Perception of Risk and the Attribution of Responsibility for Accidents

Accidents, one often hears, “happen”; we accept, and even expect, that they will be part of daily life. But in situations in which injury or death result, judgments of responsibility become critical. How might our perceptions of risk influence the ways in which we allocate responsibility for an accident? Drawing from attribution and risk perception theory, this study investigates how perceived controllability and desirability of risk, in addition to perceived danger and recreational risk‐taking, relate to attributions of responsibility for the cause of unintentional injury in a unique setting: U.S. national parks. Three parks, Mount Rainier, Olympic, and Delaware Water Gap, provide the setting for this survey‐based study, which considers how park visitors (N = 447) attribute responsibility for the cause of a hypothetical visitor accident. Results suggest that respondents tended to make more internal (i.e., related to characteristics of the victim), rather than external (i.e., related to characteristics of the park, or park management) attributions. As respondents viewed park‐related risk as controllable, they were more likely to attribute the cause of the accident to the victim. Moreover, among other significant variables, having experienced a similar accident predicted lower internal causal attribution. Opportunities for future research linking risk perception and attribution variables, as well as practical implications for the management of public outdoor settings, are presented.     

Sizing Up a Superstorm: Exploring the Role of Recalled Experience and Attribution of Responsibility in Judgments of Future Hurricane Risk

Research suggests that hurricane‐related risk perception is a critical predictor of behavioral response, such as evacuation. Less is known, however, about the precursors of these subjective risk judgments, especially when time has elapsed from a focal event. Drawing broadly from the risk communication, social psychology, and natural hazards literature, and specifically from concepts adapted from the risk information seeking and processing model and the protective action decision model, we examine how individuals’ distant recollections, including attribution of responsibility for the effects of a storm, attitude toward relevant information, and past hurricane experience, relate to risk judgment for a future, similar event. The present study reports on a survey involving U.S. residents in Connecticut, New Jersey, and New York (n = 619) impacted by Hurricane Sandy. While some results confirm past findings, such as that hurricane experience increases risk judgment, others suggest additional complexity, such as how various types of experience (e.g., having evacuated vs. having experienced losses) may heighten or attenuate individual‐level judgments of responsibility. We suggest avenues for future research, as well as implications for federal agencies involved in severe weather/natural hazard forecasting and communication with public audiences.     

Quantitative Risk Assessment of Human Salmonellosis in Canadian Broiler Chicken Breast from Retail to Consumption

The current quantitative risk assessment model followed the framework proposed by the Codex Alimentarius to provide an estimate of the risk of human salmonellosis due to consumption of chicken breasts which were bought from Canadian retail stores and prepared in Canadian domestic kitchens. The model simulated the level of Salmonella contamination on chicken breasts throughout the retail‐to‐table pathway. The model used Canadian input parameter values, where available, to represent risk of salmonellosis. From retail until consumption, changes in the concentration of Salmonella on each chicken breast were modeled using equations for growth and inactivation. The model predicted an average of 318 cases of salmonellosis per 100,000 consumers per year. Potential reasons for this overestimation were discussed. A sensitivity analysis showed that concentration of Salmonella on chicken breasts at retail and food hygienic practices in private kitchens such as cross‐contamination due to not washing cutting boards (or utensils) and hands after handling raw meat along with inadequate cooking contributed most significantly to the risk of human salmonellosis. The outcome from this model emphasizes that responsibility for protection from Salmonella hazard on chicken breasts is a shared responsibility. Data needed for a comprehensive Canadian Salmonella risk assessment were identified for future research.     

Opportunism in Capital Budget Recommendations: The Effects of Past Performance and Its Attributions*

This study uses an experiment to examine the separate and combined effects of managers' loss aversion and their causal attributions about their divisions' performance on tendencies to make goal‐incongruent capital budget recommendations. We find that managers' recommendations are biased by their loss aversion. In particular, managers of high‐performing divisions are more likely than managers of low‐performing divisions to propose investments that maximize their division's short‐term profits at the expense of the firm's long‐term value. We also find that managers' recommendations are biased by their causal attributions. In particular, managers are more likely to propose investments that maximize their division's short‐term profits at the expense of the firm's long‐term value when they attribute their division's performance to external causes (e.g., task difficulty or luck) rather than to internal causes (e.g., managerial ability or effort). Further, the effects of causal attributions are greater for managers of high‐performing divisions than for managers of low‐performing divisions. The study's findings are important because loss aversion and causal attributions are often manifested in firms. Thus, they may bias managers' decisions, which in turn may be detrimental to the firms' long‐term value.     

A Stochastic Model to Assess the Effect of Meat Inspection Practices on the Contamination of the Pig Carcasses

The objective of meat inspection is to promote animal and public health by preventing, detecting, and controlling hazards originating from animals. With the improvements of sanitary level in pig herds, the hazards profile has shifted and the inspection procedures no longer target major foodborne pathogens (i.e., not risk based). Additionally, carcass manipulations performed when searching for macroscopic lesions can lead to cross‐contamination. We therefore developed a stochastic model to quantitatively describe cross‐contamination when consecutive carcasses are submitted to classic inspection procedures. The microbial hazard used to illustrate the model was Salmonella, the data set was obtained from Brazilian slaughterhouses, and some simplifying assumptions were made. The model predicted that due to cross‐contamination during inspection, the prevalence of contaminated carcass surfaces increased from 1.2% to 95.7%, whereas the mean contamination on contaminated surfaces decreased from 1 logCFU/cm² to ?0.87 logCFU/cm², and the standard deviations decreased from 0.65 to 0.19. These results are explained by the fact that, due to carcass manipulations with hands, knives, and hooks, including the cutting of contaminated lymph nodes, Salmonella is transferred to previously uncontaminated carcasses, but in small quantities. These small quantities can easily go undetected during sampling. Sensitivity analyses gave insight into the model performance and showed that the touching and cutting of lymph nodes during inspection can be an important source of carcass contamination. The model can serve as a tool to support discussions on the modernization of pig carcass inspection.     

An Agent‐Based Model for Pathogen Persistence and Cross‐Contamination Dynamics in a Food Facility

We used an agent‐based modeling (ABM) framework and developed a mathematical model to explain the complex dynamics of microbial persistence and spread within a food facility and to aid risk managers in identifying effective mitigation options. The model explicitly considered personal hygiene practices by food handlers as well as their activities and simulated a spatially explicit dynamic system representing complex interaction patterns among food handlers, facility environment, and foods. To demonstrate the utility of the model in a decision‐making context, we created a hypothetical case study and used it to compare different risk mitigation strategies for reducing contamination and spread of Listeria monocytogenes in a food facility. Model results indicated that areas with no direct contact with foods (e.g., loading dock and restroom) can serve as contamination niches and recontaminate areas that have direct contact with food products. Furthermore, food handlers’ behaviors, including, for example, hygiene and sanitation practices, can impact the persistence of microbial contamination in the facility environment and the spread of contamination to prepared foods. Using this case study, we also demonstrated benefits of an ABM framework for addressing food safety in a complex system in which emergent system‐level responses are predicted using a bottom‐up approach that observes individual agents (e.g., food handlers) and their behaviors. Our model can be applied to a wide variety of pathogens, food commodities, and activity patterns to evaluate efficacy of food‐safety management practices and quantify contamination reductions associated with proposed mitigation strategies in food facilities.     

Emotions,Trust, and Perceived Risk: Affective and Cognitive Routes to Flood Preparedness Behavior

Despite the prognoses of the effects of global warming (e.g., rising sea levels, increasing river discharges), few international studies have addressed how flood preparedness should be stimulated among private citizens. This article aims to predict Dutch citizens’ flood preparedness intentions by testing a path model, including previous flood hazard experiences, trust in public flood protection, and flood risk perceptions (both affective and cognitive components). Data were collected through questionnaire surveys in two coastal communities (n= 169, n= 244) and in one river area community (n= 658). Causal relations were tested by means of structural equation modeling (SEM). Overall, the results indicate that both cognitive and affective mechanisms influence citizens’ preparedness intentions. First, a higher level of trust reduces citizens’ perceptions of flood likelihood, which in turn hampers their flood preparedness intentions (cognitive route). Second, trust also lessens the amount of dread evoked by flood risk, which in turn impedes flood preparedness intentions (affective route). Moreover, the affective route showed that levels of dread were especially influenced by citizens’ negative and positive emotions related to their previous flood hazard experiences. Negative emotions most often reflected fear and powerlessness, while positive emotions most frequently reflected feelings of solidarity. The results are consistent with the affect heuristic and the historical context of Dutch flood risk management. The great challenge for flood risk management is the accommodation of both cognitive and affective mechanisms in risk communications, especially when most people lack an emotional basis stemming from previous flood hazard events.     

Expecting the Unexpected: Predicting Physiological and Psychological Wildfire Preparedness from Perceived Risk,Responsibility, and Obstacles

People who live in wildfire‐prone communities tend to form their own hazard‐related expectations, which may influence their willingness to prepare for a fire. Past research has already identified two important expectancy‐based factors associated with people's intentions to prepare for a natural hazard: Perceived risk (i.e., perceived threat likelihood and severity) and perceived protection responsibility. We expanded this research by differentiating the influence of these factors on different types of wildfire preparedness (e.g., preparations for evacuation vs. for defending the house) and measured actual rather than intended preparedness. In addition, we tested the relation between preparedness and two additional threat‐related expectations: the expectation that one can rely on an official warning and the expectation of encountering obstacles (e.g., the loss of utilities) during a fire. A survey completed by 1,003 residents of wildfire‐prone areas in Perth, Australia, revealed that perceived risk (especially risk severity) and perceived protection responsibility were both positively associated with all types of preparedness, but the latter did not significantly predict preparedness after controlling for other predictors and demographics. Also, the two new expectancy‐based factors were significantly associated with all types of preparedness, and remained significant predictors of some types of preparedness after controlling for other predictors and demographics: the expectation of being able to rely on an official fire warning and expecting to lose electricity both still predicted less preparedness around house resilience, and expecting to lose water still predicted increased planning preparedness. We discuss public policy implications that follow from this research.     

Does Size Matter? A Study of Risk Perceptions of Global Population Growth

The global human population now exceeds 7 billion and is projected to reach 10 billion around 2060. While population growth has been associated with certain benefits (e.g., economies of scale, technological advancements), theoretical models, probabilistic projections, and empirical evidence also indicate that this growth could increase the likelihood of many adverse events (e.g., climate change, resource shortages) and the impact of these events, as more people are exposed to the outcomes. While concerns about these issues are well‐documented in the academic literature, there is little evidence concerning the public's perceptions of the risks associated with global population growth (GPG) and how these perceptions are likely to influence related decisions. To address these issues, we conducted a U.K.‐based study that examined respondents’ risk perceptions of GPG, their willingness to embrace mitigation/precautionary behaviors, and reasons for variations in these two factors. We found that GPG is perceived as a moderate‐to‐high risk, with concerns about the increased likelihood of resource shortages, ecological damage, and violent conflict being foremost. Respondents believed that the worst effects of GPG would arrive around 2050 and would be experienced by the world's poorest people. Respondents who perceived greater levels of risk from GPG were generally those who indicated a greater willingness to embrace mitigation behaviors (e.g., reduce resource consumption) and preventative actions (e.g., support political action to limit growth). We discuss how our findings might be utilized to better manage the potential challenges associated with GPG and we suggest several directions for further research.     

Maximizing Business Returns to Corporate Social Responsibility (CSR): The Role of CSR Communication

By engaging in corporate social responsibility (CSR) activities, companies can not only generate favorable stakeholder attitudes and better support behaviors (e.g. purchase, seeking employment, investing in the company), but also, over the long run, build corporate image, strengthen stakeholder–company relationships, and enhance stakeholders' advocacy behaviors. However, stakeholders' low awareness of and unfavorable attributions towards companies' CSR activities remain critical impediments in companies' attempts to maximize business benefits from their CSR activities, highlighting a need for companies to communicate CSR more effectively to stakeholders. In light of these challenges, a conceptual framework of CSR communication is presented and its different aspects are analyzed, from message content and communication channels to company- and stakeholder-specific factors that influence the effectiveness of CSR communication.     

Multilevel causal attributions on transboundary risk: Effects on attributions of responsibility,psychological distance,and policy support

This study used two randomized experiments in a prospective design (Study 1 N = 297, Study 2 N = 296) to examine how multilevel causal attribution dimensions (internal vs. external to an individual or a country) shape domestic and foreign policy support to counter transboundary risk. Results from Study 1 and 2 showed that external-country (vs. internal-country) causal attribution reduced perceptions of internal-country attributions of responsibility, which had a cross-lagged effect on support for domestic-industry policies to mitigate the risk. In contrast, perceptions of external-country attributions of responsibility increased support for foreign policies in a 2-week follow up. This study offers theoretical insights into the demarcation of multilevel causal attribution dimensions in studying media framing effects. It also highlights some important causal mechanisms of how media frames shape public support for policies aimed at transboundary risk mitigation.     

Human and Nature‐Caused Hazards: The Affect Heuristic Causes Biased Decisions

People are more concerned about the negative consequences of human hazards compared with natural hazards. Results of four experiments show that the same negative outcome (e.g., number of birds killed by an oil spill) was more negatively evaluated when caused by humans than when caused by nature. Results further show that when identical risk information was provided, participants evaluated nuclear power more negatively compared with solar power. The affect associated with the hazard per se influenced the interpretation of the new information. Furthermore, the affect experienced in the situation fully mediated the evaluation of the negative outcomes of a hazard. People's reliance on the affect heuristic is a challenge for acceptance of cost‐benefit analyses because equally negative outcomes are differently evaluated depending on the cause. Symbolically significant information and the affect evoked by this information may result in biased and riskier decisions.     

A Practical Framework for the Construction of a Biotracing Model: Application to Salmonella in the Pork Slaughter Chain

A novel purpose of the use of mathematical models in quantitative microbial risk assessment (QMRA) is to identify the sources of microbial contamination in a food chain (i.e., biotracing). In this article we propose a framework for the construction of a biotracing model, eventually to be used in industrial food production chains where discrete numbers of products are processed that may be contaminated by a multitude of sources. The framework consists of steps in which a Monte Carlo model, simulating sequential events in the chain following a modular process risk modeling (MPRM) approach, is converted to a Bayesian belief network (BBN). The resulting model provides a probabilistic quantification of concentrations of a pathogen throughout a production chain. A BBN allows for updating the parameters of the model based on observational data, and global parameter sensitivity analysis is readily performed in a BBN. Moreover, a BBN enables “backward reasoning” when downstream data are available and is therefore a natural framework for answering biotracing questions. The proposed framework is illustrated with a biotracing model of Salmonella in the pork slaughter chain, based on a recently published Monte Carlo simulation model. This model, implemented as a BBN, describes the dynamics of Salmonella in a Dutch slaughterhouse and enables finding the source of contamination of specific carcasses at the end of the chain.     

Risk and the Media: A Comparison of Print and Televised News Stories of a Canadian Drinking Water Risk Event

This article explores the utility of using media analyses as a method for risk researchers to gain an initial understanding of how the public may perceive a risk issue or event based on how it is presented and communicated in news media stories. In the area of risk research, newspapers consistently provide coverage of both acute and chronic risk events, whereas televised news broadcasts report primarily acute risk events. There is no consensus in the literature about which news format (print vs. televised) may be better to study public conceptualizations of risk, or if one format (e.g., print) may be used as a surrogate measure for another format (e.g., televised). This study compares Canadian national televised and newspaper coverage of the same risk event: the E. coli contamination of a public drinking water supply. Using a content analysis, this study empirically demonstrates the overall similarity in story content coverage in both televised and print coverage, noting that televised coverage promotes primarily emotional story themes while print coverage tends to also include coverage of analysis and process. On this basis, the research draws two conclusions: 1) given its more comprehensive coverage, newspaper broadsheets may provide a better measure of media coverage of a risk event than televised coverage (if only one format can be studied); and 2) when the risk area of interest is chronic, and/or if the scale of analysis is at a community/local level (i.e., when it is unlikely that archived televised coverage is available), then a researcher may find the print media to be a more useful format to study.     

Salient Beliefs About Earthquake Hazards and Household Preparedness

Prior research has found little or no direct link between beliefs about earthquake risk and household preparedness. Furthermore, only limited work has been conducted on how people's beliefs influence the nature and number of preparedness measures adopted. To address this gap, 48 qualitative interviews were undertaken with residents in three urban locations in New Zealand subject to seismic risk. The study aimed to identify the diverse hazard and preparedness‐related beliefs people hold and to articulate how these are influenced by public education to encourage preparedness. The study also explored how beliefs and competencies at personal, social, and environmental levels interact to influence people's risk management choices. Three main categories of beliefs were found: hazard beliefs; preparedness beliefs; and personal beliefs. Several salient beliefs found previously to influence the preparedness process were confirmed by this study, including beliefs related to earthquakes being an inevitable and imminent threat, self‐efficacy, outcome expectancy, personal responsibility, responsibility for others, and beliefs related to denial, fatalism, normalization bias, and optimistic bias. New salient beliefs were also identified (e.g., preparedness being a “way of life”), as well as insight into how some of these beliefs interact within the wider informational and societal context.     

Risk Perception,Experience, and Objective Risk: A Cross‐National Study with European Emergency Survivors

Understanding public risk perceptions and their underlying processes is important in order to learn more about the way people interpret and respond to hazardous emergency events. Direct experience with an involuntary hazard has been found to heighten the perceived risk of experiencing the same hazard and its consequences in the future, but it remains unclear if cross‐over effects are possible (i.e., experience with one hazard influencing perceived risk for other hazards also). Furthermore, the impact of objective risk and country of residence on perceived risk is not well understood. As part of the BeSeCu (Behavior, Security, and Culture) Project, a sample of 1,045 survivors of emergencies from seven European countries (i.e., Germany, the Czech Republic, Poland, Sweden, Spain, Turkey, and Italy) was drawn. Results revealed heightened perceived risk for emergency events (i.e., domestic and public fires, earthquakes, floods, and terrorist attacks) when the event had been experienced previously plus some evidence of cross‐over effects, although these effects were not so strong. The largest country differences in perceived risk were observed for earthquakes, but this effect was significantly reduced by taking into account the objective earthquake risk. For fires, floods, terrorist attacks, and traffic accidents, only small country differences in perceived risk were found. Further studies including a larger number of countries are welcomed.     

Quantitative risk assessment-epidemic curve prediction model for leafy green outbreak investigation

The objective of this study was to leverage quantitative risk assessment to investigate possible root cause(s) of foodborne illness outbreaks related to Shiga toxin-producing Escherichia coli O157:H7 (STEC O157) infections in leafy greens in the United States. To this end, we developed the FDA leafy green quantitative risk assessment epidemic curve prediction model (FDA-LG QRA-EC) that simulated the lettuce supply chain. The model was used to predict the number of reported illnesses and the epidemic curve associated with lettuce contaminated with STEC O157 for a wide range of scenarios representing various contamination conditions and facility processing/sanitation practices. Model predictions were generated for fresh-cut and whole lettuce, quantifying the differing impacts of facility processing and home preparation on predicted illnesses. Our model revealed that the timespan (i.e., number of days with at least one reported illness) and the peak (i.e., day with the most predicted number of reported illnesses) of the epidemic curve of a STEC O157-lettuce outbreak were not strongly influenced by facility processing/sanitation practices and were indications of contamination pattern among incoming lettuce batches received by the facility or distribution center. Through comparisons with observed number of illnesses from recent STEC O157-lettuce outbreaks, the model identified contamination conditions on incoming lettuce heads that could result in an outbreak of similar size, which can be used to narrow down potential root cause hypotheses.     

Predicting the Probability that a Chemical Causes Steatosis Using Adverse Outcome Pathway Bayesian Networks (AOPBNs)

Adverse outcome pathway Bayesian networks (AOPBNs) are a promising avenue for developing predictive toxicology and risk assessment tools based on adverse outcome pathways (AOPs). Here, we describe a process for developing AOPBNs. AOPBNs use causal networks and Bayesian statistics to integrate evidence across key events. In this article, we use our AOPBN to predict the occurrence of steatosis under different chemical exposures. Since it is an expert-driven model, we use external data (i.e., data not used for modeling) from the literature to validate predictions of the AOPBN model. The AOPBN accurately predicts steatosis for the chemicals from our external data. In addition, we demonstrate how end users can utilize the model to simulate the confidence (based on posterior probability) associated with predicting steatosis. We demonstrate how the network topology impacts predictions across the AOPBN, and how the AOPBN helps us identify the most informative key events that should be monitored for predicting steatosis. We close with a discussion of how the model can be used to predict potential effects of mixtures and how to model susceptible populations (e.g., where a mutation or stressor may change the conditional probability tables in the AOPBN). Using this approach for developing expert AOPBNs will facilitate the prediction of chemical toxicity, facilitate the identification of assay batteries, and greatly improve chemical hazard screening strategies.     

Bayesian Inventory Management with Potential Change‐Points in Demand

We consider the inventory management problem of a firm reacting to potential change points in demand, which we define as known epochs at which the demand distribution may (or may not) abruptly change. Motivating examples include global news events (e.g., the 9/11 terrorist attacks), local events (e.g., the opening of a nearby attraction), or internal events (e.g., a product redesign). In the periods following such a potential change point in demand, a manager is torn between using a possibly obsolete demand model estimated from a long data history and using a model estimated from a short, recent history. We formulate a Bayesian inventory problem just after a potential change point. We pursue heuristic policies coupled with cost lower bounds, including a new lower bounding approach to non‐perishable Bayesian inventory problems that relaxes the dependence between physical demand and demand signals and that can be applied for a broad set of belief and demand distributions. Our numerical studies reveal small gaps between the costs implied by our heuristic solutions and our lower bounds. We also provide analytical and numerical sensitivity results suggesting that a manager worried about downside profit risk should err on the side of underestimating demand at a potential change point.     

Informing Ex Ante Event Studies with Macro‐Econometric Evidence on the Structural and Policy Impacts of Terrorism

Economic consequence analysis is one of many inputs to terrorism contingency planning. Computable general equilibrium (CGE) models are being used more frequently in these analyses, in part because of their capacity to accommodate high levels of event‐specific detail. In modeling the potential economic effects of a hypothetical terrorist event, two broad sets of shocks are required: (1) physical impacts on observable variables (e.g., asset damage); (2) behavioral impacts on unobservable variables (e.g., investor uncertainty). Assembling shocks describing the physical impacts of a terrorist incident is relatively straightforward, since estimates are either readily available or plausibly inferred. However, assembling shocks describing behavioral impacts is more difficult. Values for behavioral variables (e.g., required rates of return) are typically inferred or estimated by indirect means. Generally, this has been achieved via reference to extraneous literature or ex ante surveys. This article explores a new method. We elucidate the magnitude of CGE‐relevant structural shifts implicit in econometric evidence on terrorist incidents, with a view to informing future ex ante event assessments. Ex post econometric studies of terrorism by Blomberg et al . yield macro econometric equations that describe the response of observable economic variables (e.g., GDP growth) to terrorist incidents. We use these equations to determine estimates for relevant (unobservable) structural and policy variables impacted by terrorist incidents, using a CGE model of the United States. This allows us to: (i) compare values for these shifts with input assumptions in earlier ex ante CGE studies; and (ii) discuss how future ex ante studies can be informed by our analysis.