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This study aims to develop the application of the strategic choice approach (SCA) in Karaj City (Iran) as the case study for facilitating the decision-making process concerning uncertainties associated with determining fault trace, fault buffer zone and fault setback, as well as inherent uncertainties related to urban issues – known as wicked problems – which make a clear definition unachievable for them.

The SCA was recycled in this study to manage uncertain situations in planning. It is designed to deal with problems caused by uncertainty, insufficient knowledge and unpredictable policy outcomes. In combination with the risk-based planning approach, the paper proposes a new model to deal with uncertainty and hazard risk at the same time.

The results indicated that such decision-making tools are helpful for urban planners who impose changes in the fault zone to save lives and properties where the process seems problematic and ambiguous. Further analysis from the SCA development not only shows that urban planning restrictions should be applied gradually and in pace with the completion of fault displacement geological data but also reveals that defining the implementation process and institutional structure is as important as the preparation of binding fault zoning documents.

The paper will push forward global research and practice because the results can be generalized for use in other earthquake-prone countries, as the considerations and obstacles have a universal character and more specifically because many earthquake-prone countries have not started to define fault avoidance zone restrictions up to now.

Identifying and prioritizing supply chain risk is significant from any product’s quality and reliability perspective. Under an input-process-output workflow, conventional risk prioritization uses a risk priority number (RPN) aligned to the risk analysis. Imprecise information coupled with a lack of dealing with hesitancy margins enlarges the scope, leading to improper assessment of risks. This significantly affects monitoring quality and performance. Against the backdrop, a methodology that identifies and prioritizes the operational supply chain risk factors signifies better risk assessment.

The study proposes a multi-criteria model for risk prioritization involving multiple decision-makers (DMs). The methodology offers a robust, hybrid system based on the Intuitionistic Fuzzy (IF) Set merged with the “Technique for Order Performance by Similarity to Ideal Solution.” The nature of the model is robust. The same is shown by applying fuzzy concepts under multi-criteria decision-making (MCDM) to prioritize the identified business risks for better assessment.

The proposed IF Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for risk prioritization model can improve the decisions within organizations that make up the chains, thus guaranteeing a “better quality in risk management.” Establishing an efficient representation of uncertain information related to traditional failure mode and effects analysis (FMEA) treatment involving multiple DMs means identifying potential risks in advance and providing better supply chain control.

In a company’s supply chain, blockchain allows data storage and transparent transmission of flows with traceability, privacy, security and transparency (Roy et al., 2022). They asserted that blockchain technology has great potential for traceability. Since risk assessment in supply chain operations can be treated as a traceability problem, further research is needed to use blockchain technologies. Lastly, issues like risk will be better assessed if predicted well; further research demands the suitability of applying predictive analysis on risk.

The study proposes a hybrid framework based on the generic risk assessment and MCDM methodologies under a fuzzy environment system. By this, the authors try to address the supply chain risk assessment and mitigation framework better than the conventional one. To the best of their knowledge, no study is found in existing literature attempting to explore the efficacy of the proposed hybrid approach over the traditional RPN system in prime sectors like steel (with production planning data). The validation experiment indicates the effectiveness of the results obtained from the proposed IF TOPSIS Approach to Risk Prioritization methodology is more practical and resembles the actual scenario compared to those obtained using the traditional RPN system (Kim et al., 2018; Kumar et al., 2018).

This study provides mathematical models to simulate the supply chain risk assessment, thus helping the manufacturer rank the risk level. In the end, the authors apply this model in a big-sized organization to validate its accuracy. The authors validate the proposed approach to an integrated steel plant impacting the production planning process. The model’s outcome substantially adds value to the current risk assessment and prioritization, significantly affecting better risk management quality.

This paper aims to examine how significantly the COVID-19 pandemic affects supply chain operations and how the firms have reacted to the COVID-19-induced supply chain crisis. In addition, this paper investigates whether the micro, small and medium enterprises (MSMEs) are affected disproportionally by the COVID-19-induced supply chain crisis.

This paper developed a series of hypotheses and tested them using cross-tabulation, canonical correlation, discriminant and exploratory factor analyses of the empirical data.

The descriptive data analysis and hypothesis test results revealed that the labor productivity of the manufacturing, logistics and healthcare industry sectors was affected disproportionally by the COVID-19-induced crisis. However, outsourcing and global sourcing practices themselves did not necessarily exacerbate the severity of supply chain disruptions caused by COVID-19. The authors also found that MSMEs were adversely affected by the COVID-19 pandemic to a different degree than their large counterparts.

This paper is one of the first of its kind to assess the extent of the COVID-19 adverse impact on supply chain operations using the exploratory analysis of the data collected from the questionnaire survey of US firms representing various industries sectors.

The COVID-19 pandemic created not only a public health crisis but also the largest disruption to the global economies. The purpose of the paper is to investigate the adverse impacts of the pandemic on self-employment including job loss, income reduction and cut back in work hours and how these impacts were related to the well-being the self-employed. The authors also examine how self-employers responded to adversity in different cultural settings.

The main sample was obtained the Gallup World Poll that covers more than 39,000 individuals across 55 countries over the period from October 2020 to June 2021. The ordinary least square was the main choice of methodology. The paper employs the Oaxaca-Blinder decomposition technique to quantify the gap in financial loss between self-employed and employed individuals. Finally, the moderated mediation analysis allows the authors to examine how financial loss mediates the reduction in well-being of self-employers.

The paper finds that self-employers were 29% more likely to lose their businesses than paid individuals to lose their jobs and perhaps as a result, they were 50% more likely to experience lower work hours and less income. The findings suggest that the impact of the COVID-19 pandemic varied across countries. The financial gap between self-employment and full-time employment was narrower in countries with individualism, low uncertainty avoidance and propensity for long-term future. Finally, the paper shows that although financial loss associated with the coronavirus situation mediated the relationship between self-employment and reduced wellbeing, the positive relationship between self-employment and life satisfaction (wellbeing) held amid the pandemic. Despite all the pecuniary setbacks relative to full-time employment, self-employers report higher subjective wellbeing than regular wage earners during difficult times.

The earnings gap between self-employers and employees persists (and increase) during adverse conditions may cast into doubt the efficiency of the economic system that ensures no one is left behind. In addition, contextual factors such as cultural values should also be taken into consideration in reducing the earning gap between self-employment and regular employment. It is also implied that the self-employed choose to engage in self-employment due to psychological and emotion benefits rather than material achievements.

This study has quantified the income gap between self-employment and employed individuals in the context of adverse economic conditions. This study also highlights the fact that despite all the financial setbacks, self-employers are happier than employed individuals and they engage in self-employment as an important way to pursue happiness. This highlights well-being as the critical non-pecuniary benefits of the career choice of and transition into self-employment that have been confirmed in extant entrepreneurship literature.

In this paper, the authors investigate the inbound tourism demand elasticities of the Middle East and North African (MENA) countries. The authors emphasize the role of external and internal conflicts, world gross domestic product and relative prices over the period 1995–2017.

This study applies the heterogeneous panel data estimators based on the fully modified-OLS (FM-OLS), dynamic-OLS (DOLS) and the recently developed method of moments quantile regression (MMQR).

The empirical results indicate that the effect of external and internal conflicts on inbound tourism demand is negative and inelastic with external conflict having a stronger effect. The effect of both classifications of conflicts diminishes as the market share of the tourist destination increases. In addition, the role of the world GDP on tourism demand is positive and elastic, suggesting that tourism is a luxury good while an increase in relative prices diminishes inbound tourism demand.

The paper, therefore, concludes that if policy measures are not put in place to curtail incidences of conflicts, economic growth in these countries may suffer setbacks. This by implications could affect the attainment of the sustainable development goals (SDGs) targets.

This study is aimed at developing an understanding of the consequences of the pandemic on families' socioeconomic resilience, and the strategies adopted by the families in overcoming social vulnerabilities amid uncertainty.

The materials for this study consist of semi-structured interviews with 21 families spread across the South Sumatra Province, Indonesia. Families in the study represent four different income levels, namely very high, high, middle and low, and who also work in the informal sector. Each family has at least 1 or more members who fall into the vulnerable category (children, the elderly, people with disabilities unemployed or having potential economic vulnerability).

Two main findings are outlined. Regardless of their socioeconomic status, many of the families analyzed adopted similar strategies to remain resilient. Among the strategies are classifying the urgency of purchasing consumer goods based on financial capacity rather than needs, leveraging digital economic opportunities as alternative sources of income, utilizing more extensive informal networks and going into debt. Another interesting finding shows that the pandemic, to some extent, has saved poor families from social insecurity. This is supported by evidence showing that social distancing measures during the pandemic have reduced the intensity of sociocultural activities, which require invited community members to contribute financially. The reduction of sociocultural activities in the community has provided more potential savings for the poor.

In this study, informants who provided information about their family conditions represent a major segment of the workforce and tend to be technologically savvy and younger, due to the use of Zoom as a platform for conducting interviews. Therefore, there may be a bias in the results. Another limitation is that since the interviewees were recommended by our social network in the fields, there is a risk of a distorted selection of participants.

This study offers insights that are critical in helping to analyze family patterns in developing countries in mitigating the risks and uncertainties caused by COVID-19. In addition, the literature on social policy and development could benefit from further research on COVID-19 as an alternative driver to identify mechanisms that could bring about change that would result in “security.” Critical questions and limitations of this study are presented at the end of the paper to be responded to as future research agenda.

The aim is to understand how goal orientation may help entrepreneurs maintain life satisfaction when faced with multiple crisis episodes. To do so, the authors study the early impact of COVID-19 on the life satisfaction of entrepreneurs who were previously faced with venture distress.

Data on 164 Belgian entrepreneurs, who were faced with previous venture distress, are used. The analysis is quantitative, and uses survey data, in combination with databases. The survey was administered in the beginning of the COVID-19 pandemic (June 2020).

Entrepreneurs high in learning goal orientation are less likely, and those high in performance-avoidance orientation are more likely to experience a decline in life satisfaction. Additionally, the time span between the two crises moderates the relationship between learning goal orientation and life satisfaction. Particularly, entrepreneurs high in learning goal orientation are less likely to experience a decline in life satisfaction if more time has passed between venture distress and the COVID-19 crisis.

Often, entrepreneurs are faced with multiple episodes of distress. The authors study entrepreneurs who have been in venture distress before and are then confronted with the COVID-19 crisis. The study shows that goal orientation, which is a type of self-regulation, plays an important role in how entrepreneurs’ life satisfaction is affected. Also, the authors find that the time between distress events matters. These are important insights for practitioners, including entrepreneurs and support providers.

The study adds to the emerging discussion on the impact of crises on entrepreneurs. It studies life satisfaction (a measure of well-being) and complements studies that examine the impact of crises, on entrepreneurs’ actions and firm performance.

Central to the entire discipline of construction safety management is the concept of construction accidents. Although distinctive progress has been made in safety management applications over the last decades, construction industry still accounts for a considerable percentage of all workplace fatalities across the world. This study aims to predict occupational accident outcomes based on national data using machine learning (ML) methods coupled with several resampling strategies.

Occupational accident dataset recorded in Turkey was collected. To deal with the class imbalance issue between the number of nonfatal and fatal accidents, the dataset was pre-processed with random under-sampling (RUS), random over-sampling (ROS) and synthetic minority over-sampling technique (SMOTE). In addition, random forest (RF), Naïve Bayes (NB), K-Nearest neighbor (KNN) and artificial neural networks (ANNs) were employed as ML methods to predict accident outcomes.

The results highlighted that the RF outperformed other methods when the dataset was preprocessed with RUS. The permutation importance results obtained through the RF exhibited that the number of past accidents in the company, worker's age, material used, number of workers in the company, accident year, and time of the accident were the most significant attributes.

The proposed framework can be used in construction sites on a monthly-basis to detect workers who have a high probability to experience fatal accidents, which can be a valuable decision-making input for safety professionals to reduce the number of fatal accidents.

Practitioners and occupational health and safety (OHS) departments of construction firms can focus on the most important attributes identified by analysis results to enhance the workers' quality of life and well-being.

The literature on accident outcome predictions is limited in terms of dealing with imbalanced dataset through integrated resampling techniques and ML methods in the construction safety domain. A novel utilization plan was proposed and enhanced by the analysis results.

Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.

In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.

Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.

The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

This chapter provides an overview on the traditions and values of teaching students with traumatic brain injury (TBI). First, we discuss the prevalence, identification, and characteristics associated with TBI and how those characteristics affect learning, behavior, and daily life functioning. Next, we focus on instructional and behavioral interventions used in maintaining the traditions in classrooms for working with students with TBI. Findings from a review of the literature conclude that there are no specific academic curriculums designed specifically for teaching students with TBI; however, direct instruction and strategy instruction have been shown to be effective educational interventions. Current research on students with TBI is predominately being conducted in medical centers and clinics focusing on area of impairments (e.g., memory, attention, processing speed) rather than academic achievement and classroom interventions. Finally, we conclude with a list of accommodations and a discussion of recommendations for future work in teaching students with TBI.