Human Resource Capacity of Quality Management Based on Digital Technologies

Vasiliev, Victor A.; Aleksandrova, Svetlana V.

doi:10.1109/itqmis53292.2021.9642792

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…The 21st century is a globalized era of market and information, and it is dominated by knowledge. In an environment under new economic conditions, the HRM in enterprises must make proper changes accordingly [1][2][3][4][5]. The latest in-formation technologies have greatly accelerated the digitalization progress of HRM and many useful techniques and tools have been developed for that purpose.…”

Section: Introductionmentioning

confidence: 99%

Risk Prediction of Digital Human Resource Management Based on Artificial Intelligence

2023

CIT. J. comput. inf. technol

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The latest information technologies have greatly accelerated the digitalization progress of Human Resource Management (HRM) and many useful techniques and tools have been developed for that purpose. However, in terms of risk management, effective enough tools and methods are still insufficient. Existing studies generally fail to give a turnkey solution to the operational risks in digital HRM system, and the macro measurement models are not suitable for dealing with the risks in the digital HRM system of each single enterprise. In view of these defects, this paper studied the prediction of risks in digital HRM systems based on Artificial Intelligence (AI). Firstly, the paper outlined the functions of a digital HRM system, defined the risk management mechanism of a HRM system, and built a conceptual model for it. Then, this paper proposed a novel method for predicting the risks in the digital HRM system, which innovatively integrates the digital HRM risk event chains with the risk event graph. After that, the paper elaborated on the structures and building principles of the risk event representation layer, risk event chain module, risk event graph module, and attention fusion module. At last, experimental results verified that the proposed model has obvious advantages in digital HRM risk prediction in terms of both stability and accuracy.

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