ÂÜÀòÉç¹ÙÍø

AI-Driven Marketing

Introduction

Agents, Interfaces, and Ecosystems, Special issue of the Journal of the Academy of Marketing Science; Deadline 31 Jul 2026

INTEREST CATEGORY: INNOVATION AND TECH
POSTING TYPE: Calls: Journals

Posted by: Venky Shankar

Journal of the Academy of Marketing Science
Call for Papers for a Special Issue

AI-Driven Marketing: Agents, Interfaces, and Ecosystems

Guest Editors

  • Dennis Herhausen, Vrije Universiteit Amsterdam, The Netherlands
  • Stephan Ludwig, Monash University, Australia
  • Venky Shankar, Cox School of Business, Southern Methodist University, US

Submission Window: June 1 – July 31, 2026

Introduction and Background

As artificial intelligence (AI) becomes increasingly agentic, interactive, and embedded in platforms and processes, marketing is undergoing a radical transformation. AI systems are no longer passive tools or intermediaries—they are actors, interfaces, and architects of customer value creation and capturing in marketing exchange. From autonomous agents that transact on behalf of consumers to machine-driven negotiations between firms, the next generation of AI is reshaping how value is co-created, communicated, and captured across marketing ecosystems. This special issue invites contributions that explore how AI-driven marketing is altering the roles of consumers, firms, technologies, public policy officials, and regulators. We particularly welcome research with clear managerial implications, including insights derived from or applicable to current practices in industry. As the field rapidly evolves, academic understanding can benefit greatly from the strategies and challenges faced by practitioners. The areas of focus include but are not limited to customer interactions and targeting, innovation management (Shankar 2025), demand prediction (Lu 2025), and public policy and regulation (Shankar 2024).

Building on marketing in computer-mediated environments (Hoffman and Novak 1996; Yadav and Pavlou 2014), we seek research that reflects today’s shift from technology as a passive infrastructure to AI-enabled ecosystems in which agents—both human and non- human—participate autonomously, interact adaptively, and evolve over time.

What is AI-Driven Marketing?

AI-driven marketing occurs when AI systems participate actively in the initiation, facilitation, or completion of marketing interactions and exchange. These AI systems engage directly with humans or other AI agents and may act autonomously (e.g., recommend, decide, or transact on behalf of humans), learn and adapt (e.g., personalize offers, modify messaging, evolve through feedback), and orchestrate multi-party or platform-based ecosystems (e.g., coordinating supply chains or B2B sales transactions). AI technologies involved in AI-driven exchange include but are not limited to:

  • Agentic AI systems such as digital twins, shopping agents, and customer bots (e.g., Huang, Chen, and Chan 2024; Ringel 2023; Shavit et al. 2023),
  • Generative AI systems such as large language models (LLMs) (e.g., Hermann and Puntoni 2025; Rubera, Li, and Hulland 2025) and large reasoning models (LRMs) (e.g., Xu et al. 2025),
  • Conversational AI and voice interfaces (e.g., Liu-Thompkins, Okazaki, and Li 2022; Schindler et al. 2024),
  • Smart contracts and autonomous negotiation tools (e.g., Ahearne et al. 2022),
  • Predictive and prescriptive analytics (e.g., Agarwal et al. 2020; De Luca et al. 2021),
  • Algorithmic recommendation and pricing engines (e.g., Qiu et al. 2025).

Topics of Interest

We welcome submissions on the substantive issue of AI-driven marketing across diverse paradigms—empirical, conceptual, analytical, qualitative, or mixed-methods.

Theoretical and Conceptual Advances. The special issue welcomes theoretical and conceptual work that revisits foundational marketing theories such as relationship marketing, brand theory, and service-dominant logic in the context of AI-driven agency. Researchers may explore how AI reshapes actor roles in marketing, including its potential to act as a customer, brand ambassador, competitor, or collaborator. There is also interest in frameworks that incorporate both human and non-human agents in value co-creation processes.

Design, Deployment, and Governance of AI. In the area of design, deployment, and governance, we encourage studies that examine how marketing organizations delegate tasks and collaborate with AI systems. Ethical and responsible applications of AI in consumer interactions are of particular interest. Research may also address how firms manage autonomous agents through control mechanisms, explainability, escalation procedures, and oversight. Additionally, we seek insights into the organizational capabilities required to implement AI successfully. Legal and regulatory challenges, such as intellectual property rights and the ownership of AI-generated branded content, are also increasingly relevant.

Consumer Psychology and Experience. From a consumer psychology perspective, we welcome studies on how individuals form mental models and use heuristics to interpret AI behavior and decisions. Topics such as anthropomorphism, empathy, and the perceived social presence of AI interfaces are highly relevant. In addition, we welcome research into newer frontiers of AI deployment, such as causal AI, which aims to uncover cause-effect relationships in marketing data, and embodied AI, where AI agents interact with the physical world through robotic or sensor-enabled systems. Researchers may also investigate the paradox of choice and cognitive overload in AI-augmented decision environments.

Market and Ecosystem-Level Dynamics. At the market and ecosystem level, we are interested in emergent dynamics within AI-populated environments, including patterns of competition and cooperation between agents. Work exploring how AI contributes to disintermediation or re-intermediation is welcome, as is research on how AI shapes consumer attention, engagement, and the flow of information in digital markets.

Methodological and Analytical Innovations. Finally, we invite methodological and analytical innovations that push the boundaries of how we study AI in marketing. This includes agent-based modeling of multi-party exchanges, simulations of human–AI and agent–agent interactions, and experimental designs that capture adaptive learning over time. Contributions may also draw on novel data sources such as conversational logs, behavioral telemetry, and digital trace data.

Types of Marketing Exchange and Potential Research Questions

Specifically, we invite submissions that explore four key types of marketing exchange, each transformed by AI’s emergence as an active participant.

1. Consumer to Business (C2B) Exchange
Consumer behavior in response to firms’ use of agentic AI

  • How do consumers perceive and evaluate interactions with firm-deployed AI agents (e.g., chatbots, smart advisors, AI avatars)?
  • What drives consumer trust, resistance, or dependence on AI in purchase, service, and consumption contexts?
  • How does personalization by AI influence privacy boundaries, agency perceptions, or psychological ownership?
  • How and when do consumers employ their own AI agents?

2. Business to Consumer (B2C) Exchange
Firms’ strategies and tactics involving AI interactions with consumers

  • How should firms design and manage AI systems and digital touchpoints across the customer journey?
  • What governance, brand control, or escalation mechanisms are needed for agentic AI?
  • How can firms balance standardization and customization through adaptive AI?
  • How do new AI systems redefine customer value creation and capture?
  • How do new AI systems reshape business models?
  • How should firms and regulators manage the unintended consequences of AI?

3. Consumer to Consumer (C2C) Exchange
Consumer behavior mediated or augmented by AI in peer-to-peer exchange

  • How do AI tools enable or alter word-of-mouth, social proof, and peer influence (e.g., AI-generated reviews, deepfakes, synthetic influencers)?
  • What new forms of social identity, signaling, or collaboration emerge when mediated by AI?
  • How do consumers detect, interpret, or co-create content with AI in C2C contexts (e.g., co-created experiences, generative communities)?
  • How do consumer AI agents interact with each other?

4. Business to Business (B2B) Exchange
Firms’ use of AI in interorganizational exchange and ecosystems

  • How does AI in general and AI agents in particular support/disrupt B2B marketing functions such as lead qualification, account management, and procurement, and pricing?
  • What roles do AI agents play in interfirm negotiation, coordination, and alliance governance?
  • How do platforms and ecosystems evolve when populated by autonomous or semi-autonomous agents?
  • What is the role of the salesforce and supply management in a world where AT agents fulfill (parts of) their traditional job profiles?

Submission Guidelines

Papers targeting this special issue should be submitted through the JAMS submission system

()

and will undergo a similar review process as regularly submitted papers but will be managed by the guest editors for this issue. Submissions for the special issue begin June 1, 2026, with the final deadline for submissions being July 31, 2026. Please direct questions pertaining to the special issue to one of the special issue editors: Dennis Herhausen (dennis.herhausen@vu.n), Stephan Ludwig (stephan.ludwig@monash.edu), or Venky Shankar (vshankar@smu.edu).

Estimated publication date for this issue is late 2027.

All contributors to JAMS are expected to adhere to the AMS Code of Publishing Ethics

().

Pre-Submission Activities

The Special Issue Call for Papers will be supported with special sessions and a panel discussion at
the 2026 AMS conference in Savannah, Georgia (US) to foster engagement with potential authors:

.

We encourage all interested authors to submit an extended abstract to receive feedback on their work. However, acceptance to the special sessions does not guarantee final acceptance to the special issue.

Other supporting activities for the Special Issue:

Biographies

Dennis Herhausen is Full Professor of Marketing and head of the department of Marketing at Vrije Universiteit Amsterdam. Before joining academia, he worked as an International Marketing Manager for a German Food Producer and a Sales and Marketing Consultant. Dennis’ research, teaching, and executive education revolve around the themes of digital communication, customer journeys and experience, multichannel management, digital capabilities, and social media management. His work has been funded by national and international research grants, has received several awards, and is published in the Journal of Marketing, Journal of Marketing Research, Journal of the Academy of Marketing Science, and Harvard Business Review, among others.

Stephan Ludwig is an Professor of Marketing at the Monash University and an expert on marketing communications, digital marketing and marketing analytics. His research focuses on marketing communication design – the way we communicate – which reflects who we are, our intentions, the relationships we are in, and our likely impact on respective audiences. His work is regularly covered in the world-leading journals of marketing and information systems as well as popular outlets like the HBR. He is also a Co-editor at the Journal of the Academy of Marketing Science (JAMS), Area Editor at the Journal of Retailing, on the Editorial Review Board of the Journal of Marketing, and invited Reviewer across top-tier marketing-, information systems- and strategy journals.

Venky Shankar is the Harold M. Brierley Endowed Professor of Marketing and Academic Director, The Brierley Institute of Customer Engagement, Cox School of Business, Southern Methodist University. He has been recognized as one among the World’s Most Influential Scientific Minds, as one among the Top 1% of Marketing Scientists, as a Top 10 Innovation Scholar, and as a Top Retail Influencer. Among others, he is a winner of the 2024 Charles Coolidge Parlin Award, 2023-2024 ÂÜÀòÉç¹ÙÍø (ÂÜÀòÉç¹ÙÍø) Fellow Award, 2024 Institute for Study of Business Markets (ISBM) Distinguished Research Fellow Award, 2022 Margaret Blair Award for Marketing Accountability, 2017 AMS/Cutco Vector Outstanding Marketing Educator Award, , 2012 Vijay Mahajan Award for Lifetime Contributions to Marketing Strategy, and 2006 Robert Clarke Award for the Outstanding Direct and Interactive Marketing Educator. The Shankar-Spiegel Award from the Direct Marketing Educational Foundation is named in his honor. He has published in academic journals such as the Journal of Marketing Research, Management Science, Marketing Science, Strategic Management Journal, Journal of Marketing, Journal of Public Policy and Marketing, Journal of Retailing, Harvard Business Review, and Sloan Management Review. He is Editor Emeritus of the Journal of Interactive Marketing and is an ex-academic Trustee of the MSI. He is also ex-associate editor of the Journal of Marketing Research, ex-associate editor of Management Science, and ex-Area Editor, Journal of Marketing. He is a three-time winner of the Krowe Award for Teaching. He has been a visiting faculty at Stanford University, MIT, INSEAD, Singapore Management University, SDA Bocconi, Chinese European International Business School, and the Indian School of Business. He is co-editor of the Handbook of Marketing Strategy and the author of Shopper Marketing.

References

Agarwal, Ritu, et al. (2020). Emerging technologies and analytics for a new era of value- centered marketing in healthcare. Journal of the Academy of Marketing Science, 48, 9- 23. Ahearne, Michael, et al. (2022). The future of buyer–seller interactions: A conceptual framework and research agenda. Journal of the Academy of Marketing Science, 1-24.

De Luca, Luigi M., et al. (2021). How and when do big data investments pay off? The role of marketing affordances and service innovation. Journal of the Academy of Marketing Science, 49(4), 790-810.

Hermann, Erik, and Stefano Puntoni (2025). Empowering GenAI stakeholders. Journal of the Academy of Marketing Science, 1-7

Hoffman, Donna and Thomas P. Novak (1996). Marketing in hypermedia computer- mediated environments: Conceptual foundations. Journal of Marketing, 60 (3), 50-68.

Huang, Lexie Lan, Rocky Peng Chen, and Kimmy Wa Chan (2024). Pairing up with anthropomorphized artificial agents: Leveraging employee creativity in service encounters. Journal of the Academy of Marketing Science, 52(4), 955-975.

Liu-Thompkins, Yuping, Shintaro Okazaki, and Hairong Li. (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.

Qiu, Liying, et al. (2025). Personalization, consumer search, and algorithmic pricing. Marketing Science, forthcoming.

Ringel, Daniel (2023). Creating synthetic experts with generative artificial intelligence. Kenan Institute of Private Enterprise Research Paper, 4542949.

Rubera, Gaia, Weifeng Li, and John Hulland (2025). Generative artificial intelligence: Marketing’s death knell or ringing in a new era? Journal of the Academy of Marketing Science, (53), 673-676.

Schindler, David, et al. (2024). How speaking versus writing to conversational agents shapes consumers’ choice and choice satisfaction. Journal of the Academy of Marketing Science, 52(3), 634-652.

Shankar, Venkatesh (2024). Managing the twin faces of AI: A commentary on ‘Is AI changing the world for better or worse?’ Journal of Macromarketing, 44 (4), 892-899.

Shankar, Venkatesh (2025), Marketing of technology-intensive products: An AI-driven approach. Marketing Strategy Journal, forthcoming.

Shavit, Yonadav, et al. (2023). Practices for governing agentic AI systems. Research Paper, OpenAI.

Xu, Fengli et al. (2025). Towards large reasoning models: A survey of reinforced reasoning with large language models, arXiv:2501.09686.Yadav, Manjit S., and Paul A. Pavlou (2014). Marketing in computer-mediated environments: research synthesis and new directions. Journal of Marketing, 78(1), 20-40.