Special Issue Call for Paper: Changing Consumer Behavior in AI Marketing and AI Agents

The rapid evolution of artificial intelligence (AI) is transforming marketing practices, consumer–brand relationships, and the very nature of consumer behavior. AI-powered agents, whether embodied as chatbots, digital assistants, or virtual influencers, now play a central role in shaping experiences across industries (Yim, 2024; Zhou & Wang, 2025). As firms integrate these technologies, understanding the behavioral, ethical, and strategic implications becomes critical. This special issue aims to bring together research that not only investigates consumer responses to AI agents but also highlights future research opportunities in this transformative space.

Trust remains a core challenge in human–AI interactions. Recent work has shown that AI-powered agents introduce new forms of algorithmic trust and distrust, influenced by factors such as transparency, perceived autonomy, and anthropomorphism (Zhao et al., 2024). For example, consumers often attribute less blame to virtual influencers during service failures compared to human influencers, as lower “mind perception” reduces responsibility attribution, thereby increasing forgiveness (Zhao et al., 2024). This introduces opportunities for brands to mitigate service crises but also raises questions about accountability and authenticity.

Further, scholars highlight pressing issues of deception, discrimination, and surveillance that accompany the use of AI agents in digital markets (Borau, 2025). For example, Wang and Zhou (2025) demonstrate that allowing users to name AI agents fosters psychological ownership and, in turn, encourages more responsible behaviors within shared economy platforms. Such findings highlight how seemingly small design choices can have meaningful implications for marketing practices and consumer engagement. However, consumers may resist AI if they perceive manipulative persuasion, opaque targeting, or bias embedded in algorithms. This underscores the need for ethical AI governance in marketing, balancing personalization with consumer rights. The rise of generative AI and large language models (LLMs) adds further complexity, reshaping creativity, branding, and content strategies, while raising urgent ethical concerns around authenticity and intellectual property (Yigit et al., 2025). Future research should investigate how transparency cues, regulatory frameworks, and consumer literacy can reduce perceptions of unfairness and increase acceptance.

AI agents also redefine how consumers perceive service experiences and brand interactions. A growing body of research shows that virtual influencers can outperform human ones in engagement and forgiveness, suggesting that AI agents are more than functional tools—they are becoming social actors (Zhao et al., 2024). At the same time, generative AI tools such as LLMs are reshaping how consumers seek information, evaluate alternatives, and even co-create brand narratives (Zhang et al., 2025). Furthermore, recent work has emphasized the distinction between modular AI agents and distributed agentic AI systems, noting their divergent design philosophies and implications for adaptive customer engagement (Sapkota et al., 2026). This creates both opportunities for hyper-personalized experiences and risks of misinformation or overreliance on automated systems.

At the organizational level, AI agents are also redefining work processes, customer management, and compliance practices. Research suggests that agentic AI is not only enhancing productivity but also reshaping human resource systems, decision-making workflows, and managerial oversight (Kshetri et al., 2025; Taroun et al., 2025). Similarly, the emergence of an "AI workforce" signals a future where firms must adapt structurally and culturally to integrate agentic technologies into daily operations (Lee, 2025). This reconfiguration of labor and management dynamics underscores the need for marketing researchers to link consumer-facing AI applications with organizational adoption strategies.

We welcome discussions around the opportunities and challenges that AI marketing and AI agents face in advancing marketing and business research and practice. Given the expanding research on the topic, how can we leverage AI and AI agents to create more effective marketing strategies and ultimately impact consumer behavior? Discussion points surrounding the following areas are encouraged:

1. Hybrid Influence Models – How do consumers navigate between human and AI agents in joint service environments, and what balance yields optimal trust and engagement? In what ways do consumer perceptions and adoption patterns differ when engaging with AI agents versus agentic AI, and how does this distinction influence broader market acceptance of AI-driven solutions?
2. Ethics and Governance – What frameworks can ensure AI agents in marketing avoid bias, deception, and exploitation, while preserving consumer autonomy? How can firms balance personalization with fairness, ensuring compliance with emerging global AI regulations?
3. Consumer Identity and Co-Creation – How do AI agents reshape consumer identity, empowerment, and participation in marketing, retailing, and service?
4. Agent-to-Agent Marketing – As agents increasingly negotiate on behalf of consumers and firms, how will marketing strategies evolve for B2A (Business-to-Agent) and A2A (Agent-to-Agent) contexts?
5. Cross-Cultural Differences – Given cultural differences in perceptions of authority, technology, and relational trust, how do AI agents operate in global versus local contexts? How should firms structurally and culturally manage the integration of AI agents into the workforce and managerial processes?

 

References

Borau, S. (2025). Deception, Discrimination, and Objectification: Ethical Issues of Female AI Agents: Deception, Discrimination, and Objectification: Ethical Issues of Female AI Agents. Journal of Business Ethics, 198(1), 1-19.

Lee, H. S. S. (2025). Rise of the agentic AI workforce. IEEE Micro, 45(1), 4–5.

Kshetri, N. (2025). Redefining Human Resource Practices With AI Agents and Agentic AI: Automated Compliance and Enhanced Productivity. Computer, 58(6), 119-124.

Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2025). AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges.

Taroun, A., Li, X., & Zhou, L. (2025). The rise of agentic AI in global workforce systems. Industrial Marketing Management, 119, 63–74.

Yigit, Y., Ferrag, M. A., Ghanem, M. C., Sarker, I. H., Maglaras, L. A., Chrysoulas, C., Moradpoor, N., Tihanyi, N., & Janicke, H. (2025). Generative AI and LLMs for Critical Infrastructure Protection: Evaluation Benchmarks, Agentic AI, Challenges, and Opportunities. Sensors, 25(6), 1666.

Yim, A., Cui, A.P. and Walsh, M. (2024), The role of cuteness on consumer attachment to artificial intelligence agents, Journal of Research in Interactive Marketing, 18(1), 127-141.

Zhao, T., Ran, Y., Wu, B., Wang, V. L., Zhou, L., & Wang, C. L. (2024). Virtual versus human: Unraveling consumer reactions to service failures through influencer types. Journal of Business Research, 178, 114657.

Zhang, Z., Li, H., & Ma, X. (2025). Emerging trends in AI agents and consumer behavior: A systems perspective. Journal of Retailing and Consumer Services, 75, 103564.

Zhou, L., & Wang, V. L. (2025). “What’s in a Name?”: The Effect of AI Agent Naming on Psychological Ownership and Responsible Behaviors in the Shared Economy. Journal of Applied Business & Behavioral Sciences, 1(2), 145-163. https://doi.org/10.63522/jabbs.102008

 

Guest Editors:

Xin Song*, Research Professor, Member of the National Researcher System, Faculty of Economics and Business, Universidad Anáhuac México, xin.song@anahuac.mx

Pavel Reyes Mercado, Research Professor, Marketing Research Coordinator, Faculty of Economics and Business, Universidad Anáhuac México, pavel.reyes@anahuac.mx

Adrianela Citláli Ángeles Garnica, Research Professor, Member of the National Researcher System, Faculty of Economics and Business, Universidad Anáhuac México, adrianela.angeles@anahuac.mx

*For information and inquiries, please send to the executive guest editor, Professor Xin Song at xin.song@anahuac.mx

 

All submission should go through JABBS submission system at the official website: https://jabbs.credamopress.com/index.php/jabbs/about/submissions. In the cover letter, please indicate the submission to the SI-AI Agent