Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/320897 
Year of Publication: 
2024
Citation: 
[Journal:] Administrative Sciences [ISSN:] 2076-3387 [Volume:] 14 [Issue:] 4 [Article No.:] 75 [Year:] 2024 [Pages:] 1-20
Publisher: 
MDPI, Basel
Abstract: 
An organization's human resource management practices are essential for its competitive advantage. This study specifically examined human resource (HR) practices that predict corporate performance (employee turnover and firm sales) based on a backpropagation neural network (BPN)- based causality analysis. This study aims to test how to optimize human resource practices to improve organizational performance. This study elucidated the effect of HR practices and organizational level factors on predicting employee turnover and firm sales. The BPN-based causality analysis revealed the relative importance of explanatory variables on firm performance. To test the model, it employed the Human Capital Corporate Panel open data on Korean companies' HR practices and other characteristics. The analysis identifies causal relationships between specific HR practices and firm performance. The results show that compensation-related HR practices are most influential in predicting firm sales and employee turnover. Moreover, training-related HR practices were modest, and talent acquisition and performance management practices had relatively weak effects on the two outcomes. The study provides insights into how human resource practices can be optimized to improve firm performance and enhance organizational effectiveness. The findings of this study contribute to the growing body of research on the use of machine learning in HR management and suggest practical implications for managers' insights to optimize HR practices.
Subjects: 
BPN-based causality analysis
firm performance
human corporate capital panel
humanresource management
machine learning
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Appears in Collections:

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.