The Law of Data-driven on Employee Growth in Enterprise Human Resource Management in the Era of Digital Transformation

Yiming Liu ()
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Yiming Liu: Faculty of Business, City University of Macau, Taipa Macau 999078, P. R. China

Journal of Information & Knowledge Management (JIKM), 2025, vol. 24, issue 01, 1-23

Abstract: In the era of digital transformation, the popularity of artificial intelligence technology has driven the development of the field of Human Resource Management (HRM) in enterprises, and data-driven HRM has become the key to the development and success of enterprises. Due to the late start, data-driven HRM is still in the development stage in China. To address the problem that traditional HRM lacks accuracy and real-time, this study proposes a Growth Potential Prediction (GPP) model based on the combination of all class features and a Random Forest (RF) classifier. The results of the study showed that the prediction accuracy of interpersonal background, work ability, educational status and organisational environment characteristics under the screening index based on F1 value were 85.01%, 66.34%, 66.41%, 44.19% and 64.27%, respectively. The accuracy of ROS under the RF classifier and the macro F1 value were 87.31% and 87.24%. After adding the network features, the accuracy and macro F1 values of the feature ensemble could reach 90%, and the accuracy and macro F1 values of all class feature combinations were 89.01% and 89.10%, respectively. In summary, the GPP model based on the combination of all class features and the RF classifier proposed in the study can accurately predict the growth potential of employees and provide some guarantee for mining the growth pattern of employees.

Keywords: Random forest classifier; growth state labelling; HRM domain; growth pattern; network features (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S021964922450103X

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