Hasil untuk "Risk in industry. Risk management"

A. Igwaran, A. Kayode, K. Moloantoa et al.

Cyanobacteria harmful algal blooms (cHABs) are increasingly becoming an emerging threat to aquatic life, ecotourism, and certain real estate investments. Their spontaneous yet sporadic occurrence has made mitigation measures a cumbersome task; moreover, current trends regarding anthropogenic activities, especially in agriculture and industry portend further undesirable events. Apart from the aesthetic degeneration they create in their respective habitats, they are equally capable of secreting toxins, which altogether present grave environmental and medical consequences. In this paper, we gave an update on factors that influence cHABs, cyanotoxin exposure routes, and environmental public health implications, especially impacts on fish, pets, and livestock. We discussed social economic impacts, risk assessment, and management problems for cHABs and, thereafter, assessed the extant management approaches including prevention, control, and mitigation of the proliferation of cyanobacterial blooms. In light of this, we suggest that more intensified research should be directed to the standardization of procedures for cyanotoxin analysis. Also, the provision of standardized reference material for the quantification of cyanotoxins is vital for routine monitoring as well as the development of strong in situ sensors capable of quantifying and detecting HABs cells and toxins in waterbodies to prevent the adverse impacts of cHABs. Also, more investigations into the natural and environmentally friendly approach to cyanobacteria management and the necessary and appropriate deployment of artificial intelligence are required. Finally, we wish to redirect the focus of public health authorities to protecting drinking water supply sources, agriculture products, and food sources from cyanotoxins contamination as well as to implement proper monitoring and treatment procedures to protect citizens from this potential health threat.

A. Rashid, Rizwana Rasheed, A. Ngah et al.

Purpose Supply chain (SC) management is more challenging than ever. Significantly, the pandemic has provoked global and economic destruction that appeared in the manufacturing industry as a “black swan.” Therefore, the purpose of this study was to examine the role of information processing and digital supply chain in supply chain resilience through supply chain risk management. Design/methodology/approach This study examines SC risk management and resilience from an information processing theory perspective. The authors used data collected from 251 SC professionals in the manufacturing industry, and the authors used a quantitative method to analyze the data. The data was analyzed using partial least squares-structural equation modeling. To confirm the higher-order measurement model, the authors used SmartPLS version 4 software. Findings This study found that information processing capability (disruptive orientation and visibility in high-order) and digital SC significantly and positively affect SC risk management and resilience. Similarly, SC risk management positively mediates the relationship between information processing capability and digital SC. However, information processing capability was found to have a more substantial effect on SC risk management than the digital SC. Research limitations/implications This study has both academic and practical contributions. It contributed to existing information processing theory, and manufacturing firms can improve their performance by proactively responding to SC disruptions by recognizing the pivotal role of study variables in risk management for a resilient SC. Originality/value The conceptual model of this study is based on information processing theory, which asserts that synchronizing information processing capabilities and digital SCs allows a firm to deal with unplanned events. SC disruption orientation and visibility are considered risk controllers as they allow the firms to be more proactive. An integrated model of conceptualizing the disruption orientation, visibility (higher-order) and digital SC with information processing theory makes this research novel.

Andrea Petrone, Paulo Borges, Fernando Pereira et al.

The Azores Archipelago is known for its important natural heritage, yet its ecosystems face a “green tsunami” in the form of numerous exotic and invasive species. This influx has wrought serious biodiversity loss and degradation of ecosystem services, representing one of the greatest threats to conservation across the islands. Originating from accelerated global trade and travel, these invasions impact human activities, public health and economic sectors alike. The PRIBES project intends to contribute to "The Regional Strategy for the Management of Terrestrial and Freshwater Exotic and Invasive Species in the Azores" (PRIBES-LIFE-IP- Estratégia regional para o controlo e prevenção de espécies exóticas invasoras - no âmbito do projeto LIFE IP AZORES NATURA, LIFE17 IPE/PT/000010). Recently, a plan was delivered to the Azorean government that proposes as key strategy: an unified Azores Invasive Species Task Force, a central coordination unit and island‐level focal points defined clear leadership roles for agencies and stakeholders (Axis 1), while stringent pre‐export controls, quarantine measures and risk analyses blocked new arrivals (Axis 2); parallel early‐detection teams and citizen‐science networks screened ports, airports and nurseries and triggered rapid eradication protocols (Axis 3), guided by a tiered framework of eradication, containment, control and mitigation chosen on feasibility and cost–benefit grounds (Axis 4). Simultaneously, national and international partnerships with IUCN (International Union for Conservation of Nature) ISSG (Invasive Species Specialist Group), CABI (Commonwealth Agricultural Bureaux International) and other island regions fostered data exchange (Axis 5), targeted scientific research investigated invasion pathways and management efficacy (Axis 6) and a central observatory consolidated occurrence records and risk assessments (Axis 7). Meanwhile, outreach campaigns, industry training and school programmes rallied public awareness (Axis 8). The AZORES BIOPORTAL (ABP) is a regional e-infrastructure dedicated to the mobilisation, curation and dissemination of biodiversity data from the Azores. It provides centralised data repository for researchers, policy-makers and educators; validated species checklists, including endemic, native and introduced species; integration with national and international biodiversity networks, including PORBIOTA, GBIF and LifeWatch ERIC; and tools for data visualisation and access, supporting conservation, ecological research and environmental management. ABP follows the FAIR (Findable, Accessible, Interoperable, Reusable) and supports open science. Mapping the occurrence of both native (endemic and non endemic) and exotic species is of key importance for the PRIBES project and the ABP intiative.A total of 243 vascular plant taxa were recorded across São Jorge Island, encompassing 89 families. These records correspond to 4,524 individual plant occurrences, including repeated observations of the same species across different sites. As each photographic observation is tied to unique geographic coordinates, all recorded specimens represent new spatial records for the Island’s flora. Amongst the taxa, 53 are considered endemic to the Azores, 131 are introduced, 58 are native and one species (Dracaena draco (L.) L.) is of indeterminate status. These correspond to 1,773 individual occurrences of endemic taxa, 1779 introduced, 970 native and one with indeterminate status. At the family level, 31 families include endemic taxa, 63 include introduced taxa, 34 include native taxa and one family contains a taxon of indeterminate status.The inventory includes several noteworthy Azorean endemics, spanning both ferns and flowering plants. Amongst the ferns, notable records include Crisped Buckler Fern Dryopteris crispifolia Rasbach, Reichst. & Vida, Azorean Buckler Fern Dryopteris azorica (Christ) Alston and Azorean Rockcap Fern Polypodium macaronesicum subsp. azoricum (Vasc.) Rumsey, Carine & Robba. Iconic flowering species and woody endemics recorded during the survey comprise Azorean Cherry Prunus lusitanica subsp. azorica (Mouill.) Franco, Azorean Buckthorn Frangula azorica Grubov, Azorean Eyebright Euphrasia grandiflora Hochst. ex Seub., Azorean Greater-hawkbit Leontodon filii (Hochst. ex Seub.) Paiva & Ormonde and Narrow-lipped Butterfly Orchid Platanthera micrantha (Hochst. ex Seub.) Schltr. Additional endemic taxa include Azorean Dock Rumex azoricus Rech.f., Azorean Holly Ilex azorica Gand., Azorean Umbrella Milkwort Tolpis azorica (Nutt.) P. Silva and the hemiparasitic Azorean Dwarf Mistletoe Arceuthobium azoricum Wiens & Hawksw. Other significant native species recorded include the ferns Wilson's Filmy-fern Hymenophyllum wilsonii Hook., Killarney Fern Vandenboschia speciosa (Willd.) G.Kunkel and Scaly Tongue-fern Elaphoglossum hirtum (Sw.) C.Chr., Cretan Thyme Thymus caespititius Brot., Many-stalked Spike-rush Eleocharis multicaulis (Sm.) Desv. and the more common native Firetree Morella faya (Aiton) Wilbur.Amongst the most problematic surveyed exotic invasive plant species are the Ginger Lily Hedychium gardnerianum Sheppard ex Ker-Gawl., Knotweed Persicaria capitata (Buch.-Ham. ex D.Don) H.Gross, Bigleaf Hydrangea Hydrangea macrophylla (Thunb.) Ser., Crofton Weed Ageratina adenophora (Spreng.) R.M.King & H.Rob., Australian Cheesewood Pittosporum undulatum Vent. and the Wandering Jew Tradescantia fluminensis Vell., as well as the American Pokeweed Phytolacca americana L.

Uzoma Okwudili Nnaji, Lucky Bamidele Benjamin, Nsisong Louis et al.

This review paper delves into the transformative potential of advanced risk management models in enhancing the resilience and efficiency of supply chain finance (SCF). By examining the application and development of Artificial Intelligence (AI), Machine Learning (ML), Big Data analytics, and blockchain technology, the paper highlights their role in transitioning from traditional reactive strategies to proactive and predictive risk management approaches. Despite the promising advantages, the paper also addresses the significant implementation challenges, model limitations, and regulatory and ethical considerations accompanying these technological advancements. Recommendations for effective deployment and areas for future research, particularly in overcoming existing hurdles and exploring emerging technologies, are also discussed. This comprehensive analysis aims to guide academics, industry professionals, and policymakers in harnessing advanced risk management models for a more robust SCF ecosystem.

Jianming Song, Matakala Munyinda, Philip Adu Sarfo

This study investigates the impact of risk management practices on sustainable project performance, specifically focusing on the mediating role of stakeholder engagement in the construction industry within emerging economies. It examines how risk identification, assessment, and mitigation contribute to sustainability’s environmental, economic, and social dimensions. Utilizing data collected from construction project managers involved in sustainability-driven projects, the findings confirm that all three risk management practices significantly enhance sustainable project performance. Furthermore, stakeholder engagement through communication, collaboration, and decision-making involvement plays a crucial mediating role, strengthening the effectiveness of risk management strategies in achieving sustainability goals.

Raghad Almashhour, M. Al-Mhdawi, A. Daghfous et al.

PurposeThis research seeks to systematically review studies on risk management (RM) in the construction industry, tracing its evolution from traditional approaches focused on risk identification, assessment, and mitigation to modern approaches that prioritize sustainability, resilience, and adaptability. It aims to develop a clear definition of sustainable risk management (SRM) and propose a framework for its implementation to ensure cost-efficient, resilient, and flexible operations.Design/methodology/approachA systematic literature review, guided by the PRISMA framework, analyzed 79 peer-reviewed articles (2014–2024) from Scopus, IEEE Xplore, Wiley, and other databases. Thematic grouping was used to categorize key SRM components, identifying emerging trends, gaps, and challenges in its adoption.FindingsThe study identifies key SRM pillars and attributes, demonstrating how SRM enhances the sustainability and resilience of RM practices. The proposed framework provides a structured approach to integrating SRM principles into construction operations, addressing implementation barriers such as regulatory misalignment, industry resistance, and technological integration. The findings also highlight the broader impact of SRM on shaping proactive and future-proof RM strategies.Practical implicationsConstruction firms and policymakers can benefit from the findings of this study by understanding the key pillars, attributes, and challenges of SRM. The proposed framework provides practical guidance for firms to evaluate and improve their current RM practices, particularly in addressing complex industry challenges and enhancing resilience and sustainability. Policymakers can use these insights to align regulations with SRM principles, supporting RM processes that are both effective and future-proof. Additionally, the study equips industry professionals with tools to enhance adaptability and long-term RM effectiveness.Originality/valueAs one of the first comprehensive reviews of SRM in the construction industry, this study consolidates key insights and provides a structured framework for both researchers and practitioners. It advances discussions on integrating sustainability within RM and highlights the need for empirical validation, particularly in assessing the role of digital transformation in SRM. By bridging theoretical gaps and practical applications, this research establishes a foundation for future studies on sustainable and technology-driven RM strategies.

Ion Popa, Sorina-Geanina Stănescu, Anișoara Duică et al.

The food industry faces complex challenges in managing supply chains, significantly affecting operational performance and costs. This paper explores the critical factors influencing the efficiency of food supply chains, such as product perishability, seasonality, climate change, and high logistics costs. The study uses an applied approach based on modelling a cost function that integrates the main components of supply chain expenses — procurement, transportation, warehousing, production and distribution — and how they are affected by industry-specific challenges. The proposed cost function allows for assessing the impact of these variables on total costs and identifying critical areas for optimisation. The results obtained demonstrate that monitoring logistical conditions, adjusting stocks based on seasonal forecasts and optimising transport routes are essential measures to reduce losses and increase the competitiveness of companies in the food industry. The study's applied impact consists of providing a practical cost optimisation tool applicable to manufacturers and distributors. The conclusions emphasise the importance of an integrated approach to risk and cost management in the food industry, providing recommendations for sustainable practices and strategies to increase long-term competitiveness.

Hail Jung, Daejin Kim

We study the effectiveness of textual information in predicting the returns of crude oil futures and understanding the behavior of market participants. Using a machine learning method to extract oil market sentiment from news articles, we find that the computed sentiment is significantly effective in explaining the crude oil futures returns, while existing textual analyses based on pre-defined dictionaries may mislead the contexts in the oil market. Consistent with previous findings that returns help explain the change in traders’ positions, the sentiment scores based on the machine learning method are also useful in explaining the behavior of different types of traders. Our empirical findings underscore the fact that accurately identifying textual information can increase the accuracy of oil price predictions and explain divergent behaviors of oil traders.

Dongri He, Ming Yang, Rong Jiang et al.

Abstract In recent years, the financial industry has become a disaster area for information leakage, which has serious implications for user privacy security. In the absence of risk identification and assessment, the risk will be difficult to prevent, and once the risk occurs it will directly cause serious losses. Therefore, this study plans to construct a comprehensive assessment framework combining fuzzy analytic hierarchy process (FAHP) and Dempster-Shafer (D-S) theory, aiming at assessing the weights and risk levels of the privacy security risks of financial services. (Privacy security risks refer to integrated factors in management, security, or other aspects that may lead to user privacy leakage, and they are considered an integrated concept.) The case study illustrates that the model and method proposed in this paper are effective and feasible. Finally, a comparison with the current mainstream privacy security assessment methods demonstrates that the method proposed in this paper is more capable of objectively and quantitatively reflecting the real privacy risks, providing users with more perspectives of the assessment results, and helping users to reasonably manage their personal privacy information, so as to effectively prevent and control the privacy risks.

Asid Ur Rehman, Vassilis Glenis, Elizabeth Lewis et al.

Flood risk managers seek to optimise Blue-Green Infrastructure (BGI) designs to maximise return on investment. Current systems often use optimisation algorithms and detailed flood models to maximise benefit-cost ratios for single rainstorm return periods. However, these schemes may lack robustness in mitigating flood risks across different storm magnitudes. For example, a BGI scheme optimised for a 100-year return period may differ from one optimised for a 10-year return period. This study introduces a novel methodology incorporating five return periods (T = 10, 20, 30, 50, and 100 years) into a multi-objective BGI optimisation framework. The framework combines a Non-dominated Sorting Genetic Algorithm II (NSGA-II) with a fully distributed hydrodynamic model to optimise the spatial placement and combined size of BGI features. For the first time, direct damage cost (DDC) and expected annual damage (EAD), calculated for various building types, are used as risk objective functions, transforming a many-objective problem into a multi-objective one. Performance metrics such as Median Risk Difference (MedRD), Maximum Risk Difference (MaxRD), and Area Under Pareto Front (AUPF) reveal that a 100-year optimised BGI design performs poorly when evaluated for other return periods, particularly shorter ones. In contrast, a BGI design optimised using composite return periods enhances performance metrics across all return periods, with the greatest improvements observed in MedRD (22%) and AUPF (73%) for the 20-year return period, and MaxRD (23%) for the 50-year return period. Furthermore, climate uplift stress testing confirms the robustness of the proposed design to future rainfall extremes. This study advocates a paradigm shift in flood risk management, moving from single maximum to multiple rainstorm return period-based designs to enhance resilience and adaptability to future climate extremes.

Sylvester Willys Namagwa

As life expectancy in Kenya increases, so does the need for efficient pension schemes that can secure a dignified retirement and protect members from old age poverty. Limited research, however, has explored the efficiency of these schemes under existing governance structures. This study addresses that gap by examining the combined effects of corporate governance, risk management, and industry regulation on pension scheme efficiency in Kenya. Using a quantitative design, we conducted a panel regression analysis on a seven-year secondary dataset of 128 Kenyan pension schemes, totaling 896 observations. Our results reveal significant insights That the presence of employee representatives on the board and effective risk management have a significant positive effect on efficiency. Conversely, independent board members exhibit a significant negative effect. Other factors, including top management representation, female board members, and industry regulation, showed no significant effect on efficiency in the joint model. These findings suggest that the impact of governance and risk management on efficiency is nuanced, with specific factors like employee representation playing a more prominent role. We propose that the electoral process for employee board members may introduce a Self Cleaning Mechanism that progressively enhances scheme efficiency. This mechanism offers a novel theoretical extension of Agency Theory, explaining the convergence of interests between elected trustees and scheme members.

Sylvester Willys Namagwa

The efficiency of pension schemes in Kenya invites elevated interest owing to the increasing pension contribution amounts and the expectation that benefits paid out of these schemes would protect members from old age poverty. The study investigates the intervening effect of risk management on the relationship between corporate governance and the efficiency of pension schemes in Kenya. The study employs panel data consisting of 896 observations from 128 schemes in a sample period from 2015 to 2021. The study finds that risk management significantly mediates the relationship between employee representatives on the board of trustees, as a component of corporate governance, and the efficiency of pension schemes. Consequently, the mediation effect of risk management indicates that when employee representatives are involved in governance, the presence of strong risk management practices ensures that their contributions lead to improved efficiency. Risk management, therefore, serves as a critical safeguard that enables governance structures to function more effectively and contribute to the overall performance of the scheme.

Adedoyin Tolulope Oyewole, Chinwe Chinazo Okoye, Onyeka Chrisanctus Ofodile et al.

The oil and gas industry is renowned for its inherent operational risks and complex safety challenges, necessitating robust Human Resource Management (HRM) strategies for effective safety measures and risk mitigation. This comprehensive review explores the evolving landscape of HRM practices within the oil and gas sector, focusing on their pivotal role in enhancing workplace safety and reducing operational risks. The review delves into the dynamic nature of the oil and gas industry, characterized by hazardous work environments, intricate technological processes, and a global workforce. Analyzing existing literature and case studies, the paper underscores the critical need for HRM strategies that prioritize safety culture, employee training, and proactive risk management. Key aspects include recruitment and selection processes tailored to identify candidates with a strong safety mindset, ongoing training programs to enhance competencies and awareness, and the establishment of a safety-centric organizational culture. Furthermore, the review examines the integration of technology and data analytics in HRM practices within the oil and gas sector. The utilization of advanced technologies for personnel training, real-time monitoring, and predictive analytics is discussed as a means to pre-emptively identify potential safety risks and proactively address them. Additionally, the paper highlights the importance of fostering communication and collaboration among employees, emphasizing the role of HRM in facilitating a transparent and open reporting culture. The findings of this review contribute to a deeper understanding of the multifaceted role played by HRM in promoting safety and mitigating risks within the oil and gas industry. As the industry continues to evolve, the adoption of innovative HRM strategies becomes imperative for organizations seeking to maintain a secure and resilient operational environment while safeguarding the well-being of their workforce. Keywords:  HR, Management, Safety, Risk Mitigation, Oil and Gas, Industry, Review.

Aynaz Monazzam, Jason Crawford

This study empirically examines the role of enterprise risk management (ERM) in developing and maintaining resilience resources and capabilities that are necessary for an organisation’s strategic transformation towards sustainability. Data was collected through 25 semi-structured interviews, one non-participant observation, and secondary sources in the context of a Swedish mining company undergoing a high-risk strategic transformation towards full decarbonisation. Following the temporal bracketing approach (Langley in Academy of Management Review 24:691–70, 1999) and employing thematic analysis (Gioia in Organizational Research Methods 16:15–31), the data was structured and analysed according to three phases from 2012 to 2023. The findings show: first, different ERM practices, such as risk governance frameworks, risk culture, risk artefacts, and risk awareness, influence resilience resources and capabilities. Second, the evolution of risk management practices from traditional risk management to ERM is an ongoing developmental process to ensure that risk management continues to be aligned with the company’s strategy. Third, in tandem with strategic changes, resilience in terms of resources and capabilities emerges over time and develops through a series of events, gradually enhancing the company’s ability to manage risks and uncertainties associated with multidimensional sustainability challenges. These results contribute to the ERM literature that follows the dynamic capability approach and also focuses on the relationship between ERM and strategy by adding more detailed empirical evidence from the risk management literature in relation to resilience resources and capabilities. Additionally, the results contribute to the resilience literature that follows a developmental perspective.

Adeoye Taofik Aderamo, H. Olisakwe, Yetunde Adenike Adebayo et al.

The oil and gas industry faces numerous health, safety, and environmental (HSE) risks due to the complexity of its operations. Traditional HSE management systems often rely on manual processes and reactive approaches, which can lead to inefficiencies and delayed responses to potential hazards. This paper proposes the integration of Artificial Intelligence (AI) into HSE management systems to enhance real-time safety monitoring and predictive risk management. By leveraging AI-driven technologies such as machine learning, computer vision, and predictive analytics, companies can proactively identify and mitigate risks, significantly reducing accidents, equipment failures, and environmental incidents. AI-enabled systems can process vast amounts of data from various sensors, drones, and other IoT devices in real-time, enabling continuous monitoring of hazardous conditions. Furthermore, predictive models can analyze historical data and operational patterns to foresee potential risks before they materialize, providing actionable insights to decision-makers. This approach allows for more dynamic, data-driven safety protocols, optimizing risk management strategies and improving compliance with regulatory standards. The paper will also explore the role of AI in automating routine safety checks, enhancing worker safety through real-time alerts, and minimizing human error. It will highlight case studies where AI-driven HSE systems have been successfully implemented, leading to substantial improvements in safety performance and operational efficiency. Additionally, the challenges and limitations of integrating AI into existing HSE frameworks, such as data security, workforce training, and technology costs, will be discussed. Ultimately, this paper demonstrates that AI-driven HSE management systems offer a transformative solution to risk mitigation in the oil and gas industry. By adopting AI technologies, companies can enhance safety, reduce operational risks, and create more resilient, efficient operations in an industry known for its hazardous environments.

Juan Vicente Barraza de la Paz, L. A. Rodríguez-Picón, Victor Morales-Rocha et al.

The large amount of information handled by organizations has increased their dependance on information technologies, which has made information security management a complex task. This is mainly because they cover areas such as physical and environmental security, organization structure, human resources and the technologies used. Information security frameworks can minimize the complexity through the different documents that contain guidelines, standards, and requirements to establish the procedures, policies, and processes for every organization. However, the selection of an appropriate framework is by itself a critical and important task, as the framework must adapt to the characteristics of an organization. In this paper, a general vision of the newest versions of the NIST CSF, ISO/IEC 27001:2022, and MAGERIT frameworks is provided by comparing their characteristics in terms of their approaches to the identification, assessment, and treatment of risks. Furthermore, their key characteristics are analyzed and discussed, which should facilitate the consideration of any of these frameworks for the risk management of complex manufacturing organizations.

Sonjai Kumar, Purnima Rao, M. Barai

The research aims to describe the state of enterprise risk management (ERM) in the insurance sector. It highlights emerging trends in the application of risk management in the insurance sector and thereby reports the prominent research gaps and new avenues for research in ERM. The research adopts a systematic literature review (SLR) approach, using 187 research papers spanning 44 years (1977–2021). The paper identifies the fact that most ERM and insurance sector research is performed in North America and Europe, while developing economies in Asia and Africa lag. The paper establishes a three-way relationship between ERM, risk management (RM) and risk-based capital (RBC) where RM is a subset of ERM and RBC is a driver of ERM. The research shows that very few studies are conducted on risk culture, three lines of defence and the role of chief risk officers. The determinants of ERM identified are board, firm size, audit and risk management committee and corporate governance. The determinants identified for firm value are return on assets, return on equity, profit, Tobin's Q, among others. This research provides a way for academicians, practitioners and policy makers to design effective strategies for implementing ERM in organisations.

Gobena Zelalem, Hirpa Eyob, Fikadu Yobsan et al.

Infectious bursal disease virus (IBDV) causes infectious bursal disease in poultry and poses a major challenge to the poultry industry globally. This study aimed to measure seroprevalences and so detect exposure to IBDV in backyard poultry in the selected zone of Horro Guduru Wollega. A cross-sectional study was conducted from January 2021 to November 2022. Blood samples were collected for serum extraction from 384 backyard chickens in the Horro and Horro Bulluq districts. IBDV antibody detection was conducted using an indirect ELISA serological diagnostic test. Questionnaires assessed poultry owners’ knowledge and health/hygiene management practices regarding the disease. The over all seroprevalence of IBDV was 14.84%. Significant variations in seroprevalence were seen based on district, bird age, bird sex, and flock size. Limited owner experience (just 1-3 years), disposing of carcasses in pits, and poor hygiene on the backyard premises were associated with higher IBDV seroprevalence. In conclusion, IBDV seroprevalence was linked to chicken management practices. Recommendations include improving poultry management among owners to control IBDV. The study indicates backyard poultry in the region have considerable IBDV exposure, and control should focus on improving management practices identified as high-risk, such as pit disposal of carcasses and poor hygiene.

R. A. Bahamid, S. Doh, M. A. Khoiry et al.

Construction is a critical sector of any economy in terms of value production, labor, and contributing to the gross national product. Managing risk is a relatively young area in Yemen’s construction sector, but it is gaining traction as building activity and competition rise. Construction firms mitigate risk by using a variety of risk management methods. Therefore, there is a need to assess these procedures in order to detect shortcomings. This research aims to establish the existing risk management strategies used in Yemeni building projects. Survey questionnaires were used to collect data. Respondents were drawn from Yemeni construction businesses. Risk management is not executed systematically, intentionally, or continuously, and most firms’ risk management procedures are reactive, semipermanent, informal, and unstructured, with no or few dedicated resources to address risks. This strategy is inconsistent with generally accepted risk management principles. Nonetheless, the findings suggest a general understanding of risk management and a willingness to learn from previous errors. The study of the findings suggests that risk identification approaches such as judgment and historical data are employed for risk analysis, and that the industry typically attempts to avoid or transfer risks in Yemeni building projects. The results shed light on the shortcomings of Yemen’s project management practices. To guarantee that construction projects obtain maximum value for money, project managers of big construction businesses in Yemen need a strong understanding of and training in globally accepted systematic risk management procedures. Finally, this study can help future stakeholders determine how to work together to manage risk.

Yudi Fernando, M. Tseng, Ika Sari Wahyuni-TD et al.

ABSTRACT This study aims to investigate the direct and indirect effects of information system security practices that observed the relationship effect between cyber supply chain risk management and supply chain performance. In Industry 4.0 era, a cyber-attack becomes unavoidable and needs to adopt cyber supply chain risk management to improve the firm. The data were collected from 105 firms in Malaysia through online surveys. The partial least squares structural equation modeling technique examined the model’s goodness and research hypothesis. The results revealed that operations, directly and indirectly, influence (via mediators) supply chain performance. In contrast, governance directly affects supply chain flexibility and indirect (via mediators) influence on supply chain performance; in addition, systems integration did not, directly, and indirectly, influence supply chain performance. This framework indicates the manufacturing industry and related parties with a better understanding of cyber supply chain risk management. Graphical Abstract