A multi-granular linguistic model for management decision-making in performance appraisal

被引:0
|
作者
Rocío de Andrés
José Luis García-Lapresta
Luis Martínez
机构
[1] University of Valladolid,PRESAD Research Group, Departamento de Fundamentos del Análisis Económico e Historia e Instituciones Económicas
[2] University of Valladolid,PRESAD Research Group, Departamento de Economía Aplicada
[3] University of Jaén,Department of Computer Science
来源
Soft Computing | 2010年 / 14卷
关键词
Performance appraisal; Multi-criteria decision-making; Linguistic information; Linguistic 2-tuple; Aggregation operators;
D O I
暂无
中图分类号
学科分类号
摘要
The performance appraisal is a relevant process to keep and improve the competitiveness of companies in nowadays. In spite of this relevance, the current performance appraisal models are not sufficiently well-defined either designed for the evaluation framework in which they are defined. This paper proposes a performance appraisal model where the assessments are modelled by means of linguistic information provided by different sets of reviewers in order to manage the uncertainty and subjectivity of such assessments. Therefore, the reviewers could express their assessments in different linguistic scales according to their knowledge about the evaluated employees, defining a multi-granular linguistic evaluation framework. Additionally, the proposed model will manage the multi-granular linguistic labels provided by appraisers in order to compute collective assessments about the employees that will be used by the management team to make the final decision about them.
引用
收藏
页码:21 / 34
页数:13
相关论文
共 50 条
  • [21] Uncertain linguistic terms with weakened hedges for multi-granular linguistic decision making with its application to evaluating communication technologies
    Jin, Chen
    Xu, Zeshui
    Zeng, Xiaojun
    APPLIED INTELLIGENCE, 2022, 52 (14) : 16758 - 16774
  • [22] On multi-granular fuzzy linguistic modeling in group decision making problems: A systematic review and future trends
    Morente-Molinera, J. A.
    Perez, I. J.
    Urena, M. R.
    Herrera-Viedma, E.
    KNOWLEDGE-BASED SYSTEMS, 2015, 74 : 49 - 60
  • [23] A novel multi-criteria group decision-making method for heterogeneous and dynamic contexts using multi-granular fuzzy linguistic modelling and consensus measures
    Morente-Molinera, J. A.
    Wu, X.
    Morfeq, A.
    Al-Hmouz, R.
    Herrera-Viedma, E.
    INFORMATION FUSION, 2020, 53 : 240 - 250
  • [24] An incomplete multi-granular linguistic model and its application in emergency decision of unconventional outburst incidents
    Xu, Yejun
    Ma, Feng
    Xu, Weijun
    Wang, Huimin
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2015, 29 (02) : 619 - 633
  • [25] An integrated fuzzy multi-attribute decision-making model for employees' performance appraisal
    Manoharan, T. R.
    Muralidharan, C.
    Deshmukh, S. G.
    INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT, 2011, 22 (03): : 722 - 745
  • [26] DECISION-MAKING IN A PERFORMANCE APPRAISAL SITUATION
    SHERIDAN, AJ
    CARLSON, RE
    PERSONNEL PSYCHOLOGY, 1972, 25 (02) : 339 - &
  • [27] A Model for Linguistic Dynamic Multi-criteria Decision-Making
    Jiang, Le
    Liu, Hongbin
    Martinez, Luis
    Cai, Jianfeng
    FOUNDATIONS OF INTELLIGENT SYSTEMS (ISKE 2013), 2014, 277 : 939 - 949
  • [28] A large-scale group decision-making with incomplete multi-granular probabilistic linguistic term sets and its application in sustainable supplier selection
    Song, Yongming
    Li, Guangxu
    JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 2019, 70 (05) : 827 - 841
  • [29] A multi-granular linguistic distribution-based group decision making method for renewable energy technology selection
    Liang, Yingying
    Ju, Yanbing
    Martinez, Luis
    Dong, Peiwu
    Wang, Aihua
    APPLIED SOFT COMPUTING, 2022, 116
  • [30] Tri-level multi-attribute group decision making based on regret theory in multi-granular linguistic contexts
    Wang, Zelin
    Wang, Ying-Ming
    Wang, Liang
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2018, 35 (01) : 793 - 806