36 research outputs found
Global sensitivity analysis of stochastic computer models with joint metamodels
The global sensitivity analysis method used to quantify the influence of uncertain input variables on the variability in numerical model responses has already been applied to deterministic computer codes; deterministic means here that the same set of input variables gives always the same output value. This paper proposes a global sensitivity analysis methodology for stochastic computer codes, for which the result of each code run is itself random. The framework of the joint modeling of the mean and dispersion of heteroscedastic data is used. To deal with the complexity of computer experiment outputs, nonparametric joint models are discussed and a new Gaussian process-based joint model is proposed. The relevance of these models is analyzed based upon two case studies. Results show that the joint modeling approach yields accurate sensitivity index estimatiors even when heteroscedasticity is strong
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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