Effect sizes should generally be calculated and presented along with p-values for statistically significant results, and observed effect sizes should be discussed qualitatively through direct and explicit comparisons with the effects in related literature.

In this journal, Zimmerman (2004, 2011) has discussed preliminary tests that researchers often use to choose an appropriate method for comparing locations when the assumption of normality is doubtful. The conceptual problem with this approach is that such a two-stage process makes both the power and the significance of the entire procedure uncertain, as type I and type II errors are possible at both stages. A type I error at the first stage, for example, will obviously increase the probability of a type II error at the second stage. Based on the idea of Schmider et al. (2010), which proposes that simulated sets of sample data be ranked with respect to their degree of normality, this paper investigates the relationship between population non-normality and sample non-normality with respect to the performance of the ANOVA, Brown-Forsythe test, Welch test, and Kruskal-Wallis test when used with different distributions, sample sizes, and effect sizes. The overall conclusion is that the Kruskal-Wallis test is considerably less sensitive to the degree of sample normality when populations are distinctly non-normal and should therefore be the primary tool used to compare locations when it is known that populations are not at least approximately normal.

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Purpose – The purpose of this paper is twofold: first, to empirically test whether a “one size fits all” strategy fits the fashion e-commerce business and second, to evaluate whether consumer returns are a central aspect of the creation of profitability and, if so, to discuss the role of returns management (RM) in the supply chain strategy. Design/methodology/approach – Transactional sales and return data were analysed and used to categorise customers based on their buying and returning behaviours, measuring each customer's net contribution margins. Findings – The e-commerce business collects a vast quantity of data, but these data are seldom used for the development of service differentiation. This study analysed behaviour patterns and determined that the segmentation of customers on the basis of both sales and return patterns can facilitate a differentiated service delivery approach. Research limitations/implications – This research empirically supports the theory that customer buying and returning behaviours can be used to appropriately categorise customers and thereby guide the development of a more differentiated service approach. Practical implications – The findings support a differentiated service delivery system that utilises a more dynamic approach, conserving resources and linking the supply chain and/or organisational strategies with customers' buying and returning behaviours to avoid over and underservicing customers. Originality/value – Consumer returns are often viewed as a negative aspect of doing business; interestingly, however, the authors revealed that the most profitable customer is a repeat customer who frequently returns goods.