Reducing budgetary slack through government internal control system: insights from structural equation modelling and artificial neural network approach

Research aims: This study investigates the important role of Government Internal Control System (GICS) elements in reducing budgetary slack in Public Higher Education Institutions (HEIs).Design/Methodology/Approach: Partial Least Squares Structural Equation Modelling (PLS-SEM) and Artificial Neural Network (ANN) were used to test the significance and strength of the relationship between GICS and budgetary slack. Survey data was collected from managers of Public HEIs in Indonesia. Statistical analysis was conducted using WarpPLS 8.0 for PLS-SEM, while ANN was implemented using SPSS.Research findings: Government Internal Control Systems are effective in reducing budgetary slack. However, the relationship between elements of ICS and budgetary slack is not always linear and can be influenced by interactions between elements.Theoretical contribution/Originality: This study contributes to the literature on management accounting by providing insights into a more complex process that explains the vital role of effective implementation of GICS in Public HEIs in reducing budgetary slack in the budgeting process.Practitioner/Policy implication: This study demonstrates that the Government's Internal Control System effectively reduces budgetary slack in Higher Education Institutions. Therefore, practitioners should strengthen GICS implementation, particularly focusing on the control of the environment and recognizing the complex interplay between GICS elements. Furthermore, policymakers should prioritize strengthening regulations and oversight, encouraging innovation, and integrating GICS with other systems to enhance accountability and resource allocation.Research limitation/Implication: Collecting data through questionnaires may lead to common method bias. This can be mitigated by the implementation of a longitudinal design and the collection of data at a number of points in time.