Mapping the Research Landscape of Artificial Intelligence Adoption in Marketing: A Bibliometric Analysis (2019–2025)

Abstract

The purpose of this study is to systematically map and analyze the scholarly landscape of Artificial Intelligence (AI) adoption in marketing from 2019 to 2025. The study aims to identify key trends, productive authors, and leading countries contributing to this emerging field, thereby providing insights into the development and diffusion of AI technologies in marketing practices. This research is important because AI adoption increasingly shapes marketing strategies, business competitiveness, and global innovation. A bibliometric approach was employed, using data retrieved from the Scopus database. Bibliomagika was used for data cleaning, analysis, and visualization. The study focused on publication trends, authorship productivity, and country-level contributions. Quantitative measures such as total publications, citations, h-index, g-index, and m-index were analyzed to determine research impact, collaboration patterns, and emerging thematic areas. The analysis shows a significant growth in publications on AI adoption in marketing between 2019 and 2025, indicating a rising global interest. The most productive authors and countries were identified, revealing collaboration networks and research hubs. The study also highlights influential papers and emerging trends, such as the integration of AI in customer engagement, personalization, and digital marketing strategies. The study is limited to publications indexed in Scopus, potentially excluding relevant research from other databases. Nonetheless, the findings provide valuable insights for researchers, practitioners, and policymakers, guiding future research directions, identifying gaps, and informing strategies for AI adoption in marketing contexts. This study contributes to the literature by providing a comprehensive bibliometric analysis of AI adoption in marketing, highlighting the evolution, influential contributors, and emerging trends. Its originality lies in systematically combining analyses of productivity, impact, and collaboration to provide a holistic view of the field, offering a foundation for future studies and strategic decisions in AI-driven marketing.