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In the face of increasingly severe global environmental problems, green transformation of the manufacturing industry has become a key issue. This study aims to explore the interaction between green innovation, intelligent decision-making and risk management in manufacturing industry. The purpose of this study is to explore the relationship between intelligent decision-making, risk management and green innovation, as well as the role and impact of risk management in the process of green innovation in manufacturing companies by building a statistical model. In this study, we investigated the intelligent decision-making problem and the risk management problem in the green transformation process through the literature survey method and statistical analysis. The results of the study show that intelligent decision-making positively affects the green innovation capability of manufacturing enterprises, there is a negative correlation between the complexity of risk management and the green innovation capability of manufacturing enterprises, and when manufacturing enterprises rely more on intelligent decision-making, the impact of risk management (positively or negatively) on green innovation is amplified, indicating an interactive effect between intelligent decision-making and risk management. This study provides valuable insights for the manufacturing industry to enhance smart decision-making in the face of environmental challenges. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aim: To explore Remote Area Nurses' experiences of the implementation of workplace health and safety policies and risk mitigation strategies in Australian very remote primary health clinics. Design: This qualitative study used online semi‐structured interviews, with participants purposively sampled to maximize variation in work location and service type. Data were analysed using a reflexive thematic analysis approach. Coding was carried out inductively, with NVivo 12 aiding data management. Setting: The interviews were conducted from 24 Februrary 2021 to 06 March 2021 with Remote Area Nurses from very remote primary health clinics in Australia. Participants: Fifteen Remote Area Nurses participated in the study. Results: Thematic analysis revealed varied approaches to workplace safety among the different health services and regions. While the spread of 'never alone' policies in many clinics addressed one of the significant risks faced by Remote Area Nurses, gaps remained even for hazards specifically highlighted in existing work health and safety legislation. Meaningful collaboration with staff and the community, local orientation, preparation for the role and providing quality care were protective factors for staff safety. Understaffing, unsafe infrastructure and inadequate equipment were common concerns among Remote Area Nurses. Conclusion: Health services need to prioritize workplace safety and take a continuous quality improvement approach to its implementation. This will include ensuring safety strategies are appropriate for the local context, improving infrastructure maintenance, and establishing sustainable second responder systems such as a pool of drivers with local knowledge. Implications for the Profession: Poor personal safety contributes to burnout and high turnover of staff. Nurses' insights into the barriers and enablers of current workplace safety strategies will aid policymakers and employers in future improvements. Reporting Method: COREQ reporting guidelines were followed. PIPE Statement: A panel of six Remote Area Nurses collaborated in the development of this project. [ABSTRACT FROM AUTHOR]

A novel model called Multi-Skilled, Multi-Mode Resource-Constrained Project Scheduling Problem with Discrete Time-Cost-Quality-Risk Trade-Off (MS-MRCPSP-DTCQRTP) was formulated, focusing primarily on minimizing project risks. This integer programming-based model generates various schedules that aim to optimize time, cost, quality, and risk, either individually or by achieving a balanced combination of these factors. By allocating accessible resources to the tasks of the project and adhering to precedence and resource constraints dictated by availability and skills, a collection of viable schedules was generated. Using a uniform distribution for time, cost, quality, and risk, three project case studies were derived from existing literature and expanded upon. These case studies were then employed to showcase the model. To generate optimal solutions, an open-source Python-MIP package employing the branch-and-cut methodology was employed. It was found that time is not a factor in minimizing the risk of a project, except for large-scale projects. Meanwhile, cost is a factor in all sizes of the project. Quality has no significant impact on risk. Project managers must strike a balance between resources that contain skills set with short to medium duration, moderate to high cost, and moderate to high-quality level to minimize project risk. [ABSTRACT FROM AUTHOR]

Background: Uncertainty poses a pervasive challenge in decision analysis and risk management. When the problem is poorly understood, probabilistic estimation exhibits high variability and bias. Analysts then utilize various strategies to find satisficing solutions, and these strategies can sometimes adequately address even highly complex problems. Previous literature proposed a hierarchy of uncertainty, but did not develop a quantitative score of analytical complexity. Methods: In order to develop such a score, this study reviewed over 90 strategies to cope with uncertainty, including methods utilized by expert decision-makers such as engineers, military planners and others. Results: It found that many decision problems have pivotal properties that enable their solution despite uncertainty, including small action space, reversibility and others. The analytical complexity score of a problem could then be defined based on the availability of these properties. [ABSTRACT FROM AUTHOR]

The Intergovernmental Panel on Climate Change (IPCC) exists to provide policy-relevant assessments of the science related to climate change. As such, the IPCC has long grappled with characterizing and communicating uncertainty in its assessments. Decision Making under Deep Uncertainty (DMDU) is a set of concepts, methods, and tools to inform decisions when there exist substantial and significant limitations on what is and can be known about policy-relevant questions. Over the last twenty-five years, the IPCC has drawn increasingly on DMDU concepts to more effectively include policy-relevant, but lowerconfidence scientific information in its assessments. This paper traces the history of the IPCC's use of DMDU and explains the intersection with key IPCC concepts such as risk, scenarios, treatment of uncertainty, storylines and highimpact, low-likelihood outcomes, and both adaptation and climate resilient development pathways. The paper suggests how the IPCC might benefit from enhanced use of DMDU in its current (7th) assessment cycle. [ABSTRACT FROM AUTHOR]

Why you should read this article: • To better understand the areas of risk that may become apparent across the life course of dementia • To recognise the ethical issues posed by trying to eliminate the risk of harm for people living with dementia • To consider how the principles of safeguarding apply to risk management in dementia care. What constitutes a risk for a person living with dementia may be perceived and prioritised differently by nurses from varying clinical backgrounds. Furthermore, risk may be perceived differently according to the context. This article outlines some of the social, psychological and physical risk factors relevant to people living with dementia across the life course of the condition. It is important that nurses understand their role in identifying, assessing and managing risk and are aware of the resources, policies, legislation and processes designed to support decision-making and minimise the risk of harm to people living with dementia, their families and carers. The authors hope that this article will support nurses to become more confident in identifying risk while encouraging them to adopt a proactive and person-centred approach to risk assessment and management. [ABSTRACT FROM AUTHOR]

This paper describes an assessment framework tool to analyze the performance of nature-based solutions (NBS) for hydro-meteorological risk management. The tool is based on multi-criteria decision analysis within the context of NBSs, an umbrella concept currently in focus that promotes nature and provides ecological and socio-economic benefits. The proposed tool includes the selection and application of key performance indicators (KPIs) for the co-benefits and costs associated with the implementation of NBSs. To ensure high societal impact, the tool relies on a participatory approach. Stakeholder preferences are taken into account within the assessment process. As such, the assessment framework can be used as a design and selection tool for NBSs and other alternative measures, including grey and hybrid solutions. The proposed procedure can be adapted to the specific socio-environmental context and hydro-meteorological risk by tailoring the set of relevant KPIs. The assessment framework is useful for monitoring the implemented measures and to document their effectiveness. The methodology provides quantitative and transparent documentation of hydro-meteorological risk management processes, useful for decision- and policy-makers, and stakeholders dealing with NBS measures. [ABSTRACT FROM AUTHOR]

Energy markets are crucial for integrating Distributed Energy Resources (DER) into modern power grids. However, this integration presents challenges due to the inherent variability and decentralized nature of DERs, as well as poorly adapted regulatory environments. This paper proposes a medium-term decision-making approach based on a comprehensive suite of computational tools for integrating DERs into Colombian energy markets. The proposed framework consists of modular tools that are aligned with the operation of a Commercial Virtual Power Plant (CVPP). The tools aim to optimize participation in bilateral contracts and short-term energy markets. They use forecasting, uncertainty management, and decision-making modules to create an optimal portfolio of DER assets. The suite's effectiveness and applicability are demonstrated and analyzed through its implementation with heterogeneous DER assets across various operational scenarios. [ABSTRACT FROM AUTHOR]

This study highlights the disproportionate number of fatal and non-fatal accidents in general aviation (GA) compared to airline carriers, emphasizing the need to investigate the contributing factors to these incidents. It identifies poor decision-making and a lack of situational awareness as key issues and presents a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to analyze preflight information used by GA pilots. The findings underscore the significance of operational factors in ensuring a successful flight and suggest modifications to pilot license renewal processes, with an emphasis on the adoption of digital preflight tools. A new theoretical framework based on the operational factors identified is also introduced, which could serve as a foundation for future studies and interventions aimed at enhancing safety in general aviation. [ABSTRACT FROM AUTHOR]

The purpose of the research was to create and validate a safety performance decision-making tool to transform a reactive safety model into a predictive, decision-making tool, specific to large, collegiate Title 14 of the Code of Federal Regulations (CFR) Part 141 flight training organizations, to increase safety and aid in operational decision-making. Using Monte Carlo simulation, the study conducted simulation runs based on true operational ranges to simulate the operating conditions possible within large, collegiate CFR Part 141 flight training organizations with varying levels of controllable resources in terms of personnel (Aviation Maintenance Technicians and Instructor Pilots) and expenditures (active flight students and available aircraft). The study compared the output from three different Verification Scenarios. ANOVA testing indicated no significant differences appeared among the three different groups. Four What-if Scenarios were conducted by manipulating the controllable inputs. Changes to the controllable inputs are reflected by variations to the outputs demonstrating the utility and potential for the safety performance decision-making tool. The outputs could be utilized by safety personnel and administrators to make more informed safety-related decisions without expending unnecessary resources. [ABSTRACT FROM AUTHOR]

Explaining farmer decision making using cumulative prospect theory is of increasing importance. We present a systematic review on European farmers' preferences under the cumulative prospect theory framework. We identified 17 studies covering 2324 farmers from 12 European countries. All studies report that (on average) farmers are: (i) risk averse, (ii) loss averse, and (iii) overweight small probabilities and underweight large probabilities. However, there is a large heterogeneity across and within studies. These findings have implications for the analysis and design of policy and insurance. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is advancing across technology domains including healthcare, commerce, the economy, the environment, cybersecurity, transportation, etc. AI will transform healthcare systems, bringing profound changes to diagnosis, treatment, patient care, data, medicines, devices, etc. However, AI in healthcare introduces entirely new categories of risk for assessment, management, and communication. For this topic, the framing of conventional risk and decision analyses is ongoing. This paper introduces a method to quantify risk as the disruption of the order of AI initiatives in healthcare systems, aiming to find the scenarios that are most and least disruptive to system order. This novel approach addresses scenarios that bring about a re-ordering of initiatives in each of the following three characteristic layers: purpose, structure, and function. In each layer, the following model elements are identified: 1. Typical research and development initiatives in healthcare. 2. The ordering criteria of the initiatives. 3. Emergent conditions and scenarios that could influence the ordering of the AI initiatives. This approach is a manifold accounting of the scenarios that could contribute to the risk associated with AI in healthcare. Recognizing the context-specific nature of risks and highlighting the role of human in the loop, this study identifies scenario s.06—non-interpretable AI and lack of human–AI communications—as the most disruptive across all three layers of healthcare systems. This finding suggests that AI transparency solutions primarily target domain experts, a reasonable inclination given the significance of "high-stakes" AI systems, particularly in healthcare. Future work should connect this approach with decision analysis and quantifying the value of information. Future work will explore the disruptions of system order in additional layers of the healthcare system, including the environment, boundary, interconnections, workforce, facilities, supply chains, and others. [ABSTRACT FROM AUTHOR]

Blockchain technology holds considerable amount of potential for all types of industries by executing transactions in a verifiable, efficient, and permanent channel. It has been widely viewed as a standard requirement for making industry ready for the future, but when it comes to practical applications, it still arouses numerous risks/challenges that need to be addressed. Therefore, it is essential to address this gap and establish a comprehensive and effective practical framework to align the information technology revolution with sustainable value creation. The purpose of this research is to realize to what extent an enterprise legacy system's transformation benefits a blockchain-based system and to minimize its specific risk through a hybrid fuzzy MRDM (multiple rule-based decision making) model that integrates data envelopment analysis with rough set theory (DEA-RST) and the fuzzy DEMATEL approach grounded on a questionnaire derived from domain experts. We aim to point out the inherent risks of blockchain-based technology adoption and to assist senior engineers in designing or adopting a suitable architecture for practical operation and planning of any future integration and development. The potential risk evaluation of business blockchain adoption reveals that the priority improvement sequence based on dimensions is smart contract risk, value transfer risk, and standard risk. Furthermore, law and regulation are the most critical criteria. [ABSTRACT FROM AUTHOR]

Copyright of Revista Cubana de Ciencias Informáticas is the property of Universidad de las Ciencias Informaticas (UCI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: While artificial intelligence (AI) has shown its promise in assisting human decision, there exist barriers to adopting AI for decision-making. This study aims to identify barriers in the adoption of AI for automated organisational decision-making. AI plays a key role, not only by automating routine tasks but also by moving into the realm of automating decisions traditionally made by knowledge or skilled workers. The study, therefore, selected respondents who experienced the adoption of AI for decision-making. Design/methodology/approach: The study applied an interpretive paradigm and conducted exploratory research through qualitative interviews with 13 senior managers in South Africa from organisations involved in AI adoption to identify potential barriers to using AI in automated decision-making processes. A thematic analysis was conducted, and AI coding of transcripts was conducted and compared to the manual thematic coding of transcripts with insights into computer vs human-generated coding. A conceptual framework was created based on the findings. Findings: Barriers to AI adoption in decision-making include human social dynamics, restrictive regulations, creative work environments, lack of trust and transparency, dynamic business environments, loss of power and control, as well as ethical considerations. Originality/value: The study uniquely applied the adaptive structuration theory (AST) model to AI decision-making adoption, illustrated the dimensions relevant to AI implementations and made recommendations to overcome barriers to AI adoption. The AST offered a deeper understanding of the dynamic interaction between technological and social dimensions. [ABSTRACT FROM AUTHOR]

This paper presents a generic component for Analytic Hierarchy Process (AHP)-based decision support in risk management. The component was originally dedicated to railway transportation issues; however, its generality enabled it to extend its functionality for other domains too. To show the generality of the module and possibility of its application in other domains, an environmental case was run. Its goal was to select methods for planning the post-mining heap revitalization process, especially decision-making focusing on the selection of the most advantageous revitalization option on the basis of the Analytic Hierarchy Process and different, non-financial factors, e.g., social, environmental, technological, political, etc. Taking into account expert responses, the suggested solution was related to energy production. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic caused most collegiate recreation facilities and programs to shut down to slow the spread of the virus. Public health guidelines from the state/province, federal, and global level have inundated recreation professionals with messaging to inform reopening strategies. Rasmussen's risk management framework served as a guide to explore the stakeholders, decisions, and environmental conditions influencing COVID-19 reopening plans and policies in collegiate recreation. A case study of four collegiate recreation departments was conducted, with document analysis employed to examine and interpret reopening plans. The findings highlight campus offices and leadership as major stakeholders in reopening guidelines; risk management decisions utilizing a phased reopening approach that emphasized screening, mask mandates, social distancing and touch points, equipment and floor plans, and cleaning; and communication as a critical environmental condition. Implications for collegiate recreation practitioners include strategies for decision making, dissemination of information, staff training, risk mitigation, and policy enforcement. [ABSTRACT FROM AUTHOR]

The focus of this study is to examine the short‐ and long‐term causal effects of natural disaster shocks on the stock market and business confidence level in the time‐frequency domain by studying the co‐movements between earthquake data and the Istanbul Stock Index closing prices, using the Wavelet Coherence and Phase Difference analyses. The empirical findings reveal that the relationship between earthquake events and financial markets is not stable over time and across different time horizons. The linkage becomes stronger in the long term when the impact of the earthquake event coincides with a financial crisis reflecting a combined effect. This nexus is also strong during the years 2007–2011 reflecting a similar combined effect of both an earthquake event and the global financial crisis. Meanwhile, co‐movement between the earthquake and the financial market index implies a negative effect in the period 2011–2012, indicating short‐run effects of stock market shocks. Differentiating these short‐term and long‐term effects has implications for risk management and policymaking. [ABSTRACT FROM AUTHOR]

Risk Management is an important task that helps to monitor the business application to eliminate the political, financial, cultural, and social consequences. The organization's risk decision is affected by several characteristics, such as lack of accountability and risk decision-making. The difficulties are resolved by applying the Machine-Learning related Business Decision Making Approach (ML-BDMA). The created framework helps to reduce the difficulties in decision-making while managing the organization's risk. The Business Decision Making process works along with the Optimistic Predictive Techniques (OPT) that are used to identify the risk which leads to attaining the business objective. This process categorizes the risk according to the qualitative characteristics of business data. The system's effectiveness was evaluated using the experimental result in which the system ensures a 98.93% performance rate, 92.25% reliability rate, 93.47% authenticity rate, 91.11% risk management rate, and 97.77% development rate while making a business decision. [ABSTRACT FROM AUTHOR]

Decentralized finance (DeFi) promises a revolution in financial accessibility, transparency, and automation. Yet, its very novelty exposes participants to a number of additional risks and challenges. This study aims to address the risks associated with DeFi, while also conducting a comparative analysis to those of classical/traditional finance (TradFi). After introducing DeFi and its defining characteristics, such as the use of smart contracts, blockchain technology, and decentralized governance, the paper outlines the principal risks associated with DeFi. Drawing insights from an extensive literature review of 200 recent articles, of which 50 were thoroughly analyzed, the study compares risks of DeFi and TradFi, categorizing these into systematic and unsystematic risks. Furthermore, we introduce the 'risk wheel', an innovative tool tailored to understand and navigate the subtleties of DeFi risks, finding potential applications in risk assessment, management, and even education. This paper's primary objective is to provide a detailed and impartial examination of the risks associated with DeFi and their comparison to traditional finance in order to assist stakeholders in making informed decisions and mitigating possible losses. [ABSTRACT FROM AUTHOR]

Purpose: The pharmaceutical industry faces multiple risks that adversely affect its performance. Within these risks, some dependencies have been observed, which help in streamlining the mitigation efforts. Therefore, the present work identifies and categorizes various risks/sub-risks in cause–effect groups, considering uncertainty in the decision-making process. Design/methodology/approach: An extensive literature review and experts' opinions were utilized to identify and finalize the risks faced by the pharmaceutical industry. For further analysis, data collection was done using a questionnaire focusing on finalized risks. Based on the data, the causal relation under uncertainty between various risks/sub-risks was identified using a multi-criteria decision making (MCDM) technique, i.e. intuitionistic fuzzy DEMATEL, in a pairwise manner. Findings: The results show that the three most prominent risk categories are operational, demand/customer/market and financial. Also, out of the seven main risks, only supplier and operational are categorized within the effect group and the rest, i.e. financial, demand, logistics, political and technology within the cause group. The sub-risks within each category have also been categorized into cause–effect groups. The mitigation of cause group risks will help in economize the financial resources and improve the performance and resilience of the industry. Originality/value: There is insufficient research on identifying the causality among the pharmaceutical industry risks. Additionally, an extensive discussion on the identified cause–effect groups is also missing in the literature. Therefore, in this work, efforts have been made to determine the prominent risks for the Indian pharmaceutical industry that will be helpful for channelizing the resources to mitigate risks for a resilient industry. [ABSTRACT FROM AUTHOR]

Avalanche studies have undergone a transition in recent years. Early research focused mainly on environmental factors. More recently, attention has turned to human factors in decision making, such as behavioural and cognitive biases. This article adds a social component to this human turn in avalanche studies. It identifies lessons for decision making by groups of skiers from the perspective of social choice theory, a sub-field of economics, decision theory, philosophy and political science that investigates voting methods and other forms of collective decision making. In the first part, we outline the phenomenon of wisdom of crowds, where groups make better decisions than their individual members. Drawing on the conceptual apparatus of social choice theory and using idealised scenarios, we identify conditions under which wisdom of crowds arises and also explain how and when deciding together can instead result in worse decisions than may be expected from individual group members. In the second part, we use this theoretical understanding to offer practical suggestions for decision making in avalanche terrain. Finally, we make several suggestions for risk management in other outdoor and adventure sports and for outdoor sports education. [ABSTRACT FROM AUTHOR]

Background: This paper proposes a risk analysis of transport requalification projects in the urban mobility problem caused by a mining disaster related to irregular rock salt extraction in the city of Maceió, Brazil. Methods: The model is composed of three main steps: problem definition, risk management, and decision analysis. For this purpose, we used the Picture Fuzzy-Delphi method for data collection and experts' judgment elicitation and the Delphi method was used to assess the problem without interference from others. In addition, we used Picture Fuzzy Sets (PFSs) to incorporate uncertain information in the decision-making process. Results: The results of the proposed model demonstrated consistency and relevance to the discussion. The application of methods shows the risks of the project based on a general perspective. It evaluates the sustainability tripod: economic, environmental, and social points of view, assessing the occurrence risk and intensity of the risk. Conclusions: The main objective of the work was achieved; however, some limitations of this study are related to the methods used to assess risks and the options of projects of requalification available at the moment of data analysis. This paper contributes because it systematizes the risk management of projects related to requalification in urban mobility. [ABSTRACT FROM AUTHOR]

Objectives. The objective of this study is to use bibliometric and systemic terms to select and analyze a set of articles dealing with occupational risk management in the civil construction industry. Methods. Knowledge development process – constructivist (ProKnow-C) methodology was used as an intervention instrument to guide the selection of the bibliographic portfolio and to support systemic analysis. Results. The bibliographic portfolio was created and included 20 articles. Bibliometric highlights were relevant articles, authors, countries of origin, journals and keywords. Systemic analysis showed opportunities to contribute to the state of the art in all areas of knowledge, enabling the researcher to understand theoretical bases, gaps and research opportunities for the theme and thus establish their research question in accordance with the established knowledge vision. Conclusions. The systemic analysis of each article in the bibliographic portfolio based on the decision support model and areas of knowledge present in each stage of its construction measures the amplitude of the knowledge at each stage, and therefore the completeness of or gaps in each article. This clarifies the overall state of and gaps in the field, showing opportunities for new research. [ABSTRACT FROM AUTHOR]

In asset management for energy portfolios, quantitative methodologies are typically employed. In Brazil, the NEWAVE computational model is universally used to generate scenarios of hydraulic production and future prices, which result in revenue distributions. These distributions are then used to estimate the portfolio's revenue and assess its risk. Although this is a well-established analysis, it has some shortcomings that are not always considered. The validity of the revenue series constructed by NEWAVE, especially in long-term analysis, is a real problem for agents concerning the acquisition of assets such as power plants. Another issue is the disregard for other objectives that are important for the operationality of the management task and are often ignored, such as operational risk. To address these limitations, this work combines the areas of multicriteria decision making under uncertainty and risk management and presents a methodology for evaluating the acquisition of long-term energy assets, as well as a practical application of the proposed method. Investment alternatives are evaluated in multiple developed scenarios, so it is possible to measure how robust a given option is. By analyzing several scenarios simultaneously, a larger region of uncertainties can be covered, and therefore, decision making becomes more secure. The proposed methodology includes six objectives, designed to address a wider range of stakeholder needs. This approach is applied to an illustrative portfolio, producing results that allow for a more comprehensive understanding of decision attributes. Therefore, this work not only addresses the current limitations in the field but also adds an original contribution by considering simultaneously several scenarios and integrating multiple objectives in a robust and secure decision-making framework. [ABSTRACT FROM AUTHOR]

As a way forward in assessing how the Old Testament wisdom tradition might speak to decisions in a modern medical context, in this paper, I propose exploring the iconographic function of the "tree of life" in the Old Testament, which is consistently associated with both wisdom as well as life and health, in order to tease out two-related issues that can help in providing a Christian theological framework for thinking about the problem of the medicalization of risk: first, how should the natural and good human desire for health and long life be framed in terms of the pursuit of wisdom? And, second, how might a sapientially formed character approach risk and uncertainty in making medical decisions? The answers to these questions help establish a framework in which more specific questions related to the medicalization of risk can be assessed. As such, this paper is deliberately programmatic and perspectival rather than prescriptive. [ABSTRACT FROM AUTHOR]

In recent decades, one of the most interesting innovations has undoubtedly been the application of resilience principles to the study and mitigation of seismic risk. However, although new rigorous mathematical models have become available in the context of seismic resilience assessment, their applications to real case studies focus on a local scale, or even single structures. Consequently, new models and procedures are absolutely necessary to adopt resilience measurements in the formulation of mitigation strategies on a national or subnational scale. Given the crucial role of residential buildings in the global resilience of Italian cities against major earthquakes, a new framework for large-scale applications is proposed to roughly measure the seismic resilience of communities through the integration of an empirical recovery function based on the reconstruction process of housing systems in the aftermath of the 2012 Northern Italy Earthquake. As a first attempt, the framework is applied to housing systems in the southern regions of Italy by modelling their physical damage with vulnerability curves defined on the basis of macroseismic approaches. The main results are presented and discussed in terms of average functionality levels over time in order to compare and understand the recovery capacity of the considered housing systems. [ABSTRACT FROM AUTHOR]

he article focuses on current issues in the field of application of Decision Support Systems (DSS) in the management of photovoltaic (PV) projects. In the era of renewable energy growth and the increasing need for environmentally sustainable solutions, PV projects are becoming increasingly important. In order to successfully execute such projects, informed decision-making and optimal resource utilization are required. One effective solution in this direction is the implementation of an integrated DSS, which supports the decision-making process. The article assesses the functions of DSS in PV projects, with a particular emphasis on its capabilities for data management and analysis, scenario analysis and simulation, as well as risk assessment and management. Using the case method, the importance of DSS in improving decision-making, optimizing resource allocation and increasing the efficiency of PV projects is argued. [ABSTRACT FROM AUTHOR]

The primary purpose of this research was to understand the importance of supply chain strategies in the field of supply chain risk management, emphasizing the effectiveness and efficiency of agile and lean strategies to create resilience and robustness in the supply chain. Data was collected from 392 supply chain experts working in Iran's automotive industry to test hypotheses through structural equation modeling. The findings of this study show that Market Orientation (MO) (as an external force) has more signicant impact on the development of Agile Strategy (AS) than Lean Strategy (LS). In contrast, the Quality Management (QM) system (as an internal force) is highly correlated with the development of lean supply chain strategies. Moreover, agile and lean strategies also have a signicant impact on a resilient and Robust Supply Chain (RB). The proposed model helps organizations understand and create an ideal supply chain by implementing the right combination of both agile and lean supply chain strategies, which in turn helps to create a resilient and RB. Therefore, the findings of this study help policymakers to improve supply chain strategies by incorporating new management practices. This is original research that has various valuable insights for academic researchers and also supply chain strategy professionals as it reveals empirical evidence of the past vital concepts. [ABSTRACT FROM AUTHOR]

This paper offers a framework for understanding how management decision errors may lead to iatrogenic outcomes. Organizational iatrogenesis is the unintentional genesis of qualitatively different problems due to mistakes such as unwise intervention strategies, well-intended work on the wrong problems, or ignorance of significant correlations. Iatrogenic outcomes sometimes involve "black swan" (Taleb, 2007) scenarios. Three such error types are well documented in the literature: type I, or α (alpha), type II, or β (beta), and type III. Type I and type II errors form the foundation of interpreting data in statistics. Mitroff and Betz (1972) introduced type III, "error of the third kind," a meta-error of focusing on or solving the "wrong" problem(s). We add innovation error (type IV), action errors (type V and type VI), and the cascading iatrogenic error of the seventh kind (type VII), a dangerous source of irreversible organizational iatrogenesis. While many undesirable outcomes result from uncontrollable interaction of exogenous black swans with unavoidable endogenous ignorance, many are the result of controllable endogenous factors such as poor choices, faulty tactics, poor vision, "gung-ho" attitudes toward action, lack of patience, ignorance, and faulty data analysis. Our framework clarifies for leaders where and why to drill down in the decision process to lower risk of uncontrollable iatrogenic outcomes. [ABSTRACT FROM AUTHOR]

This paper aims to stimulate debate and prompt further research regarding the training, development and Higher Education (HE) of aspiring adventure professionals (AP's). The design of the paper draws on 10 years of observations and trends within the HE outdoor degree teaching sector, in tandem with the authors' own extensive professional outdoor careers. Initial findings indicate the role and significance of Professional Judgement and Decision Making (PJDM) in professional practice within adventure sports (AS) coaching and leadership. Consequently, PJDM is found to be a vital element of the education of the AP. The training and practice of these PJDM skills is a situated cognition that is conspicuous in its absence within many coach and leader education programmes where the AP is required to balance pedagogical and welfare demands in consequential environments. A cognitive apprenticeship approach is advocated which builds on recent articles highlighting the importance of PJDM in such domains. [ABSTRACT FROM AUTHOR]

Pathogenic variants in moderate penetrance breast cancer susceptibility genes, such as ATM and CHEK2, confer a two‐ to five‐fold increased lifetime risk for breast cancer. The National Comprehensive Cancer Network has guidelines for breast surgeons to utilize when counseling women with pathogenic variants in these genes; however, previous studies indicate that other factors impact breast surgeons' recommendations to patients. This study investigated factors influencing management recommendations presented by breast surgeons to women with pathogenic variants in moderate penetrance breast cancer susceptibility genes. Focus groups and interviews were conducted with breast surgeons practicing in Ohio, Kentucky, and Indiana. A total of 15 breast surgeons from eight different hospitals participated in five focus groups and three individual interviews. Participants discussed factors they consider when making management recommendations for risk reduction in women with pathogenic variants in moderate penetrance breast cancer susceptibility genes. Participants provided risk management recommendations for given scenarios. Patient motivation/opinion, family history, patient current health status, patient personal preference, and patient anxiety level were among the most common factors mentioned. It appeared that how these factors are valued and applied in practice varies. There was no consensus among breast surgeons on which risk‐reducing management options they would recommend in each scenario. There are many factors breast surgeons take into consideration when making recommendations for this patient population. This information could inform future research on decision making around treatment for individuals with pathogenic variants in moderate penetrance breast cancer susceptibility genes. [ABSTRACT FROM AUTHOR]

Objective Mobility dysfunction can have an impact on safety, and this fact is particularly relevant to hospital settings. There are no clear standards for how health care professionals should approach the aim to encourage mobility without compromising safety. The objective of this scoping review was to identify factors that shape health care professionals' decision making for people in hospitals, in situations in which mobility and safety are taken into consideration. Methods For this scoping review, 4 databases (Medline, CINAHL, PubMed, and Scopus) were searched. Terms previously agreed upon were used to identify peer-reviewed articles related to decision making by health care professionals in hospital settings (acute and rehabilitation), in which safety and mobility were factors for consideration. An initial screening of titles and abstracts was conducted by a single reviewer. Two reviewers independently screened the full texts of the remaining articles. The key findings of the articles were synthesized to determine common themes. Results After 10,717 articles were screened, 28 met the inclusion criteria. Ten themes influencing decision making were identified: Clinical factors assessed; health care professional experience; limited use of standardized tests; impact of institutional governance; risk/benefit trade-off; capacity for decision making; role of the multidisciplinary team; families and others; communication; and fear of negative outcomes. Conclusion A wide range of factors influence health care professionals' decision making. The decisions can be complex, and individual priorities may vary in the balancing of safety with promotion of independence. Health care professionals rely on experience, clinical judgment, and shared decision making. Impact This review brings attention to many factors that contribute to decision making when mobility and safety are the factors. Health care professionals should include patients' values and monitor the impact of their personal preferences on this process. [ABSTRACT FROM AUTHOR]

Copyright of Business Consultant / Poslovni Konsultant is the property of FINconsult Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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The utility of decision-making tools for the risk governance of nanotechnology is at the core of this paper. Those working in nanotechnology risk management have been prolific in creating such tools, many derived from European FP7 and H2020-funded projects. What is less clear is how such tools might assist the overarching ambition of creating a fair system of risk governance. In this paper, we reflect upon the role that tools might and should play in any system of risk governance. With many tools designed for the risk governance of this emerging technology falling into disuse, this paper provides an overview of extant tools and addresses their potential shortcomings. We also posit the need for a data readiness tool. With the EUs NMP13 family of research consortia about to report to the Commission on ways forward in terms of risk governance of this domain, this is a timely intervention on an important element of any risk governance system. [ABSTRACT FROM AUTHOR]

Featured Application: This paper presents a review of literature on urban resilience, highlighting research gaps and suggesting solutions such as using asset and disaster risk management methods combined with GIS-based decision-making tools to improve resilience in urban areas. This can be applied in the field of urban planning and design, disaster risk management and asset management planning decisions to enhance the ability of cities and communities to optimally withstand and recover from disruptions. Urban Resilience (UR) enables cities and communities to optimally withstand disruptions and recover to their pre-disruption state. There is an increasing number of interdisciplinary studies focusing on conceptual frameworks and/or tools seeking to enable more efficient decision-making processes that lead to higher levels of UR. This paper presents a systematic review of 68 Scopus-indexed journal papers published between 2011 and 2022 that focus on UR. The papers covered in this study fit three categories: literature reviews, conceptual models, and analytical models. The results of the review show that the major areas of discussion in UR publications include climate change, disaster risk assessment and management, Geographic Information Systems (GIS), urban and transportation infrastructure, decision making and disaster management, community and disaster resilience, and green infrastructure and sustainable development. The main research gaps identified include: a lack of a common resilience definition and multidisciplinary analysis, a need for a unified scalable and adoptable UR model, margin for an increased application of GIS-based multidimensional tools, stochastic analysis of virtual cities, and scenario simulations to support decision making processes. The systematic literature review undertaken in this paper suggests that these identified gaps can be addressed with the aid of asset and disaster risk management methods combined with GIS-based decision-making tools towards significantly improving UR. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) applications are among the major trends of nowadays. Billions of connected devices are creating great business profits and performing a multitude of automated tasks in many daily human activities. In healthcare service delivery, IoT capabilities are difficult to overestimate, they are progressively becoming entangled and commonly coined Internet of Medical Things (IoMT). The participating nodes in IoMT networks generate, collect and exchange huge amounts of extremely private and sensitive data. Numerous security vulnerabilities arise due to the complexity and the heterogeneity of the technology. New risks, born out of IoMT systems, cannot easily be supported by existing risk management frameworks. The existing cyber-security risk assessment methods and approaches, deployed in several organizations, will not address the IoMT inherent risks properly. This study includes a comprehensive review of IoMT systems. Popular risk assessment methods are discussed and their suitability to IoMT is dealt with in detail. Based on this study, we propose a framework to enhance trust and help with decision making in e-healthcare environments given its high-risk exposure. The proposal is based on a quantified risk assessment approach. Our aim is to define a novel approach/model for improving trust and risk management in an e-health context. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for prioritising bridge retrofits within a regional road network subject to uncertain seismic hazard, using a technique that accounts for network performance while avoiding the combinatoric computational costs of exhaustive searches. Using global variance-based sensitivity analysis, a probabilistic ranking of bridges is determined according to how much their retrofit statuses influence the expected cost of the road network disruption. Bridges' total-order sensitivity (Sobol') indices are estimated with respect to the expected cost using the hybrid-point Monte Carlo approximation method. A bridge's total-order Sobol' index measures how much its retrofit status influences the variance of the expected cost of the road network performance and accounts for the effect of its interactions with other bridges' retrofit states. For 71 highway bridges in San Francisco, a retrofit strategy based on bridges' total-order Sobol' indices outperforms other heuristic strategies. The proposed method remains computationally tractable while accounting for the probabilistic nature of the seismic hazard, the uniqueness of individual bridges, network effects, and decision-makers' priorities. Because this method leverages existing risk assessment tools and models without imposing further assumptions, it should be extensible to other types of networks under different types of hazards and to other decision variables. [ABSTRACT FROM AUTHOR]

Social workers in England are key professionals involved in addressing safeguarding concerns affecting adults with learning disabilities, including the risk of harm from domestic abuse. This article reports the findings from an empirical study conducted with 15 social workers who participated in a 2-stage interview process. The findings and discussion examine social workers' approaches to risk management interventions in cases of domestic abuse against adults with learning disabilities. Informed by Beck's Risk Society theory, our analysis finds that interventions often focus on individuals taking responsibility for managing risk, with either the victim or the social worker becoming the risk decision-maker. Furthermore, in carrying out their work, social workers used bureaucratic tasks to protect the organisation and individual decision-makers from blame. The article concludes with recommendations for practice which explores more holistic understandings of risk and which seeks to promote more collective responses to risk management. [ABSTRACT FROM AUTHOR]

The study aimed to predict cyber claims in companies in the banking sector using a decision tree. To this end, 683 cases of cyber losses were extracted from an operational risk database. The independent variables considered in the modeling were the region of domicile, the size of the company and, as main explanatory variable, revenue. The classification reached 89% of global hits. The modeling in question guarantees a good classification quality and better fit when compared to traditional GLM modeling. The results of this work are useful and can act in an innovative way as a tool to support the decision making of insurers, aiming at useful responses to the management of cyber risks. [ABSTRACT FROM AUTHOR]

As one of the leading causes of project failures, requirements changes are inevitable in any software project. Hence, we propose an intelligent approach to facilitate the risk analysis of a change request by providing information about past cases found in similar change requests, the solutions adopted, and a support tool. The proposed approach uses casebased reasoning to retrieve previous cases similar to the current case. This approach also uses association rules to analyze patterns in the dataset and calculate the probability of risks associated with change requests. We prepared a case study to validate the proposal by analyzing the most frequent challenges in change management and considering how it can solve or minimize such problems. Results show that the proposed approach successfully assists decision-making, predicts potential risks, and suggests coherent solutions to the user. We have developed a support tool to evaluate this approach in practice with experts and obtained four different outcomes. Only a small set of cases failed to provide relevant results to the user. The use of case-based reasoning and association rules has proven to be advantageous in change management despite validity threats associated with the small number of test cases and experts involved. [ABSTRACT FROM AUTHOR]

A common assumption made by construction professionals is that complex projects are more likely than simple ones to entail cost overruns and delays. Although several researchers have studied the critical causes of complexity and have identified their indicators (CIs), few of them have investigated how they actually impact a project's schedule and cost performance during the design, procurement, and construction phases. This research aimed to study the impact of each CI and determine the most effective best practices (BPs) for preventing it. Data from 44 case studies were collected and analyzed, and the results revealed that implementing the BPs of team building, front-end planning (FEP), constructability, alignment, partnering, and change management significantly reduces cost overruns and delays in all three phases of complex construction projects and, in fact, can improve the cost and schedule performance, due to fewer complex challenges. The outcomes of this research will help project managers identify the challenges that stem from complex aspects of projects and lead to phase-base cost overruns and time delays. They will also assist decision-makers in deciding which are the most effective BPs for mitigating challenges stemming from the characteristics that make projects complex and will them improve phase-based cost and schedule performance in complex construction projects. [ABSTRACT FROM AUTHOR]