Thematic Evolution of Digital Accounting Information Systems: A Bibliometric Mapping (2000-2025) - Rahmi Nadiar; Annisa Tri Hidhayati; Maulida Hirdianti Bandi; Kristianto Tricahya Prabowo; Anto Andreawan (Owner : Riset dan Jurnal Akuntansi, 2026)
This study maps the research landscape of Accounting Information Systems in the context of digital transformation by examining three key technological domains: Enterprise Resource Planning, cloud computing, and Artificial Intelligence. The dataset comprises journal articles and conference papers indexed in Google Scholar and published between 2000 and 2025 that address Accounting Information Systems in relation to at least one of these technologies. Records were retrieved using Publish or Perish and screened through purposive sampling with predefined inclusion and exclusion criteria, followed by duplicate removal, bibliographic normalization, and manual term validation, resulting in a final sample of 117 publications. Bibliometric mapping was conducted using VOSviewer to visualize co-authorship networks, keyword co-occurrence patterns, and thematic clusters, while descriptive citation indicators were employed to capture scholarly influence. The analysis identifies three dominant research clusters: Enterprise Resource Planning integration and implementation as a mature and highly cited stream; cloud-based accounting systems as a rapidly expanding stream, particularly after 2018; and Artificial Intelligence, enabled accounting and decision-support applications as an emerging yet comparatively underexplored stream. Across the studied period, publication output exhibits a sustained upward trend, accompanied by a gradual shift from system implementation studies toward platform-based and intelligent accounting applications. However, empirical research explicitly linking these technologies to organizational performance and governance outcomes remains limited. Overall, the findings reveal the evolving knowledge structure of digital Accounting Information Systems research and emphasize the need for future studies employing robust empirical designs, cross-technology integration, and clearly defined performance and accountability measures.