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Performance appraisal based on distance function methods

delete2010-12-01
delete21
PRE
AI
R
Rocío de Andrés Calle
J
José Luis Garcı́a-Lapresta *
J
Jacinto González‐Pachón
DOI:10.1016/j.ejor.2010.06.012delete
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Abstract

Abstract

En 中文
Performance appraisal is a process used by some firms to evaluate their employees' efficiency and productivity in order to plan their promotion policy, salary policy, layoffs policy, etc. Initially this process was just carried out by the executive stall, but recently it has evolved into an evaluation process based on the opinion of different reviewers, supervisors, collaborators, customers and the employees themselves (360-degree method). In such an evaluation process the reviewers evaluate some indicators related to employees performance appraisal. In this paper we propose an evaluation framework where there are different sets of reviewers taking part in the evaluation process. Since reviewers have a different knowledge about the evaluated employee, it seems suitable to offer a flexible framework in which different reviewers can express their assessments in different finite scales according to their knowledge. The final aim is to compute a global evaluation for each employee, that can be used by the management team to make their decisions regarding their human resources policy. In this way, to obtain a global evaluation for each employee, we propose a methodology able to aggregate individual valuation in a metric L framework. In this context, the associated optimization problems can be reduced to an Extended Goal Programming formulation that is very easy to compute. (C) 2010 Elsevier B.V. All rights reserved.
Keywords:
Performance appraisal
Multi-criteria decision making
Finite scales
Extended Goal Programming approach

Journal

European Journal of Operational Research cover
European Journal of Operational Research
IF:
6
Papers:
2.2W
Citations:
6.4W

Organization

U
Universidad Politecnica de Madrid
Scholars:
1.4W
Papers: 1.2W
Citations: 10
U
Universidad de Valladolid
Scholars:
8.1K
Papers: 6.7K
Citations: 5.9K