Harnessing Neural Networks for Workforce Engagement Analytics: A Pathway to Employee Retention

DOI

10.2991/978-94-6463-978-0_9How to use a DOI?

Keywords

Employee engagement; workforce analytics; neural networks; employee retention; HR analytics

Abstract

The employee engagement is an important aspect of stability of the workforce and organization. Traditional analytics usually use linear models which do not reflect non-linear correlation that affect retention. The present study is based on a neural network framework and provides an analysis of the engagement factors, which include job satisfaction, organizational commitment, career development, leadership quality, work-life balance, and workplace philosophy. The model was trained and tested using a dataset of survey retorts (n=400) in conjunction with HRIS records. The neural network was found to have 88 percent predictive accuracy, which is better than the performance of the logistic regression (78 percent(and decision trees (81 percent). The strongest predictors were job satisfaction and workplace culture. The results reveal the usefulness of AI-based HR analytics in the creation of specific retention strategies and illustrate how neural networks can improve employee lifecycle management decision-making.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - V. R. Meghana
AU  - B. Anitha
PY  - 2025
DA  - 2025/12/31
TI  - Harnessing Neural Networks for Workforce Engagement Analytics: A Pathway to Employee Retention
BT  - Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)
PB  - Atlantis Press
SP  - 83
EP  - 94
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-978-0_9
DO  - 10.2991/978-94-6463-978-0_9
ID  - Meghana2025
ER  -