Artificial Intelligence-Based Human Resource Performance Assessment for Good University Governance: Meta-Analysis and Systematic Literature Review - Shinta Ratnawati; Miftachul Mujib; Ikhwan Alfath Nurul Fathony; Khairul Ikhwan; Dewi Anggraeni; Bagus Fauzan Azhari (Annals of Human Resource Management Research, 2026)
Purpose: This study examines the role of Artificial Intelligence (AI)-based Human Resource (HR) performance evaluation in enhancing Good University Governance (GUG), particularly in improving accountability, transparency, efficiency, and responsiveness in higher education institutions. Research Methodology: A Systematic Literature Review (SLR) and meta-analysis were conducted on 65 peer-reviewed articles published between 2015 and 2025, sourced from Scopus, Web of Science, and ScienceDirect. The effect sizes were calculated, and heterogeneity tests were performed to ensure the robustness of the findings. Results: The results reveal that AI-based HR performance evaluation has a moderate to strong positive relationship with governance effectiveness (r = 0.45) and a moderate positive relationship with governance transparency (r = 0.33). These findings indicate that AI enhances data accuracy, reduces subjective bias, and supports more efficient and consistent decision-making in higher education governance. Conclusions: This study concludes that AI integration in HR performance evaluation significantly contributes to the implementation of GUG principles. It offers both theoretical contributions to digital governance literature and practical implications for university leaders and policymakers. Limitations: This study is limited by the scope of the 65 selected articles, which may not fully represent all existing research on AI-based HR evaluation in higher education contexts.