Setting socially mediated engagement parameters: A topic modeling and text analytic approach to examining polarized discourses on Gillette’s campaign

Opinions on social and political issues can be easily polarized in socially mediated contexts. Using an inductive topic modeling and text analytical approach, the study analyzes more than 100,000 original tweets on polarized discussions on Gillette’s controversial campaign addressing toxic masculinity. Results suggest that influencers serve as ideological hyper-prototypes that attract significant attention equally from supporters and detractors, while hashtags and users’ reference of current events span the conversations across multiple contexts. Study results also indicate that horizontal information cascades from external ideologically centered sources that serve users’ identity and ideological signaling are a potential contributor of discourse polarization. Based on the study results, an identity- and representation-based approach to understanding socially mediated discourses is proposed, and four parameters of socially mediated engagement identified to further theorize engagement for public relations research in digital contexts.     

Macro-Economic Factors in Credit Risk Calculations: Including Time-Varying Covariates in Mixture Cure Models

The prediction of the time of default in a credit risk setting via survival analysis needs to take a high censoring rate into account. This rate is because default does not occur for the majority of debtors. Mixture cure models allow the part of the loan population that is unsusceptible to default to be modeled, distinct from time of default for the susceptible population. In this article, we extend the mixture cure model to include time-varying covariates. We illustrate the method via simulations and by incorporating macro-economic factors as predictors for an actual bank dataset.     

Examining multiplicity and dynamics of publics’ crisis narratives with large-scale Twitter data

The new reality of networked publics on social media calls for crisis communication practitioners and researchers to understand the narratives generated by publics on social media during organizational crises. As social media publics possess diverse, unique characteristics and communicative needs during a crisis, they form interpretative communities and co-create various symbolic interpretations of the crisis. Extending the public-centric and narrative perspective to the context of social media crises, we examined what crisis narratives were constructed by social media publics (i.e., multiplicity) and how these narratives changed by crisis stages (i.e., dynamics). Using topic modelling based on large-scale Twitter data of the Chipotle E. coli crisis (N?=?40,610), we identified ten narratives subsumed under two themes (i.e., sharing-based and conversation-based) based on publics’ social constructions of their perceived risks and crisis experience. On the one hand, sharing-based narratives, heavily impacted by publics’ shared media coverage, reflected media crisis narratives and salient risk perceptions aligning with the news agenda. On the other hand, conversation-based narratives, fueled by publics’ opinion expression and emotional venting, demonstrated publics’ interpretations of their experience with the organization in the crisis with less salient but more diversified risk perceptions. Crisis managers are recommended to produce and deliver compelling narratives resonating with different groups of social media publics during crises.     

Communicative Correlates of Satisfaction,Family Identity,and Group Salience in Multiracial/Ethnic Families

Guided by the Common Ingroup Identity Model ( S. L. Gaertner & J. F. Dovidio, 2000 ) and Communication Accommodation Theory ( C. Shepard, H. Giles, & B. A. LePoire, 2001 ), we examined the role of identity accommodation, supportive communication, and self‐disclosure in predicting relational satisfaction, shared family identity, and group salience in multiracial/ethnic families. Additionally, we analyzed the association between group salience and relational outcomes as well as the moderating roles of multiracial/ethnic identity and marital status. Individuals who have parents from different racial/ethnic groups were invited to complete questionnaires on their family experiences. Participants (N = 139) answered questions about relationships with mothers, fathers, and grandparents. The results of the multilevel modeling analyses are discussed in terms of implications for understanding multiracial/ethnic families and family functioning.     

消费者网络购物感知风险概念及测量模型研究

感知风险在消费者行为研究中是一个重要的变量,它对消费者信息处理及购物决策产生重要影响.随着网络购物的兴起,消费者的信息搜寻和购买决策过程都发生了重大的改变.本文对消费者网络购物感知风险进行了界定,提出感知风险可以由五个具体的网络购物感知风险维度构成,并在此基础上开发了15个项目作为测量指标.论文还对消费者网络购物感知风险不同结构的模型进行了探索比较,根据拟合程度和简约特点界定了消费者网络购物感知风险的概念和测量模型.     

Quality Risk Ratings in Global Supply Chains

Extended enterprises face many challenges in managing the product quality of their suppliers. Consequently characterizing the quality risk posed by value‐chain partners has become increasingly important. There have been several recent efforts to develop frameworks for rating the quality risk posed by suppliers. We develop an analytical model to examine the impact of such quality ratings on suppliers, manufacturers, and social welfare. While it might seem that quality ratings would benefit high‐quality suppliers and hurt low‐quality suppliers, we show that this is not always the case. We find that such quality ratings can hurt both types of suppliers or benefit both, depending on the market conditions. We also find that quality ratings do not always benefit the most demanding manufacturers who desire high‐quality suppliers. Finally, we find that social welfare is not always improved by risk ratings. These results suggest that public policy initiatives addressing risk ratings must be carefully considered.     

基于产业集群的企业重点投资地区决策的仿真

用多Agent仿真技术,研究了基于产业集群的台资IT企业在大陆重点投资地区的决策问题。提出了针对这一问题的、将集群竞争力评价的GEM模型、多Agent仿真技术及演化博弈相结合的思路与方法。仿真模型对集群的研究方法以及多Agent的应用领域有拓展意义,而仿真发现与结论可为政府制定有关政策提供参考。     

Universal versus contextual effects on TQM: a triangulation study using neural networks

The objective of this study is to extend previous research on total quality management (TQM)-context-performance relationships and ‘fit’ using multiple methods. We combine artificial neural networks (ANNs) with structural equation modelling (SEM) to analyse several hypotheses and propositions. This is the first study in this area of research that utilises ANNs and a triangulation technique in the presence of several contextual factors. The SEM analyses suggest that company size and industry type may have contingency effects on some of the TQM practices and/or TQM-performance relationships. However, the ANN models have shown that these two contingency factors do not moderate TQM outcomes, implying that all organisations can benefit from TQM regardless of size and type. As well, these models show that formal TQM implementation and/or ISO certifications do not add any predictive power to the ANN models except in one case: TQM implementation and/or ISO certification added to organisational effectiveness and customer results to predict financial and market (F&M) results. The results further indicate that even though implementing TQM alone has a bigger impact on F&M results than obtaining ISO certification alone, combining the two will have an even greater impact on these results. Joint implementation leads to greater improvements in organisational effectiveness, which, in turn, has a positive effect on customer results and consequently F&M results. This is a unique finding within the context of moderator effects on TQM-performance relationships.     

Incorporating Time‐Dose‐Response into Legionella Outbreak Models

A novel method was used to incorporate in vivo host–pathogen dynamics into a new robust outbreak model for legionellosis. Dose‐response and time‐dose‐response (TDR) models were generated for Legionella longbeachae exposure to mice via the intratracheal route using a maximum likelihood estimation approach. The best‐fit TDR model was then incorporated into two L. pneumophila outbreak models: an outbreak that occurred at a spa in Japan, and one that occurred in a Melbourne aquarium. The best‐fit TDR from the murine dosing study was the beta‐Poisson with exponential‐reciprocal dependency model, which had a minimized deviance of 32.9. This model was tested against other incubation distributions in the Japan outbreak, and performed consistently well, with reported deviances ranging from 32 to 35. In the case of the Melbourne outbreak, the exponential model with exponential dependency was tested against non‐time‐dependent distributions to explore the performance of the time‐dependent model with the lowest number of parameters. This model reported low minimized deviances around 8 for the Weibull, gamma, and lognormal exposure distribution cases. This work shows that the incorporation of a time factor into outbreak distributions provides models with acceptable fits that can provide insight into the in vivo dynamics of the host‐pathogen system.     

A Blueprint for Full Collective Flood Risk Estimation: Demonstration for European River Flooding

Floods are a natural hazard evolving in space and time according to meteorological and river basin dynamics, so that a single flood event can affect different regions over the event duration. This physical mechanism introduces spatio‐temporal relationships between flood records and losses at different locations over a given time window that should be taken into account for an effective assessment of the collective flood risk. However, since extreme floods are rare events, the limited number of historical records usually prevents a reliable frequency analysis. To overcome this limit, we move from the analysis of extreme events to the modeling of continuous stream flow records preserving spatio‐temporal correlation structures of the entire process, and making a more efficient use of the information provided by continuous flow records. The approach is based on the dynamic copula framework, which allows for splitting the modeling of spatio‐temporal properties by coupling suitable time series models accounting for temporal dynamics, and multivariate distributions describing spatial dependence. The model is applied to 490 stream flow sequences recorded across 10 of the largest river basins in central and eastern Europe (Danube, Rhine, Elbe, Oder, Waser, Meuse, Rhone, Seine, Loire, and Garonne). Using available proxy data to quantify local flood exposure and vulnerability, we show that the temporal dependence exerts a key role in reproducing interannual persistence, and thus magnitude and frequency of annual proxy flood losses aggregated at a basin‐wide scale, while copulas allow the preservation of the spatial dependence of losses at weekly and annual time scales.