Beating the virus: an examination of the crisis communication approach taken by New Zealand Prime Minister Jacinda Ardern during the Covid-19 pandemic

ABSTRACT

The Covid-19 pandemic has severely tested the leadership and communication abilities of political leaders globally. Guiding an effective response to the global pandemic has required leaders to demonstrate not only effective planning and coordination skills, but the ability to communicate clear consistent messages in an empathetic manner as well. In New Zealand the first confirmed case of Covid-19 was recorded on February 28 and over the course of March and April 2020, 1,132 further cases of Covid-19 were confirmed and 19 deaths – a much lower transmission rate than most industrialized nations. On 27 April 2020, New Zealand Prime Minister Jacinda Ardern announced that they had won the battle against community transmission of Covid-19. This paper analyses the speeches and public statements (n = 40) made by Prime Minister Ardern in March and April 2020 through the lens of crisis leadership and crisis communication. In particular, it looks at the use of different mediums (parliamentary statements, daily briefings, Facebook Live broadcasts and podcasts) as mechanisms for engaging in narrative and dialogue with the public. The paper underscores the importance of communication in crisis management and looks at how positive and consistent messaging inspires confidence and social solidarity.     

Structural elements of material well-being: An empirical test among people on social security

The paper reports on a study of indicators of well-being, including objective indicators of well-being, global, affective indicators of well-being, and indicators of socio-economic well-being. Socioeconomic well-being is (narrowly) defined as that part of the individual's universal well-being which is strongly related to money and material means.It was hypothesized that these 3 classes of indicators of well-being have different meanings for the various groups of people on social security. Therefore, data were collected from a Dutch, nationally representative sample of 3 categories of individuals drawing benefits, namely: people receiving social benefits; unemployed, and disabled people, and a group of people in work. Hypotheses were tested regarding the following issues:

Transforming technology management education: Value creation-learning in the early twenty-first century

Management education is often criticized as irrelevant, out of touch, too “trade-school,” too interested in training financial services professionals and consultants, and insufficiently focused on innovation, the major driver of the economy. Technology management (TM) education has always focused on practical and relevant issues and innovation has been a major theme. We believe however that rapid changes in the global environment of business demand changes in the underlying assumptions of TM. Starting with a brief overview of the field, this paper examines the major environmental changes that must be addressed by TM and the skills that future graduates will require.     

Harnessing the Theoretical Foundations of the Exponential and Beta‐Poisson Dose‐Response Models to Quantify Parameter Uncertainty Using Markov Chain Monte Carlo

Dose‐response models are the essential link between exposure assessment and computed risk values in quantitative microbial risk assessment, yet the uncertainty that is inherent to computed risks because the dose‐response model parameters are estimated using limited epidemiological data is rarely quantified. Second‐order risk characterization approaches incorporating uncertainty in dose‐response model parameters can provide more complete information to decisionmakers by separating variability and uncertainty to quantify the uncertainty in computed risks. Therefore, the objective of this work is to develop procedures to sample from posterior distributions describing uncertainty in the parameters of exponential and beta‐Poisson dose‐response models using Bayes's theorem and Markov Chain Monte Carlo (in OpenBUGS). The theoretical origins of the beta‐Poisson dose‐response model are used to identify a decomposed version of the model that enables Bayesian analysis without the need to evaluate Kummer confluent hypergeometric functions. Herein, it is also established that the beta distribution in the beta‐Poisson dose‐response model cannot address variation among individual pathogens, criteria to validate use of the conventional approximation to the beta‐Poisson model are proposed, and simple algorithms to evaluate actual beta‐Poisson probabilities of infection are investigated. The developed MCMC procedures are applied to analysis of a case study data set, and it is demonstrated that an important region of the posterior distribution of the beta‐Poisson dose‐response model parameters is attributable to the absence of low‐dose data. This region includes beta‐Poisson models for which the conventional approximation is especially invalid and in which many beta distributions have an extreme shape with questionable plausibility.     

Choosing a Pretest-Posttest Analysis

Pretest-posttest designs serve as building blocks for other more complicated repeated-measures designs. In settings where subjects are independent and errors follow a bivariate normal distribution, data analysis may consist of a univariate repeated-measures analysis or an analysis of covariance. Another possible analysis approach is to use seemingly unrelated regression (SUR). The purpose of this article is to help guide the statistician toward an appropriate analysis choice. Assumptions, estimates, and test statistics for each analysis are approached in a systematic manner. On the basis of these results, the crucial choice of analysis is whether differences in pretest group means are conceived to be real or the result of pure measurement error. Direct consultation of the statistician with a subject-matter person is important in making the right choice. If pretest group differences are real, then a univariate repeated-measures analysis is recommended. If pretest group differences are the result of pure measurement error, then a conditional analysis or SUR analysis should be used. The conditional analysis and the SUR analysis will produce similar results. Smaller variance estimates can be expected based on the SUR analysis, but this gain is partially mediated by a lack of an exact distribution for test statistics.     

Corporate long-range planning groups—Some different approaches

This paper investigates several atypical roles of formal long-range planning groups and some reasons for their appearance. The study is based on three separate surveys covering 115 companies in eight industries located in both England and the United States. The authors first identify functions based on their own research and the literature which appear to make up a typical planning group role. Twenty-four of the 115 planning groups surveyed very considerable as well as sytematically from the norm. They are classified into three major categories described as (1) capital budgeting groups, (2) project development groups, and (3) think tank groups.     

Lay Understanding of Synergistic Risk: The Case of Radon and Cigarette Smoking

The combination of radon and smoking produces a synergistic risk of lung cancer. Lay understanding of this risk was examined from the perspectives of mental models theory, the psychometric approach to risk perception, and optimistic bias. As assessed by interview, participants ( N = 50) had more extensive mental models for the risks of smoking than for the risks of radon or the combination of radon and smoking; 32% knew little or nothing about radon. Despite reading an informational brochure, their risk-perception ratings of the three hazards showed no perception of the synergy between smoking and radon risk, although the combined hazard was rated as less familiar but more controllable than the average of the single hazards ( p < .01). No evidence of optimistic bias for the health consequences of radon, or the combination of radon and smoking was observed. Participants appeared to be combining the single-hazard risks subadditively to arrive at their combined-hazard risk perceptions. Further research on the integration of perceived risks would be beneficial for designing optimal communications about synergistic risk.     

Absurdly Ambitious? Teenagers’ Expectations for the Future and the Realities of Social Structure

Expectations for achievement in the US border on the unrealistic. High school students expect to obtain better jobs and more education than current cohorts have achieved. Many youth also seem unaware of how to realize their ambitions. These findings lead to several questions about the causes and consequences of ambition. First, how do American youths’ ambitions compare with those of past cohorts and what consequences stem from rising ambitions? Second, how likely is it that youth will achieve their ambitions? What structural forces hinder or assist the goal attainment process? Finally, what cultural and institutional forces shape ambition in the United States? We review available evidence for these questions. Experts agree that the youth are overly ambitious, but debate the consequences of over‐ambition. Furthermore, youth privileged by their race, class, and gender status are more likely to achieve their ambitions than less privileged youth, confirming the key sociological premise that broader social structures play an important role in whether individuals realize their dreams.