Управленец

2218-5003

2686-7923

10.29141/2218-5003-2026-17-1-7

ZHHJLK

https://upravlenets.usue.ru/ru/-2026/1779

Мобильный банкинг с поддержкой искусственного интеллекта: особенности внедрения и влияние на лояльность клиентов

Adoption of artificial intelligence-supported digital banking applications and their impact on customer loyalty

Фатих

Джевхеш Мухаммед

Фатих

Джевхеш Мухаммед

Cevher

Muhammed Fatih

mfcevher@munzur.edu.trОнен

Эзра

Онен

Эзра

Önen

Esra

esra.onen1319@gmail.com

Munzur University (Tunceli, Türkiye)Университет Мунзур (г. Тунджели, Турция)

12032026

171981130409202501122025

CC BY-NC 4.0

Мобильные банковские приложения с поддержкой искусственного интеллекта (ИИ) улучшают потребительский опыт и отношения с клиентами. Тем не менее в фокусе интереса ученых находятся, главным образом, преимущества и специфика внедрения таких приложений, а не комплексное изучение их влияния на удовлетворенность и лояльность потребителей. С помощью анализа уровня внедрения, пользовательского опыта и удовлетворенности потребителей в статье исследуется воздействие мобильного банкинга на основе ИИ на удержание клиентов. Теоретическая база основана на интегрированной исследовательской модели, сопоставляющей переменные моделей принятия технологии и подтверждения ожиданий. В работе использованы такие научные методы, как моделирование структурными уравнениями (PLS-SEM) и количественный подход. Эмпирические данные получены в ходе онлайн-опроса 510 респондентов – пользователей мобильных приложений с поддержкой ИИ турецких банков. Результаты исследования свидетельствуют о значительном влиянии воспринимаемой простоты использования мобильных ИИ-сервисов на их воспринимаемую полезность, а также о взаимосвязи между последней и удовлетворенностью потребителей банковских услуг. Выявлено, что удовлетворенность банковским ИИ-приложением в значительной степени определяет намерение клиентов продолжить его использование, что, в свою очередь, сказывается на их лояльности. В то же время влияние прямого эффекта удовлетворенности на удержание клиентов оказалось несущественным, что подтверждает ее опосредованное воздействие через намерение дальнейшего пользования приложением. Сделан вывод, что для поддержания лояльности клиентов может быть недостаточно только высокого уровня удовлетворенности: банкам необходимо разрабатывать стратегии, стимулирующие пользователей регулярно обращаться к мобильным ИИ-приложениям.

The rationale of this paper is that AI-enabled digital banking applications enhance consumer experiences and customer relationships; however, holistic findings on the impact of these applications on customer satisfaction and loyalty, beyond their benefits and adoption, are limited. The study aims to reveal the impact of AI-powered digital banking applications on customer loyalty by examining consumer adoption, experience, and satisfaction levels. The theoretical basis is grounded in an integrated research model that correlates variables from the technology acceptance model and the expectation confirmation model. The data and research methods comprise 510 participants using AI-enabled banking applications in Türkiye, employing a quantitative research design and an online survey technique. The data obtained were analysed using structural equation modelling (PLS-SEM). The findings indicate significant effects of perceived ease of use on perceived usefulness, as well as the relationship between perceived usefulness and confirmation on satisfaction. Furthermore, significant effects of satisfaction on continuance intention and continuance intention on customer loyalty were found. However, the direct effect of satisfaction on customer loyalty is not significant. This result suggests that satisfaction plays an indirect role in the formation of customer loyalty through continuance intention. Therefore, satisfaction and continuance intention are critical variables in strengthening customer loyalty in AI-enabled digital banking. Increasing customer satisfaction may not be sufficient for enhancing customer loyalty: banks must devise strategies to support customers’ intentions to use applications regularly

мобильный банкинг;искусственный интеллект;мобильные приложения;банковские услуги;удовлетворенность клиентов;лояльность клиентов;поведение потребителей;воспринимаемая простота использования;воспринимаемая полезность;Турция.

digital banking;artificial intelligence;mobile apps;banking services;customer satisfaction;customer loyalty;consumer behaviour;perceived ease of use;perceived usefulness;Türkiye.

Статья выполнена в рамках проекта «Анализ мобильных ИИ-приложений в банковском секторе с позиции удовлетворенности клиентов» при поддержке Совета Турции по научно-техническим исследованиям (TÜBİTAK 2209-A «Программа поддержки исследовательских проектов студентов университетов»).

This study was carried out within the framework of the authors’ project titled «Analysis of Artificial Intelligence Applications in the Banking Sector in Terms of Customer Satisfaction» as part of the Scientific and Technological Research Council of Türkiye (TÜBİTAK 2209-A «University Students Research Projects Support Program»).

Abdullah A.A.H., Almaqtari F.A. (2024). The impact of artificial intelligence and Industry 4.0 on transforming accounting and auditing practices. Journal of Open Innovation: Technology, Market, and Complexity, vol. 10, issue 1, 100218. https://doi. org/10.1016/j.joitmc.2024.100218

Al Amin M., Muzareba A.M., Chowdhury I.U., Khondkar M. (2023). Understanding e-satisfaction, continuance intention, and e-loyalty toward mobile payment application during COVID-19: An investigation using the electronic technology continuance model. Journal of Financial Services Marketing, vol. 29, pp. 318–340. https://doi.org/10.1057/s41264-022-00197-2

Alnaser F.M., Rahi S., Alghizzawi M., Ngah A.H. (2023). Does artificial intelligence (AI) boost digital banking user satisfaction? Integration of the expectation confirmation model and antecedents of artificial intelligence enabled digital banking. Heliyon, vol. 9, issue 8, e18930. https://doi.org/10.1016/j.heliyon.2023.e18930

Alsoukini F.A.M., Adedokun M.W., Berberoğlu A. (2025). Enhancing sustainable innovation performance in the banking sector of Libya: The impact of artificial intelligence applications and organizational learning. Sustainability, vol. 17, issue 12, 5345. https://doi.org/10.3390/su17125345

Alt M.-A., Ibolya V. (2021). Identifying relevant segments of potential banking chatbot users based on technology adoption behavior. Market-Tržište, vol. 33, no. 2, pp. 165–183. https://doi.org/10.22598/mt/2021.33.2.165

Armutcu B., Tan A., Ho S.P.S., Chow M.Y.C., Gleason K.C. (2025). The effect of bank artificial intelligence on consumer purchase intentions. Kybernetes, vol. 54, issue 10, pp. 5529–5553. https://doi.org/10.1108/K-01-2024-0145

Baffour Gyau E., Appiah M., Gyamfi B.A., Achie T., Naeem M.A. (2024). Transforming banking: Examining the role of AI technology innovation in boosting banks financial performance. International Review of Financial Analysis, vol. 96, 103700. https:// doi.org/10.1016/j.irfa.2024.103700

Balaji K., Karim S., Rao P.S. (2024). Unleashing the power of smart chatbots: Transforming banking with artificial intelligence (pp. 1536–1542). In: Proc. of the 2024 Int. conf. on advances in computing, communication and applied informatics (Chennai, May 9–10, 2024). New York: Institute of Electrical and Electronics Engineers.

Barney J. (1991). Firm resources and sustained competitive advantage. Journal of Management, vol. 17, issue 1, pp. 99–120. https://doi.org/10.1177/014920639101700108

Belanche D., Casaló L.V., Flavián C. (2019). Artificial intelligence in fintech: Understanding robo-advisors adoption among customers. Industrial Management & Data Systems, vol. 119, issue 7, pp. 1411–1430. https://doi.org/10.1108/IMDS-08-2018- 0368

Bhatnagr P., Rajesh A. (2025). Artificial intelligence features and expectation confirmation theory in digital banking apps: Gen Y and Z perspective. Management Decision, vol. 63, issue 10, pp. 3642–3675. https://doi.org/10.1108/MD-07-2023-1145

Bhattacharya C., Sinha M. (2022). The role of artificial intelligence in banking for leveraging customer experience. Australasian Accounting, Business and Finance Journal, vol. 16, issue 5, pp. 89–105. https://doi.org/10.14453/aabfj.v16i5.07

Bhattacherjee A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, vol. 25, issue 3, pp. 351–370. https://doi.org/10.2307/3250921

Bloemer J., Ruyter K. de, Peeters P. (1998). Investigating drivers of bank loyalty: The complex relationship between image, service quality and satisfaction. International Journal of Bank Marketing, vol. 16, issue 7, pp. 276–286. https://doi. org/10.1108/02652329810245984

Boustani N.M. (2022). Artificial intelligence impact on banks clients and employees in an Asian developing country. Journal of Asia Business Studies, vol. 16, issue 2, pp. 267–278. https://doi.org/10.1108/JABS-09-2020-0376

Byambaa O., Yondon C., Rentsen E., Darkhijav B., Rahman M. (2025). An empirical examination of the adoption of artificial intelligence in banking services: The case of Mongolia. Future Business Journal, vol. 11, 76. https://doi.org/10.1186/s43093-025- 00504-y

Cheng X., Qiao L., Yang B., Li Z. (2024). An investigation on the influencing factors of elderly people’s intention to use financial AI customer service. Internet Research, vol. 34, issue 3, pp. 690–717. https://doi.org/10.1108/INTR-06-2022-0402

Cintamür İ.G. (2024). Acceptance of artificial intelligence devices in banking services: Moderation role of technology anxiety and risk aversion. International Journal of Bank Marketing, vol. 42, issue 7, pp. 2143–2176. https://doi.org/10.1108/IJBM-10- 2023-0563

Damerji H., Salimi A. (2021). Mediating effect of use perceptions on technology readiness and adoption of artificial intelligence in accounting. Accounting Education, vol. 30, issue 2, pp. 107–130. https://doi.org/10.1080/09639284.2021.1872035

Davis F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, vol. 13, issue 3, pp. 319–340. https://doi.org/10.2307/249008

Deneçli S., Yıldız Balaban Ö.E., Deneçli Aribakan C. (2022). Examining the relationship between consumer innovativeness and trust in chatbot applications: A study on Turkish banking sector. Connectist: Istanbul University Journal of Communication Sciences, issue 63, pp. 59–85. https://doi.org/10.26650/CONNECTIST2022-1171397

Doumpos M., Zopounidis C., Gounopoulos D., Platanakis E., Zhang W. (2023). Operational research and artificial intelligence methods in banking. European Journal of Operational Research, vol. 306, issue 1, pp. 1–16. https://doi.org/10.1016/j. ejor.2022.04.027

Eren B.A. (2021). Determinants of customer satisfaction in chatbot use: Evidence from a banking application in Turkey. International Journal of Bank Marketing, vol. 39, issue 2, pp. 294–311. https://doi.org/10.1108/IJBM-02-2020-0056

Fares O.H., Butt I., Lee S.H.M. (2022). Utilization of artificial intelligence in the banking sector: A systematic literature review. Journal of Financial Services Marketing, vol. 28, pp. 835–852. https://doi.org/10.1057/s41264-022-00176-7

Fornell C., Larcker D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, vol. 18, no. 1, pp. 39–50. https://doi.org/10.2307/3151312

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, vol. 80, pp. 271–282. https://doi.org/10.1016/j.chb.2017.11.014

Franke G., Sarstedt M. (2019). Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Research, vol. 29, issue 3, pp. 430–447. https://doi.org/10.1108/IntR-12-2017-0515

Ghandour A. (2021). Opportunities and challenges of artificial intelligence in banking: Systematic literature review. TEM Journal, vol. 10, issue 4, pp. 1581–1587. https://doi.org/10.18421/TEM104-12

Gounaris S., Koritos C.D. (2012). Adoption of technologically based innovations: The neglected role of bounded rationality. Journal of Product Innovation Management, vol. 29, issue 5, pp. 821–838. https://doi.org/10.1111/j.1540-5885.2012.00942.x

Goyal K., Garg M., Malik S. (2025). Adoption of artificial intelligence-based credit risk assessment and fraud detection in the banking services: A hybrid approach (SEM-ANN). Future Business Journal, vol. 11, 44. https://doi.org/10.1186/s43093-025- 00464-3

Graham G., Nisar T.M., Prabhakar G., Meriton R., Malik S. (2025). Chatbots in customer service within banking and finance: Do chatbots herald the start of an AI revolution in the corporate world? Computers in Human Behavior, vol. 165, 108570. https://doi.org/10.1016/j.chb.2025.108570

Hair J.F. Jr., Sarstedt M., Hopkins L., Kuppelwieser V.G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, vol. 26, issue 2, pp. 106–121. https://doi.org/10.1108/ EBR-10-2013-0128

Hair J.F., Binz Astrachan C., Moisescu O.I., Radomir L., Sarstedt M., Vaithilingam S., Ringle C.M. (2021). Executing and interpreting applications of PLS-SEM: Updates for family business researchers. Journal of Family Business Strategy, vol. 12, issue 3, 100392. https://doi.org/10.1016/j.jfbs.2020.100392

Hair J.F., Risher J.J., Sarstedt M., Ringle C.M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, vol. 31, issue 1, pp. 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Hari H., Iyer R., Sampat B. (2022). Customer brand engagement through chatbots on bank websites – Examining the antecedents and consequences. International Journal of Human–Computer Interaction, vol. 38, issue 13, pp. 1212–1227. https://doi. org/10.1080/10447318.2021.1988487

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, vol. 43, pp. 115–135. https://doi.org/10.1007/s11747-014- 0403-8

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, vol. 40, issue 6, pp. 1299–1336. https://doi.org/10.1108/IJBM-09-2021-0417

Ho H., Han S.-M., Cha J., Pham L. (2025). Mobile banking customer satisfaction and loyalty: The roles of technology readiness. Journal of Risk and Financial Management, vol. 18, issue 7, 403. https://doi.org/10.3390/jrfm18070403

Ho S.P.S., Chow M.Y.C. (2024). The role of artificial intelligence in consumers’ brand preference for retail banks in Hong Kong. Journal of Financial Services Marketing, vol. 29, pp. 292–305. https://doi.org/10.1057/s41264-022-00207-3

Hwang S., Kim J. (2021). Toward a chatbot for financial sustainability. Sustainability, vol.13, issue 6, 3173. https://doi.org/10.3390/ su13063173

Ikhsan R.B., Fernando Y., Prabowo H., Yuniarty Y., 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, vol. 5, issue 1, 100103. https://doi.org/10.1016/j.digbus.2024.100103

Indriasari E., Gaol F.L., Matsuo T. (2019). Digital banking transformation: Application of artificial intelligence and big data analytics for leveraging customer experience in the Indonesia banking sector (pp. 863–868). In: Proc. of the 8th Int. congress on advanced applied informatics (Toyama, July 7–11, 2019). New York: Institute of Electrical and Electronics Engineers. https:// doi.org/10.1109/IIAI-AAI.2019.00175

Jain V., Wadhwani K., Eastman J.K. (2024). Artificial intelligence consumer behavior: A hybrid review and research agenda. Journal of Consumer Behaviour, vol. 23, issue 2, pp. 676–697. https://doi.org/10.1002/cb.2233

Jakšič M., Marinč M. (2019). Relationship banking and information technology: The role of artificial intelligence and FinTech. Risk Management, vol. 21, pp. 1–18. https://doi.org/10.1057/s41283-018-0039-y

Khana A., Hamid A.B.A., Saad N.M., Hussain Z., Arif A.R. (2023). Effectiveness of artificial intelligence in building customer loyalty: Investigating the mediating role of chatbot in the tourism sector of Pakistan. International Journal of Academic Research in Business and Social Sciences, vol. 13, no. 9, pp. 598–612. https://doi.org/10.6007/IJARBSS/v13-i9/18422

Kim D.J., Ferrin D.L., Rao H.R. (2009). Trust and satisfaction, two stepping stones for successful e-commerce relationships: A longitudinal exploration. Information Systems Research, vol.20, issue 2, pp. 159–316. https://doi.org/10.1287/isre.1080.0188

Kline R.B. (2023). Principles and practice of structural equation modeling. New York: Guilford Publications.

Koyluoglu A.S., Acar O.E. (2023). A study on adoption of artificial intelligence use in mobile banking. Časopis za ekonomiju i tržišne komunikacije / Economy and Market Communication Review, vol. 13, no. 2, pp. 344–361. https://doi.org/10.7251/ EMC2302344K

Kumar A., Srivastava A., Gupta P.K. (2022). Banking 4.0: The era of artificial intelligence‐based fintech. Strategic Change, vol. 31, issue 6, pp. 591–601. https://doi.org/10.1002/jsc.2526

Kumar J., Rani V., Rani G., Sandhu K. (2025). What attracts me or prevents me from using AI-enabled neo-banking services? Unveiling the nexus between service quality and customer loyalty. International Journal of Quality & Reliability Management, vol. 42, issue 10, pp. 2793–2814. https://doi.org/10.1108/IJQRM-11-2024-0421

Lăzăroiu G., Bogdan M., Geamănu M., Hurloiu L., Ionescu L., Ștefănescu R. (2023). Artificial intelligence algorithms and cloud computing technologies in blockchain-based fintech management. Oeconomia Copernicana, vol. 14, no. 3, pp. 707–730. https://doi.org/10.24136/oc.2023.021

Le X.C., Nguyen T.H. (2024). The effects of chatbot characteristics and customer experience on satisfaction and continuance intention toward banking chatbots: Data from Vietnam. Data in Brief, vol. 52, 110025. https://doi.org/10.1016/j.dib.2023.110025

Lee J.-C., 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 and Social Sciences Communications, vol. 10, 333. https://doi.org/10.1057/s41599-023-01845-1

Liu S., Huang F. (2023). Examining teachers’ influence on MOOCs learners’ continuance learning intention: The mediating effects of perceived usefulness and satisfaction. Journal of Pedagogical Research, vol. 7, issue 4, pp. 237–250. https://doi. org/10.33902/JPR.202322513

Manser Payne E.H., Peltier J., Barger V.A. (2021). Enhancing the value co-creation process: Artificial intelligence and mobile banking service platforms. Journal of Research in Interactive Marketing, vol. 15, issue 1, pp. 68–85. https://doi.org/10.1108/ JRIM-10-2020-0214

Manta A.G., Bădîrcea R.M., Doran N.M., Badareu G., Gherțescu C., Popescu J. (2024). Industry 4.0 transformation: Analysing the impact of artificial intelligence on the banking sector through bibliometric trends. Electronics, vol. 13, issue 9, 1693. https:// doi.org/10.3390/electronics13091693

Milojević N., Redzepagic S. (2021). Prospects of artificial intelligence and machine learning application in banking risk management. Journal of Central Banking Theory and Practice, vol. 10, issue 3, pp. 41–57. https://doi.org/10.2478/jcbtp-2021-0023

Mogaji E., Balakrishnan J., Nwoba A.C., Nguyen N.P. (2021). Emerging-market consumers’ interactions with banking chatbots. Telematics and Informatics, vol. 65, 101711. https://doi.org/10.1016/j.tele.2021.101711

Nguyen D.G., Ha M.-T. (2022). What makes users continue to want to use the digital platform? Evidence from the ride-hailing service platform in Vietnam. Sage Open, vol. 12, issue 1. https://doi.org/10.1177/21582440211069146

Nguyen G.-D., Dao T.-H.T. (2024). Factors influencing continuance intention to use mobile banking: An extended expectationconfirmation model with moderating role of trust. Humanities and Social Sciences Communications, vol. 11, 276. https://doi. org/10.1057/s41599-024-02778-z

Nguyen T.H., Le X.C. (2025). Artificial intelligence-based chatbots – A motivation underlying sustainable development in banking: Standpoint of customer experience and behavioral outcomes. Cogent Business & Management, vol. 12, issue 1, 2443570. https://doi.org/10.1080/23311975.2024.2443570

Oliver R.L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, vol. 17, issue 4, pp. 460–469. https://doi.org/10.1177/002224378001700405

Oliver R.L. (1999). Whence consumer loyalty? Journal of Marketing, vol. 63, issue 4, pp. 33–44. https://doi.org/10.1177/0022 2429990634s105

Papathomas A., Konteos G., Avlogiaris G. (2025). Behavioral drivers of AI adoption in banking in a semi-mature digital economy: A TAM and UTAUT-2 analysis of stakeholder perspectives. Information, vol. 16, issue 2, 137. https://doi.org/10.3390/ info16020137

Park Y., Kim J., Jiang Q., Kim K.H. (2024). Impact of artificial intelligence (AI) chatbot characteristics on customer experience and customer satisfaction. Journal of Global Scholars of Marketing Science, vol. 34, issue 3, pp. 439–457. https://doi.org/10.1080/ 21639159.2024.2362654

Pathak A., Bansal V. (2024). AI as decision aid or delegated agent: The effects of trust dimensions on the adoption of AI digital agents. Computers in Human Behavior: Artificial Humans, vol. 2, issue 2, 100094. https://doi.org/10.1016/j.chbah.2024.100094

Peyravi B., Nekrošienė J., Lobanova L. (2020). Revolutionised technologies for marketing: Theoretical review with a focus on artificial intelligence. Verslas: Teorija ir praktika / Business: Theory and Practice, vol. 21, issue 2, pp. 827–834. https://doi. org/10.3846/btp.2020.12313

Rabbani M.R., Lutfi A., Ashraf M.A., Nawaz N., Ahmad Watto W. (2022). Role of artificial intelligence in moderating the innovative financial process of the banking sector: A research based on structural equation modeling. Frontiers in Environmental Science, vol. 10, 978691. https://doi.org/10.3389/fenvs.2022.978691

Rahman M., Ming T.H., Baigh T.A., Sarker M. (2023). Adoption of artificial intelligence in banking services: An empirical analysis. International Journal of Emerging Markets, vol. 18, no. 10, pp. 4270–4300. https://doi.org/10.1108/IJOEM-06-2020-0724

Ramos R., Casaca J., Patrício R. (2025). Adoption drivers of intelligent virtual assistants in banking: Rethinking the artificial intelligence banker. Computers, vol. 14, issue 6, 209. https://doi.org/10.3390/computers14060209

Rodrigues A.R.D., Ferreira F.A.F., Teixeira F.J.C.S.N., Zopounidis C. (2022). Artificial intelligence, digital transformation and cybersecurity in the banking sector: A multi-stakeholder cognition-driven framework. Research in International Business and Finance, vol. 60, 101616. https://doi.org/10.1016/j.ribaf.2022.101616

Rogers E.M., Singhal A., Quinlan M.M. (2009). Diffusion of innovations (pp. 432–448). In: D.W. Stacks, M.B. Salwen. (Eds.). An integrated approach to communication theory and research. New York: Routledge. https://doi.org/10.4324/9780203887011

Roozen I., Raedts M., Claeys C., Di Gennaro G. (2025). Gendered AI in banking services: The influence of financial chatbots’ gender on consumer behaviour. International Journal of Bank Marketing, vol. 43, issue 6, pp. 1231–1251. https://doi.org/10.1108/ IJBM-07-2024-0421

Shaikh A.A., Kumar A., Mishra A., Elahi Y.A. (2024). A study of customer satisfaction in using banking services through artificial intelligence (AI) in India. Public Administration and Policy, vol. 27, no. 2, pp. 167–181. https://doi.org/10.1108/PAP-05-2023- 0060

Shang D., Wu W. (2017). Understanding mobile shopping consumers’ continuance intention. Industrial Management & Data Systems, vol. 117, issue 1, pp. 213–227. https://doi.org/10.1108/IMDS-02-2016-0052

Shiyyab F.S., Alzoubi A.B., Obidat Q.M., Alshurafat H. (2023). The impact of artificial intelligence disclosure on financial performance. International Journal of Financial Studies, vol. 11, issue 3, 115. https://doi.org/10.3390/ijfs11030115

Singh R.K., Mishra R., Kumar S., Bag S. (2025). Applications of artificial intelligence for optimizing banking and financial operations: A systematic literature review and future research agenda. Journal of Economic Surveys, vol. 39, issue 5, pp. 2284–2302. https://doi.org/10.1111/joes.12693

Tiwari P. (2023). Effect of innovation practices of banks on customer loyalty: An SEM-ANN approach. Benchmarking: An International Journal, vol. 30, issue 10, pp. 4536–4568. https://doi.org/10.1108/BIJ-06-2022-0392

Trivedi J. (2019). Examining the customer experience of using banking chatbots and its impact on brand love: The moderating role of perceived risk. Journal of Internet Commerce, vol. 18, issue 1, pp. 91–111. https://doi.org/10.1080/15332861.2019.15 67188

Tulcanaza-Prieto A.B., Cortez-Ordoñez A., Lee C.W. (2023). Influence of customer perception factors on AI-enabled customer experience in the Ecuadorian banking environment. Sustainability, vol. 15, issue 16, 12441. https://doi.org/10.3390/ su151612441

Upadhyay N., Kamble A. (2024). Why can’t we help but love mobile banking chatbots? Perspective of stimulus-organismresponse. Journal of Financial Services Marketing, vol. 29, pp. 855–872. https://doi.org/10.1057/s41264-023-00237-5

Yellanki S.K. (2024). The impact of digital ecosystems on consumer behavior: A cross-industry analysis of engagement and loyalty. Global Research Development, vol. 9, no. 12. https://doi.org/10.70179/eehnv092

Zaman S.U., Ansari M.A., Alam S.H., Iqbal R.F. (2024). Role of customer loyalty in online banking services through artificial intelligence (AI). Review Journal of Social Psychology & Social Works, vol. 2, no. 2, pp. 342–360. https://10.1.21.233/rjsp.v2i2.133