Biometric-based self-service technology adoption by older adult: empirical evidence from pension fund sector in Indonesia

References

• Ajzen, I. (1985). Chapter 2 - From Intentions to Actions: A Theory of Planned Behavior BT. In J. Kuhl & J. Beckmann, (Eds.), Action Control: From Cognition to Behavior (pp. 11–39). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-69746-3_2
   Google Scholar
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 1–17. https://doi.org/10.1016/0749-5978(91)90020-T
   Web of Science ®Google Scholar
• Ajzen, I. (2005). EBOOK: Attitudes, personality, and behavior. McGraw-hill education (UK).
   Google Scholar
• Al-Harby, F., Qahwaji, R., & Kamala, M. (2010). Towards an understanding of user acceptance to use biometrics authentication systems in E-commerce: Using an extension of the technology acceptance model. International Journal of E-Business Research, 6(3), 34–55. https://doi.org/10.4018/jebr.2010070103
   Google Scholar
• Allam, F. Z., Hamami-Mitiche, L., & Bousbia-Salah, H. (2022). Evaluation and comparison of the performance of biometric recognition. International Journal of Industrial Engineering and Production Research, 33(1), 1–12. https://doi.org/10.22068/ijiepr.33.1.6
   Google Scholar
• Anderson, M., & Jiang, J. (2018). Teen’s social media habits and experiences. PEW Research Center, November, 1–20. https://www.pewinternet.org/2018/11/28/teens-social-media-habits-and-experiences/.
   Google Scholar
• Anderson, M., & Perrin, A. (2017). Tech adoption climbs among older adults. Pew Research Center, May, 1–22. http://www.pewinternet.org/2017/05/17/technology-use-among-seniors/
   Google Scholar
• Arenas-Gaitán, J., Peral-Peral, B., & Ramón-Jerónimo, M. A. (2015). Elderly and internet banking: An application of UTAUT2. Journal of Internet Banking and Commerce, 20(1), 1–23. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84939509625&partnerID=40&md5=916215219c5c6985242ec6e6af0408ea.
   Google Scholar
• Boo, H. C., & Chua, B.-L. (2022). An integrative model of facial recognition check-in technology adoption intention: The perspective of hotel guests in Singapore. International Journal of Contemporary Hospitality Management, 34(11), 4052–4079. https://doi.org/10.1108/IJCHM-12-2021-1471
   Web of Science ®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
   Web of Science ®Google Scholar
• Central Bureau of Statistics (Indonesia). (2019). Information and communication technology development index 2019. Badan Pusat Statistik, 3-24, 8305012.
   Google Scholar
• Central Bureau of Statistics (Indonesia). (2021). Elderly population statistics 2021. Badan Pusat Statistik, 27–76, 41041001(December).
   Google Scholar
• Central Bureau of Statistics (Indonesia). (2022). Elderly population statistics 2022. Badan Pusat Statistik, 15-41, 4104001 (December).
   Google Scholar
• Chen, K., & Chan, A. H. S. (2014). Gerontechnology acceptance by elderly Hong Kong Chinese: A senior technology acceptance model (STAM). Ergonomics, 57(5), 635–652. https://doi.org/10.1080/00140139.2014.895855
   PubMed Web of Science ®Google Scholar
• Cheng, E. W. L. (2019). Choosing between the theory of planned behavior (TPB) and the technology acceptance model (TAM). Educational Technology Research and Development, 67(1), 21–37. https://doi.org/10.1007/s11423-018-9598-6
   Web of Science ®Google Scholar
• Cheong, Y., Shehab, R. L., & Ling, C. (2013). Effects of age and psychomotor ability on kinematics of mouse-mediated aiming movement. Ergonomics, 56(6), 1006–1020. https://doi.org/10.1080/00140139.2013.781682
   PubMed Web of Science ®Google Scholar
• Choudrie, J., Pheeraphuttranghkoon, S., & Davari, S. (2020). The digital divide and older adult population adoption, use and diffusion of mobile phones: A quantitative study. Information Systems Frontiers, 22(3), 673–695. https://doi.org/10.1007/s10796-018-9875-2
   Web of Science ®Google Scholar
• Choudrie, J., & Vyas, A. (2014). Silver surfers adopting and using Facebook? A quantitative study of Hertfordshire, UK applied to organizational and social change. Technological Forecasting and Social Change, 89, 293–305. https://doi.org/10.1016/j.techfore.2014.08.007
   Web of Science ®Google Scholar
• Ciftci, O., Choi, E. K., & Berezina, K. (2021). Let’s face it: Are customers ready for facial recognition technology at quick-service restaurants? International Journal of Hospitality Management, 95(April), 102941. https://doi.org/10.1016/j.ijhm.2021.102941
   Google Scholar
• Clodfelter, R. (2010). Biometric technology in retailing: Will consumers accept fingerprint authentication? Journal of Retailing and Consumer Services, 17(3), 181–188. https://doi.org/10.1016/j.jretconser.2010.03.007
   Google Scholar
• Contrera, K. J., Betz, J., Deal, J., Choi, J. S., Ayonayon, H. N., Harris, T., Helzner, E., Martin, K. R., Mehta, K., Pratt, S., Rubin, S. M., Satterfield, S., Yaffe, K., Simonsick, E. M., & Lin, F. R, Health ABC Study. (2016). Association of hearing impairment and anxiety in older adults. Journal of Aging and Health, 29(1), 172–184. https://doi.org/10.1177/0898264316634571
   PubMed Web of Science ®Google Scholar
• Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A Comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
   Web of Science ®Google Scholar
• Demoulin, N. T. M., & Djelassi, S. (2016). An integrated model of self-service technology (SST) usage in a retail context. International Journal of Retail & Distribution Management, 44(5), 540–559. https://doi.org/10.1108/IJRDM-08-2015-0122
   Web of Science ®Google Scholar
• Deng, Z., Mo, X., & Liu, S. (2014). Comparison of the middle-aged and older users’ adoption of mobile health services in China. International Journal of Medical Informatics, 83(3), 210–224. https://doi.org/10.1016/j.ijmedinf.2013.12.002
   PubMed Web of Science ®Google Scholar
• Ellis, M. E., Downey, J. P., Chen, A. N., & Lu, H. K. (2021). Why Taiwanese seniors use technology. Asia Pacific Management Review, 26(3), 149–159. https://doi.org/10.1016/j.apmrv.2021.01.001
   Web of Science ®Google Scholar
• Erdinest, N., London, N., Lavy, I., Morad, Y., & Levinger, N. (2021). Vision through healthy aging eyes. Vision, 5(4), 46. https://doi.org/10.3390/vision5040046
   Google Scholar
• Farage, M. A., Miller, K. W., Ajayi, F., & Hutchins, D. (2012). Design principles to accommodate older adults. Global Journal of Health Science, 4(2), 2–25. https://doi.org/10.5539/gjhs.v4n2p2
   PubMedGoogle Scholar
• Fatima, A. (2011). E-banking security issues-Is there a solution in biometrics? Journal of Internet Banking and Commerce, 16(2), 1–9. https://www.scopus.com/inward/record.uri?eid=2-s2.0-80054943515&partnerID=40&md5=836c874ad85273c1cb4c3092581a96da
   Google Scholar
• Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behaviour: An introduction to theory and research (Vol. 27).
   Google Scholar
• Fisk, A. D., Rogers, W. A., Charness, N., Czaja, S. J., & Sharit, J. (2019). In Wendy A. Rogers & Arthur D. Fisk (Eds.), Designing for Older Adults: Principles and Creative Human Factors Approaches (2nd ed.). CRC Press.
   Google Scholar
• Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19(4), 440–452. https://doi.org/10.2307/3151718
   Web of Science ®Google Scholar
• Foroudi, P., Gupta, S., Sivarajah, U., & Broderick, A. (2018). Investigating the effects of smart technology on customer dynamics and customer experience. Computers in Human Behavior, 80(November), 271–282. https://doi.org/10.1016/j.chb.2017.11.014
   Google Scholar
• Ha, J., & Park, H. K. (2020). Factors affecting the acceptability of technology in health care among older Korean adults with multiple chronic conditions: A cross-sectional study adopting the senior technology acceptance model. Clinical Interventions in Aging, 15, 1873–1881. https://doi.org/10.2147/CIA.S268606
   PubMed Web of Science ®Google Scholar
• Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E. (2018). Multivariate Data Analysis (8th ed.). Cengage Learning EMEA.
   Google Scholar
• Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Sage.
   Google Scholar
• He, C., Baranchenko, Y., Lin, Z., Szarucki, M., & Yukhanaev, A. (2020). From global mindset to international opportunities: The internationalization of Chinese SMES. Journal of Business Economics and Management, 21(4), 967–986. https://doi.org/10.3846/jbem.2020.12673
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Hoque, R., & Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International Journal of Medical Informatics, 101, 75–84. https://doi.org/10.1016/j.ijmedinf.2017.02.002
   PubMed Web of Science ®Google Scholar
• Hunsaker, A., & Hargittai, E. (2018). A review of internet use among older adults. New Media & Society, 20(10), 3937–3954. https://doi.org/10.1177/1461444818787348
   Web of Science ®Google Scholar
• Hyams, A. V., Hay-McCutcheon, M., & Scogin, F. (2018). Hearing and quality of life in older adults. Journal of Clinical Psychology, 74(10), 1874–1883. https://doi.org/10.1002/jclp.22648
   PubMed Web of Science ®Google Scholar
• International Institute for Management Development. (2022). IMD world digital competitiveness ranking 2022. IMD World Competitiveness Center, 1–184. https://www.imd.org/centers/world-competitiveness-center/rankings/world-digital-competitiveness/
   Google Scholar
• Kanak, A., & Sogukpinar, I. (2017). BioTAM: A technology acceptance model for biometric authentication systems. IET Biometrics, 6(6), 457–467. https://doi.org/10.1049/iet-bmt.2016.0148
   Web of Science ®Google Scholar
• Kim, S., Lee, K.-H., Hwang, H., & Yoo, S. (2015). Analysis of the factors influencing healthcare professionals’ adoption of mobile electronic medical record (EMR) using the unified theory of acceptance and use of technology (UTAUT) in a tertiary hospital. BMC Medical Informatics and Decision Making, 16(1), 12. https://doi.org/10.1186/s12911-016-0249-8
   Web of Science ®Google Scholar
• Kim, S., & Bernhard, B. (2014). Factors influencing hotel customers’ intention to use a fingerprint system. Journal of Hospitality and Tourism Technology, 5(2), 98–125. https://doi.org/10.1108/JHTT-11-2013-0031
   Google Scholar
• Kline, D., & Scialfa, C. (1996, May). Visual and auditory aging. In J. E. Birren & K. W. Schaie (Eds.), Handbook of the biology of aging (pp. 181–203). San Diego, CA: Academic Press.
   Google Scholar
• Kockmann, M., Farrell, K., Colibro, D., Vair, C., Alexander, A., & Kelly, F. (2021). Voice biometrics: Perspective from the industry. In García-Mateo, C. & G. Chollet (Eds.), Voice biometrics: Technology, trust and security (pp. 163–185). Institution of Engineering and Technology.
   Google Scholar
• Kuswayati, S. (2019). Gaji ke-13 dan Problem Otentikasi Pensiunan PNS. https://News.Detik.Com/Kolom/d-4607103/Gaji-Ke-13-Dan-Problem-Otentikasi-Pensiunan-Pns.
   Google Scholar
• Laudon, K. C., & Laudon, J. P. (2016). Manajemen information system: Managing the digital firm (Thirtheenth Edition). Prentice Hall.
   Google Scholar
• Lee, C., & Coughlin, J. F. (2015). PERSPECTIVE: Older adults’ adoption of technology: An integrated approach to identifying determinants and barriers. Journal of Product Innovation Management, 32(5), 747–759. https://doi.org/10.1111/jpim.12176
   Web of Science ®Google Scholar
• Lee, C. T., & Pan, L.-Y. (2023). Smile to pay: predicting continuous usage intention toward contactless payment services in the post-COVID-19 era. International Journal of Bank Marketing, 41(2), 312–332. https://doi.org/10.1108/IJBM-03-2022-0130
   Web of Science ®Google Scholar
• Li, J., Ma, Q., Chan, A. H., & Man, S. S. (2019). Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Applied Ergonomics, 75(September 2018), 162–169. https://doi.org/10.1016/j.apergo.2018.10.006
   PubMedGoogle Scholar
• Ling, L. W., Downe, A. G., Ahmad, W. F. W., & Lai, T. T. (2011, September). Determinants of computer usage among educators: A comparison between the UTAUT and TAM models. In 2011 National Postgraduate Conference (pp. 1-6). IEEE.
   Google Scholar
• Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191. https://doi.org/10.1287/isre.2.3.173
   Web of Science ®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(1), 103–114. https://doi.org/10.1016/j.dss.2013.05.010
   Google Scholar
• Moriuchi, E. (2021). An empirical study of consumers’ intention to use biometric facial recognition as a payment method. Psychology & Marketing, 38(10), 1741–1765. https://doi.org/10.1002/mar.21495
   Web of Science ®Google Scholar
• 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), 436–450. https://doi.org/10.1177/0047287511418368
   Web of Science ®Google Scholar
• Mostaghel, R., & Oghazi, P. (2017). Elderly and technology tools: A fuzzyset qualitative comparative analysis. Quality & Quantity, 51(5), 1969–1982. https://doi.org/10.1007/s11135-016-0390-6
   PubMedGoogle Scholar
• Ngugi, B., Kamis, A. & Tremaine, M. (2011). Intention to use biometric systems. E-Service Journal, 7(3), 20. https://doi.org/10.2979/eservicej.7.3.20
   Google Scholar
• Norfolk, L., & O’Regan, M. (2020). Biometric technologies at music festivals: An extended technology acceptance model. Journal of Convention & Event Tourism, 22(1), 36–60. https://doi.org/10.1080/15470148.2020.1811184
   Web of Science ®Google Scholar
• Pai, C.-K., Wang, T.-W., Chen, S.-H., & Cai, K.-Y. (2018). Empirical study on Chinese tourists’ perceived trust and intention to use biometric technology. Asia Pacific Journal of Tourism Research, 23(9), 880–895. https://doi.org/10.1080/10941665.2018.1499544
   Web of Science ®Google Scholar
• Peek, S. T. M., Luijkx, K. G., Vrijhoef, H. J. M., Nieboer, M. E., Aarts, S., Van Der Voort, C. S., Rijnaard, M. D., & Wouters, E. J. M. (2017). Origins and consequences of technology acquirement by independent-living seniors: Towards an integrative model. BMC Geriatrics, 17(1), 189. https://doi.org/10.1186/s12877-017-0582-5
   PubMedGoogle Scholar
• Rejali, S., Aghabayk, K., Esmaeli, S., & Shiwakoti, N. (2023). Comparison of technology acceptance model, theory of planned behavior, and unified theory of acceptance and use of technology to assess a priori acceptance of fully automated vehicles. Transportation Research Part A: Policy and Practice, 168(January 2021), 103565. https://doi.org/10.1016/j.tra.2022.103565
   Google Scholar
• Riddell, W. C., & Song, X. (2017). The role of education in technology use and adoption: Evidence from the Canadian workplace and employee survey. ILR Review, 70(5), 1219–1253. https://doi.org/10.1177/0019793916687719
   Web of Science ®Google Scholar
• Rogers, E. M. (2003). Diffusion of innovation (Fifth edition). Free Press.
   Google Scholar
• Scherer, A., Wünderlich, N. V., & Von Wangenheim, F, ETH Zürich. (2015). The value of self-service: Long-term effects of technology-based self-service usage on customer retention. MIS Quarterly, 39(1), 177–200. https://doi.org/10.25300/MISQ/2015/39.1.08
   Web of Science ®Google Scholar
• Servat, J. J., Risco, M., Nakasato, Y. R., & Bernardino, C. R. (2011, July). Visual impairment in the elderly: Impact on functional ability and quality of life. Clinical Geriatrics, 9(7), 2–9. https://doi.org/10.1016/B978-0-7506-1815-1.50010-6
   Google Scholar
• Shi, H., & Koh, J, Research Assistant, Peking University-Wuhan Institute for Artificial Intelligence, China. (2023). How does digital shadow work affect user emotion and behavior in self-service technologies use? Asia Pacific Journal of Information Systems, 33(1), 1–21. https://doi.org/10.14329/apjis.2023.33.1.1
   Google Scholar
• Singh, L. K., Khanna, M., Thawkar, S., & Gopal, J. (2021). Robustness for authentication of the human using face, Ear, and Gait multimodal biometric system. International Journal of Information System Modeling and Design, 12(1), 39–72. https://doi.org/10.4018/IJISMD.2021010103
   Web of Science ®Google Scholar
• Sixsmith, A., Horst, B. R., Simeonov, D., & Mihailidis, A. (2022). Older people’s use of digital technology during the COVID-19 pandemic. Bulletin of Science, Technology & Society, 42(1-2), 19–24. https://doi.org/10.1177/02704676221094731
   PubMedGoogle Scholar
• Soh, K. L., Wongand, W. P., & Chan, K. L. (2010). Adoption of biometric technology in online applications. International Journal of Business and Management Science, 3(2), 121–146.
   Google Scholar
• Sulaiman, S. N. A., & Almunawar, M. N. (2022). The adoption of biometric point-of-sale terminal for payments. Journal of Science and Technology Policy Management, 13(3), 585–609. https://doi.org/10.1108/JSTPM-11-2020-0161
   Web of Science ®Google Scholar
• Sunandi, S. D., & Koesrindartoto, D. P. (2019, August). User’s acceptance of biometric authentication system. In The 4th International Conference on Management in Emerging Markets (ICMEM 2019) (pp. 7–9). SBMITB.
   Google Scholar
• Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137–155. https://doi.org/10.1016/0167-8116(94)00019-K
   Google Scholar
• Tenneti, R., Johnson, D., Goldenberg, L., Parker, R. A., & Huppert, F. A. (2012). Towards a capabilities database to inform inclusive design: Experimental investigation of effective survey-based predictors of human-product interaction. Applied Ergonomics, 43(4), 713–726. https://doi.org/10.1016/j.apergo.2011.11.005
   PubMed Web of Science ®Google Scholar
• Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125–142. https://doi.org/10.2307/249443
   Web of Science ®Google Scholar
• Tsai, T. H., Lin, W. Y., Chang, Y. S., Chang, P. C., & Lee, M. Y. (2020). Technology anxiety and resistance to change behavioral study of a wearable cardiac warming system using an extended TAM for older adults. PLOS One, 15(1), e0227270. https://doi.org/10.1371/journal.pone.0227270
   PubMed Web of Science ®Google Scholar
• United Nations. (2023). World social report 2023: Leaving no one behind in an ageing world, 17–34. World Social Report. United Nations 2023. https://doi.org/10.18356/7f5d0efc-en
   Google Scholar
• 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), 425–478. https://doi.org/10.1006/mvre.1994.1019
   Web of Science ®Google Scholar
• 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), 157–178. https://doi.org/10.1042/bst0120672
   Web of Science ®Google Scholar
• Wang, K. H., Chen, G., & Chen, H. G. (2017). A model of technology adoption by older adults. Social Behavior and Personality, 45(4), 563–572. https://doi.org/10.2224/sbp.5778
   Web of Science ®Google Scholar
• Wang, Q., & Sun, X. (2016). Investigating gameplay intention of the elderly using an extended technology acceptance model (ETAM). Technological Forecasting and Social Change, 107, 59–68. https://doi.org/10.1016/j.techfore.2015.10.024
   Web of Science ®Google Scholar
• Wilson, G., Gates, J. R., Vijaykumar, S., & Morgan, D. J. (2021). Understanding older adults’ use of social technology and the factors influencing use. Ageing and Society, 43(1), 222–245. https://doi.org/10.1017/S0144686X21000490
   Web of Science ®Google Scholar
• Wood, E., Lanuza, C., Baciu, I., MacKenzie, M., & Nosko, A. (2010). Instructional styles, attitudes and experiences of seniors in computer workshops. Educational Gerontology, 36(10–11), 834–857. https://doi.org/10.1080/03601271003723552
   Web of Science ®Google Scholar
• Wu, Y. H., Faucounau, V., Boulay, M., Maestrutti, M., & Rigaud, A. S. (2011). Robotic agents for supporting community-dwelling elderly people with memory complaints: Perceived needs and preferences. Health Informatics Journal, 17(1), 33–40. https://doi.org/10.1177/1460458210380517
   PubMed Web of Science ®Google Scholar
• Xu, X., Wang, L., & Zhao, K. (2020). Exploring determinants of consumers’ platform usage in “double eleven” shopping carnival in china: Cognition and emotion from an integrated perspective. Sustainability, 12(7), 2790. https://doi.org/10.3390/su12072790
   Web of Science ®Google Scholar
• Yap, Y. Y., Tan, S. H., & Choon, S. W. (2022). Elderly’s intention to use technologies: A systematic literature review. Heliyon, 8(1), e08765. https://doi.org/10.1016/j.heliyon.2022.e08765
   PubMed Web of Science ®Google Scholar
• 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(December 2020), 101515. https://doi.org/10.1016/j.techsoc.2020.101515
   Google Scholar