Risk Information Seeking and Processing About Particulate Air Pollution in South Korea: The Roles of Cultural Worldview

This study integrates cultural theory of risk into the risk information seeking and processing model in the context of particulate air pollution in South Korea. Specifically, it examines how cultural worldviews (hierarchy, individualism, egalitarianism, and fatalism) influence the way people interpret risk about an environmental risk, which may in turn promote or deter their information seeking and processing about the risk. An online survey (N = 645) showed that egalitarianism was positively associated with perceptions of societal and personal risks, affective responses toward the risk, and informational subjective norms. Perceived societal risk, in particular, mediated the effect of egalitarianism on information insufficiency. Moreover, cultural worldview was a significant moderator of the relationships between information insufficiency and risk information seeking and processing. The positive relationship between information insufficiency and information seeking grew stronger with increasing egalitarianism. In contrast, the negative relationship between information insufficiency and heuristic processing was strengthened with increasing hierarchy. This study extends prior theories and models in risk communication by addressing the roles of cultural worldview, an important individual difference factor in interpreting environmental risks.     

Evaluating barriers to implementing green supply chain management: An example from an emerging economy

Abstract

This study proposes a fuzzy-based VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) framework for evaluating barriers to implementing green supply chain management (GSCM) in the context of an emerging economy. The methodology uses a mix method approach combining literature review and opinions of some selected managers from the plastic industry of Bangladesh to identify four main-barriers and twenty-five sub-barriers relevant to GSCM implementation. Fuzzy-VIKOR approach was applied to aid in the analysis of the barriers in the plastic industry of Bangladesh. The findings of the study show the order/rank of intensity and severity of the main-barriers to implementing GSCM practices in the plastic industry of Bangladesh as follows: ‘inadequate knowledge and support’, ‘insufficient technology and infrastructure’, ‘financial constraints and unsupportive organizational’ and ‘operational policies’. The results also show the rankings of the sub-barriers under each main barriers. This research contributes to the literature in a number of ways. First, it identifies multi-levels of barriers to GSCM implementation. Secondly, it identifies and proposes alternative action plans (strategies) to help mitigate and implement GSCM practices. Though this study has significant contributions, a number of limitations do exist. The barriers in this study were identified using the extant literature review and industrial managers’ opinions. A more scientific approach and empirical validation is required, especially in the plastic manufacturing industry of Bangladesh to identify more new challenging barriers. However, this study can provide managers with a better understanding of the barriers to implementing GSCM practices and motivate the researchers to further extend the investigation on the insights for developing strategic plans for implementing GSCM practices in the plastic industry of Bangladesh.     

Getting emotional: An emotion-cognition dual-factor model of crisis communication

The effect of emotional experience on cognitive processes is an under-explored phenomenon in crisis communication research, despite indications of the increasing importance of emotion-based communication in the digital era. Emerging evidence indicates that the emotional experiences of publics play a critical role during organizational crises and determine the degree to which organizations are able to successfully devise communications that help them deal with such crises. Yet no comprehensive, theoretically sound model exists for analyzing how emotions influence the cognitive processing of crisis information. Our study proposes just such a model, capable of describing how cognition and emotion, separately or interactively, influence the publics’ processing of crisis information and consequently their attitudes and behaviors. Our model describes how emotional variables determine whether the publics take a cognition-oriented approach or an emotion-oriented approach to information processing and subsequent formation of interpretations, evaluations, and judgments directed toward organizations. Moreover, our model explains how an emotion-to-cognition assessment of publics’ attitudes is possible and makes a case for how it is critical for predicting and influencing the publics’ behavior during organizational crises. The hope is that this model will aid practitioners and academics in understanding why the publics’ emotions matter during crisis communication as well as how to craft more effective communication strategies as a result. Finally, several avenues for future research are suggested in order to test the validity of our conceptual model in different contexts.     

A Bayesian semiparametric approach to learning about gene–gene interactions in case-control studies

Gene–gene interactions are often regarded as playing significant roles in influencing variabilities of complex traits. Although much research has been devoted to this area, to date a comprehensive statistical model that addresses the various sources of uncertainties, seem to be lacking. In this paper, we propose and develop a novel Bayesian semiparametric approach composed of finite mixtures based on Dirichlet processes and a hierarchical matrix-normal distribution that can comprehensively account for the unknown number of sub-populations and gene–gene interactions. Then, by formulating novel and suitable Bayesian tests of hypotheses we attempt to single out the roles of the genes, individually, and in interaction with other genes, in case-control studies. We also attempt to identify the significant loci associated with the disease. Our model facilitates a highly efficient parallel computing methodology, combining Gibbs sampling and Transformation-based MCMC (TMCMC). Application of our ideas to biologically realistic data sets revealed quite encouraging performance. We also applied our ideas to a real, myocardial infarction dataset, and obtained interesting results that partly agree with, and also complement, the existing works in this area, to reveal the importance of sophisticated and realistic modeling of gene–gene interactions.     

Nonlinear regression models for heterogeneous data with massive outliers

The income or expenditure-related data sets are often nonlinear, heteroscedastic, skewed even after the transformation, and contain numerous outliers. We propose a class of robust nonlinear models that treat outlying observations effectively without removing them. For this purpose, case-specific parameters and a related penalty are employed to detect and modify the outliers systematically. We show how the existing nonlinear models such as smoothing splines and generalized additive models can be robustified by the case-specific parameters. Next, we extend the proposed methods to the heterogeneous models by incorporating unequal weights. The details of estimating the weights are provided. Two real data sets and simulated data sets show the potential of the proposed methods when the nature of the data is nonlinear with outlying observations.     

融合特征情感和产品参数的客户感知偏好模型

在线评论作为一种公开、可获取的信息资源，蕴含了关于产品质量的各种有价值的信息，对这些信息的分析与挖掘有助于企业了解客户的需求和偏好。目前，基于在线评论的客户偏好模型考虑了特征观点和评论数量，忽视了产品参数对消费者购买决策的影响。因此，本文提出了一种融合特征情感和产品参数的客户感知偏好模型。首先，利用在线评论提取客户讨论的产品特征，并识别产品特征的情感极性，从而进一步计算特征正负面情感得分，生成产品的特征情感。然后，结合企业定义的产品参数，构建特征情感和产品参数融合作用对产品销量影响的计量经济模型，分析客户对产品的感知偏好。最后，为验证模型的有效性，获取了汽车之家网站的39款汽车产品（SUV车型）的评论文本，并持续8个月的销量跟踪。研究结果显示本文提出的模型比信息增益和TF-IDF方法能够更加准确地获取客户的感知偏好。此外，研究结果解释了特征情感和产品参数与产品销量的关联关系，为企业的市场营销和产品设计决策提供了理论基础。     

Bayesian robustness in meta‐analysis for studies with zero responses

Statistical meta‐analysis is mostly carried out with the help of the random effect normal model, including the case of discrete random variables. We argue that the normal approximation is not always able to adequately capture the underlying uncertainty of the original discrete data. Furthermore, when we examine the influence of the prior distributions considered, in the presence of rare events, the results from this approximation can be very poor. In order to assess the robustness of the quantities of interest in meta‐analysis with respect to the choice of priors, this paper proposes an alternative Bayesian model for binomial random variables with several zero responses. Particular attention is paid to the coherence between the prior distributions of the study model parameters and the meta‐parameter. Thus, our method introduces a simple way to examine the sensitivity of these quantities to the structure dependence selected for study. For illustrative purposes, an example with real data is analysed, using the proposed Bayesian meta‐analysis model for binomial sparse data. Copyright © 2016 John Wiley & Sons, Ltd.     

Assessment of hostile and benign intent attributions in early childhood: Can we elicit meaningful information?

Throughout middle childhood and adolescence, hostile intent attributions fairly consistently predict levels of aggression. Across 28 published studies in early childhood, however, researchers have found less consistent relationships. We believe this may be due to a majority of these studies using an inappropriate methodological approach for early childhood, forced‐choice questioning. We tested the use of open‐ended vs. forced‐choice questions about intent in 118 Head Start preschool children. In response to a forced choice question, only about 30% of children attributed intent correctly to a video depicting clearly purposeful behavior. And across 18 video vignettes depicting ambiguous provocation, children's intent attribution scores based on a forced‐choice approach demonstrated neither reliability nor validity. Conversely, children's intent attribution scores in response to open‐ended questions demonstrated reliability, correspondence with other aspects of social information processing, and predictive validity in the form of relations to teacher reports of social competence and aggression. Researchers should refrain from utilizing forced‐choice approaches to intent attributions in early childhood unless also conducting intent understanding checks.     

The Influence of “Made in China” on the Great Moderation of the Global Economy：An Empirical Test Based on Value Chains*

We have collected data on China’s 22 main trading partners for 1984-2012 and used System GMM to study the influence of “Made in China” on the “Great Moderation” of the global economy. Our research shows that although trade scale, real effective exchange rate, oil prices, fixed capital investment and other variables have significantly expanded global economic volatility, “Made in China” has markedly restrained output growth rate fluctuations in countries around the world and price fluctuations in developed countries, helping the global economy develop with “high growth, low volatility.” “Made in China” is a long-term variable in the context of the global value chain. China needs to take advantage of global value chain restructuring to further upgrade and develop processing and manufacturing industry and expand its presence in the international market. It could choose to adopt a “mirror strategy” and launch trade sanctions targeting counterpart industries or enterprises to counter irrational trade sanctions from the developed countries. At the same time, however, it is important for China to make its own contribution to improve global economic governance and building a new international economic order in the era of global value chains by strengthening its policy coordination with other countries.