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. For an enterprise to maintain high-efficiency output, it is necessary to study psychological factors related to business process virtualization. This paper verified the factors hindering business process virtualization based on process virtualization theory (PVT). The research was implemented on a sample of 343 teleworkers in China enterprises. The structure of the model of this study includes two aspects that hinder the business process virtualization: the psychological requirements of teleworkers (Sensory requirements, Synchronism requirements, and Relationship requirements) and the negative effects of information systems (Information overload and Communication overload). The results show that teleworkers’ sensory requirements, synchronism requirements, and communication overload negatively impact business process virtualization. However, unlike the results in the existing literature, the relationship requirements and information overload do not affect the business process virtualization. The results will help business managers, teleworkers, and information system developers develop strategies to address the negative factors hindering business process virtualization. In the so-called new “normal era,” our research will help companies to create a successful virtual work environment.

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. To address this gap, this study offers a machine learning-based forecasting method. Methods: We utilized national public data provided by the national information society agency of South Korea. To enable the data to be used in a machine learning-based binary classifier, we discretized the predictor variable. We then validated the effectiveness of our prediction model by calculating feature importance scores and prediction accuracy. Results: We identified 10 key factors that can predict user satisfaction. Furthermore, our analysis indicated that the naive Bayes (NB) classifier achieved the highest prediction accuracy rate of 0.780, followed by logistic regression (LR) at 0.767, extreme gradient boosting (XGBoost) at 0.744, support vector machine (SVM) at 0.744, K-nearest neighbor (KNN) at 0.707, and decision tree (DT) at 0.637. Conclusions: This research identifies essential indicators that can predict user satisfaction with collaboration software across four levels: institutional guidance, information and communication technology (ICT) environment, company culture, and demographics. Enterprises can use this information to evaluate their current collaboration status and develop strategies for introducing collaboration software. Furthermore, this study presents a novel approach to predicting user satisfaction and confirm the effectiveness of the machine learning-based prediction method proposed in this study, adding to the existing knowledge on the subject.

Background: Identifying priority projects in industry–education integration is critical for optimizing policy support and resource allocation. While prior studies have primarily relied on qualitative assessments, there remains a lack of interpretable, data-driven approaches for large-scale priority project evaluation. This study focuses on projects related to the Management Information Systems (MIS) discipline, enabling a discipline-specific analysis. Methods: We developed a predictive framework by benchmarking multiple algorithms (including Logistic Regression, Support Vector Machine (SVM), Random Forest, and eXtreme Gradient Boosting (XGBoost)) on structured project-level data. The optimal tree-based ensemble model was integrated with SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) to quantify feature importance at both global and local levels. The dataset included multiple project- and enterprise-related indicators such as equipment investment, firm age, and prior project experience. Model robustness was evaluated and optimized through GridSearchCV with five-fold cross-validation. Results: The optimized XGBoost model achieved a Receiver Operating Characteristic Area Under the Curve (ROC-AUC) of 0.858, demonstrating strong discriminative ability while successfully mitigating overfitting risks. Both SHAP and LIME analyses revealed that equipment investment, firm age, and project count were the top predictors. Higher equipment investment strongly contributed to positive classifications, while extremely low or high firm age often had negative impacts. Local explanations explicitly quantified the decision thresholds, reflecting heterogeneous project profiles and trade-off mechanisms influencing prediction outcomes. Conclusions: The proposed framework not only delivers high predictive accuracy but also provides interpretable insights for policy formulation and project application strategy optimization. These findings demonstrate that combining ensemble learning with explainable artificial intelligence (XAI) can enhance the transparency and effectiveness of priority project identification, with strong potential for adaptation to other resource allocation and project selection tasks.