Manajemen Algoritmik dalam Masa Depan Dunia Kerja Sebuah Tinjauan Literatur Sistematis dalam Konteks Pekerja Gig - Mufti, Hafidz; Fauzi, Daini Unifianto; Putra, Tio Ramadha (Program Studi Manajemen, Fakultas Ekonomi, Universitas Islam Majapahit, Jawa Timur, Indonesia, 2026)
Algorithmic management has become a dominant managerial mechanism in gig work, yet its implications for workers and the future of digital employment remain fragmented in the literature. This study aims to systematically examine and synthesize prior research on algorithmic management in gig work by identifying its core mechanisms, benefits, challenges, unresolved research gaps, and implications for developing fair and inclusive algorithmic systems. This study adopts a Systematic Literature Review (SLR) design guided by the PRISMA framework. Peer-reviewed journal articles published between 2020 and 2025 were identified through structured searches of major academic databases. Following the PRISMA stages of identification, screening, eligibility assessment, and inclusion, a final sample of 34 eligible studies was retained for qualitative synthesis. Data were extracted using a structured coding scheme and analyzed through a combination of deductive Input–Process–Output (IPO) mapping and inductive thematic analysis, leading to the development of an extended Input–Process–Output–Outcomes (IPOO) analytical model. The findings indicate that algorithmic management has rapidly evolved into a central managerial system that reshapes power relations, performance evaluation, worker autonomy, and well-being through data-driven control, monitoring, and decision-making. While algorithmic systems enhance efficiency and structure short-term worker behavior, they also generate long-term social, ethical, and human sustainability challenges. These challenges highlight that the future of digital work increasingly depends on fairness, transparency, and human-centered governance in algorithmic systems. The originality of this study lies in extending the traditional IPO framework into the IPOO model to capture both immediate mechanisms and long-term consequences of algorithmic management in the gig economy.