Mapping Impact and Influence in AI-Driven Advertising: A Scientometric and Network Analysis of Knowledge Ecosystem

Artificial Intelligence (AI) has become deeply embedded in the advertising industry, presenting both opportunities and challenges. Understanding this transformation requires mapping the dyad’s structure and identifying the conditions that shape its evolution. This study applies a scientometric framework, integrating bibliometric network analysis with statistical modeling, to examine AI advertising as a knowledge ecosystem. By analyzing patterns of collaboration, thematic convergence, and structural centrality, we interpret how scholarly networks generate, connect, and diffuse ideas in ways that influence both academic and industry practices. The findings reveal that the field’s growth is underpinned by interconnected clusters of expertise with strategic opportunities emerging from interdisciplinary integration and global collaboration. Simultaneously, consolidating influence among a few dominant actors raises questions about diversity, access, and the balance between innovation and ethical responsibility. Statistical analyses conducted in SPSS Statistics version 29.0.2.0 further identify the bibliometric and structural factors that most predict citation impact, strengthening the study’s contribution to understanding how influence is built and sustained in AI-driven advertising research.