A Novel Employee Performance Evaluation Model Based on the Concepts of the PageRank Algorithm

Document Type : Industry Article

Authors

1 Department of Industrial engineering, Payame noor university, Tehran, Iran

2 Department of Industrial Engineering, Payame Noor University, Tehran, Iran

Abstract

This study aims to propose a novel model for employee performance evaluation using the PageRank algorithm. Originally designed by Google to rank web pages, the PageRank algorithm assesses the importance of pages based on their mutual connections. It considers not only the number of incoming links but also the credibility of the pages providing these links. Inspired by this approach, a model is developed in which employees are scored based on the evaluations they receive and the relative credibility of the evaluators. The model is designed to address the limitations of traditional methods, such as weighted averages, and to deliver more accurate and fair assessments.
To evaluate the effectiveness of the proposed model, its performance was tested under three scenarios: random data to eliminate bias, simulated biased evaluations, and reduced influence of low-credibility evaluators. The Wilcoxon test was used to analyze differences. Results demonstrated that the proposed model effectively provides logical rankings, identifies and mitigates the impact of biased evaluations, and adjusts for the influence of low-credibility opinions across various scenarios.
This research concludes that the proposed model, leveraging the PageRank algorithm, can offer more precise and equitable evaluations, serving as an effective tool to enhance employee performance evaluation systems. By incorporating employee participation, the model can further improve the evaluation process.

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Articles in Press, Accepted Manuscript
Available Online from 14 September 2025
  • Receive Date: 12 January 2025
  • Revise Date: 02 April 2025
  • Accept Date: 03 May 2025