From realpolitik to noopolitik: The public relations of (stateless) nations in an information age

In this article, we explore ways to advance public relations approaches to nation building by drawing insights from stateless nations and by deploying two sets of theories uncommon in the field: the information society and noopolitik. The theoretical discussion is grounded in a study of Catalonia and we conclude by arguing for a shift of emphasis from nation-state governments to governance for both public and private actors.     

Examining the Ability,Motivation and Opportunity (AMO) framework in HRM research: Conceptualization,measurement and interactions

Despite the increasing popularity of the Ability, Motivation, Opportunity (AMO) framework in the Human Resource Management (HRM) field, AMO research is at a crossroads in theoretical and empirical development. This is due to (a) a lack of clarity about the conceptualisation and measurement of AMO variables, (b) the construction of AMO articles that do not distinguish between AMO differences and AMO-enhancing HRM practices and fail to integrate them, (c) a lack of understanding about how AMO variables at the individual and organizational levels interact to generate individual and organizational performance, and (d) a lack of consideration of the process (mediators and moderators) through which AMO generates performance gains. Based on the analysis of 104 quantitative HRM articles published between 1997 and 2022, this study helps to draw clearer distinctions among AMO variables and levels of analysis. The review of the empirical literature shows that there is excessive heterogeneity with regard to the conceptualization and utilisation of AMO variables, which in turn leads to scale proliferation. We find that research on AMO-enhancing HRM practices and AMO differences is rarely combined and tends to be tested at a single level rather than more logical cross-level effects between AMO-enhancing HRM practices, AMO differences and performance. We also found that whereas Ability and Motivation differences mediate the relationship between AMO-enhancing HRM practices and performance, opportunity appears to be a boundary condition in the relationship between Ability and Motivation with performance outcomes. The paper concludes with relevant avenues for future AMO research suggested for the field of HRM.     

Bayesian factor models for multivariate categorical data obtained from questionnaires

Factor analysis is a flexible technique for assessment of multivariate dependence and codependence. Besides being an exploratory tool used to reduce the dimensionality of multivariate data, it allows estimation of common factors that often have an interesting theoretical interpretation in real problems. However, standard factor analysis is only applicable when the variables are scaled, which is often inappropriate, for example, in data obtained from questionnaires in the field of psychology, where the variables are often categorical. In this framework, we propose a factor model for the analysis of multivariate ordered and non-ordered polychotomous data. The inference procedure is done under the Bayesian approach via Markov chain Monte Carlo methods. Two Monte Carlo simulation studies are presented to investigate the performance of this approach in terms of estimation bias, precision and assessment of the number of factors. We also illustrate the proposed method to analyze participants'' responses to the Motivational State Questionnaire dataset, developed to study emotions in laboratory and field settings.     

Internationalization and performance: The moderating effects of organizational learning

By proposing a conceptual outline for a general model that explains the internationalization–performance link, we test the moderating effects of organizational learning on the relationship between internationalization and performance empirically. Integrating two distinct literature streams from the organizational learning perspective and the resource-based view, we present an integrated, multidimensional framework for analyzing multinational enterprises’ (MNEs’) resources, internationalization, and organizational learning, as well as their associated impact on firm performance. Specifically, using a sample of 110 American MNEs, we find that while certain MNE resources motivate and precede internationalization, social and market learning (whereas technological learning does not) moderates the relationship between internationalization and performance. These findings extend prior research by establishing the importance of the relationships among MNE resources, internationalization, organizational learning, and firm performance.     

Modelling long-term pan-European population change from 1870 to 2000 by using geographical information systems

Summary.  The paper presents work that creates a geographical information system database of European census data from 1870 to 2000. The database is integrated over space and time. Spatially it consists of regional level data for most of Europe; temporally it covers every decade from 1870 to 2000. Crucially the data have been interpolated onto the administrative units that were available in 2000, thus allowing contemporary population patterns to be understood in the light of the changes that have occurred since the late 19th century. The effect of interpolation error on the resulting estimates is explored. This database will provide a framework for much future analysis on long-term Europewide demographic processes over space and time.     

Publicizing atrocity and legitimizing outrage: Picasso’s Guernica

Picasso created his most famous painting, Guernica, in just over three weeks in 1937 after the bombing of the little town of Guernica, located in the Basque region, during the Spanish Civil War. Thousands of innocent people were injured or killed. In its sharp lines, its confusion and its distorted shapes, Guernica shows the suffering and pain of war. Rather than using color, especially vivid reds, Picasso used only black and white paint as symbols of death, mourning and tragedy. He believed that brighter colors might distract the viewer from the agony of the scene. In Guernica, most of the figures have open mouths; hear them shouting, groaning or screaming. The aim of this paper and its relevance to public relations is to examine whether and how visual communication can publicize and frame a military event, the character of military leaders, and warfare as a generic aspect of democratic self-governance. This paper proposes that rhetorical, discursive art can contribute impact to public relations efforts, by focusing attention, making issues public, and making informative, framing, and democratizing statements. Even more important is the ability of art to express moral outrage, especially when giving voice to muted interests.     

Blending Bayesian and Classical Tools to Define Optimal Sample-Size-Dependent Significance Levels

ABSTRACT

This article argues that researchers do not need to completely abandon the p-value, the best-known significance index, but should instead stop using significance levels that do not depend on sample sizes. A testing procedure is developed using a mixture of frequentist and Bayesian tools, with a significance level that is a function of sample size, obtained from a generalized form of the Neyman–Pearson Lemma that minimizes a linear combination of α, the probability of rejecting a true null hypothesis, and β, the probability of failing to reject a false null, instead of fixing α and minimizing β. The resulting hypothesis tests do not violate the Likelihood Principle and do not require any constraints on the dimensionalities of the sample space and parameter space. The procedure includes an ordering of the entire sample space and uses predictive probability (density) functions, allowing for testing of both simple and compound hypotheses. Accessible examples are presented to highlight specific characteristics of the new tests.     

Controlling type I error in the reference‐scaled bioequivalence evaluation of highly variable drugs

Reference‐scaled average bioequivalence (RSABE) approaches for highly variable drugs are based on linearly scaling the bioequivalence limits according to the reference formulation within‐subject variability. RSABE methods have type I error control problems around the value where the limits change from constant to scaled. In all these methods, the probability of type I error has only one absolute maximum at this switching variability value. This allows adjusting the significance level to obtain statistically correct procedures (that is, those in which the probability of type I error remains below the nominal significance level), at the expense of some potential power loss. In this paper, we explore adjustments to the EMA and FDA regulatory RSABE approaches, and to a possible improvement of the original EMA method, designated as HoweEMA. The resulting adjusted methods are completely correct with respect to type I error probability. The power loss is generally small and tends to become irrelevant for moderately large (affordable in real studies) sample sizes.     

Nonlinear mixed-effects models with scale mixture of skew-normal distributions

Aiming to avoid the sensitivity in the parameters estimation due to atypical observations or skewness, we develop asymmetric nonlinear regression models with mixed-effects, which provide alternatives to the use of normal distribution and other symmetric distributions. Nonlinear models with mixed-effects are explored in several areas of knowledge, especially when data are correlated, such as longitudinal data, repeated measures and multilevel data, in particular, for their flexibility in dealing with measures of areas such as economics and pharmacokinetics. The random components of the present model are assumed to follow distributions that belong to scale mixtures of skew-normal (SMSN) distribution family, that encompasses distributions with light and heavy tails, such as skew-normal, skew-Student-t, skew-contaminated normal and skew-slash, as well as symmetrical versions of these distributions. For the parameters estimation we obtain a numerical solution via the EM algorithm and its extensions, and the Newton-Raphson algorithm. An application with pharmacokinetic data shows the superiority of the proposed models, for which the skew-contaminated normal distribution has shown to be the most adequate distribution. A brief simulation study points to good properties of the parameter vector estimators obtained by the maximum likelihood method.