Abstract:
The role of tax administration has undergone significant changes in recent years, primarily driven
by the rapid development and implementation of new technologies. However, the effectiveness of
digitalization in addressing the challenges faced by tax administrations is not solely dependent on
technological advancements. Each tax administration needs to navigate the complexities of
digitalization based on its unique context, needs, and priorities. Nonetheless, learning from the
experiences of other tax administrations can greatly enhance the success of a country's digital
transformation journey. In Tanzania, despite the increasing integration of digital platforms and
technology in tax administration, there is a lack of comprehensive understanding regarding the
current state and specific impact of the digital economy on tax administration performance. This
knowledge gap hampers the ability of policymakers and tax authorities to harness the potential
benefits of the digital economy in optimizing tax collection, enhancing compliance levels, and
improving operational efficiency. Therefore, this study aims to investigate and evaluate the current
situation of the digital economy in Tanzania and its impact on tax administration. The research will
focus on the Njombe Region as a case study, exploring the influence of the digital economy on tax
collection performance, tax compliance levels, and operational efficiency. The study will adopt the
Technology Acceptance Model (TAM) and Efficiency Theory as the theoretical frameworks to
analyze the impact of the digital economy on tax administration in Tanzania. The research approach
will be quantitative, employing a descriptive research design with correlation analysis. The target
population will include tax authorities, taxpayers, and tax administration officials within the Njombe
Region, and purposive sampling techniques will be used to select participants with specific expertise
relevant to the study objectives. Data analysis will involve descriptive statistics and inferential
analysis using statistical software such as SPSS.
