The present study aimed to determine the dynamic relationship between good governance, fiscal policy, and economic growth in Oman. In the context of the current study, researchers chose a quantitative approach to answer the research questions, utilizing the latest 2023 data from the World Bank and The Global Economy databases. The data for the current study was carefully selected using variables that represent aspects of governance, fiscal policies, and economic performance. Our analysis uses Ordinary Least Squares (OLS) regression and the Autoregressive Distributed Lag (ARDL) Model. These methods help us understand these factors’ immediate and long-term impacts on Oman’s economy. The results we obtained offer fascinating insights into the country’s economic dynamics. We observe bidirectional causal relationships between the Good Governance Index (GGI) and the Regulatory Quality Index (RQI) and economic growth, while Fiscal Policy Effectiveness (FPE), Government Efficiency Index (GEI), and the Rule of Law Index (RLI) exhibit unidirectional causality towards GDP. Budget Balance (BB) shows no causal relationship with GDP, implying external factors influence it. Additionally, moderation analysis underscores the significance of digital financial inclusion in amplifying the effects of governance and fiscal policies on economic growth. These findings hold practical implications for policymakers and stakeholders in Oman. Specifically, they highlight the importance of governance, regulatory quality, and effective fiscal policies in shaping the economic landscape. To foster sustainable economic development, efforts should improve governance, enhance fiscal policy effectiveness, and promote digital financial inclusion.

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.

This study examines the impact of innovation governance and policies on government funding for emerging science and technology sectors in Saudi Arabia, addressing key bureaucratic, regulatory, and cultural barriers. Using a mixed-methods approach, the research integrates qualitative insights from stakeholder interviews with quantitative survey data to provide a comprehensive under-standing of the current innovation landscape. Findings indicate a high level of policy awareness among stakeholders but reveal significant challenges in practical implementation due to bureaucratic inefficiencies and stringent regulations. Cultural barriers, such as a risk-averse mindset and traditional business practices, further impede innovation. Successful initiatives like the National Transformation Program (NTP) demonstrate the potential for well-coordinated efforts, highlighting the importance of regulatory reform and cultural shifts towards entrepreneurship. Strategic recommendations include streamlining bureaucratic processes, enhancing policy coordination, and fostering a culture of innovation through education and stakeholder engagement. This study contributes to the existing literature by offering actionable insights to enhance innovation governance, supporting Saudi Arabia’s Vision 2030 goals.

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.