<![CDATA[The quality of Accounting Information System (AIS) outputs depends heavily on the quality of the input data. However, the relationships among data quality, accounting information quality (AIQ), and organizational outcomes remain fragmented across separate studies. This study aims to map the data quality dimensions examined in the context of AIS, analyze the mechanisms through which data quality influences accounting information quality, identify the determinants of data quality and AIS quality, and identify existing research gaps. This study employs a Systematic Literature Review (SLR) approach guided by the PRISMA reporting principles. Relevant literature was collected from reputable databases, including Scopus, Web of Science, DOAJ, and ScienceDirect, as well as nationally accredited Indonesian journals ranked at least Sinta 4. A total of 39 articles were selected and analyzed thematically. The findings indicate that data quality dimensions, including accuracy, completeness, consistency, timeliness, and reliability, significantly influence accounting information quality through the mediating role of AIS quality (Knauer, Nikiforow, & Wagener, 2020; Sunarta & Astuti, 2023). The key determinants of data quality include data governance, top management support, user competence, internal controls, and digital technology capabilities, such as cloud computing, big data analytics, and blockchain (Xu, 2015; Asad, 2024; Fang, Liu, Ma, & Zhuo, 2023). This review also reveals that studies comprehensively integrating data governance with AIS quality evaluation frameworks remain relatively limited, particularly in public sector organizations and developing countries. This study emphasizes that investments in digital technologies for AIS can improve accounting information quality only when accompanied by adequate data governance.]]>
