Praktik HRM Berbasis Artificial Intelligence dan Rekrutmen Inklusif Terhadap Kinerja Karyawan di Sektor Publik: Systematic Literature Review (SLR)

Abstract

Penelitian ini menelaah secara sistematis bagaimana praktik Human Resource Management (HRM) berbasis Artificial Intelligence (AI) dan rekrutmen inklusif membentuk kinerja karyawan di sektor publik. Dengan pendekatan Systematic Literature Review (SLR), studi ini mengidentifikasi, mengevaluasi, dan mensintesis bukti empiris dari lima basis data akademik pada periode 2015–2026. Dari 279 artikel awal, proses seleksi mengikuti pedoman PRISMA 2020 menyaringnya menjadi 10 studi inti untuk dianalisis mendalam. Kajian ini menunjukkan satu pesan utama: AI meningkatkan kualitas proses, sementara inklusi menentukan dampaknya pada kinerja. Penerapan AI paling sering ditemukan pada rekrutmen, evaluasi kinerja, dan pengembangan talenta, yang secara konsisten mendorong efisiensi, memperkuat objektivitas, dan meningkatkan transparansi dalam pengelolaan SDM publik. Namun, manfaat tersebut menjadi lebih bermakna ketika diintegrasikan dengan rekrutmen inklusif misalnya melalui anonimisasi identitas kandidat, penerapan merit system, dan kebijakan aksesibilitas karena mekanisme ini memperkuat keadilan sekaligus memperluas representasi dalam birokrasi. Dari sisi outcome, kinerja karyawan paling sering diukur melalui produktivitas, kepuasan kerja, keterlibatan (engagement), inovasi, dan well-being. Temuan kuantitatif menegaskan bahwa inclusion memiliki pengaruh yang lebih kuat dibanding diversity terhadap kinerja, mengindikasikan bahwa “merasa dilibatkan dan diperlakukan adil” lebih menentukan daripada sekadar “beragam secara demografis”. Dampak positif ini juga cenderung lebih kuat ketika work engagement berperan sebagai mediator, serta ketika terdapat dukungan organisasi dan budaya kerja inklusif sebagai moderator. Secara strategis, integrasi AI dan kebijakan inklusi bukan hanya modernisasi administratif, tetapi tuas kelembagaan untuk memperkuat kinerja aparatur, meningkatkan akuntabilitas birokrasi, dan menjaga keberlanjutan institusi publik.
 
This study systematically examines how Artificial Intelligence (AI)–enabled Human Resource Management (HRM) practices and inclusive recruitment shape employee performance in the public sector. Using a Systematic Literature Review (SLR) approach, the study identified, appraised, and synthesized relevant empirical evidence from five academic databases covering 2015–2026. From 279 initial records, screening and eligibility assessment following PRISMA 2020 resulted in 10 core studies for in-depth analysis. The review points to a central insight: AI improves the quality of HR processes, while inclusion determines how strongly those improvements translate into performance gains. AI-based HRM is most frequently applied in recruitment, performance appraisal, and talent development, where it consistently enhances efficiency, strengthens objectivity, and increases transparency in public-sector HR processes. However, these benefits become more consequential when integrated with inclusive recruitment policies such as candidate anonymization, merit-based selection, and accessibility measures because they reinforce procedural fairness and broaden representation in public bureaucracies. Regarding outcomes, employee performance is most commonly captured through productivity, job satisfaction, engagement, innovation, and well-being. Quantitative evidence indicates that inclusion exerts a stronger effect on performance than diversity, suggesting that “being meaningfully involved and treated fairly” matters more than demographic heterogeneity alone. These relationships are further explained by work engagement as a key mediator, and are amplified when organizational support and an inclusive work culture operate as moderators. Strategically, integrating AI with inclusion policies is not merely an administrative upgrade; it functions as an institutional lever to strengthen public employee performance, enhance bureaucratic accountability, and support the long-term sustainability of public organizations