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<p>The success of mergers and acquisitions (M &amp;A) in the knowledge-intensive Integrated Device Manufacturers (IDMs) sector critically depends on effective Post-Merger Human Resource Integration (HRI). A critical research gap exists in developing robust, quantitative frameworks to select the optimal HRI strategy given the high uncertainty and non-linear interdependence among complex evaluation criteria. This study aims to propose and validate a systematic Multi-Criteria Decision-Making (MCDM) approach to objectively prioritize HRI criteria and reliably rank integration strategies for IDM executives. This research introduces the Integrated T-Spherical Fuzzy Einstein Interaction Aggregator CRITIC-CoCoSo model, which utilizes T-Spherical Fuzzy Sets to model expert ambiguity and Einstein operators to capture interdependence among criteria. The model was applied to assess four canonical HRI strategies (Preservation, Symbiosis, Holding, Absorption) against an extended McKinsey 7&#xa0;S framework comprising 21 criteria in the IDM context. The analysis also included comprehensive Sensitivity and Comparative Studies against T-SF TOPSIS, MEREC, EDAS, and VIKOR to validate the stability of the results. The T-SF CRITIC analysis identified Innovation Continuity Safeguard and Critical Talent Safeguard as the most influential criteria; subsequently, the T-SF CoCoSo ranking conclusively determined the Symbiosis Model as the most optimal HRI strategy. This work presents a novel, validated MCDM framework suitable for complex strategic decisions under uncertainty, offering IDM executives a data-driven roadmap that prioritizes talent-centric integration to enhance long-term merger success.</p>

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T-Spherical Fuzzy Einstein MCDM Approach for Post-Merger Human Resource Integration under Uncertainty

  • 期刊论文

  • 发表日期：

    2026年3月31日

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  • Chia-Nan Wang,
  • Dinh-Tien Luong

摘要

The success of mergers and acquisitions (M &A) in the knowledge-intensive Integrated Device Manufacturers (IDMs) sector critically depends on effective Post-Merger Human Resource Integration (HRI). A critical research gap exists in developing robust, quantitative frameworks to select the optimal HRI strategy given the high uncertainty and non-linear interdependence among complex evaluation criteria. This study aims to propose and validate a systematic Multi-Criteria Decision-Making (MCDM) approach to objectively prioritize HRI criteria and reliably rank integration strategies for IDM executives. This research introduces the Integrated T-Spherical Fuzzy Einstein Interaction Aggregator CRITIC-CoCoSo model, which utilizes T-Spherical Fuzzy Sets to model expert ambiguity and Einstein operators to capture interdependence among criteria. The model was applied to assess four canonical HRI strategies (Preservation, Symbiosis, Holding, Absorption) against an extended McKinsey 7 S framework comprising 21 criteria in the IDM context. The analysis also included comprehensive Sensitivity and Comparative Studies against T-SF TOPSIS, MEREC, EDAS, and VIKOR to validate the stability of the results. The T-SF CRITIC analysis identified Innovation Continuity Safeguard and Critical Talent Safeguard as the most influential criteria; subsequently, the T-SF CoCoSo ranking conclusively determined the Symbiosis Model as the most optimal HRI strategy. This work presents a novel, validated MCDM framework suitable for complex strategic decisions under uncertainty, offering IDM executives a data-driven roadmap that prioritizes talent-centric integration to enhance long-term merger success.