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<p>This study presents a systematic analysis of 147 peer-reviewed articles examining the application of multi-criteria decision-making (MCDM) methodologies in personnel selection. It explores how various MCDM techniques—such as AHP, TOPSIS, PROMETHEE, and fuzzy-based models—enhance the evaluation of candidates across multiple and often conflicting criteria. The review identifies comparative advantages, methodological gaps, and evolving trends in the use of these approaches. Furthermore, it discusses the integration of artificial intelligence and machine learning to improve the transparency, efficiency, and objectivity of decision-making processes in human resource management. The findings underscore the potential of hybrid MCDM models that combine quantitative and qualitative evaluation mechanisms to address the inherent subjectivity in recruitment. Despite significant progress, research gaps persist in managing intangible factors and ensuring interpretability within automated systems. The review contributes to the growing discourse on evidence-based human resource practices by highlighting pathways for enhancing decision-making frameworks in personnel selection.</p>

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A systematic review of multi-criteria decision-making (MCDM) methods applied to personnel selection: trends, models, and effectiveness

  • 期刊论文

  • 发表日期：

    2026年3月21日

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  • Namita Pandey,
  • Sujata Priyambada Dash,
  • Prabjot Kaur

摘要

This study presents a systematic analysis of 147 peer-reviewed articles examining the application of multi-criteria decision-making (MCDM) methodologies in personnel selection. It explores how various MCDM techniques—such as AHP, TOPSIS, PROMETHEE, and fuzzy-based models—enhance the evaluation of candidates across multiple and often conflicting criteria. The review identifies comparative advantages, methodological gaps, and evolving trends in the use of these approaches. Furthermore, it discusses the integration of artificial intelligence and machine learning to improve the transparency, efficiency, and objectivity of decision-making processes in human resource management. The findings underscore the potential of hybrid MCDM models that combine quantitative and qualitative evaluation mechanisms to address the inherent subjectivity in recruitment. Despite significant progress, research gaps persist in managing intangible factors and ensuring interpretability within automated systems. The review contributes to the growing discourse on evidence-based human resource practices by highlighting pathways for enhancing decision-making frameworks in personnel selection.