A hybrid approach to evaluate employee performance using MCDA and artificial neural networks

Malik Haddad*, David A. Sanders

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

A new hybrid approach for employee performance evaluation based on multiple criteria decision analysis (MCDA) and artificial neural network (ANN) is presented. This is the first time this type of ANNs has been used for this application. A deep ANN is created. A MCDA method used randomly generated sets for training and testing the ANN. The network provided 93.63% training accuracy and 91.91% testing accuracy when tested against the training and testing sets respectively. The new approach could be transformed into a generic employee evaluation tool suitable to accommodate any number of employees and evaluation criteria using transfer-learning. A real-life employee evaluation problem is used as an example. Six employees and six evaluation criteria are considered. The new approach successfully identified the employee most eligible for promotion and ranked the other employees according to their performance.

Original languageEnglish
Pages (from-to)58-76
Number of pages19
JournalInternational Journal of Management and Decision Making
Volume23
Issue number1
DOIs
Publication statusPublished - 4 Dec 2023

Keywords

  • artificial neural networks
  • employee evaluation
  • employee performance
  • MCDA
  • transfer-learning

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