Conversational artificial intelligence (AI) and bank operational efficiency

https://doi.org/10.35912/ijamis.v1i2.1915
Published: Aug 6, 2024

Abstract:

Purpose: The main objective of the research was to analyse the effects of conversational artificial intelligence (AI) on bank operational efficiency. The emergency of conversational artificial intelligence (AI) has revolutionised the way business interacts with its customers.

Research methodology: The study employed a mixed- method approach where interviews and questionnaires were used to collect qualitative and quantitative data. A sample of 92 bank employees was drawn from ten Zimbabwean banks.

Results: Conversational AI has a positive impact on banking operational efficiency. Specifically, conversational AI improves customer services by providing faster and more accurate responses to customer inquiries, reduces operational costs by automating routine tasks and improve workflow efficiency.

Conclusion: Conversational AI significantly improves banking operational efficiency by automating routine tasks, enhancing customer service, and reducing costs. It streamlines processes and delivers accurate, real-time responses, reinforcing the value of its integration in banking operations. Broader research across regions and sectors is suggested to validate these findings further.

Limitations: The study concentrated on the banking industry of one particular country.

Contribution: The study makes a significant contribution in understanding the advantages of adopting conversational artificial intelligence in banking operations.

Keywords:
1. Artificial intelligence
2. Conversational artificial intelligence
3. financial technology
4. Chatbot
5. Operational efficiency
Authors:
1 . Lilian Gumbo
Midlands State University, Zimbabwe
2 . Margaret Mashizha
University of Zimbabwe, Zimbabwe
3 . Chosani Simon
Midlands State University, Zimbabwe
4 . Phillipa Phiri
Midlands State University, Zimbabwe
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How to Cite
Gumbo, L., Mashizha, M., Simon, C., & Phiri, P. (2024). Conversational artificial intelligence (AI) and bank operational efficiency. International Journal of Accounting and Management Information Systems, 1(2), 79–91. https://doi.org/10.35912/ijamis.v1i2.1915
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Issue & Section
Vol. 1 No. 2 (2023): August
Articles
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

References

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    Zainol, S., Shamsudin, M. F., Hassan, S., & Mohd Noor, N. A. (2023). Understanding customer satisfaction of chatbots service and system quality in banking services. Journal of Information Technology Management, 15(Special Issue), 142-152. doi:http://dx.doi.org/10.22059/jitm.2022.89417

    Zheng, H., Han, F., Huang, Y., Wu, Y., & Wu, X. (2025). Factors influencing behavioral intention to use e-learning in higher education during the COVID-19 pandemic: A meta-analytic review based on the UTAUT2 model. Education and Information Technologies, 1-39. doi:https://doi.org/10.1007/s10639-024-13299-2

  1. Acker, A., & Murthy, D. (2020). What is Venmo? A descriptive analysis of social features in the mobile payment platform. Telematics and Informatics, 52, 101429.
  2. 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. doi:https://doi.org/10.1007/s12525-020-00414-7
  3. Brandtzaeg, P. B., & Følstad, A. (2018). Chatbots: changing user needs and motivations. interactions, 25(5), 38-43. doi:https://doi.org/10.1145/3236669
  4. BV, R. R., & Kulkarni, S. (2024). Chatbots of Indian banks–utility for prospective customers–a perceptive study. Paper presented at the ITM Web of Conferences.
  5. Chen, H., Chen, H., & Tian, X. (2022). The dual-process model of product information and habit in influencing consumers’ purchase intention: The role of live streaming features. Electronic Commerce Research and Applications, 53, 101150. doi:https://doi.org/10.1016/j.elerap.2022.101150
  6. Góralski, W. (1992). Jean Gaudemet. Le mariage en Occident. Les moeurs et le droit. Les Editions du Cerf. Paris 1987 s. 522. Ius Matrimoniale(3), 127-132. doi:http://dx.doi.org/10.21697/im.1992.1.1.10
  7. Gumbo, L., Mashizha, M., Simon, C., & Phiri, P. (2023). Conversational Artificial Intelligence (AI) and Bank Operational Efficiency. International Journal of Accounting and Management Information Systems, 1(2), 109-121.
  8. 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.
  9. Hallikainen, H., Luongo, M., Dhir, A., & Laukkanen, T. (2022). Consequences of personalized product recommendations and price promotions in online grocery shopping. Journal of Retailing and Consumer Services, 69, 103088. doi:https://doi.org/10.1016/j.jretconser.2022.103088
  10. Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of service research, 21(2), 155-172. doi:https://doi.org/10.1177/1094670517752459
  11. Jadil, Y., Rana, N. P., & Dwivedi, Y. K. (2022). Understanding the drivers of online trust and intention to buy on a website: An emerging market perspective. doi:https://doi.org/10.1016/j.jjimei.2022.100065
  12. Khan, I., Ahmad, A. R., Jabeur, N., & Mahdi, M. N. (2021). An artificial intelligence approach to monitor student performance and devise preventive measures. Smart Learning Environments, 8(1), 17.
  13. Lu, L., Cai, R., & Gursoy, D. (2019). Developing and validating a service robot integration willingness scale. International Journal of Hospitality Management, 80, 36-51. doi:https://doi.org/10.1016/j.ijhm.2019.01.005
  14. Mollaei, L., Firoozabadi, S. M. A. K., Hafshejani, K. F., & Rabiei, M. (2024). Analyzing the Factors Influencing the Implementation of Artificial Intelligence in the Iranian Banking Industry: Findings from a Qualitative Study. Digital Transformation and Administration Innovation, 2(2), 59-69.
  15. Ncube, N., & Mushayavanhu, T. (2025). Chatbot task-technology fit and conversational intelligence on customer service encounter satisfaction in the Zimbabwean banking sector. Journal of Research & Innovation for Sustainable Society (JRISS), 7(1).
  16. Nyagadza, B., Muposhi, A., Mazuruse, G., Makoni, T., Chuchu, T., Maziriri, E., & Chare, A. (2022). Prognosticating chatbots? anthropomorphic usage intention as an ebanking customer service gateway: cogitations from Zimbabwe. PSU Research Review (PRR).
  17. Oh, J., & In, J. (2023). Supplier involvement and supplier performance in new product development: Moderating effects of supplier salesperson behaviors. Journal of Business Research, 161, 113816. doi:https://doi.org/10.1016/j.jbusres.2023.113816
  18. Parthiban, E. S., & Adil, M. (2023). Examining the adoption of AI based banking chatbots: A task technology fit and network externalities perspective. Asia pacific journal of information systems, 33(3), 652-676. doi:http://dx.doi.org/10.14329/apjis.2023.33.3.652
  19. Passarelli, M., Bongiorno, G., Cucino, V., & Cariola, A. (2023). Adopting new technologies during the crisis: An empirical analysis of agricultural sector. Technological Forecasting and Social Change, 186, 122106.
  20. Przegalinska, A., Ciechanowski, L., Stroz, A., Gloor, P., & Mazurek, G. (2019). In bot we trust: A new methodology of chatbot performance measures. Business Horizons, 62(6), 785-797. doi:https://doi.org/10.1016/j.bushor.2019.08.005
  21. Ragab, M. A., & Arisha, A. (2018). Research methodology in business: A starter’s guide. doi:http://dx.doi.org/10.5430/mos.v5n1p1
  22. Rane, R. P., Szügyi, E., Saxena, V., Ofner, A., & Stober, S. (2020). Prednet and predictive coding: A critical review. Paper presented at the Proceedings of the 2020 international conference on multimedia retrieval.
  23. Shambira, L. (2020). Exploring the adoption of artificial intelligence in the Zimbabwe banking sector. European Journal of Social Sciences Studies, 5(6).
  24. 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, 11(3), 115. doi:https://doi.org/10.3390/ijfs11030115
  25. Tandon, A., Dhir, A., Islam, N., Talwar, S., & Mäntymäki, M. (2021). Psychological and behavioral outcomes of social media-induced fear of missing out at the workplace. Journal of Business Research, 136, 186-197. doi:https://doi.org/10.1016/j.jbusres.2021.07.036
  26. Zainol, S., Shamsudin, M. F., Hassan, S., & Mohd Noor, N. A. (2023). Understanding customer satisfaction of chatbots service and system quality in banking services. Journal of Information Technology Management, 15(Special Issue), 142-152. doi:http://dx.doi.org/10.22059/jitm.2022.89417
  27. Zheng, H., Han, F., Huang, Y., Wu, Y., & Wu, X. (2025). Factors influencing behavioral intention to use e-learning in higher education during the COVID-19 pandemic: A meta-analytic review based on the UTAUT2 model. Education and Information Technologies, 1-39. doi:https://doi.org/10.1007/s10639-024-13299-2