Mapping data privacy at the intersection of AI and the digital economy: a bibliometric review, taxonomy, and research agenda

Abstract The rapid integration of artificial intelligence and related emerging technologies has intensified data privacy challenges across the digital economy. Although scholarship has grown substantially, it remains fragmented across technical, legal, ethical, and sector-specific domains, limiting a clear view of the field’s structure and development. This study presents a bibliometric analysis and thematic mapping of 434 peer-reviewed articles published between 2016 and 2026 in the Web of Science Core Collection. It identifies seven thematic clusters and finds that applied and privacy-preserving technical work dominates, while business strategy, trust, and public-sector governance remain underdeveloped. The study extends this mapping with a multi-dimensional privacy taxonomy and a TCCM (theory, context, characteristics, and methodology) synthesis, moving beyond the topic- and domain-specific scope of prior reviews to diagnose where the field is structurally weak. It argues that privacy remains under-theorized as an economic and behavioral phenomenon despite its centrality to digital commerce and sets out a research agenda of economically grounded questions derived from these gaps.