Shopping motives as antecedents of e-satisfaction and e-loyalty

Customer loyalty is fundamental to the profitability and survival of e-tailers. Yet research on antecedents of e-loyalty is relatively limited. This study contributes to the literature by investigating the effect of motives for online shopping on e-satisfaction and e-loyalty. A structural equations model is developed and tested through data from an online survey involving 797 customers of two UK-based e-tailers focussing on hedonic products. The results suggest that convenience, variety seeking, and social interaction help predict e-satisfaction, and that social interaction is the only shopping motive examined with a direct relationship to e-loyalty. Data also show that e-satisfaction is a strong determinant of e-loyalty. These findings are discussed in the light of previous research and avenues of future research are proposed.

Risk perception and commitment to reduce global climate change in Spain

An online national survey among the Spanish population (n = 602) was conducted to examine the factors underlying a person’s support for commitments to global climate change reductions. Multiple hierarchical regression analysis was conducted in four steps and a structural equations model was tested. A survey tool designed by the Yale Project on Climate Change Communication was applied in order to build scales for the variables introduced in the study. The results show that perceived consumer effectiveness and risk perception are determinant factors of commitment to mitigating global climate change. However, there are differences in the influence that other factors, such as socio-demographics, view of nature and cultural cognition, have on the last predicted variable.

First order k-th moment finite element analysis of nonlinear operator equations with stochastic data

We develop and analyze a class of efficient Galerkin approximation methods for uncertainty quantification of nonlinear operator equations. The algorithms are based on sparse Galerkin discretizations of tensorized linearizations at nominal parameters. Specifically, we consider abstract, nonlinear, parametric operator equations J(\alpha ,u)=0 for random input \alpha (\omega ) with almost sure realizations in a neighborhood of a nominal input parameter \alpha _0. Under some structural assumptions on the parameter dependence, we prove existence and uniqueness of a random solution, u(\omega ) = S(\alpha (\omega )). We derive a multilinear, tensorized operator equation for the deterministic computation of k-th order statistical moments of the random solution's fluctuations u(\omega ) - S(\alpha _0). We introduce and analyse sparse tensor Galerkin discretization schemes for the efficient, deterministic computation of the k-th statistical moment equation. We prove a shift theorem for the k-point correlation equation in anisotropic smoothness scales and deduce that sparse tensor Galerkin discretizations of this equation converge in accuracy vs. complexity which equals, up to logarithmic terms, that of the Galerkin discretization of a single instance of the mean field problem. We illustrate the abstract theory for nonstationary diffusion problems in random domains.