Developing an advanced prediction model for new employee turnover intention utilizing machine learning techniques.

In recent years, the turnover phenomenon of new college graduates has been intensifying. The turnover of new employees creates many difficulties for businesses as it is difficult to recover the costs spent on their hiring and training. Therefore, it is necessary to promptly identify and effectively manage new employees who are inclined to change jobs.

Using machine learning-based binary classifiers for predicting organizational members' user satisfaction with collaboration software.

Background: In today's digital economy, enterprises are adopting collaboration software to facilitate digital transformation. However, if employees are not satisfied with the collaboration software, it can hinder enterprises from achieving the expected benefits. Although existing literature has contributed to user satisfaction after the introduction of collaboration software, there are gaps in predicting user satisfaction before its implementation.

What is holding back business process virtualization in the post-COVID-19 era? Based on process virtualization theory (PVT).

The post-pandemic COVID-19 has been influential in accelerating the digital transformation of enterprises and business process virtualization. However, in a virtual working environment with no physical interaction, the psychological requirements of the communication between teleworkers and the negative impact of information systems are hindering the business process virtualization. Studying the relationship between the interaction between organizational members and job performance is an important part of organizational psychology.