<![CDATA[This study develops a theoretically grounded conceptual framework to examine how algorithmic tax governance defined as the integration of artificial intelligence and digital infrastructures into tax administration reshapes taxpayer compliance behavior, state fiscal capacity, and public trust in developing country contexts. It addresses persistent inefficiencies in conventional tax systems alongside the uneven consequences of ongoing digital transformation. Employing a theory-building and integrative conceptual approach, the study synthesizes interdisciplinary scholarship (2020–2025) from institutional theory, behavioral economics, and fiscal sociology. It proposes a multi-layered analytical model capturing the dynamic interaction among technological systems, governance structures, and taxpayer decision-making processes. The analysis identifies three principal pathways through which algorithmic governance affects compliance: (1) the restructuring of taxpayer cognition through enhanced data-driven monitoring and perceived detection probability; (2) the signaling of administrative competence and procedural fairness via digital interfaces; and (3) the reinforcement of enforcement capacity through automated risk assessment mechanisms. However, these effects remain contingent upon institutional credibility, transparency, and equitable digital access, with deficiencies in these conditions potentially constraining compliance outcomes. The study advances the “Trust–Technology–Compliance Nexus” as an integrative framework that conceptually links digital governance, compliance behavior, and fiscal performance, while addressing fragmentation in existing literature. As a conceptual contribution, the framework is not empirically tested but is intended to guide future research and policy design. The findings emphasize the need for transparent, accountable, and inclusive digital tax systems to strengthen voluntary compliance and enhance fiscal legitimacy in developing economies.]]>
