Mapping scholarly networks in AI-driven advertising research

Artificial intelligence (AI) applications, including generative AI, large language models (LLMs), and algorithmic recommendations, are driving rapid innovation and penetrating global industries, including marketing communications, with recent advancements reshaping advertising. Although numerous studies exist, AI advertising research remains fragmented, making it difficult to identify the most critical research needs. This study identifies the most influential works cited in the field of AI advertising, assesses the current state of AI advertising research, and evaluates the impact of the key contributions in shaping the field. To provide a cohesive perspective, a bibliometric review was conducted of AI research articles published over a 10-year span in leading advertising-related journals, pinpointing influential authors, critical studies, and central themes permeating current AI advertising research. Specifically, 1,792 AI advertising-focused articles were assessed using citation and co-citation analyses. The results provide thematic insights and practical implications for researchers and practitioners, highlighting promising directions for the development of effective AI advertising strategies. The study makes a significant contribution to the literature by providing a comprehensive bibliometric study that clarifies the AI advertising intellectual landscape, enabling the identification of key directions for future research.