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Studi Ilmu Manajemen dan Organisasi
Published by Goodwood Publishing
ISSN : -     EISSN : 27457826     DOI : https://doi.org/10.35912/simo
Core Subject : Economy,
Economics, Econometrics & Finance
Studi Ilmu Manajemen dan Organisasi (SIMO) merupakan media publikasi ilmiah yang memuat artiket-artikel dibidang manajemen dan organisasi. Jurnal Penelitian Manajemen dan Organisasi didedikasikan untuk sharing idea dikalangan akademisi, industri atau praktisi serta pengambil kebijakan. SIMO menerima artikel dari berbagai pihak untuk dimuat pada jurnal ini dan diharapkan dapat berguna untuk pengembangan ilmu pengetahuan dan praktik terkait manajemen dan organisasi dimasa mendatang.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis, Menejemen, dan Akutansi (Lain-Lain)
Articles 141 Documents
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The Role of Algorithmic Management in HR Practices and Ethical Challenges Suryawan, Ryan Firdiansyah; Yusuf, Muhammad; Suhendah, Rousilita; Krisna, Nandan Lima; Kamar, Karnawi
Studi Ilmu Manajemen dan Organisasi Vol. 6 No. 3 (2025): Oktober
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/simo.v6i3.4642

Abstract

Purpose: This study aims to systematically explore the development of algorithmic management in HR practices, focusing on emerging ethical challenges. Methodology/approach: Using a Systematic Literature Review (SLR), this study analyzes findings from the past five years on the use of algorithms in managerial decision-making and their impact on workers' rights, justice, and welfare. Results/findings: While algorithms bring efficiency, they present significant ethical, social, and legal challenges. Organizations must balance technological efficiency with principles of fairness, transparency, and privacy protection. A collaborative approach between humans and technology, coupled with strict regulation, is essential. Conclusions: Algorithmic management in HR boosts efficiency but raises ethical concerns about fairness and transparency. Its success depends on creating accountable systems that balance technology with human values. Researchers advocate for human-technology collaboration, with algorithms as tools, not substitutes for human decision-making, and the integration of "responsible and explainable AI" to foster fairness and inclusivity. Limitations: The study’s focus on references from developed countries limits its applicability to developing countries like Indonesia. Additionally, most of the literature is conceptual and lacks long-term data. Contribution: The study suggests exploring contextual and participatory case studies across sectors and regions, along with both quantitative and qualitative research on algorithms’ impact on job satisfaction and employee rights. Further research on the role of national and international regulations is required.

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