Performance Management is a major concern to various stakeholders in Education System, it is considered to be key driver to improve school effectiveness and learning quality. However, the complexity of education Systems, has made it challenging to apply an effective PM model. This study paper introduces a maturity model with six dimensions, fifteen Capability Areas and forty-two Best-Practices to assess education systems’ organizational capacity for performance management. It provides deep insights into their structural and functional characteristics and serves as a framework for decision-makers to identify and implement missing practices while enhancing existing ones. The maturity model was developed following the Design Science Research methodology to ensure both rigor and relevance. A bottom-up approach guided its design, integrating insights from extensive literature reviews and lessons learned from benchmark countries. The evaluation process employed a qualitative approach, using focus groups with a carefully selected cohort of academics, experts, and practitioners. The Moroccan case study serves as part of the “Reflection and Learning” phase, providing an initial test for the model and paving the way for further empirical research. Future studies will aim to test, refine, and extend the model, facilitating its application across diverse educational contexts.

Leadership behavior is a critical component of effective management, significantly influencing organizational success. While extensive research has examined key success factors in road management, the specific role of leadership behaviors in road usage charging (RUC) management remains underexplored. This study addresses this gap by identifying and analyzing leadership behavior dimensions and their impact on management performance within the RUC context. Using a mixed-methods approach, focus group discussions with industry practitioners were conducted to define eight leadership behavior dimensions: Central-Level Leadership Guidance (LE1), Local-Level Leadership Guidance (LE2), Central-Level Leadership Commitment (LE3), Local-Level Leadership Commitment (LE4), Subordinate Understanding from Central-Level Leadership (LE5), Subordinate Understanding from Local-Level Leadership (LE6), Work Motivation (LE7), and Understanding Rights and Obligations (LE8). These dimensions were further validated through a quantitative survey distributed to 138 professionals involved in RUC management in Vietnam, with the data analyzed using structural equation modeling (SEM) and partial least squares (PLS) estimation. The findings revealed that LE3 (Central-Level Leadership Commitment) had the strongest direct impact on management performance (MP) and mediated the relationships between other leadership dimensions and management outcomes. This study contributes to the theoretical understanding of leadership in RUC management by highlighting the centrality of leadership commitment and offering practical insights for improving leadership practices to enhance organizational performance in infrastructure management.

This study aims to explore the factors influencing people’s intention to use home fitness mobile apps in the post-pandemic era. By incorporating the perspective of playfulness into the decomposed theory of planned behavior, it seeks to construct a behavioral model for the public's use of AR sports games for home exercise. The research focuses on Active Arcade users residing in Taiwan, employing the snowball sampling method to conduct an online questionnaire survey. A total of 340 valid questionnaires were collected and analyzed using linear structural equations. The study reveals three main findings: first, the behavioral model for Active Arcade users constructed based on the decomposed theory of planned behavior demonstrates a good fit; second, users’ attitudes, subjective norms, and perceived behavioral control have a positive and significant impact on behavioral intention; third, perceived usefulness, perceived ease of use, and perceived playfulness all positively and significantly influence attitudes, with perceived playfulness having the highest impact coefficient; fourth, perceived benefits of exercise are the most crucial factor affecting subjective norms; and fifth, convenience technologies are the key factor influencing perceived behavioral control. This study provides valuable insights for theory and management practice, offering guidance on the use of home fitness apps in the post-pandemic era while addressing research limitations and suggesting future directions.

The study explores improving opportunities of forecasting accuracy from the traditional method through advanced forecasting techniques. This enables companies to optimize inventory management, production planning, and reducing the travelling time thorough vehicle route optimization. The article introduced a holistic framework by deploying advanced demand forecasting techniques i.e., AutoRegressive Integrated Moving Average (ARIMA) and Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) models, and the Vehicle Routing Problem with Time Windows (VRPTW) approach. The actual milk demand data came from the company and two forecasting models, ARIMA and RNN-LSTM, have been deployed using Python Jupyter notebook and compared them in terms of various precision measures. VRPTW established not only the optimal routes for a fleet of six vehicles but also tactical scheduling which contributes to a streamlined and agile raw milk collection process, ensuring a harmonious and resource-efficient operation. The proposed approach succeeded on dropping about 16% of total travel time and capable of making predictions with approximately 2% increased accuracy than before.

In today’s fast-moving, disrupted business environment, supply chain risk management is crucial. More critically, Industry 4.0 has conferred competitive advantages on supply chains through the integration of digital technologies into manufacturing and logistics, but it also implies several challenges and opportunities regarding the management of these risks. This paper looks at some ways emerging technologies, especially Artificial Intelligence (AI), help address pressing concerns about the management of risk and sustainability in logistics and supply chains. The study, using a systemic literature review (SLR) backed by a mapping study based on the Scopus database, reveals the main themes and gaps of prior studies. The findings indicate that AI can substantially enhance resilience through early risk identification, optimizing operations, enriching decision-making, and ensuring transparency throughout the value chain. The key message from the study is to bring out what technology contributes to rendering supply chains resilient against today’s uncertainties.