Determination of training, workload and work experience on performance government internal oversight apartment (APIP) Regional Inspectorate at the Province of Riau Islands with motivation as an intervening variable

Purpose: This study examines how training, workload, and work experience affect the performance of Government Internal Oversight Apparatus (APIP) in the Riau Islands Province, with motivation as an intervening variable. The research seeks to determine both direct and indirect effects of these factors on performance outcomes. Research Methodology: The study surveyed 151 APIP employees across provincial and regency inspectorates. Data were collected using structured questionnaires with Likert scales and analyzed through Structural Equation Modeling (SEM) using AMOS 24. Confirmatory Factor Analysis was applied to validate indicators before hypothesis testing. Results: The analysis reveals that training, workload, and work experience have significant direct impacts on APIP performance. Furthermore, motivation plays a mediating role, strengthening the relationship between these variables and performance. The findings highlight that a combination of structured training, manageable workloads, and accumulated experience contributes to improved oversight effectiveness. Conclusions: Structured training programs, effective workload distribution, and leveraging employee experience are essential strategies for improving APIP performance. Motivation emerges as a critical internal driver that enhances these relationships. Strengthening motivation through recognition, career development, and supportive policies can improve oversight accountability and service delivery. Limitations: The research is limited to APIP within the Riau Islands Province, reducing the generalizability of findings. The cross-sectional design captures data at one point in time, which may not reflect dynamic changes. External factors such as leadership style and organizational culture were not considered. Contribution: Research evidences training, workload, experience, motivation enhance government oversight.