Mapping a Decade of AI-Generated Content in Digital Marketing: A Bibliometric Analysis (2016-2025) - Quan, Kai; Kasim, Azahar; Mijan, Rohana (International Review of Management and Marketing, 2026)
Artificial-intelligence-generated content (AIGC), the autonomous production of text, images, audio and video by generative models, has rapidly become a central concern for digital-marketing scholarship and practice. This study maps the intellectual structure and evolution of research at the AIGC–digital-marketing intersection. Following PRISMA guidelines, 1400 Scopus-indexed articles and reviews published between 2016 and 2025 were retrieved and analysed in Bibliometrix and VOSviewer, combining performance analysis with science mapping. Annual output grew at a compound rate of 54.15% per year across three phases: a nascent period (2016–2019), gradual exploration (2020–2022) and explosive growth after late 2022, with 2025 alone contributing 52.6% of the corpus. Production is anchored by a United States–China dual core, with India acting as a bridging node. Co-citation and keyword analyses reveal a knowledge base that fuses established technology-adoption theories and structural-equation methodology with recent generative-AI manifestos, organised around four themes: content production, distribution channels, audience reach and responsible governance. Tool-level analysis shows that the literature is overwhelmingly text-centric (ChatGPT and the GPT family), while image and video generators and Asian social platforms remain under-examined. The study offers, to the best of our knowledge, one of the first decade-scale maps of this field, together with a structured agenda for future research.