https://ojs.piscomed.com/index.php/HRMS <table> <tbody> <tr style="vertical-align: top;"> <td style="text-align: justify;"> <p><strong><em>Human Resources Management and Services</em></strong> (HRMS) is an international open access journal on theoretical and practical research in the field of human resource management. HRMS adopts a double-blind peer review model and publishes high-quality articles. It is committed to disseminating unique and insightful insights and promoting the development, innovation and understanding of human resource management. Potential readers of HRMS include scholars, practice managers, and policy makers in the field.</p> </td> <td><img src="/public/site/images/admin/HRMS_cover_12.png"><br> <div id="issn_section"><br><span class="issn_num"><span class="issn_num">ISSN: 2661-4308 (O</span></span><span class="issn_num">)</span><br><br><img src="/public/site/Open_Access.png" alt=""></div> </td> </tr> </tbody> </table> PiscoMed Publishing Pte Ltd en-US Human Resources Management and Services 2661-4308 https://ojs.piscomed.com/index.php/HRMS/article/view/4595 <p><b>Objective:</b><b>&nbsp;</b>This study synthesizes current evidence on the role of Artificial Intelligence (AI)&nbsp;and, where relevant, Open Science (OS) practices&nbsp;in enhancing Human Resource Management (HRM) performance. It focuses on recruitment processes, ethical considerations, and employee participation. <b>Methodology:</b>&nbsp;A systematic literature review was conducted in Scopus covering the period 2019–2024, following PRISMA guidelines. The initial search yielded 1486 records. After de-duplication and screening using Rayyan, 66 studies (≈ 4.4%) met the inclusion criteria, which targeted peer-reviewed works addressing AI-supported HR decision-making. A combined content and bibliometric analysis was performed in R (Bibliometrix) to identify thematic patterns and conceptual structures. <b>Results:</b>&nbsp;Analysis revealed four thematic clusters: 1) Implementation and employee participation emphasizing human-in-the-loop approaches and effective change management; 2) ethical challenges including algorithmic bias, transparency gaps, and data privacy risks; 3) data-driven decision-making delivering higher accuracy, fewer errors, and personalized recruitment and performance assessment; 4) operational efficiency enabling faster workflows and reduced administrative workloads. AI tools consistently improved selection quality, while OS practices promoted transparency and knowledge sharing. <b>Implicat</b><b>ions:</b>&nbsp;The successful adoption of AI in HRM requires employee engagement, strong ethical safeguards, and transparent data governance. Future research should address the long-term cultural, organizational, and well-being impacts of AI&nbsp;integration, as well as its sustainability.</p> Kawthar Bouzerda Selimane Hani Hasnae Rahmani Ali Hebaz Abdessamad Dibi Hasna Mharzi Copyright (c) 2025 Kawthar Bouzerda, Selimane Hani, Hasnae Rahmani, Ali Hebaz, Abdessamad Dibi, Hasna Mharzi https://creativecommons.org/licenses/by/4.0 2025-11-14 2025-11-14 7 4 10.18282/hrms4595