Conference Agenda

In recent years, rapid technological advancements have significantly reshaped the landscape of taxation. These changes are transforming how tax systems operate, how policies are implemented, and how tax administrations engage with taxpayers. The relationship between tax and technology is multifaceted, offering substantial opportunities alongside notable challenges. Innovations such as digital platforms, automation, big data, and artificial intelligence (AI) are driving a shift toward more efficient, data-driven, and real-time tax administration. These technologies have the potential to enhance compliance, reduce administrative burdens, and provide more responsive and personalized services for taxpayers. However, they also introduce concerns around data privacy, cybersecurity, algorithmic bias, and the reliability of AI-generated insights. Beyond operational improvements, these advancements influence the structure, strategy, and accountability of tax administrations. As governments worldwide adapt to this digital transformation, it is crucial to critically evaluate both the benefits of more intelligent tax administration and the risks of unintended consequences. The primary challenge is to implement and manage these technologies in ways that strengthen trust, transparency, and the overall integrity of the tax system.

This paper aims to examine the technological evolution in taxation, focusing on how advancements in technology have influenced tax systems and administrative practices over time. Using a bibliometric analysis of 8,457 documents containing the terms tax, taxation, and technology in the title, abstract, or keywords, published up to the end of 2024 and indexed in the Scopus database, the study applies both established and innovative bibliometric approaches with Python software. To complement the quantitative analysis, a qualitative content analysis of the most relevant abstracts is conducted, offering deeper insights into thematic developments and conceptual trends at the intersection of taxation and technology.

Preliminary findings indicate a sharp increase in scientific output in recent years, with the United States leading in overall impact and China demonstrating significant recent research activity. The results highlight a progressive technological transformation across core tax administration functions. This transformation begins with the adoption of foundational digital tools such as digital platforms and electronic tax filing systems, enhancing taxpayer registration, service delivery, and return processing through improved accessibility, accuracy, and administrative efficiency. Digital platforms are central to facilitating interactions between tax authorities and taxpayers, with features like online filing and self-service portals streamlining processes. As digitalization advances, emerging technologies like blockchain are increasingly applied to secure taxpayer registration, manage land records, and ensure transparency in tax audits. In tax collection and risk management, real-time reporting and advanced analytics support data-driven decision-making. Artificial intelligence, at the forefront of this evolution, is explored for automating audits, detecting fraud, and conducting predictive risk assessments. This transition from basic digital tools to AI-driven solutions exemplifies how technology is reshaping tax administration into a more efficient, transparent, and intelligent system.

Over the past three decades, accrual-based accounting has been adopted in the public sector (Cohen et al., 2019). Accrual-based accounting offers the advantage of more accurately assessing the governments’ fiscal performance while also potentially fostering the opportunistic behavior through discretionary timing of revenues and expense recognition.

Earning management, defined as “the discretion exercised by producers of financial reports within the accounting process to alter financial performance” (Donatella et al., 2024:269), has been extensively studied in private firms (Healy, 1985; Jones, 1991), yet there is comparatively less research on its determinants in public entities (Bisogno & Donatella, 2022), leaving public-sector characteristics underexplored.

We address this gap by examining factors influencing earning management in 398 local public enterprises before and after the adoption of K-IFRS. In doing so, we draw on multiple theoretical perspectives—the political budget cycle, performance management, and administrative traditions—alongside existing theories of agency and institutionalism. IFRS adoption has often been viewed as a key driver of earning management in private firms across various countries (Barth et al., 2008; Chen et al., 2010; Christensen et al., 2015). However, the literature provides mixed outcomes: some studies find decreased earning management after voluntary adoption (Barth et al., 2008), while others show an increase under mandatory adoption (Ahmed et al., 2013; Christensen et al., 2015). Such inconclusive results extend to differences between accrual-based (AEM) and real earning management (REM), as well as across developed (e.g., Barth et al., 2008; Chen et al., 2010) and emerging economies (Saona & Muro, 2018; Mongrut & Winkelried, 2019).

Building on agency and institutional theories, we posit that IFRS adoption in public settings may either reinforce or curb AEM and REM. In addition, we explore more this is contingent on political budget cycles and performance regimes. Using quasi-experimental methods—including a difference-in-differences design—we seek to enrich the literature on public-sector accounting and governance. Our findings will enhance understanding of how K-IFRS interacts with public-sector contexts and guide policymakers, practitioners, and scholars interested in the broader implications of IFRS adoption in local public enterprises.