<![CDATA[The increasing complexity of organizational needs demands a recruitment process that is more objective, efficient and systematic. Decision Support Systems (DSS) have been widely applied to assist multi-criteria–based decision making in employee recruitment. However, studies on DSS for recruitment remain fragmented and have not yet provided a comprehensive overview of methodological trends, recurring weaknesses, and future development directions. This study aims to map the DSS methods used in employee recruitment, identify consistent patterns of weaknesses, and propose directions for the development of recruitment DSS. The research employed a Systematic Literature Review (SLR) of 17 relevant research articles published over the last ten years. The results indicate that classical Multi-Attribute Decision Making (MADM) methods continue to dominate recruitment DSS research due to their ease of implementation and computational transparency. Nevertheless, most DSS implementations still rely on static weighting based on expert judgment, utilize limited evaluation criteria, and lack post-recruitment performance evaluation. This study concludes that although DSS enhances efficiency and consistency in the selection process, conceptual objectivity and long-term decision validity remain significant challenges. Therefore, future development of recruitment DSS should focus on integrating data-driven approaches, machine learning techniques, and longitudinal evaluation to create more adaptive and sustainable recruitment systems]]>
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