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The cities of Paris, London, Chicago, and New York (among many others) have set up largescale bike-share systems to facilitate the use of bicycles for urban commuting. This paper estimates the impact on bike-share ridership of two facets of system performance: accessibility (how far the user must walk to reach stations) and bike-availability (the likelihood of finding a bicycle). Our analysis is based on a structural demand model for spatially differentiated products that includes distinct mechanisms for the short and long-term effects of bike-availability (via lost sales and increased user-interest, respectively). The bike-share context, and the distinct mechanisms require us to go beyond past work in incorporating real time changes in product (bike)-availability information, and including much finer data on potential demand sources. These enhancements render traditional estimation methods computationally infeasible; we transform our estimation from the time domain to the “local-stockout- state” domain to address this. Our estimates for the Velib’ bike-share system in Paris suggest that a 10% increase in station density would increase ridership by 5.09% (±0.45%), while a 10% increase in bike-availability would increase ridership by 12.29% (±0.39%), three-fourths of which comes from fewer lost-sales, and the rest from increased user interest. We illustrate the use of our estimates in identifying neighborhoods and times to target for improvements, and in comparing alternate operational improvements and station networks.

Retailers often offer free shipping contingent on an order satisfying a pre-specified threshold amount (Contingent Free Shipping, CFS). As a response, customers may pad up below-threshold orders to avoid paying shipping charges. From a retailer’s standpoint, such order-padding behavior can economize logistics costs provided that customers do not engage in padding behavior that yields bubble purchases---padded orders with above-par return propensity. Bubble purchases can financially strain a retailer via increased returns handling cost. In this study, we empirically examine how the customers’ engagement in bubble purchases relates to: (i) CFS policy’s threshold and shipping fee levers; and (ii) a contextual lever---ease of product return---that is elemental to customers’ return consideration. To do so, we collaborate with a large online retailer who switched over time between multiple CFS policies. Our empirical strategy builds on the induced quasi-natural experiments by these switches, and variation in customers’ experience in processing returns. We find that, in response to our retailer’s CFS policies, customers pad 12.4\% to 28.4\% of below-threshold orders. Both policy levers considerably affect their order-padding and bubble-purchase propensity. Interestingly, the share of bubble purchases in padded orders is strongly moderated by the customers’ ease-of-return experience. In markets with a customer-friendly return process, this share varies from 8.4\% to 14.7\% and is altogether eliminated in markets with modest inconveniences in the return process. Our findings suggest managers should carefully select CFS policy terms in accordance with (and not in isolation from) their returns policy features to drive its efficacy.

Background Myocardial extracellular volume (ECV) quantification by cardiovascular magnetic resonance (CMR) T1 mapping technique has shown great promises in detecting diffuse fibrosis and interstitial diseases of the myocardium. In hypertensive and left ventricle hypertrophy (LVH) patients, increased ECV has been found to be associated with impaired myocardial strain, which is an early indicator of cardiac dysfunction. We hypothesized that in a heterogeneous patient population, ECV was significantly associated with myocardial strain, on the global and the more localized segmental bases.