Vol 22 | Issue 3 | 79-102 | September 2026
Dr Mohammed Abul Khair
Department of Marketing, College of Business
Al-Baha University
mohammed.abulkhair@gmail.com
https://orcid.org/0000-0001-9761-5308
*Corresponding author
Abstract
Purpose: This study examines customer satisfaction with AI-enabled banking by distinguishing relational evaluations (algorithmic trust and perceived risk) from functional evaluations (perceived usefulness and perceived ease of use), and by examining whether their associations with satisfaction differ across age segments.
Design/methodology/approach: Survey data from 450 Indian customers with recent experience of AI-enabled banking services were analysed using partial least squares structural equation modelling (PLS-SEM). Direct effects were estimated for trust, risk, usefulness and ease of use. Age-based heterogeneity was examined through multi-group analysis comparing respondents aged 18–35 and 36+, after assessment of measurement invariance.
Findings: Algorithmic trust, perceived usefulness and perceived ease of use were positively associated with customer satisfaction, whereas perceived risk was negatively associated with satisfaction. Multi-group results indicated that the trust–satisfaction association was stronger in the 18–35 segment and the risk–satisfaction association was stronger in the 36+ segment. Usefulness and ease-of-use effects did not differ significantly between the two age segments.
Practical implications: Banks can standardize functional design across age groups while adapting relational communication. Trust-building matters more for younger customers; risk-reduction and security communication matter more for older customers.
Originality/value: The study shows that age-related heterogeneity in AI-enabled banking is not uniform across customer evaluations. The observed group differences are concentrated in trust and risk rather than usefulness and ease of use. Socioemotional selectivity theory (SST) is used as an interpretive lens for this pattern; because perceived time horizon was not measured directly, the findings are presented as consistent with SST rather than as a direct test of its motivational mechanism.
Keywords: AI-enabled banking; customer satisfaction; algorithmic trust; perceived risk; socioemotional selectivity theory; age differences
References
Alnaser, F. M., Rahi, S., Alghizzawi, M., & Ngah, A. H. (2023). Does artificial intelligence (AI) boost digital banking user satisfaction? Integration of expectation confirmation model and antecedents of artificial intelligence enabled digital banking. Heliyon, 9(8), e18930. https://doi.org/10.1016/j.heliyon.2023.e18930
Anderson, E. W., & Sullivan, M. W. (1993). The antecedents and consequences of customer satisfaction for firms. Marketing Science, 12(2), 125–143. https://doi.org/10.1287/mksc.12.2.125
Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396–402. https://doi.org/10.1177/002224377701400320
Bharti, S. S., Prasad, K., Sudha, S., & Kumari, V. (2023). Customer acceptability towards AI-enabled digital banking: A PLS-SEM approach. Journal of Financial Services Marketing, 28(4), 779–793. https://doi.org/10.1057/s41264-023-00241-9
Carstensen, L. L. (2006). The influence of a sense of time on human development. Science, 312(5782), 1913–1915. https://doi.org/10.1126/science.1127488
Charles, S. T., & Carstensen, L. L. (2010). Social and emotional aging. Annual Review of Psychology, 61, 383–409. https://doi.org/10.1146/annurev.psych.093008.100448
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., … & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519
Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage Publications.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Henseler, J., Ringle, C. M., & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405–431. https://doi.org/10.1108/IMR-09-2014-0304
Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9
Ikhsan, R. B., Fernando, Y., Prabowo, H., Yuniarty, Gui, A., & Kuncoro, E. A. (2025). An empirical study on the use of artificial intelligence in the banking sector of Indonesia by extending the TAM model and the moderating effect of perceived trust. Digital Business, 5, 100103. https://doi.org/10.1016/j.digbus.2024.100103
Jussupow, E., Benbasat, I., & Heinzl, A. (2021). Why are we averse towards algorithms? A comprehensive literature review on algorithm aversion. Journal of Management Information Systems, 38(2), 311–347. https://doi.org/10.1080/07421222.2021.1912932
Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544–564. https://doi.org/10.1016/j.dss.2007.07.001
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
Kumar, N. R., Janani, M., & Pavithra, R. (2026). Assessing the influence of AI adoption and trust on customer satisfaction in digital banking services. SAM Advanced Management Journal, 91(2), 165–183. https://doi.org/10.1108/SAMAMJ-12-2024-0100
Löckenhoff, C. E., & Carstensen, L. L. (2004). Socioemotional selectivity theory, aging, and health: The increasingly delicate balance between regulating emotions and making choices. Current Directions in Psychological Science, 13(6), 221–225. https://doi.org/10.1111/j.0963-7214.2004.00315.x
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734. https://doi.org/10.2307/258792
Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 58(3), 20–38. https://doi.org/10.2307/1252308
Oliver, R. L. (1999). Whence consumer loyalty? Journal of Marketing, 63(Special Issue), 33–44. https://doi.org/10.2307/1252099
Palmatier, R. W., Dant, R. P., Grewal, D., & Evans, K. R. (2006). Factors influencing the effectiveness of relationship marketing: A meta-analysis. Journal of Marketing, 70(4), 136–153. https://doi.org/10.1509/jmkg.70.4.136
Ringle, C. M., Wende, S., & Becker, J. M. (2022). SmartPLS 4. Oststeinbek: SmartPLS GmbH.
Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, 102551. https://doi.org/10.1016/j.ijhcs.2020.102551
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/10.2307/30036540
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/10.2307/41410412
Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world: Service robots in the frontline. Journal of Service Management, 29(5), 907–931. https://doi.org/10.1108/JOSM-04-2018-0119