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A Consensus-Based 360 Degree Feedback Evaluation Method with Linguistic Distribution Assessments

Author

Listed:
  • Chuanhao Fan

    (Business School, Hohai University, Nanjing 211100, China)

  • Jiaxin Wang

    (Business School, Hohai University, Nanjing 211100, China)

  • Yan Zhu

    (Business School, Sichuan University, Chengdu 610065, China)

  • Hengjie Zhang

    (Business School, Hohai University, Nanjing 211100, China)

Abstract

The 360 degree feedback evaluation method is a multidimensional, comprehensive assessment method. Evaluators may hesitate among multiple evaluation values and be simultaneously constrained by the biases and cognitive errors of the evaluators, evaluation results are prone to unfairness and conflicts. To overcome these issues, this paper proposes a consensus-based 360 degree feedback evaluation method with linguistic distribution assessments. Firstly, evaluators provide evaluation information in the form of linguistic distribution. Secondly, utilizing an enhanced ordered weighted averaging (OWA) operator, the model aggregates multi-source evaluation information to handle biased evaluation information effectively. Subsequently, a consensus-reaching process is established to coordinate conflicting viewpoints among the evaluators, and a feedback adjustment mechanism is designed to guide evaluators in refining their evaluation information, facilitating the attainment of a unanimous evaluation outcome. Finally, the improved 360 degree feedback evaluation method was applied to the performance evaluation of the project leaders in company J, thereby validating the effectiveness and rationality of the method.

Suggested Citation

  • Chuanhao Fan & Jiaxin Wang & Yan Zhu & Hengjie Zhang, 2024. "A Consensus-Based 360 Degree Feedback Evaluation Method with Linguistic Distribution Assessments," Mathematics, MDPI, vol. 12(12), pages 1-20, June.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:12:p:1883-:d:1416483
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    References listed on IDEAS

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    1. Federico Bizzarri & Chiara Mocenni & Silvia Tiezzi, 2023. "A Markov Decision Process with Awareness and Present Bias in Decision-Making," Mathematics, MDPI, vol. 11(11), pages 1-12, June.
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