For online apparel retailers, sizing has proven to be a particularly thorny challenge—no matter how much information is provided online, it is often impossible for an online shopper to determine exactly which size of an apparel item will fit her best. Given the increasing ubiquity of free returns (full refund, no return shipping charge), consumers often manage this size uncertainty by bracketing their online apparel purchases, that is, ordering two additional sizes (one smaller, one larger) with the intention of keeping the one that fits best and returning the others. Since bracketing is a relatively new phenomenon in retailing, its net effect on retailer profits is not well‐understood. From a retailer's perspective, bracketing is detrimental since it leads to an increase in consumer returns and the associated costs, which can be quite substantial. On the other hand, bracketing can also be beneficial to the retailer by alleviating consumers' concerns about size, and driving higher net sales. In this paper, we establish how price can be used as a lever to influence consumer bracketing behavior, and examine the impact of bracketing on the profits of a monopolist retailer who offers free returns. We provide prescriptions for when retailers should encourage or discourage bracketing based on three key parameters: the retailer's reverse logistics cost, consumers' return hassle cost, and the product's match probability. As expected, in many cases it is optimal for the retailer to price such that consumers are discouraged from bracketing, and thus the related returns processing costs are avoided. However, we also find that when the product's match probability is moderate, consumers' hassle cost is low and retailer's reverse logistics cost is low, a price that encourages bracketing is the optimal strategy for the retailer, challenging the conventional wisdom that bracketing by consumers is always bad for retailers.

This study investigates factors contributing to the injury severity of truck-involved work zones crashes in South Carolina (SC). The outcome of interest is injury or property damage only crashes, and the explanatory factors examined include the occupant, vehicle, collision, roadway, temporal, and environmental characteristics. Two mixed (random parameter) logit models are developed, one for non-interstates with speed limits less than 60 miles per hour (mph) and one for interstates with speed limits greater than or equal to 60 mph, using South Carolina statewide truck-involved work zone crash data from 2014 to 2020. Results of log-likelihood ratio tests indicate that separate speed models are warranted. The factors that were found to contribute to injury at the 90% confidence level in both models (interstate and non-interstate) are (1) dark lighting conditions, (2) female (at-fault) drivers, and (3) driving too fast for roadway conditions. Significant factors that apply only to non-interstates are SC or US primary roadways, activity area of the work zone, at-fault drivers under 35, sideswipe collision, presence of workers in the work zone, and collision with fixed objects. Significant factors that apply only to interstates are three or more vehicles, rear-end collision, location before the first work zone sign, and weekdays.