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a b s t r a c tPressure continues to build on Internet retailers to squeeze out inefficiencies from their day-to-day operations. One major source of such inefficiencies is product returns. Indeed, product returns in Internet retailing have been shown to be, on average, as high as 22% of sales. Yet, most retailers accept them as a necessary cost of doing business. This is not surprising since many retailers do not have a clear understanding of the causes of product returns. While it is known that return policies of retailers, along with product attributes, are two important factors related to product return incidents, little is known about which aspects of the online retail transaction make such a purchase more return-prone. In the current study, we seek to address this issue. We use a large data set of customer purchases and returns to identify how process attributes in physical distribution service (PDS) influence product returns. The first attribute involves perceptions of scarcity conditions in inventory availability among consumers when retailers reveal to consumers information on inventory levels for the products that they intend to buy. Our results show that orders in which items are sold when these conditions are revealed to shoppers have a higher likelihood of being returned than orders in which these conditions are not revealed. While prior research has argued that inventory scarcity perceptions have an effect on purchases, our findings suggest that they are also related to the likelihood of these purchases being returned. The second attribute involves the reliability in the delivery of orders to consumers. We find that the likelihood of orders being returned depends on the consistency between retailer promises of timeliness in the delivery of orders and the actual delivery performance of the orders. Moreover, we find that the effect that consistency in the delivery has in the likelihood of returns, is stronger for orders that involve promises for expedited delivery than for orders with less expeditious promises. That is, although the occurrence of returns depends on the delays in the delivery of orders to consumers relative to the initial promises made by the retailers, this effect is more notable for orders that involve promises of fast delivery.

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Telephone-based Latina Breast Cancer Survivorship Intervention reached Latina breast cancer survivors for survivorship education and support. Self-management of pain and fatigue showed improvement over time.

Deriving statistical models to predict one variable from one or more other variables, or predictive modeling, is an important activity in obesity and nutrition research. To determine the quality of the model, it is necessary to quantify and report the predictive validity of the derived models. Conducting validation of the predictive measures provides essential information to the research community about the model. Unfortunately, many articles fail to account for the nearly inevitable reduction in predictive ability that occurs when a model derived on one dataset is applied to a new dataset. Under some circumstances, the predictive validity can be reduced to nearly zero. In this overview, we explain why reductions in predictive validity occur, define the metrics commonly used to estimate the predictive validity of a model (e.g., R2, mean squared error, sensitivity, specificity, receiver operating characteristic, concordance index), and describe methods to estimate the predictive validity (e.g., cross-validation, bootstrap, adjusted and shrunken R2). We emphasize that methods for estimating the expected reduction in predictive ability of a model in new samples are available and this expected reduction should always be reported when new predictive models are introduced.