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Communication is an element that permeates the lives of individuals, from birth to death. Through it, it is possible to create new shared meanings, relationships, and social networks. The importance of the communication process emerges in different contexts, one of which is undoubtedly the work context: several types of research have affirmed that efficient organizational communication produces positive outcomes at the level of workers and organizations, such as increased work commitment, satisfaction, and the reduction of phenomena such as emotional exhaustion and cynicism. Starting from the theoretical framework of the Job Demands-Job Resources model, which divides the characteristics of each work context according to two general categories - namely job demands (representing workers' efforts in performing their job tasks) and job resources (facilitating aspects of work that can provide opportunities for personal growth) - the aim of this research is to explore the role of communication between colleagues and superiors in the relationship between a job resource (increased social resources) and a negative outcome, such as emotional exhaustion, by means of a non-parametric structural equation model (PLS-SEM), in a sample of 479 workers. Reliable structural and measurement models yielded excellent fit indices of the hypothesized latent variables. The results show that, for workers, enhancing social resources by asking for feedback from colleagues and bosses does not protect against the risks of emotional exhaustion, but in fact improves communication. The exclusive use of social resources at work does not have a significant impact on the risk of emotional exhaustion if it is not accompanied by perceived communication appropriate to the circumstances and contexts. Communication, therefore, fully mediates the relationship between increased social resources and emotional exhaustion and stands out as a crucial protective factor. [ABSTRACT FROM AUTHOR]

Fuzzy statistics provides useful techniques for handling real situations which are affected by vagueness and imprecision. Several fuzzy statistical techniques (e.g., fuzzy regression, fuzzy principal component analysis, fuzzy clustering) have been developed over the years. Among these, fuzzy regression can be considered an important tool for modeling the relation between a dependent variable and a set of independent variables in order to evaluate how the independent variables explain the empirical data which are modeled through the regression system. In general, the standard fuzzy least squares method has been used in these situations. However, several applicative contexts, such as for example, analysis with small samples and short and fat matrices, violation of distributional assumptions, matrices affected by multicollinearity (ill-posed problems), may show more complex situations which cannot successfully be solved by the fuzzy least squares. In all these cases, different estimation methods should instead be preferred. In this paper we address the problem of estimating fuzzy regression models characterized by ill-posed features. We introduce a novel fuzzy regression framework based on the Generalized Maximum Entropy (GME) estimation method. Finally, in order to better highlight some characteristics of the proposed method, we perform two Monte Carlo experiments and we analyze a real case study. [ABSTRACT FROM AUTHOR]

This paper presents a review of the original method recently developed by the authors with the Generalized Maximum Entropy (GME) estimator for the simple linear Measurement Error Model (MEM) and the Structural Equation Model (SEM). In socio-economic research, these two models often concern subjective or psychological variables (composite indicators), and represent relations between latent variables. In this review, two applications to the statistical modelling of economic perception and job satisfaction are presented. [ABSTRACT FROM AUTHOR]