School of Economics and Management, Anyang University, Anyang, China.
PLoS One. 2023 Jul 21;18(7):e0288795. doi: 10.1371/journal.pone.0288795. eCollection 2023.
This research delves into the application effects of Fuzzy Comprehensive Evaluation (FCE) and quantitative weight analysis in the structure management of human resources (SMHR) to optimize the structure management. The research begins by analyzing the existing problems in SMHR, such as incomplete performance feedback and error-prone outsourcing decisions. By leveraging human resource management (HRM) characteristics, the researchers construct the SMHR evaluation index system. The Analytical Hierarchy Process (AHP) is employed to establish a hierarchical human resource structure model to determine the relative weight of each HRM indicator. Subsequently, the FCE method is utilized to build an SMHR optimization model, which is then scrutinized and assessed by means of an example. The findings indicate that the consistency ratio (C.R.) values of the first and second-level evaluation factors of the constructed model are less than 0.1, thus passing the consistency test and demonstrating credibility. Ultimately, the research effectively grades SMHR in the enterprise through the analysis of HRM optimization. Accordingly, this research presents a set of optimization suggestions and measures, including the establishment of a professional HRM operation team, acceleration of the construction of a professional talent team, enhancement of the intelligent level of the HRM center, and transition towards digital sharing. These proposed measures can serve as valuable experimental references for the optimization and improvement of HRM structures in future enterprises.
本研究探讨了模糊综合评价(FCE)和定量权重分析在人力资源结构管理(SMHR)中的应用效果,以优化结构管理。研究首先分析了 SMHR 中存在的问题,如绩效反馈不完整和外包决策容易出错。通过利用人力资源管理(HRM)的特点,研究人员构建了 SMHR 评价指标体系。采用层次分析法(AHP)建立了人力资源结构模型,确定了每个 HRM 指标的相对权重。然后,利用 FCE 方法构建了 SMHR 优化模型,并通过实例进行了检验和评估。研究结果表明,构建模型的第一级和第二级评价因素的一致性比率(C.R.)值均小于 0.1,通过一致性检验,具有可信度。最终,通过对 HRM 优化的分析,有效地对企业的 SMHR 进行了分级。因此,本研究提出了一系列优化建议和措施,包括建立专业的 HRM 运营团队、加快专业人才团队建设、提高 HRM 中心的智能化水平以及向数字化共享转型。这些建议措施可为未来企业优化和改进 HRM 结构提供有价值的实验参考。