Artificial Intelligence in Marketing: A Bibliometric Analysis and Integrated AI Marketing Knowledge Framework

Abstract

Artificial intelligence (AI) has rapidly transformed contemporary marketing by enabling organizations to enhance customer experiences, automate marketing processes, and support data-driven decision-making. Despite the rapid expansion of AI marketing research, the literature remains fragmented across diverse technologies, applications, and disciplinary perspectives. This study aims to map the intellectual structure of AI marketing research and propose an integrated conceptual framework that synthesizes the field's major knowledge domains. A bibliometric co-citation analysis was conducted using 438 journal articles retrieved from the Web of Science Core Collection, from which 34,829 cited references were extracted. The 25 most frequently cited references were analyzed using multidimensional scaling (MDS) through the PROXSCAL algorithm to identify the underlying intellectual structure of the field. The findings reveal ten research groups that collectively describe the evolution of AI marketing research and can be synthesized into three higher-order marketing application domains: Strategic and Service AI, Consumer AI, and Conversational AI. Building on these findings, the study develops an Integrated AI Marketing Knowledge Framework, derived from bibliometric evidence, which synthesizes the field's major knowledge domains into a unified conceptual model explaining how AI capability levels, consumer response mechanisms, and marketing application domains collectively create marketing value.