Determinants affecting customer intention to use chatbots in the banking sector
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DOIhttp://dx.doi.org/10.21511/im.19(4).2023.21
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Article InfoVolume 19 2023, Issue #4, pp. 257-268
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The study aims to analyze the factors that influence customers’ inclination to utilize chatbots in banking services. The paper employed the technology acceptance model and utilized structural equation modeling to examine the factors affecting consumers’ willingness to embrace chatbot services. The survey evaluated various determinants, including perceived usefulness, perceived ease of use, trust, privacy concerns, and customer satisfaction. Data were collected from 250 bank customers in the Bombay region of India through an online survey employing a random sampling method. The collected data were analyzed using IBM SPSS AMOS. This study identifies the aspects of chatbot technology in the banking sector, such as user interface, content, security, and convenience, that influence customers’ decisions to adopt this innovative technology. The results of the analysis revealed path coefficients indicating a significant relationship between information security and perceived usefulness (β = 0.286; p = 0.005) and between perceived usefulness and intention to use (β = 0.489; p < 0.001). Additionally, the path coefficients for design, security, and facilitating conditions were β = 0.281, β = 0.193, and β = 0.136, respectively, all of which held nearly equal significance in the study. The inter-correlations among the variables ranged from 0.346 to 0.854 and were statistically significant. In the banking sector, customers’ intention to use chatbots is influenced by convenience, efficiency, trust, and personalized experiences. Customers are more likely to embrace chatbots when they provide seamless support and tailored solutions, ultimately enhancing customer satisfaction and engagement.
Acknowledgment
This study is supported via funding from Prince Sattam Bin Abdulaziz University project number (PSAU/2023/R/1445).
- Keywords
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JEL Classification (Paper profile tab)M30, M31
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References38
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Tables5
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Figures2
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- Figure 1. Conceptual framework
- Figure 2. Casual model
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- Table 1. Details of respondents from selected SMEs
- Table 2. Factor loadings of variables
- Table 3. Cronbach’s alpha and correlation of the variables
- Table 4. Structural model path coefficients
- Table 5. Overall model fit
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- 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.
- Afthanorhan, A., Awang, Z., & Aimran, N. (2020). An extensive comparison of CB-SEM and PLS-SEM for reliability and validity. International Journal of Data and Network Science, 4(4), 357-364.
- Ahmad, S., Bhatti, S. H., & Hwang, Y. (2020). E-service quality and actual use of e-banking: Explanation through the technology acceptance model. Information Development, 36(4), 503-519.
- Albayrak, N., Özdemir, A., & Zeydan, E. (2018). An overview of artificial intelligence based chatbots and an example chatbot application. 2018 26th signal processing and communications applications conference (SIU) (pp. 1-4). IEEE.
- 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) (pp. 284-288). London, UK.
- Barakat, K. A., & Dabbous, A. (2019). Understanding the factors that affect the sustained use of chatbots within organizations. IADIS International Journal on WWW/Internet, 17(2), 71-84.
- Brandtzaeg, P. B., & Følstad, A. (2017). Why people use chatbots. In I. Kompatsiaris, J. Cave, A. Satsiou, G. Carle, A. Passani, E. Kontopoulos, S. Diplaris, & D. McMillan (Eds.), Internet Science (pp. 377-392). Springer International Publishing.
- Buhalis, D., & Cheng, E. S. Y. (2020). Exploring the use of chatbots in hotels: technology providers’ perspective. In J. Neidhardt & W. Wörndl (Eds.), Information and Communication Technologies in Tourism 2020 (pp. 231-242). Springer International Publishing.
- Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd ed.). Routledge.
- Cardona, R. D., Werth, O., Schönborn, S., & Breitner. (2019). A mixed methods analysis of the adoption and diffusion of chatbot technology in the German insurance sector. 25th Americas Conference on Information Systems (AMCIS).
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
- Eren, B. A. (2021). Determinants of customer satisfaction in chatbot use: Evidence from a banking application in Turkey. International Journal of Bank Marketing, 39(2), 294-311.
- Gangwar, H., Date, H., & Raoot, A. D. (2014). Review on IT adoption: Insights from recent technologies. Journal of Enterprise Information Management, 27(4), 488-502.
- Gao, L., & Waechter, K. A. (2017). Examining the role of initial trust in user adoption of mobile payment services: An empirical investigation. Information Systems Frontiers, 19, 525-548.
- George, A., & Kumar, G. G. (2013). Antecedents of customer satisfaction in internet banking: Technology acceptance model (TAM) redefined. Global Business Review, 14(4), 627-638.
- Gupta, A., & Sharma, D. (2019). Customers’ attitude towards chatbots in banking industry of India. International Journal of Innovative Technology and Exploring Engineering, 8(11), 1222-1225.
- Hair Jr, J. F., Sarstedt, M., Matthews, L. M., & Ringle, C. M. (2016). Identifying and treating unobserved heterogeneity with FIMIX-PLS: Part I–method. European Business Review, 28(1), 63-76.
- Hanafizadeh, P., & Marjaie, S. (2021). Exploring banking business model types: A cognitive view. Digital Business, 1(2), 100012.
- Juniper Research. (2019). Bank Cost Savings via Chatbots to Reach $7.3 Billion by 2023, as Automated Customer Experience Evolves.
- Kasilingam, D. L. (2020). Understanding the attitude and intention to use smartphone chatbots for shopping. Technology in Society, 62, 101280.
- Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford Publications.
- Lee, K. C., & Chung, N. (2009). Understanding factors affecting trust in and satisfaction with mobile banking in Korea: A modified DeLone and McLean’s model perspective. Interacting with Computers, 21(5-6), 385-392.
- Liao, C., Palvia, P., & Chen, J.-L. (2009). Information technology intention to use behavior life cycle: Toward a technology continuance theory (TCT). International Journal of Information Management, 29(4), 309-320.
- Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Science, 38(6), 937-947.
- Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: An extension of the technology acceptance model. Internet Research, 14(3), 224-235.
- Ponte, E. B., Carvajal-Trujillo, E., & Escobar-Rodríguez, T. (2015). Influence of trust and perceived value on the intention to purchase travel online: Integrating the effects of assurance on trust antecedents. Tourism Management, 47, 286-302.
- Quah, J. T., & Chua, Y. W. (2019). Chatbot assisted marketing in financial service industry. In J. Ferreira, A. Musaev, & L. J. Zhang (Eds.), Services Computing – SCC 2019 (pp. 107-114). Springer International Publishing.
- 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 Management, 10(3), 107-118.
- 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.
- Sathye, M. (1999). Intention to use 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), 209-21.
- Shaikh, A. A., & Karjaluoto, H. (2015). Mobile banking adoption: A literature review. Telematics and Informatics, 32(1), 129-142.
- Shankar, A. (2016). Factors affecting mobile banking adoption behavior in India. Journal of Internet Banking and Commerce, 21(1).
- Silva, P. (2015). Davis’ technology acceptance model (TAM) (1989). In M. Al-Suqri & A. Al-Aufi (Eds.), Information Seeking Behavior and Technology Adoption: Theories and Trends (pp. 205-219). IGI Global.
- Tarbal, J. (2020). Chatbots in financial services: Benefits, use cases and key features.
- 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), 91-111.
- Westland, J. C. (2015). An introduction to structural equation models. In Structural Equation Models (pp. 1-8). Cham: Springer.
- Zaato, S. G., Zainol, N. R., Khan, S., Rehman, A. U., Faridi, M. R., & Khan, A. A. (2023). The mediating role of customer satisfaction between antecedent factors and brand loyalty for the Shopee application. Behavioral Sciences, 13(7), 563.