Journal of Marketing Management

Artificial Intelligence and Marketing: Rethinking Knowledge through a Human-in-the-Loop Perspective

Artificial Intelligence (AI) is increasingly embedded in marketing research and practice, most notably in creativity and content generation (Mouritzen et al, 2024; Magni et al., 2024; Chen et al, 2019), personalisation (Lu & Kannan, 2026; Mortati et al., 2026) and to some extent, research analysis (Pitt et al., 2020; Kozinets & Seraj-Aksit, 2024). Yet much of this work has focused on efficiency, scalability, and predictive performance. Comparatively little attention has been paid to what these shifts mean for the production, validation, and establishment of marketing knowledge. The present Special Issue invites scholars to examine that question directly.

The organising concept is the Human-in-the-Loop (HITL), which we define as a set of practices in which human judgement remains an active and accountable part of generating, checking, and interpreting insights. HITL plays a central role in how marketing knowledge is produced, interpreted, and validated, as seen in emerging practices such as AI-assisted coding, where outputs are compared and validated against human judgement (Bali et al., 2024), and AI-supported literature synthesis, where iterative human curation is used to refine and reconcile findings (Anesbury & Stocchi, 2025).

Existing research already points to the growing complexity of human–AI relations in marketing. Studies of AI-enabled devices show how consumers form expectations, satisfaction, and even relational attachments to these systems (Brill et al., 2019; Schweitzer et al., 2019), and broader work highlights the ethical, societal, and strategic implications of AI’s expanding role (Letheren et al., 2020). Emerging research in digitalised marketing practice demonstrates that technologies are not simply tools but active participants in shaping marketing work, expertise, and decision-making (Bourne, 2025; Ryan, 2025). Methodological developments such as AI-assisted netnography further illustrate how human and machine inputs are increasingly interlinked in the production and interpretation of marketing knowledge (Kozinets & Seraj-Aksit, 2024).

Drawing on the Journal of Marketing Management’s (JMM’s) performativity tradition (Mason et al., 2015), this Special Issue invites attention to how marketing knowledge is not merely discovered but produced and made consequential through practice. From this perspective, AI-assisted tools and methods can be understood as part of the socio-material arrangements that shape markets, consumers, and knowledge itself.

The result is the raising of some important questions. When parts of the analysis are automated, what role does human expertise still play? How should we think about rigour, validity, and generalisability when AI is involved in producing results? More broadly, how does the use of AI in research change how marketing knowledge is built, interpreted, and trusted?

Scope & Focus

We define HITL as research arrangements where human judgement, interpretation, oversight, or intervention is structurally integrated into AI-assisted processes. This Special Issue focuses on how such arrangements are designed, justified, and evaluated, and what they reveal about the trustworthiness of marketing knowledge. Submissions need not explicitly adopt a HITL label but should examine the relationship between human and AI agency in knowledge production.

We welcome conceptual, empirical, methodological, and critical contributions, particularly those engaging with the broader disciplinary and institutional implications of AI in marketing research, beyond its technical performance. Submissions that do not explicitly engage with the role of human judgement, interpretation, or intervention in AI-assisted knowledge production are unlikely to be suitable.

Indicative Topics

Relevant contributions may include, but are not limited to:

• Conceptual and theoretical work examining HITL and how AI-assisted research tools shape marketing knowledge and markets
• Socio-material and more-than-human perspectives on human–AI collaboration in marketing research and practice
• Methodological innovation in AI-assisted qualitative research, including netnography, discourse analysis, and ethnographic approaches
• Critical examination of how AI shapes the labour, expertise, and interpretive practices of marketing researchers and practitioners
• The design and evaluation of HITL systems in marketing analytics, consumer research, and decision-making, including how such systems shape knowledge production and use
• The use of AI for synthetic data generation or research alternatives, examining of human oversight and its implications for knowledge claims
• Ethical and societal implications of AI in marketing knowledge production
• Research revisiting established marketing findings using AI-assisted methods (e.g., replication and generalisation across contexts), evaluating how these approaches contribute to the validation, interpretation, and enactment of marketing knowledge

Submissions that use AI as part of their research process are expected to document that use transparently, including the nature of human involvement at each stage, particularly the creation of AI inputs and the interpretation of AI outputs. Authors are expected to clearly show the relationship between human - AI in their work and reflect on what this means for how their findings should be read and assessed.

We encourage reflexivity about the limits and conditions of AI-assisted methods, not merely their capabilities, especially for work involving AI-generated text, synthetic data, automated coding, or large-language-model-assisted analysis.

The full Call for Papers including references can be found at the JMM blog site: https://jmmnews.blog/artificial-intelligence-and-marketing/