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Factors Influencing Consumer Acceptance of Chatbots: Evidence from Azerbaijan

Authors

Mansimli Kamran, Ramiz Orujaliyev

Rubric:Economics and Management
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The rise of digital communication has prompted businesses to adopt innovative tools such as chatbots to enhance consumer engagement. Using the Technology Acceptance Model (TAM), this study investigates Azerbaijani online consumers' attitudes and intentions toward chatbot adoption. Survey data from 325 respondents indicate that perceived usefulness significantly shapes positive attitudes, whereas perceived ease of use and perceived risk were not statistically significant. Additionally, consumer attitudes significantly predict their behavioural intentions toward chatbot use. The findings suggest that emphasizing the practical benefits of chatbots can drive greater consumer acceptance and usage. The study extends TAM to a new cultural context and provides practical insights for businesses seeking to leverage chatbots effectively

Keywords

Chatbots
Consumer attitudes
Technology Acceptance Model
Perceived Usefulness
Perceived Risk
Behavioural Intention.

Authors

Mansimli Kamran, Ramiz Orujaliyev

Rubric:Economics and Management
1670
14

References:

Abu Shawar, B., & Atwell, E. (2007). Chatbots: Are they Really Useful? Journal for Language Technology and Computational Linguistics, 22(1), 29–49. https://doi.org/10.21248/jlcl.22.2007.88

Adamopoulou, E., & Moussiades, L. (2020a). An Overview of Chatbot Technology. In I. Maglogiannis, L. Iliadis, & E. Pimenidis (Eds.), Artificial Intelligence Applications and Innovations (Vol. 584, pp. 373–383). Springer International Publishing. https://doi.org/10.1007/978-3-030-49186-4_31

Adamopoulou, E., & Moussiades, L. (2020b). Chatbots: History, technology, and applications. Machine Learning with Applications, 2, 100006. https://doi.org/10.1016/j.mlwa.2020.100006

Belhadi, A., Mani, V., Kamble, S. S., Khan, S. A. R., & Verma, S. (2024). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: An empirical investigation. Annals of Operations Research, 333(2–3), 627–652. https://doi.org/10.1007/s10479-021-03956-x

Camilleri, M. A., & Falzon, L. (2020). Understanding motivations to use online streaming services: Integrating the technology acceptance model (TAM) and the uses and gratifications theory (UGT). Spanish Journal of Marketing - ESIC, 25(2), 217–238. https://doi.org/10.1108/SJME-04-2020-0074

Joo, J., & Sang, Y. (2013). Exploring Koreans’ smartphone usage: An integrated model of the technology acceptance model and uses and gratifications theory. Computers in Human Behavior, 29(6), 2512–2518. https://doi.org/10.1016/j.chb.2013.06.002

Lee, S. B. (2020). Chatbots and Communication: The Growing Role of Artificial Intelligence in Addressing and Shaping Customer Needs. Business Communication Research and Practice, 3(2), 103–111. https://doi.org/10.22682/bcrp.2020.3.2.103

Liao, Y., Deschamps, F., Loures, E. D. F. R., & Ramos, L. F. P. (2017). Past, present and future of Industry 4.0—A systematic literature review and research agenda proposal. International Journal of Production Research, 55(12), 3609–3629. https://doi.org/10.1080/00207543.2017.1308576

Liu, M., Yang, Y., Ren, Y., Jia, Y., Ma, H., Luo, J., Fang, S., Qi, M., & Zhang, L. (2024). What influences consumer AI chatbot use intention? An application of the extended technology acceptance model. Journal of Hospitality and Tourism Technology, 15(4), 667–689. https://doi.org/10.1108/JHTT-03-2023-0057

Luo, M. M., Chea, S., & Chen, J.-S. (2011). Web-based information service adoption: A comparison of the motivational model and the uses and gratifications theory. Decision Support Systems, 51(1), 21–30. https://doi.org/10.1016/j.dss.2010.11.015

Mariani, M. M., Perez-Vega, R., & Wirtz, J. (2022). AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology & Marketing, 39(4), 755–776. https://doi.org/10.1002/mar.21619

Mohebbi, S., Khatibi, V., & Keramati, A. (2012). A Household Internet Adoption Model Based on Integration of Technology Acceptance Model, Theory of Planned Behavior, and Uses and Gratifications Theory. International Journal of E-Adoption, 4, 51–69. https://doi.org/10.4018/jea.2012010104

Oetama, S. (2022). Influence Of Brand Communication, Brand Image And Brand Trust Through Online Media On Brand Loyalty In E-Commerce. International Journal of Science, Technology & Management, 3(2), 502–511. https://doi.org/10.46729/ijstm.v3i2.494

Pillai, R., & Sivathanu, B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(10), 3199–3226. https://doi.org/10.1108/IJCHM-04-2020-0259

Savastano, M., Biclesanu, I., Anagnoste, S., Laviola, F., & Cucari, N. (2024). Enterprise chatbots in managers’ perception: A strategic framework to implement successful chatbot applications for business decisions. Management Decision, ahead-of-print(ahead-of-print). https://doi.org/10.1108/MD-10-2023-1967

Shahzad, M. F., Xu, S., An, X., & Javed, I. (2024). Assessing the impact of AI-chatbot service quality on user e-brand loyalty through chatbot user trust, experience and electronic word of mouth. Journal of Retailing and Consumer Services, 79, 103867. https://doi.org/10.1016/j.jretconser.2024.103867

Van den Broeck, E., Zarouali, B., & Poels, K. (2019). Chatbot advertising effectiveness: When does the message get through? Computers in Human Behavior, 98, 150–157. https://doi.org/10.1016/j.chb.2019.04.009

Xia, Z., & Shannon, R. (2025). Navigating the Digital Frontier: Exploring the Dynamics of Customer–Brand Relationships Through AI Chatbots. Sustainability, 17(5), 2173. https://doi.org/10.3390/su17052173

Yadav, M. S., & Pavlou, P. A. (2020). Technology-enabled interactions in digital environments:a conceptual foundation for current and future research. Journal of the Academy of Marketing Science, 48(1), 132–136. https://doi.org/10.1007/s11747-019-00712-3

Yaiprasert, C., & Hidayanto, A. N. (2024). AI-powered ensemble machine learning to optimize cost strategies in logistics business. International Journal of Information Management Data Insights, 4(1), 100209. https://doi.org/10.1016/j.jjimei.2023.100209

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