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Notre article vise à améliorer la compréhension des liens entre les dimensions économique, sociale, et sociétale (incluant l’environnement) de la performance globale, en mobilisant un cadre théorique mixte (la théorie des parties prenantes et la théorie des ressources). Dans ce cadre, nous développons un modèle de représentation et de mesure de la performance globale explicitant simultanément les relations structurelles entre ses différentes dimensions. Ce modèle conceptuel est validé sur des données collectées auprès des sociétés coopératives et participatives (SCOP) et ses implications sont discutées. A model of representation and evaluation of the global performanceOur paper aims to improve the understanding of the links between the social, economic and environmental performance by mobilizing a joint theoretical framework (the stakeholder theory and the resource-based view theory). Also, we develop a measurement model of the performance clarifying simultaneously the structural relations between the three dimensions (economic, social, and environmental). This conceptual model is validated on worker cooperatives data, then we examine the theoretical and managerial implications.

Reviewers of research reports frequently criticize the choice of statistical methods. While some of these criticisms are well-founded, frequently the use of various parametric methods such as analysis of variance, regression, correlation are faulted because: (a) the sample size is too small, (b) the data may not be normally distributed, or (c) The data are from Likert scales, which are ordinal, so parametric statistics cannot be used. In this paper, I dissect these arguments, and show that many studies, dating back to the 1930s consistently show that parametric statistics are robust with respect to violations of these assumptions. Hence, challenges like those above are unfounded, and parametric methods can be utilized without concern for “getting the wrong answer”.

We consider structural equation models in which variables can be written as a function of their parents and noise terms, which are assumed to be jointly independent. Corresponding to each structural equation model is a directed acyclic graph describing the relationships between the variables. In Gaussian structural equation models with linear functions, the graph can be identified from the joint distribution only up to Markov equivalence classes, assuming faithfulness.In this work, we prove full identifiability in the case where all noise variables have the same variance: the directed acyclic graph can be recovered from the joint Gaussian distribution.Our result has direct implications for causal inference: if the data follow a Gaussian structural equation model with equal error variances, then, assuming that all variables are observed, the causal structure can be inferred from observational data only. We propose a statistical method and an algorithm based on our theoretical findings.

We develop estimation for potentially high-dimensional additive structural equation models. A key component of our approach is to decouple order search among the variables from feature or edge selection in a directed acyclic graph encoding the causal structure. We show that the former can be done with nonregularized (restricted) maximum likelihood estimation while the latter can be efficiently addressed using sparse regression techniques. Thus, we substantially simplify the problem of structure search and estimation for an important class of causal models. We establish consistency of the (restricted) maximum likelihood estimator for low- and high-dimensional scenarios, and we also allow for misspecification of the error distribution. Furthermore, we develop an efficient computational algorithm which can deal with many variables, and the new method's accuracy and performance is illustrated on simulated and real data.

We consider the problem of regularized maximum likelihood estimation for the structure and parameters of a high-dimensional, sparse directed acyclic graphical (DAG) model with Gaussian distribution, or equivalently, of a Gaussian structural equation model. We show that the ℓ 0 -penalized maximum likelihood estimator of a DAG has about the same number of edges as the minimal-edge I-MAP (a DAG with minimal number of edges representing the distribution), and that it converges in Frobenius norm. We allow the number of nodes p to be much larger than sample size n but assume a sparsity condition and that any representation of the true DAG has at least a fixed proportion of its nonzero edge weights above the noise level. Our results do not rely on the faithfulness assumption nor on the restrictive strong faithfulness condition which are required for methods based on conditional independence testing such as the PC-algorithm.

This study examined both the mediation effects of loneliness and self-esteem for the relationship between social support and life satisfaction. Three hundred and eighty nine Chinese college students, ranging in age from 17 to 25 (M = 20.39), completed the emotional and social loneliness scale, the self-esteem scale, the satisfaction with life scale and measure of social support. Structural equation modeling showed full mediation effects of loneliness and self-esteem between social support and life satisfaction. The final model also revealed a significant path from social support through loneliness and self-esteem to life satisfaction. Furthermore, a multi-group analysis found that the paths did not differ across sexes. The findings provided the external validity for the full mediation effects of loneliness and self-esteem and valuable evidence for more complicated relations among the variables.

The purpose of this study is to examine the relationship between innovativeness, quality, growth, profitability, and market value at the firm level. Building on concepts from a resource-based view of a firm and organizational learning, innovation and quality literature, we propose the innovativeness--quality--performance model, which describes how a firm's capability to balance innovativeness with quality drives growth and profitability, and in turn drives superior market value. Results of structural equation models indicate that (1) innovativeness mediates the relationship between quality and growth, (2) quality mediates the relationship between innovativeness and profitability, (3) both innovativeness and quality have mediation effects on market value, and (4) both growth and profitability have mediation effects on market value. Implications for theories and practices are discussed.

Due to the coupled or interconnected relationships among frequent climate extremes, unique geological conditions, discontinuous soil distribution, rugged geomorphology, and highly heterogeneous landscapes in different karst watersheds, few studies were conducted to decouple the relative magnitudes of the climate, lithology, soil, topography, and landscape on soil erosion in karst regions. The objective of this study was to quantify the relative importance of these influencing factors on runoff and sediment yield (SY) in 40 typical karst watersheds in southwest China. To address this issue, the Pearson correlation and random forest were first to select the dominant factors influencing runoff and SY. Subsequently, the partial least squares‐structural equation model (PLS‐SEM) was used to decouple the complex relationships among runoff, SY and their potential influencing factors. Results showed that climate, lithology, soil, topography and landscape could explain 79% of the runoff variation, and only climate factors have significant impact on runoff for heterogeneous karst watersheds (P < 0.01, path coefficient (β) = 0.589). The explanation of five factors to SY variability is 59%, and the landscape has the greatest impact on SY (P < 0.01, β = −0.458). Different from runoff, climatic factors have no significant influence on SY. By elucidating a complex coupled relationship framework, this study can provide a scientific basis for the formulation of soil and water loss program, and the optimization of land resources and ecological environment sustainable development in karst watersheds. Plain Language Summary The variations of runoff and sediment yield (SY) among heterogeneous watersheds are expected to be influenced by climate, lithology, soil, topography and landscape, which are generally interconnected and coupled. Therefore, it is important to decouple the complex relationship between runoff, SY and their potential influencing factors, especially in karst areas where ecologically fragile regions experience severe soil erosion. The results show that only climate factors play a significant influence on runoff. Unlike runoff, all factors have significant effects on SY except climate factors. Interestingly, landscape factors have the greatest influence on SY. Our findings are conducive to better understanding the hydrological and sediment transport characteristics of karst watersheds, and provide scientific basis for soil erosion control and sustainable development of ecological environment. Key Points We decoupled the effects of climate, lithology, soil, topography and landscape on runoff and sediment yield (SY) Climate and landscape exerted the largest influence on runoff and SY, respectively Lithology, soil, topography, and landscape significantly affected SY rather than runoff variability

The relationship between autistic traits, stress, and anxiety experienced by the general population was investigated using an adult sample that evaluated the suitability of three theoretical models proposed by Green and Ben-Sasson. Participants completed online questionnaires that were analysed using structural equation modelling and partial correlation analyses. Of the models tested, the model that proposed SOR and stress as mediators of the relationship between autistic traits and anxiety was able to explain the variance in the data better than the other models. Based on these findings, we suggest that sensory neutral environments should be considered for the prevention and management of anxiety and stress symptoms for people in the general population with higher levels of autistic traits.

A person's socioeconomic status (SES) can affect health (social causation) and health can affect SES (health selection). The findings for each of these pathways may depend on how SES is measured. We study (1) whether social causation or health selection is more important for overall health inequalities, (2) whether this differs between stages of the life course, and (3) between measures of SES. Using retrospective survey data from 10 European countries (SHARELIFE, n{\\thinspace}={\\thinspace}18,734), and structural equation models in a cross-lagged panel design, we determine the relative explanatory power of social causation and health selection through childhood, adulthood, and old age. We use three ways to measure SES: First, as a latent variable capturing different aspects of SES, second as material wealth, and third as occupational skill level. Between childhood and adulthood, social causation and health selection are equally important. In the transition from adulthood to old age, social causation becomes more important than health selection, making it the dominant mechanism in old age. The three measures of SES produce similar results. Only material wealth shows a stronger effect on health (between childhood and adulthood); it is also more affected by health (between adulthood and old age) than the other measures.