DOES AGE INFLUENCE AI-ENABLED MOBILE BANKING APP USAGE? ANALYZING COGNITIVE FACTORS AND SUSTAINED INTENTIONS
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Abstract
This research investigates the impact of cognitive aspects and AI attributes on the continued adoption of AI-powered mobile banking applications in Malaysia. It examines the relatively recent incorporation of AI into Malaysian mobile banking and the extent to which age influences user intentions. A total of 398 participants were surveyed, with data analysis conducted using SPSS. The results indicate that factors such as perceived usefulness, ease of use, enjoyment, and intelligence significantly contribute to users' continued engagement with AI-enabled banking apps, with age playing a moderating role. However, perceived anthropomorphism did not have a statistically significant effect on user intention, nor did age significantly moderate the connection between perceived intelligence and continuance intention. The study’s findings aim to enhance AI-enabled banking applications, fostering a more user-friendly and satisfying experience across different age groups. These insights provide valuable direction for software developers and financial institutions aiming to optimize user satisfaction and engagement with AI-powered mobile banking systems.
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References
Abdulquadri, A., Mogaji, E., Kieu, T. A., & Nguyen, N. P. (2021). Digital transformation in financial services provision: A Nigerian perspective to the adoption of chatbot. Journal of Enterprising Communities: People and Places in the Global Economy, 15(2), 258-281. https://doi-org.tarc.idm.oclc.org/10.1108/JEC-06-2020-0126
Alkhowaiter, W. A. (2022). Use and behavioural intention of m-payment in GCC countries: Extending meta-UTAUT with trust and Islamic religiosity. Journal of Innovation & Knowledge, 7(4), 100240. https://doi.org/10.1016/j.jik.2022.100240
Ambalov, I. A. (2021). An investigation of technology trust and habit in IT use continuance: a study of a social network. Journal of Systems and Information Technology, 23(1), 53-81. https://doi-org.tarc.idm.oclc.org/10.1108/JSIT-05-2019-0096
Andrade, C. (2020). Sample size and its importance in research. Indian Journal of Psychological Medicine, 42(1), 102-103. https://doi:10.4103/IJPSYM.IJPSYM_504_19
Arifin, W. N., & Yusoff, M. S. B. (2016). Confirmatory factor analysis of the Universiti Sains Malaysia emotional quotient inventory among medical students in Malaysia. Sage Open, 6(2), 2158244016650240. https://doi.org/10.1177/2158244016650240
Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020). I, Chatbot: Modelling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, 101473. https://doi.org/10.1016/j.tele.2020.101473
Azeem, M., Ahmed, M., Haider, S., & Sajjad, M. (2021). Expanding competitive advantage through organizational culture, knowledge sharing and organizational innovation. Technology in Society, 66, 101635. https://doi.org/10.1016/j.techsoc.2021.101635
Baabdullah, A. M., Alalwan, A. A., Rana, N. P., Kizgin, H., & Patil, P. (2019). Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model. International Journal of Information Management, 44, 38-52. https://doi.org/10.1016/j.ijinfomgt.2018.09.002
Balakrishnan, J., Abed, S. S., & Jones, P. (2022). The role of Meta-UTAUT factors, perceived anthropomorphism, perceived intelligence, and social self-efficacy in chatbot-based services?. Technological Forecasting and Social Change, 180, 121692. https://doi.org/10.1016/j.techfore.2022.121692
Bartneck, C., Kulić, D., Croft, E., & Zoghbi, S. (2009). Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. International Journal of Social Robotics, 1, 71-81. https://doi.org/10.1007/s12369-008-0001-3
Bassiouni, D. H., Hackley, C., & Meshreki, H. (2019). The integration of video games in family-life dynamics: an adapted technology acceptance model of family intention to consume video games. Information Technology & People, 32(6), 1376-1396. https://doi-org.tarc.idm.oclc.org/10.1108/ITP-11-2017-0375
Belanche, D., Casaló, L.V., & Flavián, C. (2019). Artificial intelligence in fintech: understanding robo-advisors adoption among customers. Industrial Management & Data Systems, 119(7), 1411-1430. https://doi-org.tarc.idm.oclc.org/10.1108/IMDS-08-2018-0368
Bennett, K., Heritage, B., & Allen, P. (2023). SPSS Statistics: A Practical Guide 5th Edition. Cengage Learning Australia Pty Limited. https://research-repository.uwa.edu.au/en/publications/spss-statistics-a-practical-guide-5th-edition
Bergmann, M., Maçada, A. C. G., de Oliveira Santini, F., & Rasul, T. (2023). Continuance intention in financial technology: a framework and meta-analysis. International Journal of Bank Marketing, 41(4), 749-786. https://doi-org.tarc.idm.oclc.org/10.1108/IJBM-04-2022-0168
Bergur Thormundsson. (2022, Mar 17). Projected spending on artificial intelligence by industry group worldwide in 2020. Statista.
https://www.statista.com/statistics/940783/ai-spending-by-industry-group/
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 351-370. https://doi.org/10.2307/3250921
Blut, M., Wang, C., Wünderlich, N. V., & Brock, C. (2021). Understanding anthropomorphism in service provision: a meta-analysis of physical robots, chatbots, and other AI. Journal of the Academy of Marketing Science, 49, 632-658. https://doi.org/10.1007/s11747-020-00762-y
Borau, S., Otterbring, T., Laporte, S., & Fosso Wamba, S. (2021). The most human bot: Female gendering increases humanness perceptions of bots and acceptance of AI. Psychology & Marketing, 38(7), 1052-1068. https://doi.org/10.1002/mar.21480
Buckley, R. P., Zetzsche, D. A., Arner, D. W., & Tang, B. W. (2021, April). Regulating artificial intelligence in finance: Putting the human in the loop. Sydney Law Review, The, 43(1), 43-81.
Cao, Y., Qin, X., Li, J., Long, Q., & Hu, B. (2022). Exploring seniors’ continuance intention to use mobile social network sites in China: a cognitive-affective-conative model. Universal Access in the Information Society, 21, 1-22. https://doi.org/10.1007/s10209-020-00762-3
Chandler, J., & Schwarz, N. (2010). Use does not wear ragged the fabric of friendship: thinking of objects as alive makes people less willing to replace them. Journal of Consumer Psychology, 20(2), 138-145. https://doi.org/10.1016/j.jcps.2009.12.008
Chaouali, W., Ben Yahia, I., Lunardo, R., & Triki, A. (2019). Reconsidering the “what is beautiful is good” effect: When and how design aesthetics affect intentions towards mobile banking applications. International Journal of Bank Marketing, 37(7), 1525-1546. https://doi-org.tarc.idm.oclc.org/10.1108/IJBM-12-2018-0337
Chawla, D., & Joshi, H. (2020). The moderating role of gender and age in the adoption of mobile wallet. Foresight, 22(4), 483-504. https://doi-org.tarc.idm.oclc.org/10.1108/FS-11-2019-0094
Chen, C. Y., Yang, H. C. P., Chen, C. W., & Chen, T. H. (2008). Diagnosing and revising logistic regression models: effect on internal solitary wave propagation. Engineering Computations, 25(2), 121-139. https://doi-org.tarc.idm.oclc.org/10.1108/02644400810855940
Choi, K., Wang, Y., & Sparks, B. (2019). Travel app users' continued use intentions: it's a matter of value and trust. Journal of Travel & Tourism Marketing, 36(1), 131-143. https://doi:10.1080/10548408.2018.1505580
Chung, J. E., Park, N., Wang, H., Fulk, J., & McLaughlin, M. (2010). Age differences in perceptions of online community participation among non-users: An extension of the Technology Acceptance Model. Computers in Human Behavior, 26(6), 1674-1684. https://doi.org/10.1016/j.chb.2010.06.016
Chung, M., Ko, E., Joung, H., & Kim, S. J. (2020). Chatbot e-service and customer satisfaction regarding luxury brands. Journal of Business Research, 117, 587-595. https://doi.org/10.1016/j.jbusres.2018.10.004
Danyali, A. A. (2018). Factors influencing customers’ change of behaviors from online banking to mobile banking in Tejarat Bank, Iran. Journal of Organizational Change Management, 31(6), 1226-1233. https://doi-org.tarc.idm.oclc.org/10.1108/JOCM-07-2017-0269
De Winter, J. C., Gosling, S. D., & Potter, J. (2016). Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data. Psychological Methods, 21(3), 273. https://doi.org/10.1037/met0000079
Flavián, C., Pérez-Rueda, A., Belanche, D., & Casaló, L. V. (2022). Intention to use analytical artificial intelligence (AI) in services–the effect of technology readiness and awareness. Journal of Service Management, 33(2), 293-320. https://doi-org.tarc.idm.oclc.org/10.1108/JOSM-10-2020-0378
Gong, X., Liu, Z., Zheng, X., & Wu, T. (2018). Why are experienced users of WeChat likely to continue using the app?. Asia Pacific Journal of Marketing and Logistics, 30(4), 1013-1039. https://doi-org.tarc.idm.oclc.org/10.1108/APJML-10-2017-0246
Grazzini, L., Viglia, G., & Nunan, D. (2023). Dashed expectations in service experiences. Effects of robots human-likeness on customers’ responses. European Journal of Marketing, 57(4), 957-986. https://doi-org.tarc.idm.oclc.org/10.1108/EJM-03-2021-0220
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202
Hair, J.F., Hult, G.T.M., Ringle, C.M., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). SAGE Publications. https://www.google.com.my/books/edition/A_Primer_on_Partial_Least_Squares_Struct/JDWmCwAAQBAJ?hl=en&gbpv=1
Hassan, H. E., & Wood, V. R. (2020). Does country culture influence consumers’ perceptions toward mobile banking? A comparison between Egypt and the United States. Telematics and Informatics, 46, 101312. https://doi.org/10.1016/j.tele.2019.101312
Hentzen, J. K., Hoffmann, A., Dolan, R., & Pala, E. (2022). Artificial intelligence in customer-facing financial services: a systematic literature review and agenda for future research. International Journal of Bank Marketing, 40(6), 1299-1336. https://doi-org.tarc.idm.oclc.org/10.1108/IJBM-09-2021-0417
Hidayat-ur-Rehman, I., Ahmad, A., Khan, M. N., & Mokhtar, S. A. (2021). Investigating mobile banking continuance intention: a mixed-methods approach. Mobile Information System, 2021, 1-17. https://doi.org/10.1155/2021/9994990
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
Humbani, M., & Wiese, M. (2019). An integrated framework for the adoption and continuance intention to use mobile payment apps. International Journal of Bank Marketing, 37(2), 646-664. https://doi-org.tarc.idm.oclc.org/10.1108/IJBM-03-2018-0072
Iancu, I., & Iancu, B. (2020). Designing mobile technology for elderly. A theoretical overview. Technological Forecasting and Social Change, 155, 119977. https://doi.org/10.1016/j.techfore.2020.119977
Illum, S. F., Ivanov, S. H., & Liang, Y. (2010). Using virtual communities in tourism research. Tourism Management, 31(3),335-340. https://doi.org/10.1016/j.tourman.2009.03.012
Jo, H. (2023). Understanding the key antecedents of users’ continuance intention in the context of smart factory. Technology Analysis & Strategic Management, 35(2), 153-166. https://doi.org/10.1080/09537325.2021.1970130
Kamide, H., Kawabe, K., Shigemi, S., & Arai, T. (2013). Development of a psychological scale for general impressions of humanoid. Advanced Robotics, 27(1), 3-17. https://doi.org/10.1080/01691864.2013.751159
Karimi Mazidi, A., Rahimnia, F., Mortazavi, S., & Lagzian, M. (2021). Cyberloafing in public sector of developing countries: job embeddedness as a context. Personnel Review, 50(7/8), 1705-1738. https://doi-org.tarc.idm.oclc.org/10.1108/PR-01-2020-0026
Khalilzadeh, J., & Tasci, A. D. (2017). Large sample size, significance level, and the effect size: Solutions to perils of using big data for academic research. Tourism Management, 62, 89-96. https://doi.org/10.1016/j.tourman.2017.03.026
Kim, H., So, K. K. F., & Wirtz, J. (2022). Service robots: Applying social exchange theory to better understand human–robot interactions. Tourism Management, 92, 104537. https://doi.org/10.1016/j.tourman.2022.104537
Koç, D. L., & Erkan Can, M. (2023). Reference evapotranspiration estimate with missing climatic data and multiple linear regression models. PeerJ, 11, e15252. https://doi.org/10.7717/peerj.15252
Königstorfer, F., & Thalmann, S. (2020). Applications of Artificial Intelligence in commercial banks–A research agenda for behavioral finance. Journal of Behavioral and Experimental Finance, 27, 100352. https://doi.org/10.1016/j.jbef.2020.100352
Kwateng, K. O., Atiemo, K. A. O., & Appiah, C. (2019). Acceptance and use of mobile banking: an application of UTAUT2. Journal of Enterprise Information Management, 32(1), 118-151. https://doi-org.tarc.idm.oclc.org/10.1108/JEIM-03-2018-0055
Larson, K. (2020). Serious games and gamification in the corporate training environment: A literature review. TechTrends, 64(2), 319-328. https://doi.org/10.1007/s11528-019-00446-7
Lee, J. C., & Chen, X. (2022). Exploring users’ adoption intentions in the evolution of artificial intelligence mobile banking applications: the intelligent and anthropomorphic perspectives. International Journal of Bank Marketing, 40(4), 631-658. https://doi-org.tarc.idm.oclc.org/10.1108/IJBM-08-2021-0394
Lee, J., Tang, Y., & Jiang, S. (2023). Understanding continuance intention of artificial intelligence (AI)-enabled mobile banking applications: An extension of AI characteristics to an expectation confirmation model. Humanities & Social Sciences Communications, 10(1), 333. https://doi.org/10.1057/s41599-023-01845-1
Letheren, K., Kuhn, K. A. L., Lings, I., & Pope, N. K. L. (2016). Individual difference factors related to anthropomorphic tendency. European Journal of Marketing, 50(5/6), 973-1002. https://doi-org.tarc.idm.oclc.org/10.1108/EJM-05-2014-0291
Liébana-Cabanillas, F., Singh, N., Kalinic, Z., & Carvajal-Trujillo, E. (2021). Examining the determinants of continuance intention to use and the moderating effect of the gender and age of users of NFC mobile payments: A multi-analytical approach. Information Technology and Management, 22, 133-161. https://doi.org/10.1007/s10799-021-00328-6
Lin, R. R., & Lee, J. C. (2023). The supports provided by artificial intelligence to continuous usage intention of mobile banking: evidence from China. Aslib Journal of Information Management, 2050-3806. https://doi-org.tarc.idm.oclc.org/10.1108/AJIM-07-2022-0337
Lin, X., Featherman, M., & Sarker, S. (2017). Understanding factors affecting users’ social networking site continuance: A gender difference perspective. Information & Management, 54(3), 383-395. https://doi.org/10.1016/j.im.2016.09.004
Liu, Q., Liu, Y., Zhang, C., An, Z., & Zhao, P. (2021). Elderly mobility during the COVID-19 pandemic: A qualitative exploration in Kunming, China. Journal of Transport Geography, 96, 103176.
Liu, X., He, X., Wang, M., & Shen, H. (2022). What influences patients' continuance intention to use AI-powered service robots at hospitals? The role of individual characteristics. Technology in Society, 70, 101996. https://doi.org/10.1016/j.techsoc.2022.101996
Makanyeza, C. (2017). Determinants of consumers’ intention to adopt mobile banking services in Zimbabwe. International Journal of Bank Marketing, 35(6), 997-1017. https://doi-org.tarc.idm.oclc.org/10.1108/IJBM-07-2016-0099
Malhotra, G., & Ramalingam, M. (2023). Perceived anthropomorphism and purchase intention using artificial intelligence technology: examining the moderated effect of trust. Journal of Enterprise Information Management. https://doi-org.tarc.idm.oclc.org/10.1108/JEIM-09-2022-0316
Mamun, M. R. A., Prybutok, V. R., Peak, D. A., Torres, R., & Pavur, R. J. (2023). The role of emotional attachment in IPA continuance intention: an emotional attachment model. Information Technology & People, 36(2), 867-894. https://doi-org.tarc.idm.oclc.org/10.1108/ITP-09-2020-0643
Mattila, M., Karjaluoto, H., & Pento, T. (2003). Internet banking adoption among mature customers: early majority or laggards?. Journal of Services Marketing, 17(5), 514-528. https://doi-org.tarc.idm.oclc.org/10.1108/08876040310486294
McLean, G., Osei-Frimpong, K., Al-Nabhani, K., & Marriott, H. (2020). Examining consumer attitudes towards retailers’ m-commerce mobile applications–an initial adoption vs. continuous use perspective. Journal of Business Research, 106, 139-157. https://doi.org/10.1016/j.jbusres.2019.08.032
Mehra, A., Rajput, S., & Paul, J. (2022). Determinants of adoption of latest version smartphones: Theory and evidence. Technological Forecasting and Social Change, 175,121410. https://doi.org/10.1016/j.techfore.2021.121410
Modiba, M. (2023). User perception on the utilisation of artificial intelligence for the management of records at the council for scientific and industrial research. Collection and Curation, 42(3), 81-87. https://doi-org.tarc.idm.oclc.org/10.1108/CC-11-2021-0033
Montes, G. A., & Goertzel, B. (2019). Distributed, decentralized, and democratized artificial intelligence. Technological Forecasting and Social Change, 141, 354-358. https://doi.org/10.1016/j.techfore.2018.11.010
Morris, M. G., & Venkatesh, V. (2000). Age differences in technology adoption decisions: Implications for a changing work force. Personnel Psychology, 53(2), 375-403. https://doi.org/10.1111/j.1744-6570.2000.tb00206.x
Mostafa, R. B. (2020). Mobile banking service quality: a new avenue for customer value co-creation. International Journal of Bank Marketing, 38(5), 1107-1132. https://doi-org.tarc.idm.oclc.org/10.1108/IJBM-11-2019-0421
Moussawi, S., Koufaris, M., & Benbunan-Fich, R. (2021). How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents. Electronic Markets, 31, 343-364. https://doi.org/10.1007/s12525-020-00411-w
Napitupulu, D., Kadar, J. A., & Jati, R. K. (2017). Validity testing of technology acceptance model based on factor analysis approach. Indonesian Journal of Electrical Engineering and Computer Science, 5(3), 697-704. https://doi.org/10.11591/ijeecs.v5.i3.pp697-704
Naqvi, M., Li, S., Jiang, Y., & Naqvi, M. H. A. (2020). The rise of social networking sites: an empirical investigation applying demographic differences and the technology acceptance model. Asia Pacific Journal of Marketing and Logistics, 32(1), 232-252. https://doi-org.tarc.idm.oclc.org/10.1108/APJML-01-2019-0029
Nkansah, B. K. (2011). On the Kaiser-meier-Olkin’s measure of sampling adequacy. Math. Theory Model, 8, 52-76. https://api.core.ac.uk/oai/oai:ojs.localhost:article/44386
Pan, W. T. (2010). Performing stock price prediction use of hybrid model. Chinese Management Studies, 4(1), 77-86. https://doi-org.tarc.idm.oclc.org/10.1108/17506141011033016
Pelau, C., Dabija, D. C., & Ene, I. (2021). What makes an AI device human-like? The role of interaction quality, empathy and perceived psychological anthropomorphic characteristics in the acceptance of artificial intelligence in the service industry. Computers in Human Behavior, 122, 106855. https://doi.org/10.1016/j.chb.2021.106855
Pillai, R., & Sivathanu, B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(10), 3199-3226. https://doi-org.tarc.idm.oclc.org/10.1108/IJCHM-04-2020-0259