Abstract
Mobile device growth is rapid, and it has a significant impact on our private and professional lives. All mobile users want to be guaranteed that their data is safe, which is why biometrics exists for mobile devices. Countless surveys have been performed on the adoption of biometric verification techniques, however, only a limited number of these surveys have concentrated on mobile device-based biometrics, including this current research. This quantitative study’s overall model was put to test on 302 mobile users via survey questionnaire distribution and analyzed utilizing the Statistical Package for Social Science (SPSS). Validity and reliability tests were performed and the model proved to be fit. The findings showed that the SSN variable of the proposed model was not supported, indicating that more research is required. In addition, the research model’s functional elements have a greater impact on the participants’ desire to use the mobile biometric system than the social elements. The study adds to academic knowledge by proposing new constructs that combine MBTAM to assess the likelihood of mobile device users adopting biometric verification techniques.
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References
Malatji, W.R., Zuva, T., VanEck, R.: Acceptance of biometric authentication security technology on mobile devices. In: Book Acceptance of biometric authentication security technology on mobile devices’ (Springer Nature, (2020), edn.), pp. 145–156
https://www.qualitymag.com/articles/96205/technology-adoption. Accessed 1 April 2021
Tscherning, H., Mathiassen, L.: Early adoption of mobile devices-a social network perspective. J. Inf. Technol. Theory Appl. 11(1), 23–42 (2010)
Kadena, E., Ruiz, L.: Adoption of biometrics in mobile devices. In: Book Adoption of Biometrics in Mobile Devices, edn., pp. 140–148 (2017)
Ashbourn, J.: Biometrics in the world: The cloud, mobile technology and pervasive identity. Springer International Publishing (2018)
Jain, A.K., Ross, A.A., Nandakumar, K.: Introduction to Biometrics. Springer (2011)
Dhraief, M.Z., Bedhiaf-Romdhania, S., Dhehibib, B., Oueslati-Zlaouia, M., Jebali, O., Ben Youssef, S.: Factors affecting the adoption of innovative technologies by livestock farmers in Arid area of Tunisia. FARA Res. Rep. 3(5), 22 (2018)
https://www.enterpriseedges.com/biometric-banking-authentication-industry. Accessed 1 April 2021
Clarke, N.L., Furnell, M.: Authentication of users on mobile telephones—a survey of attitudes and practices. Comput. Security 24, 519–527 (2005)
James, T., Pririm, T., Katherine, B., Reithel, B., Barkhi, R.: Determining the intention to use biometric devices: an application and extension of the technology acceptance model. J. Organ. End User Comput. (JOEUC) 18(3), 24 (2006)
Chau, A., Stephens, G., Jamieson, R.: Biometrics acceptance - perceptions of use of biometrics. In: Book Biometrics Acceptance - Perceptions of Use of Biometrics (edn.) (2004)
https://www.biometricupdate.com/202104/Biometrics-adoption-boom-from-pandemic-expected-to-continue-goode-and-ID-R&G-survey. Accessed 01 May 2021
Abd-El-Barr, M., Qureshi, K., Aljanahi, N.: Evaluation and performance comparison of a model for adoption of biometrics in online banking. Kuwati J. Sci. 48(2) (2021)
Vrana, R.: Acceptance of mobile technologies and m-learning in higher education learning: an explorative study at the Faculty of Humanities and Social Science at the University of Zagreb. Croatian Soc. Inf. Commun. Technol. Electron. Microelectron. 41, 814–819 (2018)
Mandari, H., Koloseni, D.: Biometric authentication in financial institutions: the intention of banks to adopt biometric powered ATM. Adv. Comput. Sci. Int. J. 5(4), 9–17 (2016)
Laux, D., Luse, A., Mennecke, B., Townsend, A.M.: Adoption of biometric authentication systems: implications for research and practice in the deployment of end- user security systems. J. Organ. Comput. Electron. Commer. 22(4) (2012)
Soh, K.L., Wongand, W.P., Chan, K.L.: Adoption of biometric technology in online applications. Int. J. Bus. Sci. Appl. Manage. 3(2), 121–146 (2010)
Boateng, M.S., Asiamah, K.O., Lamptey, R.B.: Impact assessment of biometric fingerprint application for time keeping at KNUST library. J. Appl. Thought 4, 256–273 (2015)
Cristian, M.: Opportunities and challenges for biometric systems in travel: a review. Travel Tourism Res. Assoc. Adv. Tourism Res. Globally 61, 1–120 (2016)
Erastus, L.R., Jere, N., Shava, F.B.: Exploring challenges of biometric technology adoption: a Namibian review. In: Proceedings of the International Conference on Emerging Trends in Network and Computer Communication, Windhoek (2015)
Ho, G., Stephens, G., Jamieson, R.: Biometric authentication adoption ıssues. In: Proceedings of the 14th Australasian Conference on Information Systems, Perth, Western Australia (2003)
Johnson, E.M., Howard, C.: A library mobile device deployment to enhance the medical student experience in a rural longitudinal integrated clerkship. J Med Libr Assoc 107(1), 30–42 (2019)
Apostolou, B., Bélanger, F., and Schaupp, L.C.: Online communities: satisfaction and continued use intention. Inf. Res. 22 (2017)
Lankton, N., McKnight, D., Tripp, J.: Technology, humanness, and trust: rethinking trust in technology. J. Assoc. Inf. Syst. 16, 880–918 (2015)
Gao, Q., Rau, P.-L., Salvendy, G.: Perception of interactivity: affects of four key variables in mobile advertising. Int. J. Hum.-Comput. Interact. 25, 479 (2009)
Asiimwe, E.N., Grönlund, A.: MLCMS actual use, perceived use, and experiences of use. ijEDict – Int. J. Educ. Dev. Inf. Commun. Technol. 11(1), 101–121 (2015)
Cheng, X., Fu, S., Sun, J., Bilgihan, A., Okumus, F.: An investigation on online reviews in sharing economy driven hospitality platforms: a viewpoint of trust. Tour. Manage. 71, 366–377 (2019)
Tuunainen, V., Pitkänen, O., Hovi, M.: Users Awareness of Privacy on Online Social Networking sites – Case Facebook (2009)
Taherdoost, H.: Sampling methods in research methodology; How to choose a sampling technique for research. Int. J. Acad. Res. Manage. 5, 18–27 (2016)
Taber, K.S.: The use of Chronbach’s Alpha when developing and reporting research instruments in Science Education. Springer 48, 1273–1296 (2017). https://doi.org/10.1007/s11165-016-9602-2
Hosokawa, R., Katsura, T.: Correction: association between mobile technology use and child adjustment in early elementary school age. PLoS ONE 13, 12 (2018)
Chao, C.M.: Factors determining the behavioral intention to use mobile learning: an application and extension of the UTAUT model. Front. Psychol 10, 1–14 (2019)
Lankton, N.K., Mcknight, D.H., Tripp, J.F.: Technology, humanness, and trust: Rethinking trust in technology. J. Assoc. Inf. Syst. 16(10), 880–918 (2015)
Acknowledgement
This work was authored by Malatji W.R. The author would like to thank Doctor Rene Van Eck and Professor Tranos Zuva for their contributions, helpfulness and support during this study.
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Malatji, W.R., VanEck, R., Zuva, T. (2021). Measuring the Adoption of Biometric Verification Technique Implementations on Mobile Devices. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Software Engineering Application in Informatics. CoMeSySo 2021. Lecture Notes in Networks and Systems, vol 232. Springer, Cham. https://doi.org/10.1007/978-3-030-90318-3_13
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