Factors influencing smartphone owners' acceptance of Biometric Authentication methods
La Ode Abdul Wahid(1); Ahmad R Pratama(2*);
(1) Universitas Islam Indonesia
(2) Universitas Islam Indonesia
(*) Corresponding Author
AbstractSmartphones are the world's most widely used personal computing devices. PINs and passcodes have long been the most popular authentication methods in smartphones and even in the pre-smartphone era. Due to the inconvenient nature of PINs and passcodes, a new biometric authentication method for smartphones was developed and has been gaining traction in terms of adoption, beginning with flagship devices and progressing to some mid-range devices. This article aims to investigate the factors influencing smartphone owners' acceptance of biometric authentication methods by developing a new model based on the Technology Acceptance Model (TAM). It also validates the data with survey data from 233 Indonesian smartphone owners via an online survey and analyzed it using Structural Equation Modeling (SEM). The results from the SEM analysis show that all nine hypotheses in the proposed model are supported. In other words, all six factors in the proposed model (i.e., attitude toward the use, perceived usefulness, perceived the ease of use, perceived enjoyment, perceived security, and social influence) have significant effects on the behavioral intention of adopting biometric authentication methods among smartphone owners. More specifically, the findings indicate that most Indonesian smartphone users have a favorable attitude toward biometric authentication, which is why they are willing to adopt it. Furthermore, it is discovered that the perceived usefulness of a biometric authentication method on smartphones outweighs its perceived ease of use. It reveals that the user's belief in the intrinsic value of biometric authentication methods in the form of perceived security outweighs both the internal user motivation of perceived enjoyment and the external user motivation of social influence in terms of their acceptance of biometric authentication methods. Keywordsauthentication method; biometrics; smartphone; technology acceptance; structural equation modeling
|
Full Text:PDF |
Article MetricsAbstract view: 566 timesPDF view: 182 times |
Digital Object Identifierhttps://doi.org/10.33096/ilkom.v14i2.1114.91-98 |
Cite |
References
M. R. Ramadhani and A. R. Pratama, “Analisis kesadaran cybersecurity pada pengguna media sosial di Indonesia,” journal.uii.ac.id, vol. 1, no. 2, pp. 1–8, 2020.
M. S. Alif and A. R. Pratama, “Analisis kesadaran keamanan di kalangan pengguna E-Wallet di Indonesia,” AUTOMATA, vol. 2, no. 1, 2021.
M. R. Akhyari and A. R. I. Pratama, “Kesadaran akan ancaman serangan berbasis backdoor di kalangan pengguna smartphone android,” AUTOMATA, vol. 2, no. 1, 2021.
D. Kunda and M. Chishimba, “A survey of android mobile phone authentication schemes,” Mob. Netw. Appl., vol. 26, no. 6, pp. 2558–2566, Dec. 2021.
A. Hadid, J. Y. Heikkila, O. Silven, and M. Pietikainen, “Face and eye detection for person authentication in mobile phones,” in 2007 First ACM/IEEE International Conference on Distributed Smart Cameras, Vienna, Austria, 2007.
Y. Yang, B. Guo, Z. Wang, M. Li, Z. Yu, and X. Zhou, “BehaveSense: Continuous authentication for security-sensitive mobile apps using behavioral biometrics,” Ad Hoc Netw., vol. 84, pp. 9–18, Mar. 2019.
A. Rattani and R. Derakhshani, “A survey of mobile face biometrics,” Comput. Electr. Eng., vol. 72, pp. 39–52, Nov. 2018.
A. Choudhary and R. Vig, “Mobile biometrics using Face Recognition,” in Communication and Computing Systems, Dronacharya College of Engineering, Gurgaon, India, 2016.
M. Conti, I. Zachia-Zlatea, and B. Crispo, “Mind how you answer me!,” in Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security - ASIACCS ’11, Hong Kong, China, 2011.
H. Bojinov, Y. Michalevsky, G. Nakibly, and D. Boneh, “Mobile device identification via sensor fingerprinting,” arXiv [cs.CR], 06-Aug-2014.
D. Afah, A. Gautam, S. Misra, A. Agrawal, R. Damaševičius, and R. Maskeliūnas, “Smartphones verification and identification by the use of fingerprint,” in Advanced Techniques for IoT Applications, Singapore: Springer Singapore, 2022, pp. 365–373.
A. K. Jain, P. Flynn, and A. A. Ross, Eds., Handbook of biometrics. New York, NY: Springer, 2010.
F. D. Davis, R. P. Bagozzi, and P. R. Warshaw, “User acceptance of computer technology: A comparison of two theoretical models,” Manage. Sci., vol. 35, no. 8, pp. 982–1003, Aug. 1989.
RStudio Team(2015), “RStudio: Integrated Development for R,” RStudio, Inc., Boston, MA, p. http://www.rstudio.com/., 2015.
Y. Rosseel, “lavaan : An R Package for Structural Equation Modeling,” Journal of Statistical Software, vol. 48, no. 2, pp. 1–36, 2012.
M. Abad, I. Díaz, and M. Vigo, “Acceptance of mobile technology in hedonic scenarios,” in Proceedings of the 2010 British Computer Society Conference on Human-Computer Interaction, BCS-HCI 2010, 2010, pp. 250–258.
A. R. Pratama, “Fun first, useful later: Mobile learning acceptance among secondary school students in Indonesia,” Education and Information Technologies, vol. 26, no. 2, pp. 1737–1753, Mar. 2021.
S.-C. Chang, C.-C. Sun, L.-Y. Pan, and M.-Y. Wang, “An extended TAM to explore behavioural intention of consumers to use M-Commerce,” Journal of Information & Knowledge Management, vol. 14, no. 02, p. 1550014, Jun. 2015.
Y. Sarwono, “Pengertian dasar Structural Equation Modeling (SEM),” Jurnal Ilmiah Manajemen Bisnis Ukrida, vol. 10, no. 3, p. 98528, 2010.
P. G. Schierz, O. Schilke, and B. W. Wirtz, “Understanding consumer acceptance of mobile payment services: an empirical analysis,” Electronic Commerce Research and Applications, vol. 9, no. 3, pp. 209–216, 2010.
W. Widiyanti, “Pengaruh kemanfaatan, kemudahan penggunaan dan promosi terhadap keputusan penggunaan E-Wallet OVO di Depok,” Moneter - Jurnal Akuntansi dan Keuangan, vol. 7, no. 1, pp. 54–68, 2020.
M. Conti, I. Zachia-Zlatea, and B. Crispo, “Mind how you answer me!: (Transparently authenticating the user of a smartphone when answering or placing a call),” Proceedings of the 6th International Symposium on Information, Computer and Communications Security, ASIACCS 2011, pp. 249–260, 2011.
H. M. Jogiyanto, Sistem Informasi Keperilakuan. Yogyakarta: Andi Offset, 2007.
C. Fornell and D. F. Larcker, “Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable variables and.pdf,” Journal of Marketing Research, vol. XVIII, no. February, pp. 39–50, 1981.
J. Henseler, C. M. Ringle, and M. Sarstedt, “A new criterion for assessing discriminant validity in variance-based structural equation modeling,” Journal of the Academy of Marketing Science, vol. 43, no. 1, pp. 115–135, Jan. 2015.
G. Franke and M. Sarstedt, “Heuristics versus statistics in discriminant validity testing: a comparison of four procedures,” Internet Research, vol. 29, no. 3, pp. 430–447, 2019.
R. B. Kline, “Principles and practice of structural equation modeling,” 2015.
L. T. Hu and P. M. Bentler, “Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives,” Structural Equation Modeling, vol. 6, no. 1, pp. 1–55, 1999.
R. C. MacCallum, M. W. Browne, and H. M. Sugawara, “Power analysis and determination of sample size for covariance structure modeling,” Psychological Methods, vol. 1, no. 2, pp. 130–149, 1996.
D. Hooper, J. Coughlan, M. R. Mullen, and E. T. Al., “Evaluating model fit : a Synthesis of the Structural Equation Modelling Literature,” Electronic Journal of business Research Methods, vol. 6, no. 1, pp. 53–60, 2008.
A. R. Pratama and F. M. Firmansyah, “Until you have something to lose! Loss aversion and two-factor authentication adoption,” Applied Computing and Informatics, vol. ahead-of-print, no. ahead-of-print, pp. 1–12, Jan. 2021.
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 La Ode Abdul Wahid, Ahmad R Pratama
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.