Strategic management of employee churn: Leveraging machine learning for sustainable development and competitive advantage in emerging markets
2
Issued Date
2024-12-01
Resource Type
eISSN
25723170
Scopus ID
2-s2.0-85208579789
Journal Title
Business Strategy and Development
Volume
7
Issue
4
Rights Holder(s)
SCOPUS
Bibliographic Citation
Business Strategy and Development Vol.7 No.4 (2024)
Suggested Citation
Agrawal P., Ghangale S., Dhar B.K., Nirmal N. Strategic management of employee churn: Leveraging machine learning for sustainable development and competitive advantage in emerging markets. Business Strategy and Development Vol.7 No.4 (2024). doi:10.1002/bsd2.70039 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/102039
Title
Strategic management of employee churn: Leveraging machine learning for sustainable development and competitive advantage in emerging markets
Author(s)
Corresponding Author(s)
Other Contributor(s)
Abstract
Employee churn or attrition presents significant challenges, especially in emerging markets, where it can disrupt business operations and inflate recruitment costs. This research leverages machine learning techniques to predict employee churn, focusing on developing sustainable and inclusive retention strategies that enhance business competitiveness. By analyzing a range of predictive algorithms and key variables associated with churn, the study identifies the most effective models for predicting attrition. A comprehensive exploratory data analysis was conducted using an indigenous machine learning model, offering practical insights for human resource management in emerging markets. The findings align with the sustainable development goals (SDGs), promoting decent work, and economic growth. This study contributes to business strategy by proposing data-driven solutions for workforce stability and sustainable development.