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Research aims: To examine the influence of transformational leadership and digital competence on employee performance in Regional Apparatus Organizations (OPDs) in the Riau Islands Province, with self-efficacy as a mediating factor and organizational support as a moderating factor.Design/Methodology/Approach: A quantitative approach using a survey method was employed. Data were collected from 185 employees in OPD using a structured questionnaire. The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) with Smart PLS software, allowing for both direct and indirect effect evaluations.Research findings: Transformational leadership and digital competence were found to significantly enhance employee performance, both directly and indirectly, with self-efficacy mediating their positive effects. Organizational support further strengthened these relationships as a moderating factor. Together, these variables accounted for 90.3% of the variance in performance, emphasizing the need for leadership that inspires and equips employees with digital skills to thrive in a technology-driven workplace.Theoretical Contribution/Originality: The study integrates leadership, digital competence, and psychological constructs (self-efficacy and organizational support) in the context of public sector organizations. It highlights the interplay between these variables, offering a comprehensive framework for improving employee performance in a digital era.Practitioners/Policy Implications: The study emphasizes the importance of developing transformational leadership through targeted training and prioritizing digital competence to address digital-era challenges. Organizations should strengthen support systems, such as continuous training and recognition programs, to boost employee confidence and engagement, fostering sustained performance, innovation, and resilience.Research Limitations/Implications: This study focuses on Regional Apparatus Organizations in Riau Islands Province, limiting generalizability. Its cross-sectional design restricts causal inference. As such, future research should consider longitudinal and qualitative approaches for deeper insights into these relationships.
Dewi et al. (Tue,) studied this question.