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A composite model for Employees' performance appraisal and improvement

机译:员工绩效考核与提升的综合模型

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Purpose - The purpose of this paper is to develop an innovative method of performance appraisal that will be useful for designing a structured training programme. Design/methodology/approach - Employees' performance appraisals are conducted using new approaches, namely data envelopment analysis and an integrated fuzzy model. Interpretive structural modelling is used to design a training programme for employees. Findings - Performance appraisals using data envelopment analysis focus on output enhancement, while an integrated fuzzy model using quality function deployment (QFD) and multi-attribute decision-making focuses on input enhancement. For overall and continuous improvement of employees' knowledge, skills and attributes, this composite model provides an in-depth analysis and also offers a means for designing a structured and effective training programme through interpretive structural modelling. Research limitations/implications - In data envelopment analysis, the number of employees for performance appraisal should be equal to or greater than three times the selected number of input and output factors. In the integrated fuzzy model, the number of main factors should not exceed seven for pairwise comparison. The size of the QFD matrix should not be more than 30. Practical implications - The factors selected for appraisal and the method of appraisal should be known by the employees concerned. Consensus among all those concerned is necessary for effective application and utilization of the model. Social implications - This model provides a means to increase the knowledge, skills and attributes of employees by adopting a structured approach to designing a training programme for employees of various categories. The approaches used are well-established and can be applied in many other fields. Originality/value - In this paper, approaches used for appraisals and designing training programmes are new to this field of study, although they have been successfully proven in many other fields. The results obtained using these methods are useful for helping management to make decisions on training needs, bonuses, incentives and promotions. For the employees, a structured training programme design improves their KSA, quality and standards.
机译:目的-本文的目的是开发一种创新的绩效评估方法,这将对设计结构化培训计划很有用。设计/方法/方法-使用新方法进行员工的绩效评估,即数据包络分析和集成的模糊模型。解释性结构建模用于为员工设计培训计划。调查结果-使用数据包络分析的绩效评估着重于输出增强,而使用质量函数展开(QFD)和多属性决策的集成模糊模型则着重于输入增强。为了全面,持续地改善员工的知识,技能和特质,此复合模型提供了深入的分析,还提供了通过解释性结构建模设计结构化且有效的培训计划的方法。研究局限性/含义-在数据包络分析中,用于绩效评估的员工人数应等于或大于所选输入和输出因子数量的三倍。在综合模糊模型中,成对比较的主要因素应不超过七个。 QFD矩阵的大小不应超过30。实际意义-有关评估人员应了解选择进行评估的因素和评估方法。所有相关人员之间的共识对于有效应用和利用模型是必要的。社会影响-该模型通过采用结构化的方法为各种类别的员工设计培训计划,提供了一种增加员工知识,技能和特质的方法。所使用的方法是行之有效的,可以应用于许多其他领域。原创性/价值-在本文中,用于评估和设计培训计划的方法是该研究领域的新手,尽管它们已在许多其他领域中得到成功证明。使用这些方法获得的结果有助于管理人员就培训需求,奖金,奖励和晋升做出决策。对于员工而言,结构化的培训计划设计可提高其KSA,质量和标准。

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