Keywords
AI
Customer Trust
Technology Familiarity
Technology Adoption
How to Cite
Abstract
The adoption of AI-enabled customer service has transformed the service industry, enabling efficiency, personalization, and emotionally intelligent interactions. However, the factors influencing customer acceptance of these systems remain underexplored, particularly in relation to emotional awareness, anthropomorphism, and technology trust. This study examines the interplay of these factors in shaping consumer attitudes and behaviors toward AI-driven customer service, drawing on Lazarus’s cognitive-motivation-emotion framework to propose an integrative adoption model. Emotional awareness, defined as the ability of AI systems to detect and respond to human emotions, is posited to enhance customer satisfaction and loyalty by creating empathetic and tailored experiences. Anthropomorphism, the attribution of human-like traits to AI agents, is explored as a mechanism for fostering emotional connections and reducing user resistance. Additionally, the moderating effects of technology trust and familiarity on customer perceptions and adoption intentions are analyzed. The findings contribute to a deeper understanding of how cognitive and emotional processes influence engagement with AI service agents. This research provides practical implications for designing AI systems that not only meet operational demands but also address the emotional and psychological needs of customers, ultimately advancing the development of human-centered AI in customer service contexts.
References
Abushakra, A., Nikbin, D., Odeh, A., & Abdulwahab, R. (2022). The effect of trust, IT knowledge, and entrepreneur’s innovativeness to embrace or shun the internet of things. Frontiers in Psychology, 13, 1035015. https://doi.org/10.3389/fpsyg.2022.1035015
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
Arfi, W. B., Nasr, I. B., Kondrateva, G., & Hikkerova, L. (2021). The role of trust in intention to use the IoT in eHealth: Application of the modified UTAUT in a consumer context. Technological Forecasting and Social Change, 167, 120688. https://doi.org/10.1016/j.techfore.2021.120688
Becker, C., & Fischer, M. (2024). Factors of trust building in conversational AI systems: A literature review. In H. Degen & S. Ntoa (Eds.), Artificial Intelligence in HCI (Vol. 14735, pp. 27–44). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-60611-3_3
Bedué, P., & Fritzsche, A. (2022). Can we trust AI? An empirical investigation of trust requirements and guide to successful AI adoption. Journal of Enterprise Information Management, 35(2), 530–549. https://doi.org/10.1108/JEIM-06-2020-0233
Blut, M., Wang, C., Wünderlich, N. V., & Brock, C. (2021). Understanding anthropomorphism in service provision: A meta-analysis of physical robots, chatbots, and other AI. Journal of the Academy of Marketing Science, 49(4), 632–658. https://doi.org/10.1007/s11747-020-00762-y
Bodó, B. (2021). Mediated trust: A theoretical framework to address the trustworthiness of technological trust mediators. New Media & Society, 23(9), 2668–2690. https://doi.org/10.1177/1461444820939922
Bromuri, S., Henkel, A. P., Iren, D., & Urovi, V. (2021). Using AI to predict service agent stress from emotion patterns in service interactions. Journal of Service Management, 32(4), 581–611. https://doi.org/10.1108/JOSM-06-2019-0163
Chandra, S., Shirish, A., & Srivastava, S. C. (2022). To Be or not to Be …human? Theorizing the role of human-like competencies in conversational artificial intelligence agents. Journal of Management Information Systems, 39(4), 969–1005. https://doi.org/10.1080/07421222.2022.2127441
Cheng, X., Qiao, L., Yang, B., & Li, Z. (2024). An investigation on the influencing factors of elderly people’s intention to use financial AI customer service. Internet Research, 34(3), 690–717. https://doi.org/10.1108/INTR-06-2022-0402
Cheng, X., Zhang, X., Cohen, J., & Mou, J. (2022). Human vs. AI: Understanding the impact of anthropomorphism on consumer response to chatbots from the perspective of trust and relationship norms. Information Processing & Management, 59(3), 102940. https://doi.org/10.1016/j.ipm.2022.102940
Cheung, G. W., Cooper-Thomas, H. D., Lau, R. S., & Wang, L. C. (2024). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management, 41(2), 745–783. https://doi.org/10.1007/s10490-023-09871-y
Chi, N. T. K., & Hoang Vu, N. (2023). Investigating the customer trust in artificial intelligence: The role of anthropomorphism, empathy response, and interaction. CAAI Transactions on Intelligence Technology, 8(1), 260–273. https://doi.org/10.1049/cit2.12133
China: Size of AI customer service by segment 2019-2027. (n.d.). Statista. Retrieved January 7, 2025, from https://www.statista.com/statistics/1440409/china-size-of-ai-customer-service-by-segment/
Choung, H., David, P., & Ross, A. (2023). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human–Computer Interaction, 39(9), 1727–1739. https://doi.org/10.1080/10447318.2022.2050543
Del Prete, M. (2021). Emotional artificial intelligence: Detecting and managing customer emotions in automated customer service. Universita degli studi di Salerno. https://doi.org/10.14273/UNISA-4563
Fan, H., Han, B., & Wang, W. (2024). Aligning (In)congruent chatbot–employee empathic responses with service-recovery contexts for customer retention. Journal of Travel Research, 63(8), 1870–1893. https://doi.org/10.1177/00472875231201505
Golossenko, A., Pillai, K. G., & Aroean, L. (2020). Seeing brands as humans: Development and validation of a brand anthropomorphism scale. International Journal of Research in Marketing, 37(4), 737–755. https://doi.org/10.1016/j.ijresmar.2020.02.007
Golzar, J., Noor, S., & Tajik, O. (2022). Convenience Sampling. International Journal of Education Language Studies, 1(2). https://doi.org/10.22034/ijels.2022.162981
Guo, J., Li, Y., Xu, Y., & Zeng, K. (2021). How Live Streaming Features Impact Consumers’ Purchase Intention in the Context of Cross-Border E-Commerce? A Research Based on SOR Theory. Frontiers in Psychology, 12, 767876. https://doi.org/10.3389/fpsyg.2021.767876
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), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Horowitz, M. C., Kahn, L., Macdonald, J., & Schneider, J. (2024). Adopting AI: How familiarity breeds both trust and contempt. AI And Society, 39(4), 1721–1735. https://doi.org/10.1007/s00146-023-01666-5
Hsu, C.-L., & Lin, J. C.-C. (2023). Understanding the user satisfaction and loyalty of customer service chatbots. Journal of Retailing and Consumer Services, 71, 103211. https://doi.org/10.1016/j.jretconser.2022.103211
Jobanputra, K. (n.d.). Council Post: Customer Service: How AI Is Transforming Interactions. Forbes. Retrieved January 7, 2025, from https://www.forbes.com/councils/forbesbusinesscouncil/2024/08/22/customer-service-how-ai-is-transforming-interactions/
Joslyn, S., & Savelli, S. (2021). Visualizing uncertainty for non-expert end users: The challenge of the deterministic construal error. Frontiers in Computer Science, 2, 590232. https://doi.org/10.3389/fcomp.2020.590232
Khaksar, S. M. S., Khosla, R., Singaraju, S., & Slade, B. (2021). Carer’s perception on social assistive technology acceptance and adoption: Moderating effects of perceived risks. Behaviour & Information Technology, 40(4), 337–360. https://doi.org/10.1080/0144929X.2019.1690046
Khan, M. I., Fatima, J. K., Bahmannia, S., Chatrath, S. K., Dale, N. F., & Johns, R. (2025). Investigating the influence of perceived humanization of service encounters on value creation of chatbot-assisted services. Journal of Service Theory and Practice, 35(1), 56–88. https://doi.org/10.1108/JSTP-10-2023-0282
Kim, J., Kang, S., & Bae, J. (2022). Human likeness and attachment effect on the perceived interactivity of AI speakers. Journal of Business Research, 144, 797–804. https://doi.org/10.1016/j.jbusres.2022.02.047
Kim, M. (2024). Unveiling the e-servicescape of ChatGPT: Exploring user psychology and engagement in AI-powered chatbot experiences. Behavioral Sciences, 14(7), 558. https://doi.org/10.3390/bs14070558
Kumar, N., Upreti, K., Upreti, S., Shabbir Alam, M., & Agrawal, M. (2021). Blockchain integrated flexible vaccine supply chain architecture: Excavate the determinants of adoption. Human Behavior and Emerging Technologies, 3(5), 1106–1117. https://doi.org/10.1002/hbe2.302
Kuuru, T.-K., Litovuo, L., Aarikka-Stenroos, L., & Helander, N. (2020). Emotions in customer experience. In H. Lehtimäki, P. Uusikylä, & A. Smedlund (Eds.), Society as an Interaction Space (Vol. 22, pp. 247–274). Springer Nature Singapore. https://doi.org/10.1007/978-981-15-0069-5_12
Lee, S., Park, G., & Chung, J. (2023). Artificial emotions for charity collection: A serial mediation through perceived anthropomorphism and social presence. Telematics and Informatics, 82, 102009. https://doi.org/10.1016/j.tele.2023.102009
Li, Q., Luximon, Y., & Zhang, J. (2023). The influence of anthropomorphic cues on patients’ perceived anthropomorphism, social presence, trust building, and acceptance of health care conversational agents: Within-subject web-based experiment. Journal of Medical Internet Research, 25, e44479. https://doi.org/10.2196/44479
Liu, J. (2024). ChatGPT: Perspectives from human–computer interaction and psychology. Frontiers in Artificial Intelligence, 7, 1418869. https://doi.org/10.3389/frai.2024.1418869
Liu, T., Wang, W., Xu, J. (David), Ding, D., & Deng, H. (2021). Interactive effects of advising strength and brand familiarity on users’ trust and distrust in online recommendation agents. Information Technology & People, 34(7), 1920–1948. https://doi.org/10.1108/ITP-08-2019-0448
Liu-Thompkins, Y., Okazaki, S., & Li, H. (2022). Artificial empathy in marketing interactions: Bridging the human-AI gap in affective and social customer experience. Journal of the Academy of Marketing Science, 50(6), 1198–1218. https://doi.org/10.1007/s11747-022-00892-5
Lv, X., Yang, Y., Qin, D., Cao, X., & Xu, H. (2022). Artificial intelligence service recovery: The role of empathic response in hospitality customers’ continuous usage intention. Computers in Human Behavior, 126, 106993. https://doi.org/10.1016/j.chb.2021.106993
Mainland China, Hong Kong firms to gain as AI drives customer experience: Report. (2023, November 17). South China Morning Post. https://www.scmp.com/business/article/3241904/mainland-china-hong-kong-companies-benefit-ai-penetrates-deeper-customer-experience-researcher-says
Majeed, M., Owusu-Ansah, M., & Ashmond, A.-A. (2021). The influence of social media on purchase intention: The mediating role of brand equity. Cogent Business & Management, 8(1), 1944008. https://doi.org/10.1080/23311975.2021.1944008
Mantello, P., Ho, M.-T., Nguyen, M.-H., & Vuong, Q.-H. (2023). Bosses without a heart: Socio-demographic and cross-cultural determinants of attitude toward emotional AI in the workplace. AI And Society, 38(1), 97–119. https://doi.org/10.1007/s00146-021-01290-1
Manthiou, A., Hickman, E., & Klaus, P. (2020). Beyond good and bad: Challenging the suggested role of emotions in customer experience (CX) research. Journal of Retailing and Consumer Services, 57, 102218. https://doi.org/10.1016/j.jretconser.2020.102218
Montaldi, D., & Kafkas, A. (2022). Neural mechanisms of familiarity. PsyArXiv. https://doi.org/10.31234/osf.io/zfq5s
Mutlu, A. Y. (2024). Users acceptance of generative artificial intelligence based on ChatBot use. 2024 9th International Conference on Computer Science and Engineering (UBMK), 1–6. https://doi.org/10.1109/UBMK63289.2024.10773553
Mweshi, G. K., & Sakyi, K. (2020). Application of sampling methods for the research design. Archives of Business Research, 8(11), 180–193. https://doi.org/10.14738/abr.811.9042
Nguyen, N. C. M. (n.d.). Artificial intelligence chatbots in telecommunications: Transform-ing customer service in the digital age.
Pentina, I., Xie, T., Hancock, T., & Bailey, A. (2023). Consumer–machine relationships in the age of artificial intelligence: Systematic literature review and research directions. Psychology & Marketing, 40(8), 1593–1614. https://doi.org/10.1002/mar.21853
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
Roemer, E., Schuberth, F., & Henseler, J. (2021). HTMT2–an improved criterion for assessing discriminant validity in structural equation modeling. Industrial Management & Data Systems, 121(12), 2637–2650. https://doi.org/10.1108/IMDS-02-2021-0082
Roesler, E., Manzey, D., & Onnasch, L. (2021). A meta-analysis on the effectiveness of anthropomorphism in human-robot interaction. Science Robotics, 6(58), eabj5425. https://doi.org/10.1126/scirobotics.abj5425
Saghafian, M., Laumann, K., & Skogstad, M. R. (2021). Stagewise overview of issues influencing organizational technology adoption and use. Frontiers in Psychology, 12, 630145. https://doi.org/10.3389/fpsyg.2021.630145
Salimzadeh, S., He, G., & Gadiraju, U. (2024). Dealing with uncertainty: Understanding the impact of prognostic versus diagnostic tasks on trust and reliance in human-AI decision making. Proceedings of the CHI Conference on Human Factors in Computing Systems, 1–17. https://doi.org/10.1145/3613904.3641905
Sesha Bhargavi Velagaleti. (2024). Empathetic algorithms: The role of AI in understanding and enhancing human emotional intelligence. Journal of Electrical Systems, 20(3s), 2051–2060. https://doi.org/10.52783/jes.1806
Shareef, M. A., Kumar, V., Dwivedi, Y. K., Kumar, U., Akram, M. S., & Raman, R. (2021). A new health care system enabled by machine intelligence: Elderly people’s trust or losing self control. Technological Forecasting and Social Change, 162, 120334. https://doi.org/10.1016/j.techfore.2020.120334
Sharma, R., Bose, P., Sharma, R., & Chopra, A. (2021). Enhancing customer engagement through AI-powered marketing personalization engines: A comparative study of collaborative filtering and natural language processing techniques. International Journal of AI Advancements, 10(1), Article 1. https://ijoaia.com/index.php/v1/article/view/32
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
Singh, A. P., Saxena, R., & Saxena, S. (2024). The human touch in the age of artificial intelligence: A literature review on the interplay of emotional intelligence and AI. Asian Journal of Current Research, 9(4), 36–50. https://doi.org/10.56557/ajocr/2024/v9i48860
Song, C. S., & Kim, Y.-K. (2022). The role of the human-robot interaction in consumers’ acceptance of humanoid retail service robots. Journal of Business Research, 146, 489–503. https://doi.org/10.1016/j.jbusres.2022.03.087
Spring, M., Faulconbridge, J., & Sarwar, A. (2022). How information technology automates and augments processes: Insights from artificial‐intelligence‐based systems in professional service operations. Journal of Operations Management, 68(6–7), 592–618. https://doi.org/10.1002/joom.1215
Sun, Y., Zhong, Y., Zhang, Z., Wang, Y., & Zhu, M. (2024). How technical features of virtual live shopping platforms affect purchase intention: Based on the theory of interactive media effects. Decision Support Systems, 180, 114189. https://doi.org/10.1016/j.dss.2024.114189
Sutherland, H., Recchia, G., Dryhurst, S., & Freeman, A. L. J. (2022). How people understand risk matrices, and how matrix design can improve their use: Findings from randomized controlled studies. Risk Analysis, 42(5), 1023–1041. https://doi.org/10.1111/risa.13822
Tsai, W.-H. S., Liu, Y., & Chuan, C.-H. (2021). How chatbots’ social presence communication enhances consumer engagement: The mediating role of parasocial interaction and dialogue. Journal of Research in Interactive Marketing, 15(3), 460–482. https://doi.org/10.1108/JRIM-12-2019-0200
Verhagen, T., Van Nes, J., Feldberg, F., & Van Dolen, W. (2014). Virtual customer service agents: Using social presence and personalization to shape online service encounters. Journal of Computer-Mediated Communication, 19(3), 529–545. https://doi.org/10.1111/jcc4.12066
Vomberg, A., & Klarmann, M. (2022). Crafting Survey Research: A Systematic Process for Conducting Survey Research. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of Market Research (pp. 67–119). Springer International Publishing. https://doi.org/10.1007/978-3-319-57413-4_4
WeChat: Active users worldwide 2024. (n.d.). Statista. Retrieved January 7, 2025, from https://www.statista.com/statistics/255778/number-of-active-wechat-messenger-accounts/
Wirani, Y., Randi, R., Romadhon, M. S., & Suhendi, S. (2021). The influence of familiarity and personal innovativeness on the acceptance of fintech lending services: A perspective from indonesian borrowers. Register: Jurnal Ilmiah Teknologi Sistem Informasi, 8(1), 81. https://doi.org/10.26594/register.v8i1.2327
Xu, K., Chen, X., & Huang, L. (2022). Deep mind in social responses to technologies: A new approach to explaining the computers are social actors phenomena. Computers in Human Behavior, 134, 107321. https://doi.org/10.1016/j.chb.2022.107321
Yang, Y., Liu, Y., Lv, X., Ai, J., & Li, Y. (2022). Anthropomorphism and customers’ willingness to use artificial intelligence service agents. Journal of Hospitality Marketing & Management, 31(1), 1–23. https://doi.org/10.1080/19368623.2021.1926037
Yuan, C., Zhang, C., & Wang, S. (2022). Social anxiety as a moderator in consumer willingness to accept AI assistants based on utilitarian and hedonic values. Journal of Retailing and Consumer Services, 65, 102878. https://doi.org/10.1016/j.jretconser.2021.102878
Zhan, E. S., Molina, M. D., Rheu, M., & Peng, W. (2024). What is there to fear? Understanding multi-dimensional fear of AI from a technological affordance perspective. International Journal of Human–Computer Interaction, 40(22), 7127–7144. https://doi.org/10.1080/10447318.2023.2261731
Zhang, J., Chen, Q., Lu, J., Wang, X., Liu, L., & Feng, Y. (2024). Emotional expression by artificial intelligence chatbots to improve customer satisfaction: Underlying mechanism and boundary conditions. Tourism Management, 100, 104835. https://doi.org/10.1016/j.tourman.2023.104835
Zhao, F., & Nakatani, K. (2023). The impact of user familiarity to ERP usage: An empirical study. International Journal of Information Systems and Change Management, 13(3), 265–283. https://doi.org/10.1504/IJISCM.2023.133360
Zhao, K., Zhang, G., Feng, R., Wang, W., Xu, D., Liu, Y., & Chen, L. (2020). Anxiety, depression and insomnia: A cross-sectional study of frontline staff fighting against COVID-19 in Wenzhou, China. Psychiatry Research, 292, 113304. https://doi.org/10.1016/j.psychres.2020.113304
Zhi, R., Wang, Y., & Wang, Y. (2024). The role of emotional intelligence and self-efficacy in EFL teachers’ technology adoption. The Asia-Pacific Education Researcher, 33(4), 845–856. https://doi.org/10.1007/s40299-023-00782-6
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright (c) 2025 Journal of Asia Social Science Practice