Study on AI-Powered Chatbots for Customer Service and Business Operations
Keywords:
artificial intelligence, chatbots, customer service, operational efficiency, SPSSAbstract
With artificial intelligence (AI) driving their capabilities, chatbots are one of the more ubiquitous technological solutions in customer support and business operations, capable of providing 24×7 service and speeding up and cutting down on service costs. Although there is a significant number of studies on how the quality of a service affects customers' satisfaction and the efficiency of a service, the empirical evidence on the relationship between the quality of a chatbot service and customer satisfaction and the efficiency of the chatbot service is still scattered, especially in emerging market contexts. The study is essentially empirical and seeks to understand the correlation between service quality of AI chatbots, the efficiency of business operations, the satisfaction of the customers and whether there is any difference in customer satisfaction in different demographic groups. A quantitative, cross-sectional survey design was used and 120 respondents who interacted with AI-powered chatbots in their customer services of retail, banking, telecommunication, and e-commerce were reached. A 12-item questionnaire that covered three constructs (chatbot service quality, operational efficiency, and customer satisfaction) was structured and along with five demographic items, it was administered. Data was analyzed using Cronbach's alpha, descriptive statistics, Pearson correlation, multiple linear regression and one way analysis of variance (ANOVA) using the Statistical Package for the Social Sciences (SPSS). Three hypotheses were developed and tested. The result indicated that there is positive relationship between Chatbot service quality and Customer satisfaction (H1 accepted), there is positive relationship between the adoption of AI Chatbots and the efficiency in business operations (H2 accepted) and there is difference between Customer satisfaction according to age group of respondents (H3 accepted). The Cronbach's alpha for all constructs were above the threshold of 0.70, which is acceptable for internal consistency. The findings contribute to the literature on technology adoption by offering an empirical correlation between the performance of chatbots and actual operational and experiential outcomes and offer actionable advice to managers seeking to maximize their customer engagement efforts using AI.
References
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. https://doi.org/10.1007/s12525-020-00414-7
Behera, R. K., Bala, P. K., & Ray, A. (2024). Cognitive chatbot for personalised contextual customer service: Behind the scene and beyond the hype. Information Systems Frontiers, 26(3), 899–919. https://doi.org/10.1007/s10796-021-10168-y
Brill, T. M., Munoz, L., & Miller, R. J. (2019). Siri, Alexa, and other digital assistants: A study of customer satisfaction with artificial intelligence applications. Journal of Marketing Management, 35(15–16), 1401–1436. https://doi.org/10.1080/0267257X.2019.1687571
Castelo, N., Boegershausen, J., Hildebrand, C., & Henkel, A. P. (2023). Understanding and improving consumer reactions to service bots. Journal of Consumer Research, 50(4), 848–863. https://doi.org/10.1093/jcr/ucad023
Chen, Q., Lu, Y., Gong, Y., & Xiong, J. (2023). Can AI chatbots help retain customers? Impact of AI service quality on customer loyalty. Internet Research, 33(6), 2205–2243. https://doi.org/10.1108/INTR-09-2021-0686
Chung, M., Ko, E., Joung, H., & Kim, S. J. (2020). Chatbot e-service and customer satisfaction regarding luxury brands. Journal of Business Research, 117, 587–595. https://doi.org/10.1016/j.jbusres.2018.10.004
Følstad, A., & Skjuve, M. (2019). Chatbots for customer service: User experience and motivation. In Proceedings of the 1st International Conference on Conversational User Interfaces (CUI 2019) (pp. 1–9). Association for Computing Machinery. https://doi.org/10.1145/3342775.3342784
Kondybayeva, S., Daribayeva, M., Fiume, R., Abilda, S., Staroverova, O., Ponkratov, V., Vatutina, L., Shapoval, G., Mikhina, E., & Nikolaeva, I. (2024). A new concept of transforming service: Impact of generative voice chatbots on customer satisfaction and banking industry productivity. Emerging Science Journal, 8(6), 2278–2311. https://doi.org/10.28991/ESJ-2024-08-06-09
Libai, B., Bart, Y., Gensler, S., Hofacker, C. F., Kaplan, A., Kötterheinrich, K., & Kroll, E. B. (2020). Brave new world? On AI and the management of customer relationships. Journal of Interactive Marketing, 51(1), 44–56. https://doi.org/10.1016/j.intmar.2020.04.002
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. https://doi.org/10.1287/mksc.2019.1192
Magno, F., & Dossena, G. (2023). The effects of chatbots' attributes on customer relationships with brands: PLS-SEM and importance–performance map analysis. The TQM Journal, 35(5), 1156–1169. https://doi.org/10.1108/TQM-02-2022-0080
Meyer von Wolff, R., Hobert, S., & Schumann, M. (2020). How may I help you? – State of the art and open research questions for chatbots at the digital workplace. In Proceedings of the 53rd Hawaii International Conference on System Sciences (pp. 4171–4180). https://doi.org/10.24251/HICSS.2020.510
Mostafa, R. B., & Kasamani, T. (2022). Antecedents and consequences of chatbot initial trust. European Journal of Marketing, 56(6), 1748–1771. https://doi.org/10.1108/EJM-02-2020-0084
Murtarelli, G., Gregory, A., & Romenti, S. (2021). A conversation-based perspective for shaping ethical human–machine interactions: The particular challenge of chatbots. Journal of Business Research, 129, 927–935. https://doi.org/10.1016/j.jbusres.2020.09.018
Nguyen, D. M., Chiu, Y. T. H., & Le, H. D. (2021). Determinants of continuance intention towards banks' chatbot services in Vietnam: A necessity for sustainable development. Sustainability, 13(14), 7625. https://doi.org/10.3390/su13147625
Pantano, E., & Pizzi, G. (2020). Forecasting artificial intelligence on online customer assistance: Evidence from chatbot patents analysis. Journal of Retailing and Consumer Services, 55, 102096. https://doi.org/10.1016/j.jretconser.2020.102096
Rese, A., Ganster, L., & Baier, D. (2020). Chatbots in retailers' customer communication: How to measure their acceptance? Journal of Retailing and Consumer Services, 56, 102176. https://doi.org/10.1016/j.jretconser.2020.102176
Sheehan, B., Jin, H. S., & Gottlieb, U. (2020). Customer service chatbots: Anthropomorphism and adoption. Journal of Business Research, 115, 14–24. https://doi.org/10.1016/j.jbusres.2020.04.030
Van Pinxteren, M. M. E., Pluymaekers, M., & Lemmink, J. G. A. M. (2020). Human-like communication in conversational agents: A literature review and research agenda. Journal of Service Management, 31(2), 203–225. https://doi.org/10.1108/JOSM-06-2019-0175
Zarouali, B., Van den Broeck, E., Walrave, M., & Poels, K. (2018). Predicting consumer responses to a chatbot on Facebook. Cyberpsychology, Behavior, and Social Networking, 21(8), 491–497. https://doi.org/10.1089/cyber.2017.0518
Downloads
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
