MODELLING THE FACTORS AFFECTING CUSTOMERS’ INTENTION TO USE ARTIFICIAL INTELLIGENCE POWERED CHATBOT SERVICES IN BANKS
DOI:
https://doi.org/10.48165/iitmjbs.2024.SI.15Keywords:
tbots and artificial intelligence have been subjects of interest and discussion due to their perceived complexity, Technological anxiety, risk, social influenceAbstract
Indian banks are using AI-powered chatbots to provide better customer service through real time communication and problem-solving. This research study aims to compare customer intent in using chatbot services offered by private and public sector banks. Additionally, the study investigates the significant factors that explain customer behavioral intent to use AI-powered chatbot services. The paper collected primary data from Indian public and private sector bank customers in Delhi-NCR through Google form links, Instagram, and Facebook. The Unified Theory of Acceptance and Use of Technology (UTAUT) 2 was assessed, and statistical tools such as paired sample t-tests and multiple regression were used to test the hypotheses. The results of the multiple regression analysis showed that hedonic motivation plays a significant role in understanding the intent of public sector customers to use chatbots, while habit plays a significant role for private bank customers. The authors observed a significant difference between public sector bank customers and private bank customers in terms of behavioral intent to use chatbot services, concluding that private bank customers are more intent on using chatbots compared to public sector customers.
References
Aaron Smith, H. N. (2020). Artificial Intelligence in Banking A Mini Review. SSRN Electronic Journal.
Aggarwal, B., Agarwal, H., & Talib, P. (2019). Application of Artificial Intelligence for Successful Strategy Implementation in India’s Banking Sector. International Journal of Advanced Research, 7(11), 157-166.
ALT, M.-A., VIZELI, I., & SĂPLĂCAN, Z. (2021). Banking with a Chatbot: A Study on Technology Acceptance. Studia Universitatis Babeș-Bolyai Oeconomica.
Almahri, F. A. J., Bell, D., & Merhi, M. (2020, March). Understanding student acceptance and use of chatbots in the United Kingdom universities: A structural equation modelling approach. In 2020 6th International Conference on Information Management (ICIM) (pp. 284-288). IEEE.
Alzaidi, A. A. (2018). Impact of Artificial Intelligence on the Performance of the Banking Industry in the Middle East. International Journal of Computer Science and Network Security, 18(10), 140-148.
Ambrish Kumar Mishra, A. P. (2021). Impact of the COVID-19 Outbreak on the Performance of the Indian Banking Sector. International Semantic Intelligence Conference.
Anetta, M. V. (2021). Banking with a Chatbot – A Study on Technology Acceptance. Studia Universitatis Babeș, 66(1), 13-35.
Ashish Bagewadi, D. D. (2020). Analysis of the Banking Sector in India: Post COVID
International Journal of Research and Analytical Reviews, 7(3), 299-308.
Cardona, D. R., Werth, O., Schönborn, S., & Breitner, M. H. (2019). A Mixed Methods Analysis of the Adoption and Diffusion of Chatbot Technology in the German Insurance Sector. Proceedings of the 25th Americas Conference on Information Systems (AMCIS), Cancun, Mexico.
Christian Catalini, C. F. (2018). Machine Intelligence vs. Human Judgment in New Venture Finance.
Das, S. R. (2019). Future of Fintech. Financial Management Wiley, 10.1111(48), 981-1007. 12. Dhingra, A. B. (2020). Analysis of the Banking Sector in India. International Journal of Research and Analytical Reviews, 7(3), 299- 308.
Donepudi, P. K. (2017). AI and Machine Learning in Banking: A Systematic Literature Review. Asian Journal of Applied Science and Engineering, Volume 6, No 3.
Dr Navleen Kaur, M. S. (2020). Banking 4.0: The Influence of Artificial Intelligence on the Banking Industry and How AI is Changing the Face of Modern-day Banks. International Journal of Management, 11(6), 577-585.
Dubey, V. (2019). Fintech Innovations in Digital Banking. International Journal of Engineering Research and Technology, 597-601.
Elena Carletti, S. C. (2020). The Bank Business Model in the Post-COVID-19 World. London, UK: IESE Banking Initiative.
Fintech Innovations in Digital Banking. (2019). International Journal of Engineering Research and Technology, 8(10), 597-601.
Gupta, A., & Sharma, D. (2019). Customers’ Attitude towards Chatbot in the Banking Industry in India. International Journal of Innovative Technology and Exploring Engineering.
Hair, J. F., Hult, G. T., Ringle, C. M., & Sarstedt, M. (2016). A Primer on Partial Least Squares Structural Equation Modeling (PLS SEM). Quantitative Techniques for Business & Management Research.
Hong, J.-C., Han, C.-J., Dai, C.-Y., Hwang, M.-Y., Lin, P.-H., & Lee, C.-C. (2012). Technology Anxiety and Implicit Learning Ability Affect Technology Leadership to Promote the Use of Information Technology at Elementary Schools. International Educational Technology Conference IETC 2012 (pp. 555-563). Procedia - Social and Behavioral Sciences, 64(2012).
Im, I., Kim, Y., & Han, H.-J. (2007). The Effect of Perceived Risk and Technology Type on Users’ Acceptance of Technology. Information and Management, ScienceDirect.
Inaki Aldasoro, I. F. (2020). Effect of COVID 19 on the Banking Sector. BIS Bulletin. 23. Jewandah, D. S. (2018). How Artificial Intelligence is Changing the Banking Sector. International Journal of Management, Technology and Engineering, 8(VII), 525- 531.
Jonathan Donner, A. T. (2008). Mobile Banking and Economic Development.pment: Linking adoption, impact and use. Asian Journal of Communication, 18(4).
Keenam, J. M. (2020). Covid, Resilience and the built environment. Springer, Environment systems and decisions, 40, 216-221.
Kunwar, M. (2019). Understanding how Automation and Machine learning are transforming the financial industry. Artificial Intelligence in Finance, p. 36.
Kurode, T. (J.2018). Review of applicability of Artificial Intelligence in various financial services. Journal of Advance Management Research, 06(01), 2019-214.
Magaji, E. B. (2021). Emerging-market consumers’ interactions with banking chatbot. Telematics and Informatics, 65.
Marie Paule, O. P. (2020, May). Banking model after Covid-19: Taking model risk management to the next level. Mckinsley and Company.
Moore, G. C., & Benbasat, I. (1991). Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. Adoption of Information Technology Innovation, Information Systems Research, 2(3), 192-222.
Namratha, S. J. (2019). Impact of Artificial Intelligence in Chosen Indian Commercial Bank- A Cost Benefit Analysis.
Phillipe Dintrans, B. H. (2019). Artificial Intelligence in Financial Services: From nice to must-have. India: Cognizant.
Paraskevi, G., Saprikis, V., & Avlogiaris, G. (2023). Modeling Nonusers’ Behavioral Intention towards Mobile Chatbot Adoption: An Extension of the UTAUT2 Model with Mobile Service Quality Determinants. Human Behavior and Emerging Technologies, 2023(1), 8859989.
Rajesh Bansal, A. B. (Sep). Recovery, Resilience and Adaptation: India from 2020 to 2030. Carnegie India, Washington DC.
Rani, R., Kanda, J., Chanchal, C., & Vij, T. (2023). A Study on chatbot in the Indian Banking Sector. Contemporary Studies of Risks in Emerging Technology, 35-47.
RBI Director, I. (2020). Artificial Intelligence in Banking. Delhi: RESERVE BANK OF INDIA.
Richad, R., Vivensius, V., Sfenrianto, S., & Kaburuan, E. R. (2019). Analysis of factors influencing millennials’ technology acceptance of chatbot in the banking industry in Indonesia.
International Journal of Civil Engineering and Technology, 1270-1281.
Salunkhe, R. T. (2019). Role of Artificial Intelligence in providing customer service with Special reference to SBI and HDFC Bank. International Journal of Recent Technology and Engineering, 8(4), 12251-12260.
Sannya, L., Susastraa, A. C., Robertsa, C., & Yusramdaleni, R. (2019). The analysis of customer satisfaction factors that influence chatbot acceptance in Indonesia. Management Science Letters, 1225-1232.
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.
Sengupta, S. M. (2020). Impact of COVID-19 on Indian economy. Mumbai: Indira Gandhi Institute of Development Research.
Sinha, S. (2022). Chatbot for banking: Everything you need to know. Ameyo. Retrieved from https://www.ameyo.com/blog/ chatbot-for-banking-everything-you-need to-know/Vedapradha, R., & H. R. (2018). Application of Artificial Intelligence in Investment Banks. Review of Economic and Business Studies, 11(2), 131-136.
Veerla, V. (January 2021). To study the impact of Artificial Intelligence as Predictive model in banking sector: Novel approach. International Journal of Innovative Research in Technology, 7(8), 94-105.
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.
Venkatesh, V., Thong, J. Y., & Xu, X. (2019). Consumer acceptance and use of Information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly.
Vijai, D. C. (April 2019). Artificial Intelligence in the Indian Banking Sector. International Journal of Advanced Research, 7(5), 1581- 1587.
Villar, A. S., & Khan, N. (2021). Robotic process automation in the banking industry: a case study on Deutsche Bank. Journal of Banking and Financial Technology.
Wicaksono, B. P., & Zahra, A. (2022). Design of the use of chatbot as a virtual assistant in banking services in Indonesia. IAES International Journal of Artificial Intelligence (IJ-AI), 23-33.
