The Women’s Empowerment in Livestock Index

Social Indicators Research - The empowerment of women in the livestock sector is fundamental to achieve gender equality. It also is instrumental for increased household productivity and improved...     

The relationship between burnout and risk-taking in workplace decision-making and decision-making style

This study aimed to investigate what decision-making styles might be exhibited by employees who experience burnout. Using a Work Risk Inventory (WRI), developed for this study, which included generic workplace scenarios, it was also explored whether such employees take relatively more risky decisions. Risk was conceptualised as the adoption of decisions that threaten one’s reputation at work, job performance and job security. The mediating effect of the likelihood and seriousness of the consequences of the worst that could happen in each given scenario on the relationships between dimensions of burnout and risk-taking was also tested. A total of 262 employees in various occupations completed an online survey, including measures on burnout, decision-making styles and the WRI. As predicted, dimensions of burnout – exhaustion, cynicism and professional inefficacy – correlated significantly with avoidant decision-making and negatively with rational decision-making. The seriousness of the consequences of the worst-case scenario occurring mediated the relationship between professional inefficacy and risk-taking. In the context of identifying mechanisms by which burnout leads to risky decision-making, the findings suggest that employees’ sense of professional inefficacy determines their risky decision-making. The contribution to theory and implications for practice are discussed.     

Can Carbon Nanomaterials Improve CZTS Photovoltaic Devices? Evaluation of Performance and Impacts Using Integrated Life‐Cycle Assessment and Decision Analysis

In emergent photovoltaics, nanoscale materials hold promise for optimizing device characteristics; however, the related impacts remain uncertain, resulting in challenges to decisions on strategic investment in technology innovation. We integrate multi‐criteria decision analysis (MCDA) and life‐cycle assessment (LCA) results (LCA‐MCDA) as a method of incorporating values of a hypothetical federal acquisition manager into the assessment of risks and benefits of emerging photovoltaic materials. Specifically, we compare adoption of copper zinc tin sulfide (CZTS) devices with molybdenum back contacts to alternative devices employing graphite or graphene instead of molybdenum. LCA impact results are interpreted alongside benefits of substitution including cost reductions and performance improvements through application of multi‐attribute utility theory. To assess the role of uncertainty we apply Monte Carlo simulation and sensitivity analysis. We find that graphene or graphite back contacts outperform molybdenum under most scenarios and assumptions. The use of decision analysis clarifies potential advantages of adopting graphite as a back contact while emphasizing the importance of mitigating conventional impacts of graphene production processes if graphene is used in emerging CZTS devices. Our research further demonstrates that a combination of LCA and MCDA increases the usability of LCA in assessing product sustainability. In particular, this approach identifies the most influential assumptions and data gaps in the analysis and the areas in which either engineering controls or further data collection may be necessary.     

Assessing Risk‐Based Upper Limits of Melamine Migration from Food Containers

Melamine contamination of food has become a major food safety issue because of incidents of infant disease caused by exposure to this chemical. This study was aimed at establishing a safety limit in Taiwan for the degree of melamine migration from food containers. Health risk assessment was performed for three exposure groups (preschool children, individuals who dine out, and elderly residents of nursing homes). Selected values of tolerable daily intake (TDI) for melamine were used to calculate the reference migration concentration limit (RMCL) or reference specific migration limit (RSML) for melamine food containers. The only existing values of these limits for international standards today are 1.2 mg/L (0.2 mg/dm²) in China and 30 mg/L (5 mg/dm²) in the European Union. The factors used in the calculations included the specific surface area of food containers, daily food consumption rate, body weight, TDI, and the percentile of the population protected at a given migration concentration limit (MCL). The results indicate that children are indeed at higher risk of melamine exposure at toxic levels than are other groups and that the 95th percentile of MCL (specific surface area = 5) for children aged 1–6 years should be the RMCL (0.07 mg/dm²) for protecting the sensitive and general population.     

Analysing critical success factors to implement sustainable supply chain practices in Indian automobile industry: a case study

The aim of this article is to analyse the critical success factors (CSFs) behind the successful implementation of sustainable supply chain practices in Indian automobile industry. Supply chain sustainability is a critical and timely topic for the developing country like India that captures increasing concerns over sustainability, whether driven by current legislation, public interest or competitive opportunity. Identification of CSFs responsible for sustainable supply chain practices and evaluating their contextual relationships has the potential to affect future government policy, current production operations and identify new business models. In response to this, this paper intends to identify CSFs based on organisational theory and model them to implement sustainable supply chain practices in Indian automobile industry. Twenty-five CSFs to implement sustainable supply chain practices based on organisational theory are recognised by means of the literature review and in discussions with experts in one case study organisation. A solution methodology based on the interpretive structural modelling (ISM) technique is used to propose a structural model, which not only helps in understanding the contextual relationship among these CSFs, but also in determining their interdependence to implement sustainable supply chain practices. Further, the importance of CSFs has been determined based on their driving and dependence power using MICMAC analysis. Our overarching thesis is that the insights gained are useful for Indian automobile industry and would help practitioners, regulators and academicians to focus their efforts towards implementation of sustainable supply chain practices.     

Evaluation of the Food and Agriculture Sector Criticality Assessment Tool (FASCAT) and the Collected Data

To protect and secure food resources for the United States, it is crucial to have a method to compare food systems’ criticality. In 2007, the U.S. government funded development of the Food and Agriculture Sector Criticality Assessment Tool (FASCAT) to determine which food and agriculture systems were most critical to the nation. FASCAT was developed in a collaborative process involving government officials and food industry subject matter experts (SMEs). After development, data were collected using FASCAT to quantify threats, vulnerabilities, consequences, and the impacts on the United States from failure of evaluated food and agriculture systems. To examine FASCAT's utility, linear regression models were used to determine: (1) which groups of questions posed in FASCAT were better predictors of cumulative criticality scores; (2) whether the items included in FASCAT's criticality method or the smaller subset of FASCAT items included in DHS's risk analysis method predicted similar criticality scores. Akaike's information criterion was used to determine which regression models best described criticality, and a mixed linear model was used to shrink estimates of criticality for individual food and agriculture systems. The results indicated that: (1) some of the questions used in FASCAT strongly predicted food or agriculture system criticality; (2) the FASCAT criticality formula was a stronger predictor of criticality compared to the DHS risk formula; (3) the cumulative criticality formula predicted criticality more strongly than weighted criticality formula; and (4) the mixed linear regression model did not change the rank‐order of food and agriculture system criticality to a large degree.     

Risk‐Cost‐Benefit Analysis for Transportation Corridors with Interval Uncertainties of Heterogeneous Data

Access management, which systematically limits opportunities for egress and ingress of vehicles to highway lanes, is critical to protect trillions of dollars of current investment in transportation. This article addresses allocating resources for access management with incomplete and partially relevant data on crash rates, travel speeds, and other factors. While access management can be effective to avoid crashes, reduce travel times, and increase route capacities, the literature suggests a need for performance metrics to guide investments in resource allocation across large corridor networks and several time horizons. In this article, we describe a quantitative decision model to support an access management program via risk‐cost‐benefit analysis under data uncertainties from diverse sources of data and expertise. The approach quantifies potential benefits, including safety improvement and travel time savings, and costs of access management through functional relationships of input parameters including crash rates, corridor access point densities, and traffic volumes. Parameter uncertainties, which vary across locales and experts, are addressed via numerical interval analyses. This approach is demonstrated at several geographic scales across 7,000 kilometers of highways in a geographic region and several subregions. The demonstration prioritizes route segments that would benefit from risk management, including (i) additional data or elicitation, (ii) right‐of‐way purchases, (iii) restriction or closing of access points, (iv) new alignments, (v) developer proffers, and (vi) etc. The approach ought to be of wide interest to analysts, planners, policymakers, and stakeholders who rely on heterogeneous data and expertise for risk management.     

Deriving Behavior Model Parameters from Survey Data: Self‐Protective Behavior Adoption During the 2009–2010 Influenza A(H1N1) Pandemic

In this paper, we demonstrate how public opinion surveys can be designed to collect information pertinent to computational behavior modeling, and we present the results of a public opinion and behavior survey conducted during the 2009–2010 H1N1 influenza pandemic. The results are used to parameterize the Health Belief Model of individual health‐protective decision making. Survey subjects were asked questions about their perceptions of the then‐circulating influenza and attitudes towards two personal protective behaviors: vaccination and avoidance of crowds. We empirically address two important issues in applying the Health Belief Model of behavior to computational infectious disease simulation: (1) the factors dynamically influencing the states of the Health Belief Model variables and (2) the appropriateness of the Health Belief Model in describing self‐protective behavior in the context of pandemic influenza.     

Private equity regulation: a comparative analysis

This article examines the existing contractual arrangements and industry standards in private equity. It shows that investors are, in principle, capable of structuring their particular investments according to their own preferences, there are a range of governance problems and risks that could be potentially hazardous for some classes of investors. We examine the circumstances where existing industry codes and legal tools can be used to address the problems that arise in relation to private equity and buyout activity.