Abstract
This article positions perceived humanness as a central construct for understanding strategic communication in an era where distinctions between human and AI agents are increasingly blurred. Integrating research on social presence, anthropomorphism, and conversational human voice, it conceptualizes perceived humanness as both an outcome of strategic design choices and a mechanism shaping attitudes, relationships, and behaviors. Drawing on prior literature and empirical work, the piece shows that perceived humanness applies to both human and artificial agents and is associated with outcomes such as trust and perceived relational investment. Implications are discussed for scholars refining measurement and theory, and for practitioners designing communication systems aligned with user needs, while addressing ethical concerns including transparency and privacy.
References
Barcelos, R. H., Dantas, D. C., & Sénécal, S. (2018). Watch your tone: How a brand's tone of voice on social media influences consumer responses. Journal of Interactive Marketing, 41(1), 60-80.
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.
Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On Seeing Human: A three-factor theory of anthropomorphism. Psychological Review, 114(4), 864–886.
Festerling, J., & Siraj, I. (2022). Anthropomorphizing technology: A conceptual review of anthropomorphism research and how it relates to children’s engagements with digital voice assistants. Integrative Psychological and Behavioral Science, 56(3), 709-738.
Greulich, R. S., & Brendel, A. B. (2022). “Feel, Don't Think!” Review of the Application of Neuroscience Methods for Conversational Agent Research. In Proceedings of the Thirtieth European Conference on Information Systems (ECIS 2022), Timisoara, Romania.
Jiang, H., Cheng, Y., Yang, J., & Gao, S. (2022). AI-powered chatbot communication with customers: Dialogic interactions, satisfaction, engagement, and customer behavior. Computers in Human Behavior, 134, 107329.
Kelleher, T. (2009). Conversational voice, communicated commitment, and public relations outcomes in interactive online communication. Journal of Communication, 59(1), 172-188.
Kelleher, T. (2015). Relationship management and social media. In J. Ledingham, E-J. Ki, & J.N. Kim (Eds.) Public Relations as Relationship Management, (pp. 281-305), 2nd ed. Mahwah, NJ: Lawrence Erlbaum Associates.
Lavan, N., Irvine, M., Rosi, V., & McGettigan, C. (2025). Voice clones sound realistic but not (yet) hyperrealistic. PLoS One, 20(9), e0332692.
Liebrecht, C., & van Hooijdonk, C. (2019, November). Creating humanlike chatbots: What chatbot developers could learn from webcare employees in adopting a conversational human voice. In International Workshop on Chatbot Research and Design (pp. 51-64). Springer International Publishing.
Liu, H., Zhang, P., Cheng, H., Hasan, N., & Chiong, R. (2025). Impact of AI-generated virtual streamer interaction on consumer purchase intention: A focus on social presence and perceived value. Journal of Retailing and Consumer Services, 85, 104290.
Lu, L., McDonald, C., Kelleher, T., Lee, S., Chung, Y. J., Mueller, S., Vielledent, M., & Yue, C. A. (2022). Measuring consumer-perceived humanness of online organizational agents. Computers in Human Behavior, 128, 107092.
MacKenzie, S. B., Lutz, R. J., & Belch, G. E. (1986). The role of attitude toward the ad as a mediator of advertising effectiveness: A test of competing explanations. Journal of marketing research, 23(2), 130-143.
Mariani, Marcello M., Novin Hashemi, and Jochen Wirtz. "Artificial intelligence empowered conversational agents: A systematic literature review and research agenda." Journal of Business Research 161 (2023): 113838.
Men, L. R., Zhou, A., & Sunny Tsai, W. H. (2022). Harnessing the power of chatbot social conversation for organizational listening: The impact on perceived transparency and organization-public relationships. Journal of Public Relations Research, 34(1-2), 20-44.
Microsoft. (2025, November 27). Understanding AI agents vs. chatbots. https://www.microsoft.com/en-us/microsoft-copilot/for-individuals/do-more-with-ai/general-ai/understanding-ai-agents-vs-chatbots.
Oh, C. S., Bailenson, J. N., & Welch, G. F. (2018). A systematic review of social presence: Definition, antecedents, and implications. Frontiers in Robotics and AI, 5, 114.
OpenAI. (2026, April 13). ChatGPT response to “Has the conversational tone of these [AI platforms] been improved with new releases?” GPT-5.3 [Large language model]. https://chat.openai.com.
Polyportis, A., & Pahos, N. (2025). Understanding students’ adoption of the ChatGPT chatbot in higher education: the role of anthropomorphism, trust, design novelty and institutional policy. Behaviour & Information Technology, 44(2), 315-336.
Radziwill, N. M., & Benton, M. C. (2017). Evaluating quality of chatbots and intelligent conversational agents. arXiv preprint arXiv:1704.04579.
Turing, A., 1950, “Computing Machinery and Intelligence,” Mind, 59 (236): 433–60.
Short, J., Williams, E., & Christie, B. (1976). The Social Psychology of Telecommunications. London: John Wiley & Sons.
Zhao, X., Sun, Y., Liu, W., & Wong, C. W. (2025). Tailoring generative AI chatbots for multiethnic communities in disaster preparedness communication: extending the CASA paradigm. Journal of Computer-Mediated Communication, 30(1), zmae022.
Zinsser, W. K. (2001). On Writing Well: The Classic Guide to Writing Nonfiction (25th Anniversary ed., 6th ed.). New York: HarperCollins.
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