In recent years, research on digital transformation (DT) and business process management (BPM) has gained significant attention in the field of business and management. This paper aims to conduct a comprehensive bibliometric analysis of global research on DT and BPM from 2007 to 2022. A total of 326 papers were selected from Web of Science and Scopus for analysis. Using bibliometric methods, we evaluated the current state and future research trends of DT and BPM. Our analysis reveals that the number of publications on DT and BPM has grown significantly over time, with the Business Process Management Journal being the most active. The countries that have contributed the most to this field are Germany (with four universities in the top 10) and the USA. The Business Process Management Journal is the most active in publishing research on digital transformation and business process management. The analysis showed that “artificial intelligence” is a technology that has been studied extensively and is increasingly asserted to influence companies’ business processes. Additionally, the study provides valuable insights from the co-citation network analysis. Based on our findings, we provide recommendations for future research directions on DT and BPM. This study contributes to a better understanding of the current state of research on DT and BPM and provides insights for future research.

Applying IoT systems in industrial production allows data collection directly from production lines and factories. These data are aggregated, analyzed, and converted into reports to support manufacturers. Business managers can quickly and easily grasp the situation, making timely and effective management decisions. In industrial sewing, IoT applications collect production data from sewing lines, especially from industrial sewing machines, and transmit that data to cloud-based systems. This allows businesses to analyze production situations, thereby improving management capacity. This article explores the implementation of IoT applications at industrial sewing enterprises, focusing on data collection during the production process and proposing a data structure to integrate this information into the company’s MIS system enterprise. In addition, the research also considers applying the Real-RCPSP problem to support businesses in planning automatic production operations.

The management information systems (MIS) for education bring many benefits to management at universities, educational institutions, and academies. The Vietnam National Academy of Education Management (NAEM) has successfully integrated information technology into the management, teaching, and learning process, bringing many benefits. Many professional activities have been included in the standard framework of the academy's faculties. However, more specific and detailed activities at specialized faculties are being actively researched and implemented for management purposes. This article presents a study on the construction and implementation of a management information system to support the management of activities at faculties, such as managing information about lecturers' teaching, information about scientific research activities, and published works, In addition, this system also allows students to engage in learning activities such as registering for internships and internships at enterprises, information about graduation thesis implementation, and lecturers' assignments to guide students. Deploying this system at faculties supports the management of detailed operations, improves data management and processing, and ensures consistency in management.

This paper examines the interaction between robotic process automation (RPA), business process management (BPM), and digital transformation (DT)-three critical components in improving operational efficiency and driving business modernization. RPA automates repetitive tasks, reduces errors, accelerates processing, and optimizes resource use. When combined with artificial intelligence (AI) and machine learning (ML), it further enhances data analysis and decision-making. BPM focuses on analyzing, designing, and optimizing business processes to ensure organizational agility. DT provides a technological foundation for broader innovation in processes and structures. The paper contributes a comprehensive and updated perspective on how RPA, BPM, and DT interrelate—not only functioning independently but also reinforcing one another to create greater business value. It emphasizes that their integration is a strategic approach to improving performance, responsiveness, and continuous innovation. Importantly, the research is relevant to both Industry 4.0 and Industry 5.0. While Industry 4.0 (I4.0) prioritizes automation and data-driven systems, Industry 5.0 (I5.0) highlights human–technology collaboration for more adaptive and human-centric organizations. This study enriches theoretical insights and offers practical guidance for building effective and sustainable DT strategies.

The classification problem is crucial in business, providing an effective method for supporting search activities in areas such as e-commerce, education, and marketing. This has become especially important in the wake of the COVID-19 pandemic, which has increased the need to promote and stimulate domestic tourism. This research focuses on recommending tourist destinations based on historical search data related to domestic tourism. The study uses techniques like term frequency-inverse document frequency (TF-IDF) weight vector analysis and similarity measures to calculate recommendation scores. Data was collected from various tourism websites, covering destinations across all 63 provinces and cities in Vietnam. Experiments were conducted using three approaches: cosine similarity, the brute force algorithm, and long short-term memory (LSTM) for long-text processing. The results indicate that similarity-based methods produce recommendations that closely match user preferences. For full-sentence queries, the brute force algorithm delivers more accurate results, while LSTM provides faster processing times. These findings offer businesses multiple strategies for improving recommender systems in practical applications.

Background: Generative Artificial Intelligence is increasingly transforming learning, work, and creative activities among young people. However, limited evidence explains how technological, social, behavioral, and individual factors jointly influence creativity in Generative Artificial Intelligence-assisted environments. Objective: This study examines the determinants of Generative Artificial Intelligence usage and their effects on youth creativity by integrating the Technology Acceptance Model 2 and the Componential Theory of Creativity. Methods: A quantitative survey was conducted with 400 young users who had prior experience with Generative Artificial Intelligence. Data were analyzed using Cronbach’s Alpha, Exploratory Factor Analysis, Confirmatory Factor Analysis, and Structural Equation Modeling. Results: The proposed model demonstrated excellent fit, with a chi-square to degrees-of-freedom ratio of 1.373, Comparative Fit Index of 0.981, Tucker-Lewis Index of 0.978, and Root Mean Square Error of Approximation of 0.031. Social influence and perceived usefulness significantly promoted habitual use, while technology trendiness and innate creativity positively influenced usage purpose. Usage purpose was the strongest predictor of creativity enhancement, followed by habitual use and innate creativity. The model explained 62% of the variance in creativity enhancement. Conclusion: Generative Artificial Intelligence serves not only as a productivity tool but also as a catalyst for youth creativity. The integration of the Technology Acceptance Model 2 and the Componential Theory of Creativity highlights how technological, social, behavioral, and individual factors jointly contribute to creativity enhancement, with practical implications for education, policy, and technology development. Keywords: GenAI, Creativity, TAM2, Componential Theory of Creativity, SEM