This is an outdated version published on 2024-03-29. Read the most recent version.

Determinants of consumer adoption of biometric technologies in mobile financial applications

Authors

DOI:

https://doi.org/10.18559/ebr.2024.1.1019

Keywords:

biometric technologies, mobile payments, mobile banking, personal finance apps, technology acceptance, FinTech, COVID-19 pandemic

Abstract

This study aims to identify what determines the use of biometric technologies in the financial applications of banks and FinTechs. The analysis uses data from a survey of 1,000 adult Polish residents. The estimated logit model indicates that the probability of using biometric solutions decreases with age and increases with the level of education and technological sophistication relating to personal innovativeness, experience with biometric technology, and the use of digital technology in both financial and non-financial areas. The work identifies the COVID-19 pandemic as a factor accelerating the adoption of biometric solutions and fostering awareness of the threat of digital technologies invading respondents’ privacy. The study demonstrates the positive impact of trust that phone manufacturers ensure the security of stored funds and data processing on the acceptance of biometric solutions in financial services. This relationship underpins the recommendation to financial institutions in the field of promoting biometric technologies.

Downloads

Download data is not yet available.

References

Al-Janahi, N, Abd-El-Barr, M, & Qureshi K (2021). Evaluation and performance comparison of a model for adoption of biometrics in online banking. Kuwait Journal of Science, 48(2). https://doi.org/10.48129/kjs.v48i2.8800
View in Google Scholar DOI: https://doi.org/10.48129/kjs.v48i2.8800

Amankwaa, A., & McCartney, C. (2020). Gaughran vs the UK and public acceptability of forensic biometrics retention. Science and Justice, 60(3), 204–205. https://doi.org/10.1016/j.scijus.2020.04.001
View in Google Scholar DOI: https://doi.org/10.1016/j.scijus.2020.04.001

Agidi, R. Ch. (2018). Biometrics: The Future of Banking and Financial Service Industry in Nigeria, International Journal of Electronics and Information Engineering, 9(2), 91-105.
View in Google Scholar

Alpar, O., Biometric touchstroke authentication by fuzzy proximity of touch locations. Future Generation Computer Systems, 86, 71–80. https://doi.org/10.1016/j.future.2018.03.030
View in Google Scholar DOI: https://doi.org/10.1016/j.future.2018.03.030

Baichoo, S., Khan, M.H.-M., Bissessur, P., Pavaday, N., Boodoo-Jahangeer, N., & Purmah, N.R. (2018). Legal and ethical considerations of biometric identity card: Case for Mauritius. Computer Law & Security Review, 34(6), 1333-1341. https://doi.org/10.1016/j.clsr.2018.08.010
View in Google Scholar DOI: https://doi.org/10.1016/j.clsr.2018.08.010

Bauer, H.H., Barnes, S.J., Reichardt, T., & Neumann, M.M. (2005). Driving consumer acceptance of mobile marketing: A theoretical framework and empirical study. Journal of Electronic Commerce Research, 6(3), 181–192.
View in Google Scholar

Breward, M., Hassanein, K., & Head M. (2017). Understanding Consumers’ Attitudes Toward Controversial Information Technologies: A Contextualization Approach. Information Systems Research, 28(4), 760-774. https://doi.org/10.1287/isre.2017.0706
View in Google Scholar DOI: https://doi.org/10.1287/isre.2017.0706

Byun, S., & Byun, S-E. (2013). Exploring perceptions toward biometric technology in service encounters: a comparison of current users and potential adopters. Behaviour & Information Technology, 32(3), 217–230. https://doi.org/10.1080/0144929X.2011.553741
View in Google Scholar DOI: https://doi.org/10.1080/0144929X.2011.553741

Carpenter, D., McLeod, A., Hicks, Ch., & Maasber, M. (2018). Privacy and biometrics: An empirical examination of employee concerns. Information Systems Frontiers, 20, 91–110. https://doi.org/10.1007/s10796-016-9667-5
View in Google Scholar DOI: https://doi.org/10.1007/s10796-016-9667-5

Cramer J. S. (2003). Logit models from economics and other fields, Cambridge University Press. Cambridge.
View in Google Scholar DOI: https://doi.org/10.1017/CBO9780511615412

Dang, V.T., Nguyen, N., Nguyen, H.V., Nguyen, H., Van Huy, L., Tran, V.T., & Nguyen, T.H. (2022). Consumer attitudes toward facial recognition payment: an examination of antecedents and outcomes. International Journal of Bank Marketing, 40(3), 511-535. https://doi.org/10.1108/IJBM-04-2021-0135
View in Google Scholar DOI: https://doi.org/10.1108/IJBM-04-2021-0135

Dhrymes P. (2017). Introductory Econometrics. Springer Cham . https://doi.org/10.1007/978-3-319-65916-9
View in Google Scholar DOI: https://doi.org/10.1007/978-3-319-65916-9

Fouad, K.M., Hassan, B.M., & Hassan, M.F. (2016). User Authentication based on Dynamic Keystroke Recognition. International Journal of Ambient Computing and Intelligence, 7(2), 1-32.
View in Google Scholar DOI: https://doi.org/10.4018/IJACI.2016070101

Gomez-Barrero, M., & Galbally, J. (2020). Reversing the irreversible: A survey on inverse biometrics. Computers & Security, 90, 101700. https://doi.org/10.1016/j.cose.2019.101700
View in Google Scholar DOI: https://doi.org/10.1016/j.cose.2019.101700

Hino H. (2015). Assessing Factors Affecting Consumers' Intention to Adopt Biometric Authentication Technology in E-shopping. Journal of Internet Commerce, 14(1), 1-20. https://doi.org/10.1080/15332861.2015.1006517
View in Google Scholar DOI: https://doi.org/10.1080/15332861.2015.1006517

Huterska, A., Piotrowska, A.I., & Szalacha-Jarmużek, J. (2021). Fear of the COVID-19 Pandemic and Social Distancing as Factors Determining the Change in Consumer Payment Behavior at Retail and Service Outlets. Energies, 14, 4191. https://doi.org/10.3390/en14144191
View in Google Scholar DOI: https://doi.org/10.3390/en14144191

Jeddy, N., Radhika, T., & Nithya S. (2017). Tongue prints in biometric authentication: A pilot study. Journal of Oral and Maxillofacial Pathology, 21(1), 176‑179.
View in Google Scholar DOI: https://doi.org/10.4103/jomfp.JOMFP_185_15

Jünger, M., & Mietzner, M. (2020). Banking goes digital: The adoption of FinTech services by German households. Finance Research Letters, 34, https://doi.org/10.1016/j.frl.2019.08.008
View in Google Scholar DOI: https://doi.org/10.1016/j.frl.2019.08.008

Kagerbauer, M., Manz, W., & Zumkeller, D. (2013). Analysis of PAPI, CATI, and CAWI Methods for a Multiday Household Travel Survey. In J. Zmud, M. Lee-Gosselin, M. Munizaga, & J.A. Carrasco (Eds.), Transport Surveys Methods: Best Practice for Decision Making (pp. 289–304). Emerald Group Publishing Limited: Bingley.
View in Google Scholar DOI: https://doi.org/10.1108/9781781902882-015

Kim, M., Kim, S., & Kim, J. (2019). Can mobile and biometric payments replace cards in the Korean offline payments market? Consumer preference analysis for payment systems using a discrete choice model. Telematics and Informatics, 38, 46–58. https://doi.org/10.1016/j.tele.2019.02.003
View in Google Scholar DOI: https://doi.org/10.1016/j.tele.2019.02.003

Kindt, E.J. (2018). Having yes, using no? About the new legal regime for biometric data. Computer law & security review, 34, 523–538. https://doi.org/10.1016/j.clsr.2017.11.004
View in Google Scholar DOI: https://doi.org/10.1016/j.clsr.2017.11.004

Kochaniak, K., & Ulman, P. (2020). Risk-Intolerant but Risk-Taking—Towards a Better Understanding of Inconsistent Survey Responses of the Euro Area Households. Sustainability, 12, 6912. https://doi.org/10.3390/su12176912
View in Google Scholar DOI: https://doi.org/10.3390/su12176912

Kufel T. (2011). Ekonometria. Rozwiązywanie problemów z wykorzystaniem programu Gretl. Wydawnictwo Naukowe PWN. Warszawa.
View in Google Scholar

Kumari, P., & Seeja, K.R. (2022). Periocular biometrics: A survey. Journal of King Saud University - Computer and Information Sciences Journal of King Saud University - Computer and Information Sciences, 34(4), 1086-1097.
View in Google Scholar DOI: https://doi.org/10.1016/j.jksuci.2019.06.003

Lumini, A., & Nanni, L. (2017). Overview of the combination of biometric matchers.
View in Google Scholar

Information Fusion, 33, 71-85. http://dx.doi.org/10.1016/j.inffus.2016.05.003
View in Google Scholar DOI: https://doi.org/10.1016/j.inffus.2016.05.003

Maddala, G.S. (1992). Introduction to Econometrics. 2nd ed. Macmillan Publishing Company.
View in Google Scholar

Miltgen, C.L., Popovič, A., & Oliveira, T. (2013). Determinants of end-user acceptance of biometrics: Integrating the “Big 3” of technology acceptance with privacy context. Decision Support Systems, 56, 103–114. http://dx.doi.org/10.1016/j.dss.2013.05.010
View in Google Scholar DOI: https://doi.org/10.1016/j.dss.2013.05.010

Morosan, C., (2011). Customers' adoption of biometric systems in restaurants: an extension of the technology acceptance model. Journal of Hospitality Marketing & Management, 20(6), 661–690. https://doi.org/10.1080/19368623.2011.570645
View in Google Scholar DOI: https://doi.org/10.1080/19368623.2011.570645

Mróz-Gorgoń, B., Wodo, W., Andrych, A., Caban-Piaskowska, K., & Kozyra, C. (2022). Biometrics Innovation and Payment Sector Perception. Sustainability, 14, 9424. https://doi.org/10.3390/su14159424
View in Google Scholar DOI: https://doi.org/10.3390/su14159424

Nguyen, K., Fookes, C., Sridharan, S., Tistarelli, M., & Nixon, M. (2018). Super-resolution for biometrics: A comprehensive survey. Pattern Recognition, 78, 23–42. https://doi.org/10.1016/j.patcog.2018.01.002
View in Google Scholar DOI: https://doi.org/10.1016/j.patcog.2018.01.002

Piotrowski, D. (2022). ICTs in the banking sector in the times of the COVID-19 pandemic: the customer’s perspective. Ekonomia i Prawo. Economics and Law, 21(3), 603-622. https://doi.org/10.12775/EiP.2022.032
View in Google Scholar DOI: https://doi.org/10.12775/EiP.2022.032

Prince, J. T., & Wallsten, S. (2022). How much is privacy worth around the world and across platforms? Journal of Economics & Management Strategy, 31(4). https://doi.org/10.1111/jems.12481
View in Google Scholar DOI: https://doi.org/10.1111/jems.12481

Rio, J.S., Moctezuma, D., Conde, C., de Diego, I. M., & Cabello, E. (2016). Automated border control e-gates and facial recognition systems. Computers & Security, 62, 49–72. http://dx.doi.org/10.1016/j.cose.2016.07.001
View in Google Scholar DOI: https://doi.org/10.1016/j.cose.2016.07.001

Sadhya, D., & Singh, S.K. (2017). Providing robust security measures to Bloom filter based biometric template protection schemes. Computers & Security, 67, 59–72. http://dx.doi.org/10.1016/j.cose.2017.02.013
View in Google Scholar DOI: https://doi.org/10.1016/j.cose.2017.02.013

Sanchez-Reillo, R., Ortega-Fernandez, I., Ponce-Hernandez, W., & Quiros-Sandoval, H.C. (2019). How to implement EU data protection regulation for R&D in biometrics. Computer Standards & Interfaces, 61, 89–96. https://doi.org/10.1016/j.csi.2018.01.007
View in Google Scholar DOI: https://doi.org/10.1016/j.csi.2018.01.007

Singh, M., Singh, R., & Ross, A. (2019). A comprehensive overview of biometric fusion. Information Fusion, 52, 187–205. https://doi.org/10.1016/j.inffus.2018.12.003
View in Google Scholar DOI: https://doi.org/10.1016/j.inffus.2018.12.003

Sleiman, K.A.A., Juanli, L., Lei, H.Z., Rong, W., Yubo, W., Li, S., Cheng, J., & Amin, F. (2023). Factors that impacted mobile-payment adoption in China during the COVID-19 pandemic. Heliyon, 9(5), e16197. https://doi.org/10.1016/j.heliyon.2023.e16197
View in Google Scholar DOI: https://doi.org/10.1016/j.heliyon.2023.e16197

Soh, K. L.; Wong, W. P., & Chan, K. L. (2010). Adoption of Biometric Technology in Online Applications. International Journal of Business and Management Science, 3(2), 121-146.
View in Google Scholar

Štitilis, D., & Laurinaitis M. (2017). Treatment of biometrically processed personal data: Problem of uniform practice under EU personal data protection law. Computer Law & Security Review, 33, 618–628. http://dx.doi.org/10.1016/j.clsr.2017.03.012
View in Google Scholar DOI: https://doi.org/10.1016/j.clsr.2017.03.012

Sun, Y., Li, H., & Li, N. (2023). A novel cancelable fingerprint scheme based on random security sampling mechanism and relocation bloom filter. Computers & Security, 125, 103021. https://doi.org/10.1016/j.cose.2022.103021
View in Google Scholar DOI: https://doi.org/10.1016/j.cose.2022.103021

Tassabehji, R., & Kamala M.A. (2012). Evaluating biometrics for online banking: The case for usability. International Journal of Information Management, 32(5), 489–494. http://dx.doi.org/10.1016/j.ijinfomgt.2012.07.001
View in Google Scholar DOI: https://doi.org/10.1016/j.ijinfomgt.2012.07.001

Tovarek, J., Voznak, M., Rozhon, J., Rezac, F., Safarik, J., & Partila, P. (2018). Different Approaches for Face Authentication as Part of a Multimodal Biometrics System. Advances in Electrical and Electronic Engineering, 16(1), 118-124. DOI:10.15598/aeee.v16i1.2547
View in Google Scholar DOI: https://doi.org/10.15598/aeee.v16i1.2547

Wang, J. S. (2021). Exploring biometric identification in FinTech applications based on the modified TAM. Financial Innovation, 7(42). https://doi.org/10.1186/s40854-021-00260-2
View in Google Scholar DOI: https://doi.org/10.1186/s40854-021-00260-2

Wang, K., Yang, G., Huang, Y., & Yin, Y. (2020). Multi-scale differential feature for ECG biometrics with collective matrix factorization. Pattern Recognition, 102, 107211. https://doi.org/10.1016/j.patcog.2020.107211
View in Google Scholar DOI: https://doi.org/10.1016/j.patcog.2020.107211

Wang, M., Hu, J., & Abbass, H. A. (2020). BrainPrint: EEG biometric identification based on analyzing brain connectivity graphs. Pattern Recognition, 105, 107381. https://doi.org/10.1016/j.patcog.2020.107381
View in Google Scholar DOI: https://doi.org/10.1016/j.patcog.2020.107381

Yu, J., Sun, K., Gao, F., & Zhu, S. (2018). Face biometric quality assessment via light CNN. Pattern Recognition Letters, 107, 25–32. http://dx.doi.org/10.1016/j.patrec.2017.07.015
View in Google Scholar DOI: https://doi.org/10.1016/j.patrec.2017.07.015

Unar, J.A., Seng, W.C., & Abbasi, A. (2014). A review of biometric technology along with trends and prospects. Pattern Recognition, 47, 2673–2688. http://dx.doi.org/10.1016/j.patcog.2014.01.016
View in Google Scholar DOI: https://doi.org/10.1016/j.patcog.2014.01.016

Zhang, Y., Huang, Y., Wang, L., & Yu S. (2019). A comprehensive study on gait biometrics using a joint CNN-based method. Pattern Recognition, 93, 228–236. https://doi.org/10.1016/j.patcog.2019.04.023 0031
View in Google Scholar DOI: https://doi.org/10.1016/j.patcog.2019.04.023

Zhang, D., Liu, Z., & Yan J. (2010). Dynamic tongueprint: A novel biometric identifier. Pattern Recognition, 43(3), 1071–1082. https://doi.org/10.1016/j.patcog.2009.09.002 10
View in Google Scholar DOI: https://doi.org/10.1016/j.patcog.2009.09.002

Zhao, Y., & Bacao, F. (2021). How Does the Pandemic Facilitate Mobile Payment? An Investigation on Users’ Perspective under the COVID-19 Pandemic. International Journal of Environmental Research and Public Health, 18, 1016. https://doi.org/10.3390/ijerph18031016
View in Google Scholar DOI: https://doi.org/10.3390/ijerph18031016

Downloads

Published

2024-03-29

Versions

Issue

Vol. 10 No. 1 (2024)

Section

Research article- regular issue

License

Copyright (c) 2024 Anna Iwona Piotrowska

Creative Commons License

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

How to Cite

Anna Iwona Piotrowska. (2024). Determinants of consumer adoption of biometric technologies in mobile financial applications. Economics and Business Review, 10(1). https://doi.org/10.18559/ebr.2024.1.1019

Similar Articles

1-10 of 148

You may also start an advanced similarity search for this article.