Poverty, as a phenomenon, remains an obstacle to global sustainable development. Although a universal malaise, it is more prevalent in underdeveloped countries, including Nigeria. However, because of its devastating impacts on the Nigerian economy, such as increasing death rates, high crime rates, insecurity difficulties, threats to national cohesion, and so on, successive administrations have implemented poverty alleviation programs to mitigate the consequences of this disease. Worryingly, despite a multiplicity of projects and massive human and natural resources invested to match global standards, Nigeria remains impoverished. The curiosity at how these programs fail, either because of implementation hiccups or because elites’ wealth and power influence these programs spurred the paper to assess poverty alleviation policies and elitist approaches in Nigeria. The study employed the desk study approach, as it examined secondary sources such as books, journals, articles, and magazines. Its theoretical underpinning was the elite theory. The paper discovered that several factors such as corruption, the elitist nature of the policies which in disguise reflect public interests, lack of continuity, lack of coordination and monitoring system, misappropriation of public resources, and others, led to the poor performances of government in alleviating poverty in Nigeria. The paper concludes that, while the rate of poverty index in Nigeria rises year after year, poverty alleviation efforts in Nigeria have had little or no influence on the Nigerian economy, since most of these projects are purely reflective of the elites’ interests rather than the masses. Therefore, the paper recommends that for there to be a reduction in poverty incidence in Nigeria, a holistic developmental approach should be adopted, the policies formulated and implemented should sync with the needs of the citizens, and quality and viable programs should be sustained and financed irrespective of change in government; public accountability should be instilled; proper coordination and monitoring system should be domesticated, etc.

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 primary objective of this paper is to explore the impact of household policies in both Saudi Arabia and Nigeria towards achieving efficient and sustainable economic growth in the 21st century. Fundamentally, the objective of the study was sparked by the basic factors of comparison the importance of culture in international relations, challenges related to terrorism which impede adequate implementations of economic policies, trade facilitation and logistics to enhance economic growth and cross-border movement of goods and services. Systematic literature review (SLR) and content analysis (CA) were used as methodological approaches of the paper. The articles explored for review were accessed using visualization of similarities (VOS) by exploring different database such as: journals, core collection of Web of Science (WOS), peer review sources and library sources. The findings demonstrated that Saudi Arabia and Nigeria have different policies regarding households in achieving sustainable economic growth. On one hand, in Saudi Arabia, the focus is on the economic burden associated with chronic non-communicable diseases (NCDs) and the out-of-pocket spending among individuals diagnosed with these diseases. In addition, the study found that households with older and more educated members, an employed head of household, higher socioeconomic status, health insurance coverage, and urban residency had significantly higher out-of-pocket expenditure in achieving sustainable economic development. On the other hand, Nigeria’s policy is centered around trade liberalization and its impact on household welfare as an integral part of sustainable economic development. The policies implemented in Saudi Arabia and Nigeria have implications for the well-being of their citizens. In Saudi Arabia, the household policies have significantly impacted the quality of life (QoL) of households, particularly those with low income, large size, male-led, urban, and with elderly heads. In Nigeria, trade liberalization policies have mixed welfare implications for households in the aspects of real income, they also induce unemployment in key sectors, such as agriculture and industry. To mitigate negative effects, it is suggested that Saudi Arabia should effectively address chronic non-communicable diseases (NCDs) among the households while Nigeria should efficiently pursue trade liberalization on a sectorial basis, focusing on sectors that do not severely undermine household welfare.

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.

Nowadays, urban ecosystems require major transformations aimed at addressing the current challenges of urbanization. In recent decades, policy makers have increasingly turned their attention to the smart city paradigm, recognizing its potential to promote positive changes. The smart city, through the conscious use of technologies and sustainability principles, allows for urban development. The scientific literature on smart cities as catalysts of public value continues to develop rapidly and there is a need to systematize its knowledge structure. Through a three-phase methodological approach, combining bibliometric, network and content analyses, this study provides a systematic review of the scientific literature in this field. The bibliometric results showed that public value is experiencing an evolutionary trend in smart cities, representing a challenging research topic for scholars. Network analysis of keyword co-occurrences identified five different clusters of related topics in the analyzed field. Content analysis revealed a strong focus on stakeholder engagement as a lever to co-create public value and a greater emphasis on social equity over technological innovation and environmental protection. Furthermore, it was observed that although environmental concerns were prioritized during the policy planning phase, their importance steadily decreased as the operational phases progressed.