Impact of artificial intellegence on customer engagement from selected banks in Abuja Metropolis, Nigeria: a mediating role of personalization
DOI:
https://doi.org/10.25299/ijbs.2025.22584Keywords:
Artificial Intelligence, Chatbot, Biometric Authentication, Personalisation, Customer EngagementAbstract
Purpose: The objective of this research is to explore how Artificial Intelligence applications drive customer engagement in selected Deposit Money Banks in Abuja Metropolis, through the mediating influence of personalization. The research is grounded in the Technology Acceptance Model, aiming to explore how chatbot and biometric technologies influence customer engagement through the lens of personalization.
Design/methodology/approach: The study employed a cross-sectional survey design, targeting online retail customers of five banks Access Bank, First Bank, GTBank, United Bank for Africa, and Zenith Bank within the Abuja Municipal Area Council. A total sample size of 119 respondents involved in the study. The data were collected via a structured five-point Likert scale questionnaire and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM).
Findings: The results revealed that Biometric Authentication has a positive and significant effect on customer engagement. Chatbots have a negative but significant effect on customer engagement. Personalization has a negative and non-significant effect on customer engagement. For the mediating relationship, personalization does not mediate the relationship between biometric authentication and customer engagement. Personalization does not serve as a mediator between chat-bots and customer engagement
Limitations and Research implications: The study is limited to online retail customers of five banks within a single geographical area (Abuja), which may affect the generalizability of the findings. Future research could expand the geographic scope and consider longitudinal data to observe trends over time.
Practical Implications: The findings suggest the need for banks to develop a comprehensive AI strategy that cohesively integrates chatbots, biometric systems, and personalized services to enhance customer engagement. Emphasis should be placed on improving the effectiveness and customer perception of biometric technologies.
Originality/value: This study contributes to the growing literature on AI adoption in banking by empirically validating the role of personalization as a mediator in the relationship between AI tools and customer engagement. It also extends the application of the Technology Acceptance Model in the context of AI-driven banking services in Nigeria
Downloads
References
Adam, M., Wessel, M., & Benlian, A. (2021). AI-based chatbots in customer service and their effects on user compliance. Electronic markets, 31(2), 427-445. https://doi.org/10.1007/s12525-020-00414-7
Ahmed, H. K. (2021). A chatbot system for Kurdish speakers based on natural language processing (Doctoral dissertation, University of Sulaimani).
Balaji, K., Karim, S., & Rao, P. S. (2024). Unleashing the power of smart chatbots: transforming banking with artificial intelligence. International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-7). IEEE. https://doi.org/10.1109/ACCAI61061.2024.10602456
Bhalerao, D., & Joseph, S. (2023). Success factors for chatbots from the perspective of an Indian customer. In AIP Conference Proceedings, 2914(1). AIP Publishing.
Chauhan, S., Akhtar, A., & Gupta, A. (2022). Customer experience in digital banking: a review and future research directions. International Journal of Quality and Service Sciences, 14(2), 311-348. https://doi.org/10.1108/IJQSS-02-2021-0027
Chen, Y., Prentice, C., Weaven, S., & Hisao, A. (2022). The influence of customer trust and artificial intelligence on customer engagement and loyalty–The case of the home-sharing industry. Frontiers in psychology, 13, 912339. https://doi.org/10.3389/fpsyg.2022.912339
Chigada, J. M. (2020). A qualitative analysis of the feasibility of deploying biometric authentication systems to augment security protocols of bank card transactions. South African Journal of Information Management, 22(1), 1-9. https://hdl.handle.net/10520/ejc-info-v22-n1-a26
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Lawrence Erlbaum Associates.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Davis, F. D. (1989). Technology acceptance model: TAM. Al-Suqri, MN, Al-Aufi, AS: Information Seeking Behavior and Technology Adoption, 205(219), 5.
Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, phone, mail, and mixed-mode surveys: The tailored design method (4th ed.). John Wiley & Sons.
Elegunde, A. F., & Osagıe, R. (2020). Artificial intelligence adoption and employee performance in the Nigerian banking industry. International Journal of Management and Administration, 4(8), 189-205. https://doi.org/10.29064/ijma.734734
Ernst & Young Global. (2023). AI adoption in European financial services: progress, challenges, and future directions.
Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). Sage Publications.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Garg, Y. A. S. H. I. K. A., & Sachdeva, K. A. N. I. K. A. (2022). Artificial Intelligence in Indian Banking Sector: A Game Changer. Dogo Rangsang Research Journal, 2347-7180.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). SAGE Publications.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). SAGE Publications.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2018). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). SAGE Publications.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2019). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage Publications.
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., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2022). Advanced issues in partial least squares structural equation modeling (PLS-SEM) (2nd ed.). SAGE Publications.
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433. https://doi.org/10.1007/s11747-011-0261-6
Henseler, J., Ringle, C. M., & Sarstedt, M. (2012). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 113(1), 1–22. https://doi.org/10.1108/IMDS-09-2015-0382
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., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20, 277–319. https://doi.org/10.1108/S1474-7979(2009)0000020014
Hepziba, E., & John, F. (2020). Role of chat‐bots in customer engagement valence. Psychology & Education, 57(9), 2181-2186.
Kumar, V., Rajan, B., Gupta, S., & Pozza, I. D. (2019). Customer engagement in service. Journal of the Academy of Marketing Science, 47(1), 138-160. https://doi.org/10.1007/s11747-017-0565-2
Lephale, T. R. (2021). Product personalisation in the era of big data: the influence on customer loyalty (Master's thesis, University of Pretoria (South Africa)).
Mandari, H. E., & Koloseni, D. (2016). Biometric authentication in financial institutions: the intention of banks to adopt biometric-powered ATM. Advances in Computer Science: an International Journal, 5(4), 9-17.
McKinsey & Company. (2023). The economic potential of generative AI: The next productivity frontier. The economic potential of generative AI: The next productivity frontier.
Menezes, A. D., Kavyashree, K., & Naik, S. G. (2024). Customer Perception of Artificial Intelligence in Public Banking: An Empirical Analysis. In ITM Web of Conferences (Vol. 68, p. 01026). EDP Sciences.
Ng, G. W., & Leung, W. C. (2020). Strong artificial intelligence and consciousness. Journal of Artificial Intelligence and Consciousness, 7(1), 63–72. https://doi.org/10.1142/S2705078520300042.
Ninness, C., & Ninness, S. K. (2020). Emergent virtual analytics: Artificial intelligence and human-computer interactions. Behavior and Social Issues, 29(1), 100-118. . https://doi.org/10.1007/s42822-020-00031-1
Noori, M. A., & Jailani, M. N. (2022). Biometric authentication technology acceptance in mobile banking in Iraq. UKM, Bangi, 3004-3019
Oyetunji, D. J. (2024). The role of artificial intelligence and machine learning in enhancing customer experience in Nigeria digital banks. International Journal of Advance Research, Ideas and Innovations in Technology, 10(1), 377-382.
Rysin, V., Prokopenko, O., Muravskyi, O., Pechenko, R., Holiachuk, N., & Zinchenko, A. (2023). Personalization of banking products (services) using digitalization technologies. WSEAS Transactions on Business and Economics, 20(4), 2528-2539.
Sarbabidya, S., & Saha, T. (2020). Role of chatbot in customer service: A study from the perspectives of the banking industry of Bangladesh. International review of business research papers, 16(1), 231-248.
Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson Education Limited.
Skandali, D., Magoutas, A., & Tsourvakas, G. (2023). Artificial intelligent applications in enabled banking services: The next frontier of customer engagement in the era of ChatGPT. Theoretical Economics Letters, 13(5), 1203-1223.
Suraña‐Sánchez, C., & Aramendia‐Muneta, M. E. (2024). Impact of artificial intelligence on customer engagement and advertising engagement: A review and future research agenda. International Journal of Consumer Studies, 48(2), 1=22. https://doi.org/10.1111/ijcs.13027.
Trunfio, M., & Rossi, S. (2021). Conceptualising and measuring social mediaengagement: A systematic literature review. Italian Journal of Marketing, 2021(3), 267–29. https://doi.org/10.1007/s43039-021-00035.
Tubaro, P., Casilli, A. A., & Coville, M. (2020). The trainer, the verifier, the imitator: Three ways in which human platform workers support artificial intelligence. Big Data & Society, 7(1), 2053951720919776. https://doi.org/10.1177/2053951720919776.
Udodiugwu, M. I., Eneremadu, K. E., Onunkwo, A. R., & Onyia, M. K. (2024). The Role of Artificial Intelligence in Enhancing the Performance of Banks in Nigeria. Arabian Journal of Business and Management Review (Oman Chapter), 11(2), 27-34.
Wang, M., Cho, S., & Denton, T. (2017). The impact of personalization and compatibility with past experience on e-banking usage. International Journal of Bank Marketing, 35(1), 45-55. https://doi.org/10.1108/IJBM-04-2015-0046
