微信时代风险信息的传播与感知——以山东“问题”疫苗事件为例

目前,以手机终端为依托的微信传播技术不仅给社会传播方式带来了前所未有的改变,而且给社会风险信息的传播获取以及公众对风险的感知与判断,甚至政府的风险决策与治理方式都带来了巨大的挑战.鉴于此,选取了比较典型的案例——山东“问题”疫苗事件作为探讨对象,结合相应的统计资料,深入分析了在此案例中微信传播呈现出的显著特征,即传播内容庞杂未见明显的“沉默螺旋”的显现,传播态度多元化但偏于激动,传播迅速集中但议题缺乏持续的关注.研究认为,正由于这样的传播特征加之疫苗风险本身的特征与个体的偏颇性解读,使公众对“问题”疫苗事件产生了强烈的风险感知,并在一定程度上造成了疫苗的污名化结果.因此建议微信时代一定要警惕媒介化的风险,风险沟通要在同理心的基础上进行,要引导公众对风险议题进行持续理性的关注等.     

How do researchers perceive research misbehaviors? A transcultural case study of Chinese and Flemish researchers

China has achieved a dramatic development in scientific research over the last few decades. However, just like many other countries, it has also seen a surge of scientific misconducts. With its expansion of international publications, retractions due to suspected research misconduct are also on the rise. A transcultural case study was conducted by investigating the perception of research misbehaviors by Chinese researchers compared to their Belgian Flemish colleagues. The study was designed to find out variation in research practices in different countries and to see how research was shaped and influenced by cultural contexts. An online questionnaire was sent to 3,236 researchers by e-mails and it received a response rate of 13.09%. They were asked to score 32 research misbehaviors on a 5-point scale. The findings suggested that compared to Flemish respondents, the Chinese had a significantly higher acceptance toward most research misbehaviors. To be more specific, the Chinese respondents felt less unacceptable toward behaviors violating such values as honesty, fairness, and verifiability than the Flemish, while their perceptions of behaviors that violated such values as responsibility, objectivity, and truth were not different compared to the Flemish. This case study implies that the Chinese research community is in an urgent need of training in responsible conduct of research and strong as well as sound guidelines for responsible research practices in place.     

Incremental Sampling Methodology: Applications for Background Screening Assessments

This article presents the findings from a numerical simulation study that was conducted to evaluate the performance of alternative statistical analysis methods for background screening assessments when data sets are generated with incremental sampling methods (ISMs). A wide range of background and site conditions are represented in order to test different ISM sampling designs. Both hypothesis tests and upper tolerance limit (UTL) screening methods were implemented following U.S. Environmental Protection Agency (USEPA) guidance for specifying error rates. The simulations show that hypothesis testing using two‐sample t ‐tests can meet standard performance criteria under a wide range of conditions, even with relatively small sample sizes. Key factors that affect the performance include unequal population variances and small absolute differences in population means. UTL methods are generally not recommended due to conceptual limitations in the technique when applied to ISM data sets from single decision units and due to insufficient power given standard statistical sample sizes from ISM.     

Spatial Risk Analysis of Power Systems Resilience During Extreme Events

The increased frequency of extreme events in recent years highlights the emerging need for the development of methods that could contribute to the mitigation of the impact of such events on critical infrastructures, as well as boost their resilience against them. This article proposes an online spatial risk analysis capable of providing an indication of the evolving risk of power systems regions subject to extreme events. A Severity Risk Index (SRI) with the support of real‐time monitoring assesses the impact of the extreme events on the power system resilience, with application to the effect of windstorms on transmission networks. The index considers the spatial and temporal evolution of the extreme event, system operating conditions, and the degraded system performance during the event. SRI is based on probabilistic risk by condensing the probability and impact of possible failure scenarios while the event is spatially moving across a power system. Due to the large number of possible failures during an extreme event, a scenario generation and reduction algorithm is applied in order to reduce the computation time. SRI provides the operator with a probabilistic assessment that could lead to effective resilience‐based decisions for risk mitigation. The IEEE 24‐bus Reliability Test System has been used to demonstrate the effectiveness of the proposed online risk analysis, which was embedded in a sequential Monte Carlo simulation for capturing the spatiotemporal effects of extreme events and evaluating the effectiveness of the proposed method.     

产需不确定下基于零售商风险规避的三级供应链组合契约模型

针对产需不确定下单一供应商、制造商和风险规避的零售商组成的三级供应链系统，建立了分散和集中情况下的最优决策模型。通过设计风险共担和GL组合契约实现了三级供应链的协调。讨论了风险规避零售商的最优订购决策，分析了风险规避对供应链期望效益的影响。比较了风险规避和风险中性两种情况下零售商的最优决策。探讨了组合契约的协调问题及契约参数之间的关系。研究表明供应链的期望利润随着产需不确定的增加而减少，风险规避下零售商的期望利润低于风险中性时的期望利润，零售商的期望利润随着风险规避程度的加大而减少，零售商最优订购量随风险规避程度的增加而变化。最后数值算例验证了模型和契约协调的有效性。     

Development of an Asset Value Map for Disaster Risk Assessment in China by Spatial Disaggregation Using Ancillary Remote Sensing Data

The extent of economic losses due to a natural hazard and disaster depends largely on the spatial distribution of asset values in relation to the hazard intensity distribution within the affected area. Given that statistical data on asset value are collected by administrative units in China, generating spatially explicit asset exposure maps remains a key challenge for rapid postdisaster economic loss assessment. The goal of this study is to introduce a top‐down (or downscaling) approach to disaggregate administrative‐unit level asset value to grid‐cell level. To do so, finding the highly correlated “surrogate” indicators is the key. A combination of three data sets—nighttime light grid, LandScan population grid, and road density grid, is used as ancillary asset density distribution information for spatializing the asset value. As a result, a high spatial resolution asset value map of China for 2015 is generated. The spatial data set contains aggregated economic value at risk at 30 arc‐second spatial resolution. Accuracy of the spatial disaggregation reflects redistribution errors introduced by the disaggregation process as well as errors from the original ancillary data sets. The overall accuracy of the results proves to be promising. The example of using the developed disaggregated asset value map in exposure assessment of watersheds demonstrates that the data set offers immense analytical flexibility for overlay analysis according to the hazard extent. This product will help current efforts to analyze spatial characteristics of exposure and to uncover the contributions of both physical and social drivers of natural hazard and disaster across space and time.     

Integrating risk and performance management in quality management systems for the development of complex bespoke systems (CBSs)

Abstract

Risk and performance management are at the core of complex bespoke systems (CBSs). CBSs are developed to customer–specified requirements in terms of structure, functionality and conformance. This article examines how risk and performance management are integrated as essential systems in the successful development of projects across multi-organizational functions in complex bespoke system (CBS) organizations. The article argues for the development of a quality management system that consists of two sub-processes: quality control and quality development. Using three case studies from engineering companies, we provide evidence and insights of the way change control, quality development and quality performance are developed in innovating business solutions.     

Media Disaster Reporting Effects on Public Risk Perception and Response to Escalating Tornado Warnings: A Natural Experiment

Previous research has evaluated public risk perception and response to a natural hazards in various settings; however, most of these studies were conducted either with a single scenario or after a natural disaster struck. To better understand the dynamic relationships among affect, risk perception, and behavioral intentions related to natural disasters, the current study implements a simulation scenario with escalating weather intensity, and includes a natural experiment allowing comparison of public response before and after a severe tornado event with extensive coverage by the national media. The current study also manipulated the display of warning information, and investigated whether the warning system display format influences public response. Results indicate that (1) affect, risk perception, and behavioral intention escalated as weather conditions deteriorated, (2) responses at previous stages predicted responses at subsequent stages of storm progression, and (3) negative affect predicted risk perception. Moreover, risk perception and behavioral intention were heightened after exposure to the media coverage of an actual tornado disaster. However, the display format manipulation did not influence behavioral responses. The current study provides insight regarding public perception of predisaster warnings and the influence of exposure to media coverage of an actual disaster event.     

Developing a Broadly Applicable Measure of Risk Perception

Decades of research identify risk perception as a largely intuitive and affective construct, in contrast to the more deliberative assessments of probability and consequences that form the foundation of risk assessment. However, a review of the literature reveals that many of the risk perception measures employed in survey research with human subjects are either generic in nature, not capturing any particular affective, probabilistic, or consequential dimension of risk; or focused solely on judgments of probability. The goal of this research was to assess a multidimensional measure of risk perception across multiple hazards to identify a measure that will be broadly useful for assessing perceived risk moving forward. Our results support the idea of risk perception being multidimensional, but largely a function of individual affective reactions to the hazard. We also find that our measure of risk perception holds across multiple types of hazards, ranging from those that are behavioral in nature (e.g., health and safety behaviors), to those that are technological (e.g., pollution), or natural (e.g., extreme weather). We suggest that a general, unidimensional measure of risk may accurately capture one's perception of the severity of the consequences, and the discrete emotions that are felt in response to those potential consequences. However, such a measure is not likely to capture the perceived probability of experiencing the outcomes, nor will it be as useful at understanding one's motivation to take mitigation action.     

Efficacy,Action, and Support for Reducing Climate Change Risks

A growing body of research demonstrates that believing action to reduce the risks of climate change is both possible (self‐efficacy) and effective (response efficacy) is essential to motivate and sustain risk mitigation efforts. Despite this potentially critical role of efficacy beliefs, measures and their use vary wildly in climate change risk perception and communication research, making it hard to compare and learn from efficacy studies. To address this problem and advance our understanding of efficacy beliefs, this article makes three contributions. First, we present a theoretically motivated approach to measuring climate change mitigation efficacy, in light of diverse proposed, perceived, and previously researched strategies. Second, we test this in two national survey samples (Amazon's Mechanical Turk N = 405, GfK Knowledge Panel N = 1,820), demonstrating largely coherent beliefs by level of action and discrimination between types of efficacy. Four additive efficacy scales emerge: personal self‐efficacy, personal response efficacy, government and collective self‐efficacy, and government and collective response efficacy. Third, we employ the resulting efficacy scales in mediation models to test how well efficacy beliefs predict climate change policy support, controlling for specific knowledge, risk perceptions, and ideology, and allowing for mediation by concern. Concern fully mediates the relatively strong effects of perceived risk on policy support, but only partly mediates efficacy beliefs. Stronger government and collective response efficacy beliefs and personal self‐efficacy beliefs are both directly and indirectly associated with greater support for reducing the risks of climate change, even after controlling for ideology and causal beliefs about climate change.