers’ Attitudes Towards Illustrated Children’s Books. Educational Policy Analysis and Strategic Research, 17(3), 222–237. https://doi.org/10.29329/epasr.2022.461.11
13. Athiyaman, A. (2002). Internet users’ intention to purchase air travel online: an empirical investigation. Marketing Intelligence & Planning, 20(4), 234–242. https://doi.org/10.1108/02634500210431630
14. Bajunaied, K., Hussin, N., & Kamarudin, S. (2023). Behavioral intention to adopt FinTech services: An extension of unified theory of acceptance and use of technology. Journal of Open Innovation: Technology, Market, and Complexity, 9(1), 100010. https://doi.org/10.1016/j.joitmc.2023.100010
15. Campino, J., Brochado, A., & Rosa, Á. (2021). Digital Business Transformation in the Banking Sector. Research Anthology on Concepts, Applications, and Challenges of FinTech, 186–215. https://doi.org/10.4018/978-1-7998-8546-7.ch012
16. Chang, S. E., Shen, W. C., & Yeh, C. H. (2017). A Comparative Study of User Intention to Recommend Content on Mobile Social Networks. Multimedia Tools and Applications, 76(4), 5399-5417. https://doi.org/10.1007/s11042-016-3966-1
17. Cheah, J.-H., Memon, M. A., Richard, J. E., et al. (2020). CB-SEM Latent Interaction: Unconstrained and Orthogonalized Approaches. Australasian Marketing Journal, 28(4), 218–234. https://doi.org/10.1016/j.ausmj.2020.04.005
18. Chen, S., Doerr, S., Frost, J., et al. (2023). The fintech gender gap. Journal of Financial Intermediation, 54, 101026. https://doi.org/10.1016/j.jfi.2023.101026
19. Chen, X., Yu, H., & Yu, F. (2015). What is the optimal number of response alternatives for rating scales? From an information processing perspective. Journal of Marketing Analytics, 3(2), 69–78. https://doi.org/10.1057/jma.2015.4
20. Cruz-Jesus, F., Oliveira, T., Bacao, F., et al. (2016). Assessing the pattern between economic and digital development of countries. Information Systems Frontiers, 19(4), 835–854. https://doi.org/10.1007/s10796-016-9634-1
21. Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, 121092. https://doi.org/10.1016/j.techfore.2021.121092
22. Davcik, N. S. (2014). The use and misuse of structural equation modeling in management research. Journal of Advances in Management Research, 11(1), 47–81. https://doi.org/10.1108/jamr-07-2013-0043
23. Diamantopoulos, A., & Siguaw, J. (2000). Introducing LISREL. SAGE Publications, Ltd. https://doi.org/10.4135/9781849209359
24. Dick, A. S., & Basu, K. (1994). Customer Loyalty: Toward an Integrated Conceptual Framework. Journal of the Academy of Marketing Science, 22(2), 99–113. https://doi.org/10.1177/0092070394222001
25. Djamaly, M. F. (2023). The Rola of Social Media in Generation Z Decision-Making Process to Watch Films in Cinemas. Journal Ekonomi, 12(2), 1201-1207.
26. Dospinescu, O., Dospinescu, N., & Agheorghiesei, D. T. (2021). FinTech Services and Factors Determining the Expected Benefits of Users: Evidence in Romania for Millennials and Generation Z. E+M Ekonomie a Management, 24(2), 101–118. https://doi.org/10.15240/tul/001/2021-2-007
27. Drăgan, M., Horeczki, R., & Munteanu, G. (2024). Ready for the Digital Era? A Comparative Analysis of Hungary and Romania in the Field of Digital Policy. Journal of Settlements and Spatial Planning, 15(1), 39–55. https://doi.org/10.24193/jssp.2024.1.04
28. Elsaman, H., Dayanandan, R., Dawood, Z., et al. (2024). Navigating fintech innovation: Performance, trust, and risk factors in UAE’s banking sector. Journal of Eastern European and Central Asian Research (JEECAR), 11(2), 332–341. https://doi.org/10.15549/jeecar.v11i2.1569
29. Frare, A. B., Fernandes, C. M. G., Santos, M. C. dos, & Quintana, A. C. (2023). Determinants of Intention to Use Fintechs Services by Accounting Students: A Mixed Methods Approach. Brazilian Business Review, 20(5), 580–599. https://doi.org/10.15728/bbr.2021.1059.en
30. Gelencsér, M., Kőmüves, Z. S., Hollósy-Vadász, G., et al. (2024). Modelling employee retention in small and medium-sized enterprises and large enterprises in a dynamically changing business environment. International Journal of Organizational Analysis. https://doi.org/10.1108/ijoa-09-2023-3961
31. George, D., & Mallery, M. (2010). SPSS for Windows Step by Step: A Simple Guide and Reference, 17.0 update (10th edition). Pearson, Boston.
32. Gopal, S., Gupta, P., & Minocha, A. (2023). Advancements in Fin-Tech and Security Challenges of Banking Industry. 2023 4th International Conference on Intelligent Engineering and Management (ICIEM), 12, 1–6. https://doi.org/10.1109/iciem59379.2023.10165876
33. Gubik, A. S., Farkas, S., & Kása, R. (2018). Applying the theory of planned behaviour to explain the evolution of entrepreneurial propensity (Hungarian). Közgazdasági Szemle, 65(01), 74–101. https://doi.org/10.18414/ksz.2018.1.74
34. Habing, B. (2003). Exploratory Factor Analysis. University of South Carolina, Columbia.
35. Haenlein, M., & Kaplan, A. M. (2004). A Beginner’s Guide to Partial Least Squares Analysis. Understanding Statistics, 3(4), 283-297.
36. Hair Jr., J. F., Gabriel, M. L. D. da S., et al. (2014). Covariance-Based Structural Equation Modeling (CB-SEM) with AMOS: Guidance on its application as a Marketing Research Tool (Portuguese). Revista Brasileira de Marketing, 13(2), 44–55. https://doi.org/10.5585/remark.v13i2.2718
37. Hair, J. F., Black, W. C., Babin, B. J., et al. (2010). Multivariate Data Analysis, 7th ed. Prentice Hall, New Jersey.
38. Hair, J. F., Hult, G. T. M., Ringle, C. M., et al. (2017). A primer on partial least squares structural equation modeling (PLS-SEM), 2nd ed. Sage, New York.
39. Hair, J. F., Hult, G. T. M., Ringle, C., et al. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage, New York.
40. Harman, H. H. (1960). Modern factor analysis. University of Chicago Press, Chicago.
41. Hewstone, M., & Martin, R. (2008). Social Influence. In: Hewstone, M., Strobe, W., Joans, K. (editors). Introduction to social psychology. Blackwell. Chichester. 216-243.
42. Hoque, M. Z., Chowdhury, N. J., Hossain, A. A., et al. (2024). Social and facilitating influences in fintech user intention and the fintech gender gap. Heliyon, 10(1), e23457. https://doi.org/10.1016/j.heliyon.2023.e23457
43. Howe, N., & Strauss, W. (2000). Millennials Rising: The Next Great Generation. Vintage Books, New York.
44. Hutabarat, Z, Suryawan, I. N., Andrew, R, Akwila, F. P. (2021). Effect Of Performance Expectancy and Social Influence on Continuance Intention In OVO. Jurnal Manajemen, 25(1), 125. https://doi.org/10.24912/jm.v25i1.707
45. Igamo, A. M., Rachmat, R. A., Siregar, M. I., et al. (2024). Factors influencing Fintech adoption for women in the post-Covid-19 pandemic. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), 100236. https://doi.org/10.1016/j.joitmc.2024.100236
46. Jihane, T., & Aziz, M. (2022). Banks and FinTech Relationship in a Digital Transformation Context. European Scientific Journal, ESJ, 18(12), 106. https://doi.org/10.19044/esj.2022.v18n12p106
47. Kálmán, B. G., & Grotte, J. K. (2023). The Impact of Travel and Tourism Sustainability on a Country’s Image and as the Most Important Factor in the Global Competitive Index: Building Brands Based on Fogel, Schultz, and Schumpeter. Sustainability, 15(22), 15797. https://doi.org/10.3390/su152215797
48. Kézai, P. K., & Kurucz, A. (2023). Crisis Resilience of Startup Companies (The Case of Hungary among the Visegrad Countries with a Focus on the Pandemic). Sustainability, 15(9), 7108. https://doi.org/10.3390/su15097108
49. Kézai, P. K., & Skala, A. (2024). Remarks on the location theories of startups: A case study on the Visegrad countries. Regional Science Policy & Practice, 16(9), 100063. https://doi.org/10.1016/j.rspp.2024.100063
50. Kini, A. N., Savitha, B., & Hawaldar, I. T. (2024). Brand loyalty in FinTech services: The role of self-concept, customer engagement behavior and self-brand connection. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), 100240. https://doi.org/10.1016/j.joitmc.2024.100240
51. Kireyeva, A. A., Kredina, A., Vasa, L., et al. (2021). Impact of financial technologies on economic development: Theories, methods and analysis. Journal of International Studies, 14(4), 286-303.
52. Koloseni, D., & Mandari, H. (2024). Expediting financial inclusion in Tanzania using FinTech: the perspective of diffusion of innovation theory. Technological Sustainability, 3(2), 171–194. https://doi.org/10.1108/techs-11-2023-0048
53. Koranteng, B., & You, K. (2024). Fintech and financial stability: Evidence from spatial analysis for 25 countries. Journal of International Financial Markets, Institutions and Money, 93, 102002. https://doi.org/10.1016/j.intfin.2024.102002
54. Kovács, G., & Vinkóczi, T. (2020). Spatial analysis of the impact of digitalisation and modernisation of banking services in the European Union (Hungarian). Külgazdaság, 64(11–12), 33–69. https://doi.org/10.47630/kulg.2020.64.11-12.33
55. Kovács, G., & Vinkóczi, T. (2022). An Analysis of digitalisation in the context of banking services in the European Union, 2017–2018 (Hungarian). Területi Statisztika, 62(1), 35–58. https://doi.org/10.15196/ts620102
56. Kowalewski, O., & Pisany, P. (2020). The Rise of Fintech: A Cross-Country Perspective. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3624456
57. Lian, S. B., & Yoong, L. C. (2023). Customers’ Satisfaction and Continuance Intention to Adopt Fintech Services. Fintech and Cryptocurrency, 105–135. Portico. https://doi.org/10.1002/9781119905028.ch6
58. Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables. Struct. Equ. Model.: Multidiscip. J., 13 (4), 497-519.
59. Lopez, E. N. B., & Abadiano, M. N. (2023). Understanding Generation Z, the New Generation of Learners: A Technological-Motivational-Learning Theory. Journal of Harbin Engineering University, 44(10), 770-784.
60. Mahmud, K., Joarder, Md. M. A., & Sakib, K. (1975). Fintech ecosystem across developing countries: cross-country exploratory comparison. Journal of Business Administration, 43(2). https://doi.org/10.58964/jbaart06
61. Marsh, H. W., & Craven, R. G. (2006). Reciprocal effects of self-concept and performance from a multidimensional perspective: Beyond seductive pleasure and unidimensional perspectives. Perspect. Psychol. Sci., 1 (2), 133-163.
62. Marsh, H. W., & Hau, K.-T. (1996). Assessing Goodness of Fit. The Journal of Experimental Education, 64(4), 364–390. https://doi.org/10.1080/00220973.1996.10806604
63. Marsh, H. W., Wen, Z., Hau, K.-T., et al. (2007). Unconstrained Structural Equation Models of Latent Interactions: Contrasting Residual- and Mean-Centered Approaches. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 570–580. https://doi.org/10.1080/10705510701303921
64. McDonald, R. P., & Ho, M.-H. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7(1), 64–82. https://doi.org/10.1037/1082-989x.7.1.64
65. Mulaik, S. A., James, L. R., Van Alstine, J., et al. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin, 105(3), 430–445. https://doi.org/10.1037/0033-2909.105.3.430
66. Münnich Á., & Hidegkuti I. (2012). Models of structural equations: examining causality and complex theories in psychological research (Hungarian). Alkalmazott Pszichológia, 1, 77-102.
67. Nathan, R. J., Setiawan, B., & Quynh, M. N. (2022). Fintech and Financial Health in Vietnam during the COVID-19 Pandemic: In-Depth Descriptive Analysis. Journal of Risk and Financial Management, 15(3), 125. https://doi.org/10.3390/jrfm15030125
68. Nguyen, T. A., Dick, M., Nguyen, B. T. T., et al. (2022). The Effect of Culture on Performance Expectancy, Intention, and Trust in Mobile Payment Adoption. International Journal of E-Services and Mobile Applications, 14(1), 1–16. https://doi.org/10.4018/ijesma.285546
69. Nugroho, M. A., & Novitasari, B. T. (2023). Fintech Risks and Continuance to use on Generation Z. International Journal of Professional Business Review, 8(6), e0541. https://www.doi.org/10.26668/businessreview/2023.v8i6.541
70. Nunnally, J. C. (1978). Psychometric testing, 2nd ed. McGraw-Hill, New York.
71. Olçum, G., & Gülova, A. A. (2023). Digitalization and Generation Z: Advantages and Disadvantages of Digitalization. In: Akkaya, B., Tabak, A. (editors). Two Faces of Digital Transformation, Emerald Publishing Limited, Leeds, 31-46. https://doi.org/10.1108/978-1-83753-096-020231003
72. Omar, Q., Yap, C. S., Ho, P. L., et al. (2021). Predictors of behavioral intention to adopt e-AgriFinance app among the farmers in Sarawak, Malaysia. British Food Journal, 124(1), 239–254. https://doi.org/10.1108/bfj-04-2021-0449
73. Pagel, M., Olafsson, A., & Carlin, B. I. (2019). Fintech and Consumer Financial Well-Being in the Information Age. Available online: https://api.semanticscholar.org/CorpusID:209442841 (accessed on 3 May 2024).
74. Pakhnenko, O., Rubanov, P., Hacar, D., et al. (2021). Digitalization of financial services in European countries: Evaluation and comparative analysis. Journal of International Studies, 14(2), 267–282. https://doi.org/10.14254/2071-8330.2021/14-2/17
75. Pirani, S. A. (2024). Navigating the complexity of sample size determination for robust and reliable results. International Journal of Multidisciplinary Research & Reviews, 3(2), 73–86. https://doi.org/10.56815/ijmrr.v3i2.2024/73-86
76. Poyda-Nosyk, N., Kálmán, B. G., & Taylor, R. K. (2022). Financial Literacy in Ukraine and Hungary with special regard to the Performance of University Students. Pacific Business Review International, 15(2), 71-85.
77. Rahi, S., Abd. Ghani, M., & MI Alnaser, F. (2017). Predicting customer’s intentions to use internet banking: the role of technology acceptance model (TAM) in e-banking. Management Science Letters, 513–524. https://doi.org/10.5267/j.msl.2017.8.004
78. Reddy, L. S., & Kulshrestha, P. (2019). Performing the KMO and Bartlett’s Test for Factors Estimating the Warehouse Efficiency, Inventory and Customer Contentment for E-retail Supply Chain. Int. J. Res. Eng. Appl. Manag., 5(9), 1-13.
79. Ritter, N. L. (2012). A comparison of distribution free and no distribution free factor analysis methods. Available online: https://www.researchgate.net/publication/291828129_A_comparison_of_distribution-free_and_non-distribution_free_methods_in_factor_analysis (accessed on 3 May 2024).
80. Rizkalla, N., Tannady, H., & Bernando, R. (2024). Analysis of the influence of performance expectancy, effort expectancy, social influence, and attitude toward behavior on intention to adopt live.on. Multidisciplinary Reviews, 6, 2023spe017. https://doi.org/10.31893/multirev.2023spe017
81. Romānova, I., & Kudinska, M. (2016). Banking and Fintech: A Challenge or Opportunity? Contemporary Studies in Economic and Financial Analysis, 21–35. https://doi.org/10.1108/s1569-375920160000098002
82. Rushda, M. U. F., & Nawarathna, L. S. (2021). The Impact and Usage of Smartphone among Generation Z: A Study Based on Data Mining Techniques. Explainable Artificial Intelligence for Smart Cities, 47–63. https://doi.org/10.1201/9781003172772-4
83. Saksonova, S., & Kuzmina-Merlino, I. (2017). Fintech as Financial Innovation - The Possibilities and Problems of Implementation. European research studies journal, 961–973. https://doi.org/10.35808/ersj/757
84. Saris, W. E., & Stronkhorst, H. (1984). Causal modelling in nonexperimental research. Sociometric Research Foundation, Amsterdam. Available online: https://real.mtak.hu/72091/1/05_S_Gubik_FarkasKasa_u.pdf (accessed on 3 May 2024).
85. Schreiber, J. B., Nora, A., Stage, F. K., et al. (2006). Reporting Structural Equation Modeling and Confirmatory Factor Analysis Results: A Review. The Journal of Educational Research, 99(6), 323–338. https://doi.org/10.3200/joer.99.6.323-338
86. Sharma, V., Jangir, K., Gupta, M., et al. (2024). Does service quality matter in FinTech payment services? An integrated SERVQUAL and TAM approach. International Journal of Information Management Data Insights, 4(2), 100252. https://doi.org/10.1016/j.jjimei.2024.100252
87. Shirowzhan, S., Sepasgozar, S. M. E., Edwards, D. J., et al. (2020). BIM compatibility and its differentiation with interoperability challenges as an innovation factor. Automation in Construction, 112, 103086. https://doi.org/10.1016/j.autcon.2020.103086
88. Shtembari, E., & Elgün, R. F. (2023). Generation Z “Life Skills” Acquired and Enhanced through Internships before and during COVID-19 Pandemic. Administrative Sciences, 13(2), 38. https://doi.org/10.3390/admsci13020038
89. Shuhaiber, A. (2016). How Facilitating Conditions Impact Students’ Intention to Use Virtual Lectures? An Empirical Evidence. In: Borcoci, E., Daimi, K., Atmaca, T. (editors). AICT 2016: The Twelfth Advanced International Conference on Telecommunications. IARIA Press. Wilmington. 68-75.
90. Siposné, N. E., & Zsidó, K. E. (2023). The Effects of Crises on the Living Standards in Romania and Hungary in the 21st Century – Similarities and Differences. Acta Marisiensis. Seria Oeconomica, 17(1), 69–82. https://doi.org/10.2478/amso-2023-0006
91. Siswanto, T., Shofiati, R., & Hartini, H. (2018). Acceptance and Utilization of Technology (UTAUT) as a Method of Technology Acceptance Model of Mitigation Disaster Website. IOP Conference Series: Earth and Environmental Science, 106, 012011. https://doi.org/10.1088/1755-1315/106/1/012011
92. Sultana, N., Chowdhury, R. S., & Haque, A. (2023). Gravitating towards Fintech: A study on Undergraduates using extended UTAUT model. Heliyon, 9(10), e20731. https://doi.org/10.1016/j.heliyon.2023.e20731
93. Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics, 5th ed. Allyn and Bacon, Boston.
94. Ton, D. A., Hammerl, L., Weber, D., et al. (2022). Why leaders are important for cross-functional teams: Moderating role of supportive leadership on knowledge hiding. Problems and Perspectives in Management, 20(3), 178-191.
95. Triandis, H. C. (1980). Beliefs, Attitudes and Values. University of Nebraska Press. Nebraska.
96. Tunn, V. S. C., van den Hende, E. A., Bocken, N. M. P., et al. (2020). Digitalised product-service systems: Effects on consumers’ attitudes and experiences. Resources, Conservation and Recycling, 162, 105045. https://doi.org/10.1016/j.resconrec.2020.105045
97. Utomo, P., Kurniasari, F., & Purnamaningsih, P. (2021). The Effects of Performance Expectancy, Effort Expectancy, Facilitating Condition, and Habit on Behavior Intention in Using Mobile Healthcare Application. International Journal of Community Service & Engagement, 2(4), 183–197. https://doi.org/10.47747/ijcse.v2i4.529
98. Varga, D. (2017). Fintech, the new era of financial services. Vezetéstudomány/Budapest Management Review, 48(11), 22–32. https://doi.org/10.14267/veztud.2017.11.03
99. 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. https://doi.org/10.2307/30036540
100. 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. https://doi.org/10.2307/41410412
101. Wang, F. (2014). Explaining the low utilization of government websites: Using a grounded theory approach. Government Information Quarterly, 31(4), 610–621. https://doi.org/10.1016/j.giq.2014.04.004
102. Wang, X., Joyce, N., & Namkoong, K. (2020). Investigating College Students’ Intentions to Seek Online Counseling Services. Communication Studies, 71(4), 550–567. https://doi.org/10.1080/10510974.2020.1750448
103. Wei, L., Wang, X., Wang, T., et al. (2023). Recommendation Systems for the Metaverse. Blockchains, 1(1), 19–33. https://doi.org/10.3390/blockchains1010003
104. Wheaton, B., Muthen, B., Alwin, D. F., et al. (1977). Assessing Reliability and Stability in Panel Models. Sociological Methodology, 8, 84. https://doi.org/10.2307/270754
105. Wibowo, N. A. P., & Sobari, N. (2023). The influence of behavioral intention, facilitating condition, and habit on use behavioral of QRIS: a study on mobile banking services. Gema Wiralodra, 14(3), 1243–1258. https://doi.org/10.31943/gw.v14i3.482
106. Windasari, N. A., Kusumawati, N., Larasati, N., et al. (2022). Digital-only banking experience: Insights from gen Y and gen Z. Journal of Innovation & Knowledge, 7(2), 100170. https://doi.org/10.1016/j.jik.2022.100170
107. Wood, W., Lundgren, S., Ouellette, J. A., et al. (1994). Minority influence: A meta-analytic review of social influence processes. Psychological Bulletin, 115(3), 323–345. https://doi.org/10.1037/0033-2909.115.3.323
108. Yang, K., & Forney, J. C. (2013). The Moderating Role of Consumer Technology Anxiety in Mobile Shopping. Journal of Electronic Commerce Research, 14(4), 334-347.
109. Yee‐Loong Chong, A., Ooi, K., Lin, B., et al. (2010). Online banking adoption: an empirical analysis. International Journal of Bank Marketing, 28(4), 267–287. https://doi.org/10.1108/02652321011054963
110. Zarco, C., Giráldez-Cru, J., Cordón, O., et al. (2024). A comprehensive view of biometric payment in retailing: A complete study from user to expert. Journal of Retailing and Consumer Services, 79, 103789. https://doi.org/10.1016/j.jretconser.2024.103789
111. Zihan, Y., Yihan, L., & Yinwen, T. (2023). The Development and Impact of FinTech in the Digital Economy. Economics. https://doi.org/10.11648/j.eco.20231201.13