The successful execution of large-scale infrastructure projects is essential for economic growth and societal development, but these projects are too often beset with financial risks. The main financial risks related to infrastructure projects, including cost overrun, funding uncertainty, currency fluctuation, and regulatory change are examined in this research. The study identifies and assesses the magnitude and frequency of these risks by combining surveys and analysis of financial reports. The findings show that current risk management strategies, including hedging, contingency funds, and public-private partnerships, are often unsuitable to respond to the specific needs of financial uncertainties. The research suggests the need for an all-encompassing financial risk management framework that relies on real-time data analysis and a cocktail of risk assessment tools. Additionally, the development of strategic tailored approaches to address financial risk recovery depends on proactive stakeholder engagement. This research complements the existing literature on risk management in infrastructure projects by highlighting the financial dimensions of risk management and suggesting future research on advanced financial tools and technologies. Ultimately, large-scale infrastructure project sustainability and success contribute to economic stability and societal well-being can only be achieved through effective financial risk management.

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.

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.

In order to diversify a portfolio, find prices, and manage risk, derivatives products are now necessary. There is a lack of understanding of the true influence of derivatives on the behavior of the underlying assets, their volatility consequences, and their pricing as complex instruments. There is a dearth of empirical research on how these instruments impact company risk exposures and inconsistent findings. This study examines corporate derivatives’ impact on stock price exposure and systematic risk in South African non-financial firms. Using a dataset of listed firms from 2013 to 2023, we employ Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models to assess the effect of derivatives on return volatility and beta, a measure of systematic risk. Additionally, we apply the Generalized Method of Moments (GMM) to address potential endogeneity between firm characteristics and derivatives use. Our findings suggest that firms using derivatives experience lower overall volatility and reduced systematic risk compared to non-users. The results are robust to various control factors, including firm size, leverage, and macroeconomic conditions. This study fills a gap in the literature by focusing on an underrepresented emerging market and provides insights relevant to global risk management practices.

The hospital is a complex system, which evolving practices, knowledge, tools, and risks. This study aims to assess the level of knowledge about risks at Hassan II Hospital among healthcare workers (HCWs) working in three COVID-19 units. The action-research method was adopted to address occupational risks associated with the pandemic. The study involved 82 healthcare professionals in the three COVID-19 units mentioned above. All participants stated they were familiar with hospital risks. Seventy-four HCPs reported no knowledge of how to calculate risk criticality, while eight mentioned the Occurrence rating, Severity rating, and Detection rating (OSD) method, considering Occurrence rating, Severity rating, and Detection rating as key elements for risk classification. Staff indicated that managing COVID-19 patients differs from other pathologies due to the pandemic’s evolving protocols. There is a significant lack of information among healthcare professionals about risks associated with COVID-19, highlighting the need for a hospital risk management plan at a subsequent stage.

The continuous escalation of social risks has exacerbated the challenges faced by aging urban communities. In this context, resilience building emerges as a critical approach, offering new perspectives and innovative solutions to address these issues. This paper applies the theories of risk society and resilience governance to establish an analytical framework for resilience governance, specifically examining the current status of resilience construction within the Jin Guang Men community in Xi’an. The findings indicate that resilience building within these aging urban communities is hindered by issues such as weak grassroots governance, deficient repair mechanisms, inadequate infrastructure, and a slow pace of information technology adoption. To effectively manage social risks, it is imperative to strengthen party leadership in governance, enhance community self-repair capacities, upgrade infrastructure, and accelerate the application of information technology. These measures are essential for bolstering the risk management capabilities of aging urban communities.