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<p>The personnel selection process aims to identify the most qualified candidate for a specific position within a company. This process is not solely based on individual assessments but is rather complex, incorporating multiple criteria. As such, various multi-criteria decision-making (MCDM) methods are employed to evaluate potential candidates. This study presents a comprehensive MCDM framework for personnel selection that integrates the Range of Value (ROV), Weighted Aggregated Sum Product Assessment (WASPAS), and Multiple Attribute Utility Theory (MAUT) methods with objective criterion weighting approaches, including entropy and standard deviation (SD) methods. Unlike prior research that primarily applies individual MCDM methods, this study systematically compares multiple methods to identify consistent patterns in candidate evaluation, thereby enhancing decision reliability. The framework enables the identification of top- and bottom-performing candidates while resolving discrepancies in intermediate rankings, offering a robust, data-driven approach to human resource decision-making. Application of the framework to a real-world personnel selection case shows that A1 consistently outperforms other candidates, whereas A4 is consistently the lowest-performing alternative across all methods. To assess the robustness of the decision-making outcomes, two types of sensitivity analyses were performed: (1) variation of the WASPAS λ parameter and (2) modification of criteria weights in the MAUT method. The results demonstrate that top and bottom performers remain consistent under both analyses, confirming the robustness and reliability of the proposed approach. This research contributes to the literature by providing a structured, multi-method framework that advances methodological rigor and supports objective, transparent, and dependable personnel selection decisions.</p>

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Personnel selection based on multi-criteria decision-making approach

  • 期刊论文

  • 发表日期：

    2026年5月23日

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  • Ceyda Nur Tokyürek,
  • Mehmet Şahin

摘要

The personnel selection process aims to identify the most qualified candidate for a specific position within a company. This process is not solely based on individual assessments but is rather complex, incorporating multiple criteria. As such, various multi-criteria decision-making (MCDM) methods are employed to evaluate potential candidates. This study presents a comprehensive MCDM framework for personnel selection that integrates the Range of Value (ROV), Weighted Aggregated Sum Product Assessment (WASPAS), and Multiple Attribute Utility Theory (MAUT) methods with objective criterion weighting approaches, including entropy and standard deviation (SD) methods. Unlike prior research that primarily applies individual MCDM methods, this study systematically compares multiple methods to identify consistent patterns in candidate evaluation, thereby enhancing decision reliability. The framework enables the identification of top- and bottom-performing candidates while resolving discrepancies in intermediate rankings, offering a robust, data-driven approach to human resource decision-making. Application of the framework to a real-world personnel selection case shows that A1 consistently outperforms other candidates, whereas A4 is consistently the lowest-performing alternative across all methods. To assess the robustness of the decision-making outcomes, two types of sensitivity analyses were performed: (1) variation of the WASPAS λ parameter and (2) modification of criteria weights in the MAUT method. The results demonstrate that top and bottom performers remain consistent under both analyses, confirming the robustness and reliability of the proposed approach. This research contributes to the literature by providing a structured, multi-method framework that advances methodological rigor and supports objective, transparent, and dependable personnel selection decisions.