The importance of tourism to nations’ socioeconomic development cannot be overemphasised as it has proven to be a significant source of revenue for many countries globally. However, sub-Saharan nations like Nigeria have not tapped into the unlimited potential of tourism in their development drive, hence the continuous grappling with underdevelopment challenges. This study examines how tourism impacts socioeconomic growth in Nigeria, focusing on well-known tourist destinations in Lagos State, Nigeria. The study adopts quantitative and qualitative mixed-method research using survey questionnaires and in-depth interviews to elicit responses from visitors at the tourist centres and the tourists’ operations. Data were analysed using simple percentages of frequency distribution tables and thematic analysis. The Neo-liberal theory was adopted as a theoretical framework for the study. The findings highlight the need for better infrastructure, security measures, destination awareness, better housing, financial help, the development of a competent workforce, solid governmental policies, the conservation of cultural and natural assets, and encouragement of collaboration. Future studies may focus primarily on three areas: the evaluation of tourism’s economic impacts, the effectiveness of specific tourist development programs, and the role of tourism in community empowerment.

This study aims to examine the role of automotive industry development in the regional growth of Hungarian counties. Through word frequency analysis, the counties were grouped, and their unique characteristics were highlighted. Some counties already play a prominent role in the domestic automotive industry hosting established Original Equipment Manufacturers (OEMs), a significant number of automotive suppliers and high R&D and innovation potential. Another group includes counties that currently lack a significant automotive industry and did not identify it as a key focus area for future development. Additionally, an intermediate group has also emerged, including counties where the automotive industry is either in its early stages of investment, or such development is prioritized in regional planning documents. The study details the direction of automotive development in counties where the industry plays a significant role, focusing on labor market characteristics and human resource development. The findings have significant implications for the future of the automotive industry in these counties, underlining the urgent and immediate need for well-managed and well-established human resource development and ensuring effective partnership to realize its full potential in the automotive industry.

Measuring the performance of healthcare organizations has become a crucial yet challenging task, which is the focus of this study. The paper’s primary goal is to identify the key factors that shape healthcare organizations’ performance management systems in Serbia, which can serve as useful guidelines for implementing sustainable solutions. Additionally, the aim is to emphasize the importance of a broad implementation of performance measurement systems to facilitate strategy implementation and enhance organizational effectiveness. The empirical research involved an online survey of 280 respondents, including managers, executives, and operational staff from both private and public healthcare organizations in Serbia. Statistical analysis was conducted using SPSS 20. The study identifies key challenges, including the lack of a developed performance measurement system, weak support from information and management systems for performance improvement, and an organizational structure that does not support performance enhancement. Furthermore, it has been found that a deeper understanding of the essence of measurement significantly contributes to identifying problems in its application in the healthcare sector. It was also observed that the more challenges identified in the measurement process, the less favourable the perception of the flexibility and adaptability of the system.

The convergence of multifaceted global challenges encompassing the rise of populism, Brexit, the climate crisis, the COVID-19 pandemic, and the Russian invasion of Ukraine has catalyzed a profound reassessment of international trade policies. This article critically examines the intricate linkages between these challenges and their profound implications for the contemporary international trading system. Traditionally, globalization debates in the 1990s underscored the social and environmental dimensions of trade, yet the current landscape reveals an undeniable entwining of societal implications with trade policies. This article delves into the interconnectedness of these global challenges with trade, evaluating how each phenomenon influences and reshapes policy discourse. In particular, the rise of populism and its attendant protectionist sentiments have engendered a reevaluation of trade relationships and multilateral agreements. The seismic geopolitical event of Brexit has disrupted regional trade dynamics, signaling a paradigm shift in established trade blocs. Simultaneously, the imperatives of addressing the escalating climate crisis have spotlighted the necessity for trade policies to align with environmental sustainability goals. The COVID-19 pandemic, acting as a disruptor on a global scale, has accentuated vulnerabilities within supply chains, emphasizing the need for resilience and adaptability in trade frameworks. Additionally, the Russian invasion of Ukraine has introduced geopolitical tensions that further complicate the trade-policy landscape. By critically evaluating these intersecting challenges, this article delineates the evolving nature of trade policies and their inextricable relationship with societal and geopolitical realities. It underscores the imperative for a holistic approach in policy formulation that integrates social, environmental, and geopolitical considerations, acknowledging the integral role of trade policies in addressing contemporary global challenges.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.