E-Government and Public Service Performance: The Role of Good Governance among Non-Metropolitan Local Governments in Jambi, Indonesia - Ferry Siswadhi; Tri Widyatuti; Ni Nyoman Sawitri; Hadita (Yayasan Sinergi Kawula Muda, 2026)
This study investigates the interplay between e-government initiatives and public service performance, focusing on the implications of good governance in non-metropolitan local governments in Jambi, Indonesia. The ongoing challenge of enhancing public service performance in decentralized governance is underscored, as many local governments struggle to provide efficient and effective services. While e-government has been widely adopted as a solution, its outcomes are inconsistent across different institutional settings, prompting an exploration of governance's role in facilitating digital transformation. This research delineates good governance as a moderating variable in the e-government-public service performance nexus, contrasting with previous studies that primarily viewed governance as a direct or mediating factor. Employing a quantitative methodology with an explanatory design, this study analyzes data from cross-sectional surveys conducted among 377 civil servants in Kerinci Regency and Sungai Penuh City, utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) for analysis. Findings reveal that e-government positively influences public service performance significantly, and good governance also exerts a significant direct impact. However, the moderating effect of good governance is found to be non-significant, indicating that both governance and e-government function as independent determinants of public service performance, rather than in concert. This research challenges the prevailing notion that governance universally enhances digital transformation efficacy within the public sector, particularly in non-metropolitan contexts. It is important to interpret these findings with caution due to limitations in measurement reliability, as indicated by relatively low alpha and marginal Average Variance Extracted values for certain constructs.