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.

The Hungarian tourism and hospitality industry has faced serious challenges in recent years. The tourism and hospitality sector has been confronted with severe challenges in recent years. Even after the end of the pandemic, the industry has not seen the expected recovery, as rising inflation, declining discretionary income and a lack of foreign tourists have further hampered the industry. The hotel market in Budapest in particular has been significantly affected by these developments. Despite the difficulties, investors continue to see opportunities in the market. One example is the purchase by a group of real estate investors of an under-utilised leisure centre in District VII, which they intend to convert into a hotel. Our study is part of this project and its primary objective is to define the parameters of the future hotel and analyse the market opportunities and challenges. Our research focuses on the hotel market in Budapest and uses methods such as benchmarking, STEEP and SWOT analyses, as well as four in-depth interviews with key players in the market. The benchmarking examined the operations of hotels in the capital, while the in-depth interviews provided practical experience and insider perspectives. On the basis of the interviews and analyses, the study identifies possible directions for improvement and factors for competitive advantage.

Food safety in supply chains remains a critical concern due to the complexity of global distribution networks. This study develops a conceptual framework to evaluate how food safety risks influence supply chain performance through predictive analytics. The framework identifies and minimizes food safety risks before they cause serious problems. The study examines the impact of food safety practices, supply chain transparency, and technological integration on adopting predictive analytics. To illustrate the complex dynamics of food safety and supply chain performance, the study presents supply chain transparency, technological integration, and food safety practices and procedures as independent variables and predictive analytics as a mediator. The results show that supply chain managers’ capacity to anticipate and control risks related to food safety can be improved by predictive analytics, leading to safer food production and distribution methods. The research recommends that businesses create scalable cloud-based predictive model solutions, combine data sources, and employ cutting-edge AI and machine learning tools. Companies should also note that strong, data-driven approaches to food safety require cooperative data sharing, regulatory compliance, training initiatives and ongoing improvement.

With the rising global consumer demand for green and healthy food, the tea industry is facing unprecedented competitive pressure. Therefore, how to build tea enterprises with sustainable competitiveness has become a key issue facing the industry. This paper firstly reviews the concept of traceability systems and their evolution and, based on the theory of enterprise competitive advantage, explores the influence mechanism of traceability as a strategic resource on the long-term competitiveness of tea enterprises; secondly, it analyzes the multi-dimensional role of traceability on enterprise competitiveness from five aspects, namely, quality and safety control and guarantee, brand image shaping and trust construction, market dynamics response and consumer feedback, risk response and product recall, as well as technological innovation and efficiency enhancement; finally, combined with the above analysis, this paper constructs a theoretical framework for the competitiveness of tea enterprises, integrates the impact of traceability in different dimensions, and proposes a multi-level competitiveness enhancement model. Through this framework, tea enterprises can more comprehensively understand and grasp the close relationship between traceability and the long-term competitive advantage of enterprises and then make strategic adjustments according to their own actual situation so as to realize sustainable competitiveness enhancement in the future market competition.