A comprehensive conceptual and bibliometric study of person-centered methodologies

Person-centered approaches, such as latent profile analysis (LPA) and qualitative comparative analysis (QCA), have gained in popularity in organizational scholarship because of their ability to provide insight into how interrelations between a group of conditions can lead to a particular outcome. Despite the growing acceptance of person-centered approaches in social science research, traditional variable-centered approaches continue to prevail, although their dominance is increasingly questioned. This paper offers in-depth analysis of the current state of QCA and LPA from both a conceptual and a bibliometric perspective. This study thus aims to contextualize the role of person-centered methodologies in organizational scholarship. This aim is important, given the significance of exploring novel approaches to advance knowledge in organizational research. This paper provides scholars with quantifiable and readily comparable information on the use of these emerging but promising methods in organizational studies. Ultimately, this scientific contribution sheds light on the current and prospective applications of person-centered methods in research. Moreover, it offers scholars who are considering applying these methods objective analysis of the scientific production in this area thus far.

     

Intrapreneurship model in the software industry

Quality & Quantity - The complexity of business dynamics demands that companies search for alternative ways to ensure their growth and survival. Thus intrapreneurship emerges as an ad-hoc...     

Bayesian Inference in Spatial Sample Selection Models

In this study, we consider Bayesian methods for the estimation of a sample selection model with spatially correlated disturbance terms. We design a set of Markov chain Monte Carlo algorithms based on the method of data augmentation. The natural parameterization for the covariance structure of our model involves an unidentified parameter that complicates posterior analysis. The unidentified parameter – the variance of the disturbance term in the selection equation – is handled in different ways in these algorithms to achieve identification for other parameters. The Bayesian estimator based on these algorithms can account for the selection bias and the full covariance structure implied by the spatial correlation. We illustrate the implementation of these algorithms through a simulation study and an empirical application.     

Who do you care about? Scientists’ personality traits and perceived impact on beneficiaries

We investigate the roots of scientists' perceptions of the impact of their work by examining stable psychological characteristics such as personality traits. An analysis of personality traits highlights the effects of policies related to gender equality, allocation of research time and skills acquisition. It improves our understanding of the conflicts related to scientists’ perceptions of the impact of their research on beneficiaries. For example, conscientiousness increases the perceived impact on clinical beneficiaries, but reduces the perceived impact on industrial beneficiaries. Organizational scientific freedom increases the effects of personality traits on perceived impact on beneficiaries such that scientists affiliated to a university are less likely than colleagues working in other research settings to perceive the simultaneous impact of their work on both industrial and clinical beneficiaries.     

Time series copulas for heteroskedastic data

We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We suggest copulas for first‐order Markov series, and then extend them to higher orders and multivariate series. We derive the copula of a volatility proxy, based on which we propose new measures of volatility dependence, including co‐movement and spillover in multivariate series. In general, these depend upon the marginal distributions of the series. Using exchange rate returns, we show that the resulting copula models can capture their marginal distributions more accurately than univariate and multivariate generalized autoregressive conditional heteroskedasticity models, and produce more accurate value‐at‐risk forecasts.     

Inequality in turbulent times: income distribution in Germany and Britain, 1900–50

Using social tables, this article provides new data on inequality in Germany and Britain on an annual basis for the first half of the twentieth century. Inequality trends in these two countries tended to follow opposite patterns. The decline in inequality in Germany was interrupted during the First World War and the Nazi period, while in Britain the reversal took place between the end of the First World War and the Great Depression. Results show that the drop in inequality during the twentieth century in Europe did not follow secular trends, thus supporting the notion of inequality cycles.     

Determinants of bank’s interest margin in the aftermath of the crisis: the effect of interest rates and the yield curve slope

Empirical Economics - This paper analyses the determinants of net interest margin, focusing on the impact of interest rates and the slope of the yield curve, using a broad panel of data from 32...     

Inflation,real economic growth and unemployment expectations: an empirical analysis based on the ECB survey of professional forecasters

Expectations are at the centre of modern macroeconomic theory and policymakers. In this article, we examine the predictive ability and the consistency properties of macroeconomic expectations using data of the European Central Bank (ECB) Survey of Professional Forecasters (SPF). In particular, we provide evidence on the properties of forecasts for three key macroeconomic variables: the inflation rate, the growth rate of real gross domestic product and the unemployment rate.     

The impact of purchasing strategy-structure (mis)fit on purchasing cost and innovation performance

The organizational design literature strongly supports the notion of “structure follows strategy”, and suggests that a misfit between the two has a negative effect on performance. Building on this line of argument, we examine to what extent the (mis)fit between purchasing strategy and purchasing structure impacts purchasing performance. We focus on cost and innovation purchase category strategies, and examine how the deviation from an ideal purchasing structure defined along three dimensions (centralization, formalization, and cross-functionality) impacts purchasing performance. Analysing data collected from 469 firms in ten countries, we demonstrate that a strategy-structure misfit negatively impacts purchasing performance in both cost and innovation strategies. We also find that purchasing proficiency is a mediator in this relationship between misfit and performance. Our findings aid managerial decision making by empirically validating the necessity of having the right purchasing structure for successfully executing different purchasing strategies.