Biometric Technology in Digital Banking: Insights from Generation Z and Millennials

Herwin Ardianto; Chitra Laksmi Rithmaya; Larasati Ayu Sekarsari

doi:10.14414/jbb.v15i1.5378

Vol. 15 No. 01 (2025), Articles

Vol. 15 No. 01 (2025)

Biometric Technology in Digital Banking: Insights from Generation Z and Millennials

Articles

DOI: 10.14414/jbb.v15i1.5378

Published 2025-12-10

Herwin Ardianto⁺⁻
Chitra Laksmi Rithmaya⁺⁻
Larasati Ayu Sekarsari⁺⁻

Herwin Ardianto

Universitas Hayam Wuruk Perbanas

Chitra Laksmi Rithmaya

Universitas Hayam Wuruk Perbanas

Larasati Ayu Sekarsari

Universitas Hayam Wuruk Perbanas

pdf

Keywords

Biometric
TAM
Generation Z
Millennial
Digital Bank

How to Cite

Herwin Ardianto, Chitra Laksmi Rithmaya, & Larasati Ayu Sekarsari. (2025). Biometric Technology in Digital Banking: Insights from Generation Z and Millennials. Journal of Business & Banking, 15(01), 39-58. https://doi.org/10.14414/jbb.v15i1.5378

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Biometric technology offers various conveniences and security features that can enhance the user experience of digital banking. This study explores the adoption of biometric technology in digital banking among Indonesian Generation Z and Millennials. Utiliz-ing the Technology Acceptance Model (TAM), we investigate factors influencing atti-tudes, intentions, and actual use of biometrics among 326 respondents. Data analysis was performed using Partial Least Squares-Structural Equation Modeling (PLS-SEM). Findings indicate that perceived usefulness, ease of use, security, and convenience signif-icantly affect user attitudes, intentions, and actual usage. Additionally, perceived use-fulness moderates the relationship between perceived ease of use and attitudes toward biometric adoption. These insights are crucial for financial institutions aiming to en-hance user acceptance of biometric systems, contributing to secure and user-friendly digi-tal banking solutions for younger consumers in Indonesia.

References

Agidi, R. . (2018). Using biometric in solving terrorism and crime activities in Nigeria. Techsplend Journal of Technology, 1(12), 91–105.

Ahmad, S. M. S., Ali, B. M., & Adnan, W. A. W. (2012). Technical issues and challenges of biometric applications as access control tools of information security. International Journal of Innovative Computing, Information and Control, 8(11).

Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.47985/dcidj.475

Alalwan, A. A., Algharabat, R. S., Baabdullah, A. M., Rana, N. P., Qasem, Z., & Dwivedi, Y. K. (2020). Examining the impact of mobile interactivity on customer engagement in the context of mobile shopping. Journal of Enterprise Information Management, 33(3), 627–653. https://doi.org/10.1108/JEIM-07-2019-0194

Armansyah, R. F. (2021). Herd behavior in using mobile payment with unified theory of acceptance and use of technology (UTAUT2). Jurnal Manajemen Dan Kewirausahaan, 23(2), 111–128. https://doi.org/10.9744/jmk.23.2.111-128

There are 61 references. More references >>

Agidi, R. . (2018). Using biometric in solving terrorism and crime activities in Nigeria. Techsplend Journal of Technology, 1(12), 91–105.

Ahmad, S. M. S., Ali, B. M., & Adnan, W. A. W. (2012). Technical issues and challenges of biometric applications as access control tools of information security. International Journal of Innovative Computing, Information and Control, 8(11).

Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.47985/dcidj.475

Alalwan, A. A., Algharabat, R. S., Baabdullah, A. M., Rana, N. P., Qasem, Z., & Dwivedi, Y. K. (2020). Examining the impact of mobile interactivity on customer engagement in the context of mobile shopping. Journal of Enterprise Information Management, 33(3), 627–653. https://doi.org/10.1108/JEIM-07-2019-0194

Armansyah, R. F. (2021). Herd behavior in using mobile payment with unified theory of acceptance and use of technology (UTAUT2). Jurnal Manajemen Dan Kewirausahaan, 23(2), 111–128. https://doi.org/10.9744/jmk.23.2.111-128

Baraba, R. A. A., & Mahmudi, M. (2023). Understanding The Millennial Generation Behavior In Using Digital Banking. Asean International Journal of Business, 2(1), 1–13. https://doi.org/10.54099/aijb.v2i1.394

Bayode, A., van der Poll, J. A., & Ramphal, R. R. (2019). 4th industrial revolution: Challenges and opportunities in the south African context. 17th JOHANNESBURG Int’l Conference on Science, Engineering, Technology & Waste Management (SETWM-19).

Cahyono, D., Fahrudin, R., Alwiyah, & Sinclair, A. (2024). Pentingnya Edukasi dalam Mengatasi Keamanan Data Mobile Banking di Indonesia. Jurnal MENTARI: Manajemen, Pendidikan Dan Teknologi Informasi, 3(1), 81–89. https://doi.org/10.33050

Chinn, W. W. (1998). The partial least squares approach to structural equation modelling. In Modern methods for business research (pp. 295–336). Lawrence Elbaum Associates.

Christiani, N., Anggela, P., Uslianti, S., Industri, J. T., Teknik, F., & Tanjungpura, U. (2024). Penerapan technology acceptance model (tam) pada penggunaan aplikasi seabank. INTEGRATE: Industrial Engineering and Management System, 8(2), 218–224.

Cohen, J. (1988). Statistical power analysis for the behavioural sciences (2nd Edition). Lawrence Earlbaum Associates.

CrowdStrike. (2025). 2025 CrowdStrike Global Threat Report.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–340. https://doi.org/10.2307/249008

Feng, Z. (2018). Biometric Identification Technology and Development Trend of Physiological Characteristics. Journal of Physics: Conference Series, 1060(1), 1–5. https://doi.org/10.1088/1742-6596/1060/1/012047

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. In Philosophy Rhetoric. Reading, MA: Addison-Wesley. https://doi.org/10.1002/cncr.26402

Gartner. (2024). 2025 Top Strategic Technology Trends.

Ghozali, I. (2014). Structural Equation Modeling, Metode Alternatif dengan Partial Least Square (PLS). Badan Penerbit Universitas Diponegoro.

Gignac, G. E., & Szodorai, E. T. (2016). Effect size guidelines for individual differences researchers. Personality and Individual Differences, 102, 74–78. https://doi.org/10.1016/j.paid.2016.06.069

Groß, M. (2015). Exploring the acceptance of technology for mobile shopping: an empirical investigation among Smartphone users. International Review of Retail, Distribution and Consumer Research, 25(3), 215–235. https://doi.org/10.1080/09593969.2014.988280

Gupta, R., & Varma, S. (2019). A structural equation model to assess behavioural intention to use biometric enabled e-banking services in India. International Journal of Business Information Systems, 31(4). https://doi.org/10.1504/IJBIS.2019.101586

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). In Sage. SAGE Publications, Inc.

Hair Jr., J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123. https://doi.org/10.1504/ijmda.2017.10008574

Hew, J. J., Lee, V. H., Ooi, K. B., & Lin, B. (2016). Mobile social commerce: The booster for brand loyalty? Computers in Human Behavior, 59, 142–154. https://doi.org/10.1016/j.chb.2016.01.027

Hoehle, H., Aljafari, R., & Venkatesh, V. (2016). Leveraging Microsoft’s mobile usability guidelines: Conceptualizing and developing scales for mobile application usability. International Journal of Human Computer Studies, 89, 35–53. https://doi.org/10.1016/j.ijhcs.2016.02.001

Hossain, M. S., & Phoha, V. V. (2021). Enhancing performance and user convenience of multi-biometric verification systems. Pattern Analysis and Applications, 24(4), 1569–1582. https://doi.org/10.1007/s10044-021-01008-5

Hosseini, S. S., & Mohammadi, S. (2012). Review Banking on Biometric in the World’s Banks and Introducing a Biometric Model for Iran’s Banking System. J. Basic. Appl. Sci. Res, 2(9).

Huda, N., Ayu, D., & Septyarini, R. (2024). Digital Economy Outlook 2025. Center of Economic and Law Studies (Celios).

Hwang, H., & Takane, Y. (2004). Generalized structured component analysis. Psychometrika, 69(1), 81–99. https://doi.org/10.1007/BF02295841

Jain, A. K., Ross, A., & Pankanti, S. (2006). Biometrics: A tool for information security. IEEE Transactions on Information Forensics and Security, 1(2), 125–143. https://doi.org/10.1109/TIFS.2006.873653

Jain, A., & Kumar, A. (2010). Biometrics of next generation: An overview. Second Generation Biometrics.

Jayawardhena, C., & Foley, P. (2000). Changes in the banking sector - The case of Internet banking in the UK. Internet Research, 10(1). https://doi.org/10.1108/10662240010312048

Julia, L., Linggam, P. S., Hibatullah, R., & Justianto, J. S. (2024). The Impact of Perceived Risk and Technology Acceptance Model on Gen Z’s Adoption of Digital Banking. WSEAS Transactions on Computer Research, 12(1), 1–18. https://doi.org/10.37394/232018.2024.12.1

Kariuki, P., Ofusori, L. O., & Goyayi, M. L. J. (2025). Internet of Things on Banking Processes in South Africa: A Systematic Reflection on Innovations, Opportunities and Challenges. Digital Business, 5(1), 100097. https://doi.org/10.1016/j.digbus.2024.100097

Kim, S. J., Wang, R. J. H., & Malthouse, E. C. (2015). The effects of adopting and using a brand’s mobile application on customers’ subsequent purchase behavior. Journal of Interactive Marketing, 31(1), 28–41. https://doi.org/10.1016/j.intmar.2015.05.004

Kizgin, H., Jamal, A., Dwivedi, Y. K., & Rana, N. P. (2021). The impact of online vs. offline acculturation on purchase intentions: A multigroup analysis of the role of education. Journal of Business Research, 130. https://doi.org/10.1016/j.jbusres.2020.05.011

Kuisma, T., Laukkanen, T., & Hiltunen, M. (2007). Mapping the reasons for resistance to Internet banking: A means-end approach. International Journal of Information Management, 27(2), 75–85. https://doi.org/10.1016/j.ijinfomgt.2006.08.006

Liang, Y., Samtani, S., Guo, B., & Yu, Z. (2020). Behavioral Biometrics for Continuous Authentication in the Internet-of-Things Era: An Artificial Intelligence Perspective. IEEE Internet of Things Journal, 7(9). https://doi.org/10.1109/JIOT.2020.3004077

Liao, Z., & Cheung, M. T. (2002). Internet-based e-banking and consumer attitudes: An empirical study. Information and Management, 39(4). https://doi.org/10.1016/S0378-7206(01)00097-0

Lim, A. C. M., Ng, L. H. X., & Taeihagh, A. (2025). Biometric data landscape in Southeast Asia: Challenges and opportunities for effective regulation. Computer Law and Security Review, 56(December 2024), 106095. https://doi.org/10.1016/j.clsr.2024.106095

MarketsandMarkets. (2025). Biometrics-As-A-Service Global Market Report 2025.

Monecke, A., & Leisch, F. (2012). SemPLS: Structural equation modeling using partial least squares. Journal of Statistical Software, 48(3), 1–32. https://doi.org/10.18637/jss.v048.i03

Morosan, C. (2012). Voluntary Steps toward Air Travel Security: An Examination of Travelers’ Attitudes and Intentions to Use Biometric Systems. Journal of Travel Research, 51(4). https://doi.org/10.1177/0047287511418368

Morosan, C., & DeFranco, A. (2016). Investigating American iPhone users’ intentions to use NFC mobile payments in Hotels. Proceeding of the Information and Communication Technologies in Tourism 2016, 427–440. https://doi.org/10.1007/978-3-319-28231-2_31

Mróz-Gorgoń, B., Wodo, W., Andrych, A., Caban-Piaskowska, K., & Kozyra, C. (2022). Biometrics Innovation and Payment Sector Perception. Sustainability (Switzerland), 14(15). https://doi.org/10.3390/su14159424

Muhammad, H. G., & Khalaf, Z. A. (2025). A Survey Of Fingerprint Identification System Using Deep Learning. International Journal of Computing and Digital Systems, 17(1), 1–17. https://doi.org/10.12785/ijcds/1571022983

Nguyen, O. T. (2020). Factors affecting the intention to use digital banking in Vietnam. Journal of Asian Finance, Economics and Business, 7(3), 303–310. https://doi.org/10.13106/jafeb.2020.vol7.no3.303

Oghuma, A. P., Chang, Y., Libaque-Saenz, C. F., Park, M. C., & Rho, J. J. (2015). Benefit-confirmation model for post-adoption behavior of mobile instant messaging applications: A comparative analysis of KakaoTalk and Joyn in Korea. Telecommunications Policy, 39(8), 658–677. https://doi.org/10.1016/j.telpol.2015.07.009

Oye, N. D., A.Iahad, N., & Ab.Rahim, N. (2014). The history of UTAUT model and its impact on ICT acceptance and usage by academicians. Education and Information Technologies, 19(1), 251–270. https://doi.org/10.1007/s10639-012-9189-9

Poon, W. C. (2008). Users’ adoption of e-banking services: The Malaysian perspective. Journal of Business and Industrial Marketing, 23(1), 59–69. https://doi.org/10.1108/08858620810841498

Populix. (2024). Studi Analisis Ekosistem dan Persepsi terhadap Bank Digital di Indonesia.

Pradhina, N. P., & Indrawati. (2019). Measuring tools for analyzing factors influencing continuance intention of digital banking adoption using UTAUT 2 model (A case study of Jenius in Indonesia). International Journal of Business and Management Invention, 8(1), 10–15.

Raksadigiri, M. W., & Wahyuni, S. (2020). Perceived Ease of Use Effect on Perceived Usefulness and Attitude Towards Use and Its Impact on Behavioural Intention To Use. International Journal of Advanced Research, 8(12), 439–444. https://doi.org/10.21474/ijar01/12167

Saputri, V. D. (2023). Implementation Of Biometric-Based Security System On Mobile Banking Application. Jurnal Komputer Indonesia, 2(1), 25–32. https://doi.org/10.37676/jki.v2i1.565

Saripudin, S., Ernawati, D., & Yuniarti, R. (2024). Exploring the Impact of Attitudes, Behavior, and Perceptions on Millennials’ Adoption of QRIS Payment Systems. Journal Of Economics, Finance and Management Studies, 7(9), 1035–1043. https://doi.org/10.47191/jefms/v7-i2-22

Taylor, D. G., & Levin, M. (2014). Predicting mobile app usage for purchasing and information-sharing. International Journal of Retail and Distribution Management, 42(8), 759–774. https://doi.org/10.1108/IJRDM-11-2012-0108

Vahdat, A., Alizadeh, A., Quach, S., & Hamelin, N. (2021). Would you like to shop via mobile app technology? The technology acceptance model, social factors and purchase intention. Australasian Marketing Journal, 29(2), 187–197. https://doi.org/10.1016/j.ausmj.2020.01.002

Venkatesh, V., & 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

Veríssimo, J. M. C. (2016). Enablers and restrictors of mobile banking app use: A fuzzy set qualitative comparative analysis (fsQCA). Journal of Business Research, 69(11), 5456–5460. https://doi.org/10.1016/j.jbusres.2016.04.155

Wang, C., Ahmad, S. F., Bani Ahmad Ayassrah, A. Y. A., Awwad, E. M., Irshad, M., Ali, Y. A., Al-Razgan, M., Khan, Y., & Han, H. (2023). An empirical evaluation of technology acceptance model for Artificial Intelligence in E-commerce. Heliyon, 9(8). https://doi.org/10.1016/j.heliyon.2023.e18349

Wu, W.-T., Chen, C.-B., & Chang, C.-C. (2016). Applying the Analytic Hierarchy Process Decision Analysis to Better Understand Adoption Intentions of Mobile Banking. Business and Economic Research, 6(1), 431. https://doi.org/10.5296/ber.v6i1.9621

Zhong, Y., Oh, S., & Moon, H. C. (2021). Service transformation under industry 4.0: Investigating acceptance of facial recognition payment through an extended technology acceptance model. Technology in Society, 64. https://doi.org/10.1016/j.techsoc.2020.101515

Biometric Technology in Digital Banking: Insights from Generation Z and Millennials

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