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Updated: Sep 17, 2025

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Employee loyalty evaluation using machine learning in technology-based small and medium-sized enterprises.

Yong Shi1, Yuan Wang2, Hongkun Zuo3

  • 1School of Computer Science, Huainan Normal University, Huainan City, Anhui, China. shiyong@hnnu.edu.cn.

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|July 2, 2025
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Summary
This summary is machine-generated.

Employee loyalty is crucial for technology-based small and medium-sized enterprises (TSMEs). Machine learning models effectively predict loyalty, aiding talent management and sustainable growth in innovative TSMEs.

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Area of Science:

  • Human Resource Management
  • Innovation Studies
  • Data Science

Background:

  • Employee loyalty is vital for the sustainable growth of technology-based small and medium-sized enterprises (TSMEs).
  • TSMEs are key drivers of innovation, possessing significant vitality and potential.
  • High employee loyalty is critical for the success and development of these innovative enterprises.

Purpose of the Study:

  • To analyze factors influencing employee loyalty in Chinese TSMEs using historical evaluation data.
  • To investigate the relationship between these factors and employee loyalty.
  • To develop an objective and applicable intelligent evaluation model for employee loyalty in TSMEs.

Main Methods:

  • Utilized historical employee evaluation data from Chinese TSMEs.
  • Applied several machine learning models and algorithms for loyalty prediction.
  • Employed decision analysis to support talent evaluation within TSMEs.

Main Results:

  • Demonstrated the feasibility of using machine learning to predict employee loyalty.
  • Identified key factors influencing employee loyalty in the context of TSMEs.
  • Provided a foundation for an intelligent talent evaluation system.

Conclusions:

  • Machine learning offers a viable approach for predicting and managing employee loyalty in TSMEs.
  • The developed model supports TSMEs in identifying, motivating, attracting, cultivating, and retaining scientific and technological talent.
  • This research contributes to promoting sustainable and high-quality management practices in TSMEs.