BANKING WITH A CHATBOT – A STUDY ON TECHNOLOGY ACCEPTANCE
DOI:
https://doi.org/10.2478/subboec-2021-0002Keywords:
Technology acceptance model, Artificial Intelligence, Chatbot, Self-service, Banking, Partial Least Squares-Structural Equation Modeling (PLS-SEM)Abstract
The implementation of chatbot technology is evolving rapidly in the banking industry, yet customer acceptance is behind. The aim of the present paper is to identify the factors that influence consumers’ intention to use chatbot technology applied in the banking industry. The measurement development and hypotheses were based on the technology acceptance model extended with compatibility, customers’ perceived privacy risk and awareness of the service. The sample contains 287 respondents, out of whom 24% have previously used a banking chatbot. The measure items were validated by a measurement model and hypotheses were tested using Partial Least Squares-Structural Equation Modeling (PLS-SEM). The findings highlight the importance of perceived compatibility and perceived usefulness in the adoption of banking chatbot technology. Awareness of the service has an effect on perceived ease of use, perceived privacy risk, and it indirectly affects usage intention of banking chatbots through perceived usefulness. Also, perceived ease of use influences perceived usefulness, and perceived compatibility has an effect on both perceived ease of use and perceived usefulness. Perceived ease of use and perceived privacy risk show no effect on usage intention.
JEL classification: M31, O33.
References
Akturan, U., Tezcan, N. (2012). Mobile banking adoption of the youth market: Perceptions and intentions. Marketing Intelligence and Planning, 30(4).
Al-somali, S. A., Gholami, R., Clegg, B. (2009). An investigation into the acceptance of online banking in Saudi Arabia. Technovation, 29(2), 130–141.
Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., Algharabat, R. (2018). Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: Extending UTAUT2 with risk. Journal of Retailing and Consumer Services, 40.
Almahri, F. A. J., Bell, D., Merhi, M. (2020). Understanding Student Acceptance and Use of Chatbots in the United Kingdom Universities: A Structural Equation Modelling Approach. 6th International Conference on Information Management (ICIM), 284–288.
Arif, I., Afshan, S., Sharif, A. (2016). Resistance to Mobile Banking Adoption in a Developing Country : Evidence from Modified TAM. Journal of Finance and Economics Research, 1(1), 25–42.
Bala, K., Kumar, M., Hulawale, S., Pandita, S. (2017). Chat-bot for college management system using AI. International Research Journal of Engineering and Technology, 4(11).
Business Insider. (2021, January 13). Artificial Intelligence in Banking 2021: How Banks Use AI. https://www.businessinsider.com/ai-in-banking-report
Cardona, R. D., 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 A. 25th Americas Conference on Information Systems (AMCIS), January.
Curs BNR. (2020). Lista bancilor din Romania . https://www.cursbnr.ro/lista-banci
Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Farah, M. F., Hasni, M. J. S., Abbas, A. K. (2018). Mobile-banking adoption: empirical evidence from the banking sector in Pakistan. International Journal of Bank Marketing, 36(7).
Fishbein, M., Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Addison-Wesley.
Forza, C., Filippini, R. (1998). TQM impact on quality conformance and customer satisfaction: A causal model. International Journal of Production Economics, 55(1).
Gefen, D., Karahanna, E., Straub, D. W. (2003). Trust and tam in online shopping: AN integrated model. MIS Quarterly: Management Information Systems, 27(1).
Giovanis, A., Athanasopoulou, P., Assimakopoulos, C., Sarmaniotis, C. (2019). Adoption of mobile banking services: A comparative analysis of four competing theoretical models. International Journal of Bank Marketing, 37(5), 1165–1189.
Giovanis, A. N., Binioris, S., Polychronopoulos, G. (2012). An extension of TAM model with IDT and security/privacy risk in the adoption of internet banking services in Greece. EuroMed Journal of Business, 7(1), 24–53.
Guesalaga, R. (2016). The use of social media in sales: Individual and organizational antecedents, and the role of customer engagement in social media. Industrial Marketing Management, 54, 71–79.
Gupta, A., Sharma, D. (2019). Customers’ Attitude towards Chatbots in Banking Industry of India. International Journal of Innovative Technology and Exploring Engineering, 8(11).
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Second Edition. In California: Sage.
Hanafizadeh, P., Khedmatgozar, H. R. (2012). The mediating role of the dimensions of the perceived risk in the effect of customers’ awareness on the adoption of Internet banking in Iran. Electronic Commerce Research, 12(2).
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).
Juniper Research. (2020). Chatbots, a Game Changer for Banking & Healthcare. https://www.juniperresearch.com/press/press-releases/chatbots-a-game-changer-for-banking-healthcare
Koenig-Lewis, N., Palmer, A., Moll, A. (2010). Predicting young consumers’ take up of mobile banking services. International Journal of Bank Marketing, 28(5).
Kolodinsky, J. M., Hogarth, J. M., Hilgert, M. A. (2004). The adoption of electronic banking technologies by US consumers. International Journal of Bank Marketing, 22(4).
Laumer, S., Maier, C., Fabian, G. (2020). Chatbot acceptance in healthcare: Explaining user adoption of conversational agents for disease diagnosis. 27th European Conference on Information Systems - Information Systems for a Sharing Society, ECIS 2019.
Li, J. P., Kishore, R. (2006). How robust is the UTAUT instrument? A multigroup invariance analysis in the context of acceptance and use of online community weblog systems. SIGMIS CPR’06 - Proceedings of the 2006 ACM SIGMIS CPR Conference, 2006.
Martins, C., Oliveira, T., Popovič, A. (2014). Understanding the internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34(1).
McKinsey. (2020). Digital adoption through COVID-19 and beyond | McKinsey. https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/the-covid-19-recovery-will-be-digital-a-plan-for-the-first-90-days
Melián-González, S., Gutiérrez-Taño, D., Bulchand-Gidumal, J. (2021). Predicting the intentions to use chatbots for travel and tourism. Current Issues in Tourism, 24(2), 192–210.
Moldovan, I.-Z., Săplăcan, Z. (2018). What makes Romanians to bank on their smarthphones? Determinants of mobile banking adoption. Studia UBB Negotia, LXIII(1), 5–33. https://econpapers.repec.org/RePEc:bbn:journl:2018_1_1_moldovan
Moore, C. G., Benbasat, I. (1991). Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. In Information Systems Research (Vol. 2, Issue 3, pp. 192–222).
Mordor Intelligence. (2019). Chatbot Market - Growth, Trends, COVID-19 Impact, and Forecasts (2021 - 2026). https://www.mordorintelligence.com/industry-reports/chatbot-market
Nguyen, Q. N., Sidorova, A. (2017). AI capabilities and user experiences: a comparative study of user reviews for assistant and non-assistant mobile apps. AMCIS 2017 - America’s Conference on Information Systems: A Tradition of Innovation, 2017-August, 1–10.
Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., Pahnila, S. (2004). Consumer acceptance of online banking: An extension of the technology acceptance model. In Internet Research (Vol. 14, Issue 3).
Quah, J. T. S., Chua, Y. W. (2019). Chatbot assisted marketing in financial service industry. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11515 LNCS.
Richad, R., Vivensius, V., Sfenrianto, S., Kaburuan, E. R. (2019). Analysis of factors influencing millennial’s technology acceptance of chatbot in the banking industry in Indonesia. International Journal of Civil Engineering and Technology, 10(4), 1270–1281.
Ringle, C. M., Wende, S., Becker, J.-M. (2015). SmartPLS 3. Bönningstedt: SmartPLS. Retrieved From.
Rogers, E. M. (1983). Difiusion of innovations. New York: Free Press.
Safeena, R., Date, H., Kammani, A., Hundewale, N. (2012). Technology Adoption and Indian Consumers: Study on Mobile Banking. International Journal of Computer Theory and Engineering.
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).
Sathye, M. (1999). Adoption of Internet banking by Australian consumers: an empirical investigation. International Journal of Bank Marketing, 17(7), 324–334.
Schierz, P. G., Schilke, O., Wirtz, B. W. (2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research and Applications, 9(3). https://doi.org/10.1016/j.elerap.2009.07.005
Shaikh, A. A., Karjaluoto, H. (2015). Telematics and Informatics Mobile banking adoption: A literature review. In Telematics and Informatics (Vol. 32, Issue 1, pp. 129–142). Elsevier Ltd.
Shankar, A., Kumari, P. (2016). Journal of Internet Banking and Commerce Factors Affecting Mobile Banking Adoption Behavior in India. Journal of Internet Banking and Commerce, 21(1).
Tabachnick, B. G., Fidell, L. S. (2007). Using Multivariate Statistics. In Boston: Pearson Allyn and Bacon (5th ed.). MA: Pearson.
Tarbal, J. (2020). Chatbots in Financial Services: Benefits, Use Cases & Key Features | 2020. https://www.artificial-solutions.com/blog/chatbots-financial-services-benefits-use-cases
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, 18(1).
Venkatesh, Thong, Xu. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1).
Venkatesh, V., Davis, F. D. (2000). Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2).
Venkatesh, V., Morris, M. G., Davis, G. B., Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, 27(3).
Vieira, A., Sehgal, A. (2017). How banks can better serve their customers through artificial techniques. In Digital Marketplaces Unleashed.
Yang, S., Lu, Y., Gupta, S., Cao, Y., Zhang, R. (2012). Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits. Computers in Human Behavior, 28(1).
Yang, Y., Liu, Y., Li, H., Yu, B. (2015). Understanding perceived risks in mobile payment acceptance. Industrial Management & Data Systems, 115(2), 253–269.
Yousafzai, S. Y. (2012). A literature review of theoretical models of Internet banking adoption at the individual level. In Journal of Financial Services Marketing (Vol. 17, Issue 3, pp. 215–226).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Studia Universitatis Babeș-Bolyai Oeconomica
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
