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Due to increase in operational risk, banks are facing huge losses. In order to avoid losses, banks need to manage operational risk. This study aims to analyze the impact of operational risk management (ORM) processes, which include identification, assessment, analysis, monitoring and control in the presence of corporate governance (CG) that can also contribute to effective ORM practices.

Operational risk management processes are used to manage operational risk along with CG. Primary data are collected through questionnaire from (167) operational risk managers of commercial banks. Multiple linear regressions has been run to analyze the data.

Results indicate significant impact of CG and operational risk identification (ORI), monitoring and control on ORM practices in commercial banks of Pakistan.

The study suggests policy makers to improve the ORM framework by CG. Beside this, in order to lessen operational risk, proper identification, monitoring and control of operational risk could also contribute.

This study aims to comprehensively identify supply chain risks and their causes, the factors influencing supply chain management and techniques to successfully mitigate and control supply chain risks in construction projects. This study developed a comprehensive framework showing various supply chain risks and how these risks that influence project execution are systematically identified and managed for the overall construction project success.

The research conducted was characterised by its descriptive, exploratory and quantitative nature. The collection of quantitative data was conducted by means of structured online questionnaires. The sample consisted of 205 construction project professionals who were selected randomly. This group included individuals with various roles in the construction industry, such as project managers, civil/structural engineers mechanical engineers, risk managers, architects, quantity surveyors, electrical engineers, construction managers, health, safety and environment managers, estate managers and other professionals. All participants were actively involved in construction projects located in the Gauteng province of South Africa. The data was analysed, using descriptive statistical methods, including factor analysis, reliability assessment and calculations of frequencies and percentages.

The result showed that predictable delivery, funding schedule, inventories, balanced demands, production capabilities, timely procurement, construction supply chain management coordination, delivery reliability, the proximity of suppliers, identification of supply chain risks in the conceptualisation stage of a project, identification of supply chain risks in the planning stage of a project, identification of supply chain risks in the execution stage and the reconciliation of material flows of the subcontractors with the contractors were identified as the key factors that influenced the construction supply chain management the most. The result also showed that subcontractor’s negative attitudes towards supply chain management, procurement delays, imbalanced demands, clients’ negative attitudes towards other project stakeholders, unpredictable delivery reliability, disorganised construction supply chain management approach, delayed funding, low delivery reliability, poor inventories, poor construction supply chain co-ordination, suppliers’ negative attitudes towards supply chain management and when the material flows of the subcontractors with the contractors are not reconciled were identified as the factors that have the greatest impacts on construction supply chain risks management.

For future research, it is recommended to incorporate fourth industrial revolution) such as machine learning prediction models and algorithms, Artificial intelligence and blockchain to identify and manage supply chain, supply chain risks and project stakeholders involved in supply chain in construction projects. Green construction or sustainable construction was not fully covered in this study. The findings will be beneficial for sustainable construction projects in developing countries for sustainability, although it did not extensively cover green buildings and related risks.

Supply chain risk is one of the major challenges facing the construction industry because construction projects are complex by nature involving a lot of activities and participants with different responsibilities and tasks therefore it is highly recommended to implement the proposed frameworks in this paper from the conceptualisation stage to the execution stage, carefully identifying parties involved in supply chain, supply chain management, stakeholders, tasks, activities, responsibilities and supply chain risks generated as a result of the interactions between stakeholders involved in supply chain management and coordination to realise project objectives. The findings will be a foundation for identifying and managing supply risks in sustainable buildings in developing countries.

Supply chain management is crucial in every enterprise. Managing supply chain risks is a major aspect of risk and disaster management and this implies that supply chain excellence is achievable by building communication, trust and mutual objectives, no blame culture, performance measurement, constant improvement and partnering.

The implementation of construction supply chain risk management framework involves assessing the impacts of these supply chain risks on the objectives of construction projects with respect to time, cost, safety, health, environment, stakeholders, financial performance, client satisfaction and quality.

Due to the globalization of trade, hundreds of millions containers pass every year through world ports. Such a situation is extremely challenging in terms of securing freight transport operations. However, costs and lead-times are still very important components of supply chains' performance models. Therefore, the drive for enhanced safety and security cannot be made at the expense of these other two factors of competitiveness, and the processes implemented by the global supply chain links, including the maritime port one, should tend to a joint optimization of trade facilitation and operational safety / security.

The research on which this paper feeds back falls within the frame of this mixed performance requirement. More specifically, the paper presents a decision-support system dedicated to managing the risks associated with land and maritime container transportation; this system is based on the modeling of the knowledge of a group of experts, and covers the three phases of risk identification, assessment and avoidance / mitigation.

This paper aims to analyze the risks faced by the Cambodian rice supply chain (RSC), including risk identification, risk investigation and risk management.

The first qualitative area of exploration from this exploratory sequential design is to identify the potential risks, in which the authors conduct in-depth interviews with ten different experts in Cambodia. Using the structural equation model (SEM) in AMOS and descriptive statistics analysis, this study investigates the risks that affect the RSC performance on an environmental, social and economic basis and subsequently proposes risk management strategies. The authors collect quantitative data from 200 Cambodian farmers through interviews and surveys.

The results illustrate that the farm households face 18 risk factors. The researchers consolidate 18 risk factors into four classifications: supply risks, production risks, demand risks and environmental risks. Nine experts out of the ten who were interviewed (90%) consider themselves “highly vulnerable” (with a rating of 4 or 5 on the Likert scale), while only one expert has a “neutral” stance (with a rating of 3 on the Likert scale); these results concerning risk identification are visualized in the likelihood effect matrix of the RSC. After investigating the risks, the authors found that RSC performance is significantly affected by the RSC risks. In particular, four groups are created, representing two different approaches to mitigate, avoid, transfer and cope with agricultural risks, i.e. ex ante and ex post risk management strategies.

This study fully answers research questions regarding risk identification, risk investigation and risk management.

Data breaches in the US healthcare sector have more than tripled in the last decade across all states. However, to this day, no established framework ranks all states from most to least at risk for healthcare data breaches. This gap has led to a lack of proper risk identification and understanding of cyber environments at state levels.

Based on the security action cycle, the National Institute of Standards and Technology (NIST) cybersecurity framework, the risk-planning model, and the multicriteria decision-making (MCDM) literature, the paper offers an integrated multicriteria framework for prioritization in cybersecurity to address this lack and other prioritization issues in risk management in the field. The study used historical breach data between 2015 and 2021.

The findings showed that California, Texas, New York, Florida, Indiana, Pennsylvania, Massachusetts, Minnesota, Ohio, and Georgia are the states most at risk for healthcare data breaches.

The findings highlight each US state faces a different level of healthcare risk. The findings are informative for patients, crucial for privacy officers in understanding the nuances of their risk environment, and important for policy-makers who must grasp the grave disconnect between existing issues and legislative practices. Furthermore, the study suggests an association between positioning state risk and such factors as population and wealth, both avenues for future research.

Theoretically, the paper offers an integrated framework, whose basis in established security models in both academia and industry practice enables utilizing it in various prioritization scenarios in the field of cybersecurity. It further emphasizes the importance of risk identification and brings attention to different healthcare cybersecurity environments among the different US states.

Since the 1980s, New public management has fostered the introduction of managerial approaches similar to those of the private sector in public administrations. Recently, the advantages of performing risk management in the public sector have been recognized; however, to the best of our knowledge, research on risk management in public administrations is underdeveloped, and there is a need to understand how risk management is performed. This paper addresses these issues and investigates whether and how risk management is performed in Italian public administration.

This study focused on a sample of 503 Italian municipalities and used a mixed research method. Through a qualitative content analysis of documents published on municipalities’ websites, data and information were collected and elaborated using quantitative indicators.

The main results are that a high percentage of large Italian municipalities perform risk management and comply with theoretical provisions on risk management, sometimes displaying isomorphic behavior in risk management practices.

This study provides a new perspective on risk management in Italian municipalities, contributes to filling a gap in the literature and suggests a theoretical perspective on municipalities’ approaches when introducing new managerial practices.

Risks can easily disrupt the demand–supply match targeted by sales and operations planning (S&OP). As surprisingly little is known of how organizations identify, assess, treat and monitor risks through tactical planning processes, this paper zooms in on the S&OP set-up and process parameters to explore how risks are managed through S&OP.

A multiple case study analyzes the S&OP processes of seven organizations in the process industry, drawing on 17 in-depth interviews with high-ranking representatives, internal and external documents, and a group meeting with participating organizations.

The study finds that organizations proactively design their S&OP based on their main risk focus stemming from the planning environment. In turn, such designs proactively support organizations' risk identification, assessment, treatment and monitoring through their S&OP execution. Reactively, a crisis S&OP meeting – making use of the structure of S&OP – can be used as a risk-treatment tool, and S&OP design can be temporarily adapted to deal with emerging risks.

This study is among the first to empirically elucidate risk management through S&OP. S&OP design, execution and adaption are identified as three interconnected strategies that allow organizations to manage risks. The design enables risk management activities in the monthly execution of S&OP. The reactive role of S&OP in risk management is particularly novel.

Despite the emergence and strategic importance of project risk management (PRM), its diffusion is limited mainly to large companies, leaving a lack of empirical evidence addressing SMEs. Given the socio-economic importance of SMEs and their need to manage risks to ensure the success of their strategic and innovative projects, this research aims to investigate how to adopt PRM in SMEs with a positive cost–benefit ratio.

This study presents an exploratory and explanatory research conducted through multiple-case studies involving 10 projects performed in Spanish and Italian small and medium-sized enterprises (SMEs).

The results obtained highlight how project features (commitment type, innovativeness, strategic relevance and managerial complexity) and firms' characteristics (sector of activity, production system and access to public incentives) influence PRM adoption, leading to different levels and types of benefits.

The paper offers practical indications about PRM phases, activities, tools and organizational aspects to be considered in different contexts to ensure the project's success and, ultimately, the company's growth and sustainability. Such indications could not be found in the literature.

The objective of this research is to investigate the capabilities of the ChatGPT GPT-4 model, a form of artificial intelligence (AI), in comparison to human experts in the context of construction project risk management.

Employing a mixed-methods approach, the study draws a qualitative and quantitative comparison between 16 human risk management experts from Finnish construction companies and the ChatGPT AI model utilizing anonymous peer reviews. It focuses primarily on the areas of risk identification, analysis, and control.

ChatGPT has demonstrated a superior ability to generate comprehensive risk management plans, with its quantitative scores significantly surpassing the human average. Nonetheless, the AI model's strategies are found to lack practicality and specificity, areas where human expertise excels.

This study marks a significant advancement in construction project risk management research by conducting a pioneering blind-review study that assesses the capabilities of the advanced AI model, GPT-4, against those of human experts. Emphasizing the evolution from earlier GPT models, this research not only underscores the innovative application of ChatGPT-4 but also the critical role of anonymized peer evaluations in enhancing the objectivity of findings. It illuminates the synergistic potential of AI and human expertise, advocating for a collaborative model where AI serves as an augmentative tool, thereby optimizing human performance in identifying and managing risks.

Organizations, would they be individual companies or large multi-firm networks, face a wide variety of potential risks requiring dedicated keen management. It all the better applies to supply-chains as risk, related to both physical and information flows, pervades the whole logistics network and has acquired a new and growing security dimension since 9/11. More specifically, as they are now under the permanent threat of terrorism, and because offering sufficient security levels is bound to become a necessary condition for global supply-chain membership, seaports need to adjust their risk management strategy and processes accordingly. In such a context, this paper aims at describing the project of a decision-support system, dedicated to container transit security-wise decision making and which features an expert-system architecture.