Technical efficiency in the retail food industry: the influence of inventory investment, wage levels, and age of the firm

The objective of this paper is to estimate technical efficiency in retailing; and the influence of inventory investment, wage levels, and firm age on this efficiency. We use the output supermarket chains’ sales volume, calculated isolating the retailer price effect on its sales revenue. This output allows us to estimate a strictly technical concept of efficiency. The methodology is based on the estimation of a stochastic parametric function. The empirical analyses applied to panel data on a sample of 42 supermarket chains between 2000 and 2002 show that inventory investment and wage level have an impact on technical efficiency. In comparison, the effect of these factors on efficiency calculated through a monetary output (sales revenue) shows some differences that could be due to aspects related to product prices.

Social anxiety and self-concept in adolescence

This study analyzes the relationships between social anxiety and dimensions of self-concept in a sample of 2022 (50.1% males) Spanish adolescents, ranging in age from 12 to 16 year-olds. The social anxiety was assessed by the Social Phobia and Anxiety Inventory (SPAI), and self-concept was measured with the Self-Description Questionnaire II (SDQ-II). Logistic regression analyses show that adolescents with social anxiety are more likely to negatively perceive their relationship with opposite sex peers and have lower scores in self-esteem than students without social anxiety. Furthermore, in most logistic models, students with social anxiety also show a higher probability of perceiving their relation with peers of the same sex in a negative way, considering worse student, less attractive and athletic, and more emotional instable than students without social anxiety. The relevance of sex and grade in the relationship between social anxiety and self-concept in adolescence is discussed.

Handling of Uncertainty in Life Cycle Inventory by Correlated Multivariate Lognormal Distributions: Application to the Design of Supply Chain Networks

Poster presented in the 24th European Symposium on Computer Aided Process Engineering (ESCAPE 24), Budapest, Hungary, June 15-18, 2014.

Handling of Uncertainty in Life Cycle Inventory by Correlated Multivariate Lognormal Distributions: Application to the Design of Supply Chain Networks

In this work, we analyze the effect of incorporating life cycle inventory (LCI) uncertainty on the multi-objective optimization of chemical supply chains (SC) considering simultaneously their economic and environmental performance. To this end, we present a stochastic multi-scenario mixed-integer linear programming (MILP) coupled with a two-step transformation scenario generation algorithm with the unique feature of providing scenarios where the LCI random variables are correlated and each one of them has the desired lognormal marginal distribution. The environmental performance is quantified following life cycle assessment (LCA) principles, which are represented in the model formulation through standard algebraic equations. The capabilities of our approach are illustrated through a case study of a petrochemical supply chain. We show that the stochastic solution improves the economic performance of the SC in comparison with the deterministic one at any level of the environmental impact, and moreover the correlation among environmental burdens provides more realistic scenarios for the decision making process.

Prosocial behavior and self-concept of Spanish students of Compulsory Secondary Education

This study analyzed the relationship between prosocial behavior and self-concept dimensions in a sample of 2022 Spanish students (51.1% males) of Compulsory Secondary Education. The prosocial behavior was measured with the Prosocial Behavior scale of the Teenage Inventory of Social Skills (TISS) and the self-concept was measured with the Self-Description Questionnaire-II (SDQ-II). Logistic regression analyses revealed that prosocial behavior is a positive and significant statistically predictor of high scores on the following self-concept dimensions: physical ability, parent relations, same-sex relations, opposite-sex relations, verbal, school, trustworthiness, and self-esteem. Those results were found in males, females and every Compulsory Secondary education year. However, prosocial behavior is not a significant statistically predictor of high scores on physical appearance, math, and emotional stability.