WHAT THE (SUBJECT-MATTER) EXPERT? THE ACCEPTANCE OF MACHINE LEARNING AT GOVERNMENT SUBJECT-MATTER EXPERTS

Machine learning is increasingly integrated into operational decision-making. This study explores the perceived usefulness (PU) and perceived ease of use (PEOU) of decision mining, in government settings, focusing on Subject-Matter Experts (SMEs). Using a mixed-method approach with surveys based on the Technology Acceptance Model (TAM), and qualitative interviews, findings reveal SMEs perceive that decision mining improves diagnostics, streamline workflows, and enhances job performance. Key enablers include seamless integration and high-quality data. However, adoption is hindered by steep learning curves, limited model interpretability, and reliance on historical data. Usability varies between technical and non-technical users, emphasizing the need for tailored training. Despite limitations such as a small sample size and lack of longitudinal analysis, the study highlights decision mining's potential provided challenges are addressed through integration, user support, and alignment with organizational goals. Future research is encouraged to examine diverse user groups, long-term impacts, and strategies for optimizing adoption and usability.