Ashish Kabra


Assistant Professor
Business Analytics, Department of Decision, Operations & Information Technologies
University of Maryland, College Park

I am an Assistant Professor of Business Analytics at University of Maryland, College Park. My research interests lie in studying the design and user-behavior of micromobility systems, food delivery services, online marketplaces and e-commerce. I use large scale observational and experimental data as well as causal inferences methods, structural estimation methods, and applied game theory.

Refereed Publications

Bike-Share Systems: Accessibility and Availability. Ashish Kabra, Elena Belavina and Karan Girotra
Management Science, 2020 (Lead Article)
First Place, MSOM Best Student Paper Competition 2015
Second Place, POMS Best Student Paper Award in Sustainability 2015
Third Place, IBM Best Student Paper Award in Service Science 2016

The cities of Paris, London, Chicago, and New York (among many others) have set up bike-share systems to facilitate the use of bicycles for urban commuting. This paper estimates the impact of two facets of system performance on bike-share ridership: accessibility (how far the user must walk to reach stations) and bike-availability (the likelihood of finding a bicycle). We obtain these estimates from a structural demand model for ridership estimated using data from the Vélib’ system in Paris. We find that every additional meter of walking to a station decreases a user’s likelihood of using a bike from that station by 0.194% (±0.0693%), and an even more significant reduction at higher distances (>300 m). These estimates imply that almost 80% of bike-share usage comes from areas within 300 m of stations, highlighting the need for dense station networks. We find that a 10% increase in bike-availability would increase ridership by 12.211% (±1.097%), three-fourths of which comes from fewer abandonments and the rest of which comes from increased user interest. We illustrate the use of our estimates in comparing the effect of adding stations or increasing bike-availabilities in different parts of the city, at different times, and in evaluating other proposed improvements.

Online Grocery Retail: Revenue Models and Environmental Impact Elena Belavina, Karan Girotra and Ashish Kabra
Management Science, 2017

This paper compares the financial and environmental performance of two revenue models for the online retailing of groceries: the per-order model, where customers pay for each delivery, and the subscription model, where customers pay a set fee and receive free deliveries. We build a stylized model that incorporates (i) customers with ongoing uncertain grocery needs and who choose between shopping offline or online and (ii) an online retailer that makes deliveries through a proprietary distribution network. We find that subscription incentivizes smaller and more frequent grocery orders, which reduces food waste and creates more value for the customer; the result is higher retailer revenues, lower grocery costs, and potentially higher adoption rates. These advantages are countered by greater delivery-related travel and expenses, which are moderated by area geography and routing-related scale economies. Subscription also leads to lower food waste–related emissions but to higher delivery-related emissions. Ceteris paribus, the per-order model is preferable for higher-margin retailers with higher-consumption product assortments that are sold in sparsely populated markets spread over large, irregular areas with high delivery costs. Geographic and demographic data indicate that the subscription model is almost always environmentally preferable because lower food waste emissions dominate higher delivery emissions.