The Journal of AI in Marketing (JAIM) is a peer-reviewed, open-access scholarly journal dedicated to publishing high-impact research at the intersection of artificial intelligence and marketing. The journal seeks to advance fundamental understanding of how AI technologies reshape marketing theory, strategy, and practice.
JAIM serves as a premier outlet for original research that makes clear theoretical, methodological, and managerial contributions to the marketing discipline. The journal welcomes work that develops new theory, extends existing frameworks, introduces innovative research and analytical methods, or provides robust empirical evidence on the role of AI in the field of marketing.
JAIM adopts an interdisciplinary orientation, encouraging research that draws from marketing, management, psychology, sociology, communication studies, information systems, computer science, economics, and related fields, while maintaining a strong grounding in marketing theory. The journal prioritizes rigor, transparency, and reproducibility, and aims to publish research that is both intellectually significant and relevant to contemporary marketing challenges.
As an open-access journal, JAIM is committed to the broad dissemination of high-quality research and to fostering global scholarly exchange in the rapidly evolving domain of AI-enabled marketing. Supported by an internal grant, JAIM currently waives submission fees or article processing charges (APCs). All articles accepted for publication are made freely and permanently available to readers worldwide, and authors retain their rights in accordance with the journal’s open-access policy.
The Journal of AI in Marketing publishes research that explores, develops, and critically evaluates artificial intelligence applications, methods, and implications in marketing. The journal welcomes conceptual, empirical, methodological, and analytical contributions that advance knowledge and practice in AI-enabled marketing.
Topics of interest include, but are not limited to:
● AI and Marketing Theory
Research that develops or extends marketing theory by incorporating AI-driven decision systems, algorithmic agents, and human–AI interactions.
● Machine Learning and Advanced Analytics in Marketing
Predictive modeling, causal inference, deep learning, reinforcement learning, natural language processing, computer vision, pricing models, and generative AI applied to marketing problems.
● Consumer Behavior and Human–AI Interaction
Consumer attitude, perceptions, trust, adoption, and evaluations of AI-driven agents, recommendations, personalization, chatbots, and virtual assistants.
● Customer Journey and UX
AI-enabled personalization, customer journey optimization, CRM, customer lifetime value modeling, and omnichannel experiences.
● Generative AI and Content Creation
AI-generated advertising, branding, creative strategy, social media content, and their effectiveness and ethical implications.
● Ethics, Governance, and Responsible AI in Marketing
Fairness, transparency, privacy, bias, regulation, and societal implications of AI use in marketing.
● AI in Sales, Retail, and Digital Platforms
Applications in sales forecasting, dynamic pricing, retail analytics, platform ecosystems, and marketplace design.
● Methodological and Data Innovations
Synthetic datasets, benchmarks, modeling approaches, and reproducible research relevant to AI in marketing.
JAIM encourages submissions that offer strong theoretical contributions, robust methodological rigor, and clear implications for marketing scholarship and practice. The journal values interdisciplinary work and welcomes collaborations between academics and industry researchers.