Purpose This study aims to evaluate the key risk factors inherent in public–private partnership (PPP) power projects in Ghana and further determine the critical risk factors affecting both the public and private sectors in PPP power projects. Design/methodology/approach Ranking-type Delphi...

Purpose Financial services firms have a significant societal responsibility to prevent issues. The three lines model helps them do that though faces challenges in its effectiveness. This paper aims to offer a behavioural perspective on these challenges and practical solutions to help improve the...

Purpose This paper is dedicated to elaborating, proposing and validating an action plan to enhance the mitigation of risks generated by the COVID-19 pandemic in the electric sector supply chain, aiming to promote a more resilient supply chain. Design/methodology/approach For this, a systematic...

Purpose - This article aims to evaluate published food cold chain (FCC) literature against risk management and supply chain sustainability concepts. Design/methodology/approach - The article uses the theory refinement logic proposed by Seuring et al. (2021) to analyze the contents of FCC...

Risk managers as corporate stewards are important gatekeepers in enterprises and they are essential to managing risks. Relatedly, a more accurate evaluation of the risk factors allows a well-founded decision-making process and reduces the number of surprising situations that could occur...

Global economic crises can have a significant impact on businesses across different sectors, often leading to difficulties or even insolvency. In such a situation, organizational resilience is often considered a means to ensure the competitive advantage. Although the concept has gained...

The paper develops a conceptual framework for the analysis and management of catastrophic risk. The framework serves to assess rare extreme events in systematic, quantitative and consistent ways. It dispenses with probabilistic extreme value theory, concentrating on descriptive statistics and...

Comparative studies investigating the estimation accuracy of statistical methods often arrive at different conclusions. Therefore, it remains unclear which method is best suited for a particular estimation task. While this problem exists in many areas of predictive analytics, it has particular...

Data-driven business models imply the inter-organisational exchange of data or similar value objects. Data science methods enable organisations to discover patterns and eventually knowledge from data. Further, by training machine learning models, knowledge is materialised in those models. Thus,...

This reprint concerns methods of data analysis for risk management in economics, finance, and business. The presented papers contain research on data analysis methods, including classical statistical methods, and machine learning methods that have emerged from statistics and are being...