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This study investigates the impact of machine learning on employee performance. Machine learning has been adopted as a metric for assessing employee performance. These indicators have a broader application than only assessments and professional progression. Furthermore, they are critical in facilitating corporate expansion and generating organizational profitability. According to research, precise measures have the potential to greatly improve employee well-being, productivity, and retention inside firms. Machine learning promises unrivaled prospects for people and brands all across the world. This study includes a complete systematic literature review (SLR) on the use of machine learning (ML) in measuring employee performance matrix. The SLR is based on Scopus indexed database where 248 recent papers were chosen from 6363 search results using a PRISMA model-based approach. The review also includes research objectives such as investigating the variety of methods used in machine learning, issues, opportunities, and barriers that are deemed significant machine learning roles, and establishing how machine learning impact employee performance metrics measurement functions and the nature of such impact, as well as a pictorial representation via cluster to justify the methodologies used in addressing the issue.

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Employees Performance Metrics Using Machine Learning: A Systematic Literature Review Using Prisma Model

  • 会议论文

  • 发表日期：

    2026年

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  • Abhilasha Dixit,
  • Rashmita Singh,
  • Nitin Dixit,
  • Shaifali Garg

摘要

This study investigates the impact of machine learning on employee performance. Machine learning has been adopted as a metric for assessing employee performance. These indicators have a broader application than only assessments and professional progression. Furthermore, they are critical in facilitating corporate expansion and generating organizational profitability. According to research, precise measures have the potential to greatly improve employee well-being, productivity, and retention inside firms. Machine learning promises unrivaled prospects for people and brands all across the world. This study includes a complete systematic literature review (SLR) on the use of machine learning (ML) in measuring employee performance matrix. The SLR is based on Scopus indexed database where 248 recent papers were chosen from 6363 search results using a PRISMA model-based approach. The review also includes research objectives such as investigating the variety of methods used in machine learning, issues, opportunities, and barriers that are deemed significant machine learning roles, and establishing how machine learning impact employee performance metrics measurement functions and the nature of such impact, as well as a pictorial representation via cluster to justify the methodologies used in addressing the issue.