The White House and public relations: Examining the linkages between presidential communications and public opinion

This study investigates the linkages between presidential public relations activities—speeches and press conferences—and public opinion towards the presidency from 1961 to 1997. The results show that there is a positive linkage between presidential news conferences and foreign policy job approval, and between presidential speeches and general as well as foreign policy job approval. Overall, the findings reveal stronger linkages between presidential news conferences and job approval than between presidential speeches and job approval, but also that the topic of the information subsidies and the specific type of job approval matters.     

Group decision analysis and its application to combining opinions

This paper is about the graphical depiction of the set of feasible gains-in-utilities accruing to three Bayesians involved in the joint estimation of a multivatiate normal mean vector. The basic theory for this problem is sketched. Then a suitable parametrization of surface contours is developed. These contours allow the surface to be mapped and graphically displayed. This is done with the LIG language for interactive graphics. As the opinions of the three Bayesians diverge, the illustrations contained in the paper show how the initially smooth balloon shaped structure develops and ‘clicks’ through a flat spot and eventually becomes highly irregular with a central indentation. The result provides insight into the nature of disensus where explicit mathematical analysis is extremely difficult.     

Pregnant women’s choice of birthing hospital: A qualitative study on individuals’ preferences

Objective

To investigate pregnant women’s decision making in relation to their choice of birthing hospital and, in particular, their priorities regarding hospital characteristics.

How does information sharing affect first-tier suppliers’ flexibility? Evidence from the automotive industry in Sweden

This study addresses the critical research issue of how supplier delivery performance can be enhanced by integrating information sharing into volume and delivery flexibility. This study developed a research model to relate information sharing on demand forecasts and inventory data between original equipment manufacturers (OEMs) and first-tier suppliers. Based on a sample of 52 suppliers from automotive industry in Sweden, partial least squares structural equations modelling (PLS-SEM) was used to assess the model. The results confirm that sharing demand forecasts is a key enabler of supplier volume and delivery flexibility while sharing inventory data is not. The study contributes to enlarging the knowledge about supply chain management from the suppliers’ perspective. It also contributes to knowledge by validating the conceptual model and operationalisation of constructs. The study also has practical contribution in which management should focus on improving communication and collaboration practices with OEMs for effective sharing of demand forecasts.     

Too much information? Predictors of information overload in the context of online news exposure

As the Internet provides massive amounts of heterogeneous information, people may perceive this medium as challenging. The difficulty to evaluate and select relevant information increases as more and more diverse sources and content are available. Information overload (IO) may be the consequence. The research presented here gives a first comprehensive overview of possible indicators for IO in the context of online news exposure. Based on an online survey (N=419), we found that younger people with less information-seeking self-efficacy were more susceptible to experience IO. Additionally, we identified motivations for media consumption and information retrieval strategies in the Internet that imply IO. With our results, we contribute to a further understanding of IO and provide an important basis for future research needed to face the challenges resulting from the rising media diversity.     

Citizen engagement and the illusion of secrecy: exploring commenter characteristics in censored online news articles

We address the conflict between citizenship engagement through news commenting, and censorship needs. News articles often contain forms of censorship to maintain security, with non-identification of individuals a means of information protection. Commonly used is the replacement of a name with a supposedly non-identifying initial, protecting the identity of military personnel, witnesses, minors, victims or suspects who need to be granted anonymity in the public sphere. We seek to understand the characteristics of commenters including awareness of the potential for social media to circumvent censorship, and attitudes towards censorship in news articles. Our study of censored articles collected from online news pages on Facebook, presents insights into participant characteristics including a strong correlation between personal network size and censorship support.     

Network autocorrelation models with egocentric data

Network autocorrelation models have been widely used for decades to model the joint distribution of the attributes of a network's actors. This class of models can estimate both the effect of individual characteristics as well as the network effect, or social influence, on some actor attribute of interest. Collecting data on the entire network, however, is very often infeasible or impossible if the network boundary is unknown or difficult to define. Obtaining egocentric network data overcomes these obstacles, but as of yet there has been no clear way to model this type of data and still appropriately capture the network effect on the actor attributes in a way that is compatible with a joint distribution on the full network data. This paper adapts the class of network autocorrelation models to handle egocentric data. The proposed methods thus incorporate the complex dependence structure of the data induced by the network rather than simply using ad hoc measures of the egos’ networks to model the mean structure, and can estimate the network effect on the actor attribute of interest. The vast quantities of unknown information about the network can be succinctly represented in such a way that only depends on the number of alters in the egocentric network data and not on the total number of actors in the network. Estimation is done within a Bayesian framework. A simulation study is performed to evaluate the estimation performance, and an egocentric data set is analyzed where the aim is to determine if there is a network effect on environmental mastery, an important aspect of psychological well-being.     

Preparing Social Work Graduates for Digital Practice: Ethical Pedagogies for Effective Learning

ABSTRACT

Debate has arisen in social work about the value of online education, with proponents providing examples of innovative approaches to online teaching and those against it citing the need for face-to-face interactions to suitably teach and assess social work skills. At the same time, social workers are increasingly making use of social media, websites, online databases, email, and text messaging services for work with clients. Consequently, social workers must be adequately prepared to make use of online technologies in their practice. Drawing on examples of online interactive activities used at two regional universities and informed by Chi’s conceptual framework of cognitive processes of learning, this article argues that learning via information and communications technology (ICT) helps prepare graduates for emerging digital practice and should be an integral part of social work education. Further, ICT enables educators to monitor the levels and quality of student rehearsal of practice skills and knowledge in interactive learning experiences helping promote competent practice, grounded in social work values.     

Dynamic approach to linear statistical calibration with an application in microwave radiometry

The problem of statistical calibration of a measuring instrument can be framed both in a statistical context as well as in an engineering context. In the first, the problem is dealt with by distinguishing between the ‘classical’ approach and the ‘inverse’ regression approach. Both of these models are static models and are used to estimate exact measurements from measurements that are affected by error. In the engineering context, the variables of interest are considered to be taken at the time at which you observe it. The Bayesian time series analysis method of Dynamic Linear Models can be used to monitor the evolution of the measures, thus introducing a dynamic approach to statistical calibration. The research presented employs a new approach to performing statistical calibration. A simulation study in the context of microwave radiometry is conducted that compares the dynamic model to traditional static frequentist and Bayesian approaches. The focus of the study is to understand how well the dynamic statistical calibration method performs under various signal-to-noise ratios, r.     

Estimation of generalized exponential distribution based on an adaptive progressively type-II censored sample

In this paper, based on an adaptive Type-II progressively censored sample from the generalized exponential distribution, the maximum likelihood and Bayesian estimators are derived for the unknown parameters as well as the reliability and hazard functions. Also, the approximate confidence intervals of the unknown parameters, and the reliability and hazard functions are calculated. Markov chain Monte Carlo method is applied to carry out a Bayesian estimation procedure and in turn calculate the credible intervals. Moreover, results from simulation studies assessing the performance of our proposed method are included. Finally, an illustrative example using real data set is presented for illustrating all the inferential procedures developed here.