Explaining the Causes and Effects of Dynamic Capabilities Generation: A Multiple‐Indicator Multiple‐Cause Modelling Approach

The purpose of this paper is to develop a multiple‐indicator multiple‐cause model to explain dynamic capabilities generation. We use one of the main common effects of dynamic capabilities (operational, structural and strategic flexibility) to design a measurement tool for dynamic capabilities generation. Based on this measurement tool, we test the influence of several factors identified in the specialized literature as potential causes that trigger and promote dynamic capabilities generation. We use data from a survey of 200 CEOs of Spanish firms to test the model. The results show that only organizations whose managers have perceived a high degree of environmental dynamism have generated dynamic capabilities. The results also show that knowledge codification and technical innovation are significantly related to dynamic capabilities generation. We attempt to shed light on current theoretical debates about dynamic capabilities generation and provide a practical guide to explain the origin and results of dynamic capabilities that have been tested empirically.     

Shrinking into the big city: influence of genetic and environmental factors on urban dragon lizard morphology and performance capacity

Urban Ecosystems - Urban wildlife faces a novel set of challenges resulting in selective pressure that can lead to population-level changes. We studied Australian water dragons (Intellagama...     

Urbanization does not affect green space bird species richness in a mid-sized city

Urban Ecosystems - Urbanization is one of the most intensive forms of landscape and habitat transformation, resulting in species loss, and taxonomic and functional homogenization of different...     

Incidence of Social Accountability in Local Governance: The Case of the Network for Fair,Democratic and Sustainable Cities and Territories in Latin America

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - In Latin America, the emergence of a series of civil society initiatives aimed at citizen engagement for social...     

Promoting Individual and Social Changes: A Hybrid Model of Social Work with Groups

Taking as a starting point two influential, yet different approaches in group work, that is, the self-directed group work and mutual aid models, this article examines a possible alternative for conducting social work with groups. Drawing from structuration theory, which makes a strong dialectical relation between agency and structure possible, this article highlights how our alternative model could lead to a greater integration of the micro- and macrodimensions in group-work practice. The Discussion section proposes three key principles for group work, namely, a belief in people’s strengths and capacities, a focus on critical thinking, and a concern for the development of a democratic culture in groups. These principles are conveyed through the group worker’s roles as consciousness raiser and process facilitator and provide a flexible and participatory process that can be used with a broad range of service-user groups. The article concludes with a discussion on the strengths and limitations of the model.     

Estimation of the Intensity Parameter of the Germ‐Grain Quermass‐Interaction Model when the Number of Germs is not Observed

The Quermass‐interaction model allows to generalize the classical germ‐grain Boolean model in adding a morphological interaction between the grains. It enables to model random structures with specific morphologies, which are unlikely to be generated from a Boolean model. The Quermass‐interaction model depends in particular on an intensity parameter, which is impossible to estimate from classical likelihood or pseudo‐likelihood approaches because the number of points is not observable from a germ‐grain set. In this paper, we present a procedure based on the Takacs–Fiksel method, which is able to estimate all parameters of the Quermass‐interaction model, including the intensity. An intensive simulation study is conducted to assess the efficiency of the procedure and to provide practical recommendations. It also illustrates that the estimation of the intensity parameter is crucial in order to identify the model. The Quermass‐interaction model is finally fitted by our method to P. Diggle's heather data set.     

An extension of the generalized half-normal distribution

In this paper we propose an extension of the generalized half-normal distribution studied in Cooray and Ananda (Commun Stat 37:1323–1337, 2008). This new distribution is defined by considering the quotient of two random variables, the one in the numerator being a generalized half normal distribution and the one in the denominator being a power of the uniform distribution on \((0,1)\) , respectively. The resulting distribution has greater kurtosis than the generalized half normal distribution. The density function of this more general distribution is derived jointly with some of its properties and moments. We discuss stochastic representation, maximum likelihood and moments estimation. Applications to real data sets are reported revealing that the proposed distribution can fit real data better than the slashed half-normal, generalized half-normal and Birnbaum–Saunders distributions.     

Treatment Effects: A Bayesian Perspective

This paper contributes to the emerging Bayesian literature on treatment effects. It derives treatment parameters in the framework of a potential outcomes model with a treatment choice equation, where the correlation between the unobservable components of the model is driven by a low-dimensional vector of latent factors. The analyst is assumed to have access to a set of measurements generated by the latent factors. This approach has attractive features from both theoretical and practical points of view. Not only does it address the fundamental identification problem arising from the inability to observe the same person in both the treated and untreated states, but it also turns out to be straightforward to implement. Formulae are provided to compute mean treatment effects as well as their distributional versions. A Monte Carlo simulation study is carried out to illustrate how the methodology can easily be applied.     

Stability of risk preference measures: results from a field experiment on French farmers

We compare two different elicitation methods for measuring risk attitudes on a sample of French farmers. We consider the lottery tasks initially proposed by Holt and Laury (Econ Rev 92:1644?C1655, 2002) and by Eckel and Grossman (Evol Hum Behav 23:281?C295, 2002; J Econ Behav Org 68:1?C7, 2008). The main empirical result from this within-subject study is that risk preference measures are affected by the type of mechanism used. We first show that this risk preference instability can be related to non-expected utility preferences of farmers. Using a risk-taking psychometric questionnaire, we then demonstrate that risk preferences of farmers are context-dependent. This may be another explanation of the observed risk preference instability.