Knowledge management through the development of knowledge repositories: towards work degradation

This paper questions the consequences of the use of electronic knowledge repositories for work and employment. Drawing on critical research suggesting that knowledge management associated with such tools presents similarities to scientific management principles, it proposes to examine the following key research question: how do employees experience the transformation of the employment relationship when a knowledge repository is introduced to the workplace? The inquiry is grounded in an exploratory qualitative case study of a knowledge management system designed to foster knowledge‐sharing in a Belgian public administration. The findings illustrate two complementary outcomes: this system resulted in employees experiencing deskilling and work degradation, and was met with resistance. Significantly, this paper considers work degradation as a reflexive phenomenon in the context studied, where knowledge‐sharing systems produced deskilling and resistance as part of a specific re‐regulation process.     

Markov-switching dynamic factor models in real time

We extend the Markov-switching dynamic factor model to account for some of the specificities of the day-to-day monitoring of economic developments from macroeconomic indicators, such as mixed sampling frequencies and ragged-edge data. First, we evaluate the theoretical gains of using data that are available promptly for computing probabilities of recession in real time. Second, we show how to estimate the model that deals with unbalanced panels of data and mixed frequencies, and examine the benefits of this extension through several Monte Carlo simulations. Finally, we assess its empirical reliability for the computation of real-time inferences of the US business cycle, and compare it with the alternative method of forecasting the probabilities of recession from balanced panels.     

Be bad but (still) look good: Can controversial industries enhance corporate reputation through CSR initiatives?

Even though the link between perceived corporate social responsibility fit (PCSR‐fit) and corporate reputation has received much attention from scholars, this tradition has ignored that the underpinnings of this association vary depending on the particular characteristics of each industry under study. To delve into this matter, we investigate in the increasingly relevant context of controversial industries (CIs) how PCSR‐fit could enhance corporate reputation and which are the mediating mechanisms of this association. Our academic contribution is twofold. First, we find that controversial sectors indeed can increase corporate reputation through CSR activities. However, we find that to achieve this goal, the nature of PCSR‐fit should be different than what extant literature indicates, because companies in these settings should directly focus on avoiding or reducing their inherent controversial harm or impact. Second, we evidence that “CSR initiatives' legitimacy” and “situational skepticism” mediate the PCSR‐fit and corporate reputation relationship in CIs. Therefore, we further unravel the underpinnings of this association to advance what we know on the matter and aid practitioners in this particular context.     

Extracting Nonlinear Signals from Several Economic Indicators

We develop a twofold analysis of how the information provided by several economic indicators can be used in Markov switching dynamic factor models to identify the business cycle turning points. First, we compare the performance of a fully nonlinear multivariate specification (one‐step approach) with the ‘shortcut’ of using a linear factor model to obtain a coincident indicator, which is then used to compute the Markov switching probabilities (two‐step approach). Second, we examine the role of increasing the number of indicators. Our results suggest that one step is generally preferred to two steps, especially in the vicinity of turning points, although its gains diminish as the quality of the indicators increases. Additionally, we also obtain decreasing returns of adding more indicators with similar signal‐to‐noise ratios. Using the four constituent series of the Stock–Watson coincident index, we illustrate these results for US data. Copyright © 2014 John Wiley & Sons, Ltd.     

When is escalation of commitment unstoppable in group settings? An iterative economic modeling approach to unveil the dark side of group decision-making

Escalation of commitment (EOC) has been usually studied from a psychological lens, and only recently have scholars approached EOC from an economic perspective. We contribute to this by focusing on iterative decision-making in group settings with a game theory approach. We study how the group members' strategic interaction may result in continuing failing courses of action. Drawing on the Byzantine generals' problem, our model considers an iterative decision-making process where committee participants vote based on private information to escalate or not. Our article demonstrates that if decision-makers reset their beliefs based on the committee's previous decision, then EOC becomes perpetual.     

Extreme Value Theory and Statistics of Univariate Extremes: A Review

Statistical issues arising in modelling univariate extremes of a random sample have been successfully used in the most diverse fields, such as biometrics, finance, insurance and risk theory. Statistics of univariate extremes (SUE), the subject to be dealt with in this review paper, has recently faced a huge development, partially because rare events can have catastrophic consequences for human activities, through their impact on the natural and constructed environments. In the last decades, there has been a shift from the area of parametric SUE, based on probabilistic asymptotic results in extreme value theory, towards semi‐parametric approaches. After a brief reference to Gumbel's block methodology and more recent improvements in the parametric framework, we present an overview of the developments on the estimation of parameters of extreme events and on the testing of extreme value conditions under a semi‐parametric framework. We further discuss a few challenging topics in the area of SUE. © 2014 The Authors. International Statistical Review © 2014 International Statistical Institute     

Antecedents of Purchase Intention in the Online Context

The understanding of the determinant factors of customer purchase intention is necessary, and it is equally important to study the online purchase context, since this context is disseminated among customers. A theoretical model has been elaborated on and tested, considering the constructs of information quality, distrust, and perceived risk as antecedents of purchase intention, and aiming to analyze the relationship among these constructs in the online purchase context. A quantitative research study has been performed by means of the application of a survey. Multivariate statistics techniques have been applied for data analyses, including structural equation modeling. This study contributes to the evolution of the empirically tested concepts by providing a greater individual understanding of each construct presented in the theoretical model, as well as the relationship among them as determinants of purchase intention; the indication is that meaningful relationships were found which may impact greater profitability and, consequently, greater competition for online retailers.     

Asymptotic comparison at optimal levels of reduced‐bias extreme value index estimators

In this article we are interested in the asymptotic comparison, at optimal levels, of a set of semi‐parametric reduced‐bias extreme value (EV) index estimators, valid for a wide class of heavy‐tailed models, underlying the available data. Again, as in the classical case, there is not any estimator that can always dominate the alternatives, but interesting clear‐cut patterns are found. Consequently, and in practice, a suitable choice of a set of EV index estimators will jointly enable us to better estimate the EV index γ, the primary parameter of extreme events.