Department of Decision, Operations & Information Technologies
Maryland Transportation Institute
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.
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.
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.
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.
In the race to establish themselves, many early-stage marketplaces choose to accelerate their growth by adding marquee (established brand name) sellers. We study the implications of marquee seller adoption on smaller, lesser-known unbranded sellers in the marketplace. While recent literature has shown that higher- quality unbranded sellers fare better than their lower-quality peers, we posit that this tendency may depend on the quality of entering marquee sellers. To this end, we collaborate with a B2B platform and exploit its two marquee sellers’ adoptions of contrasting qualities. Using a difference-in-differences framework, we causally identify the effect. We find that while higher-quality unbranded seller revenues increase relative to low-quality unbranded sellers when the entering marquee seller is of high quality (consistent with the literature), the effect is reversed when the entering marquee seller is of low quality. Further, unbranded sellers change their supply quantities such that their average supply quality shifts in the direction of marquee entrant quality. Using a stylized theoretical model, we identify two mechanisms that drive our findings – (i) new buyers brought in as a result of the marquee’s entry disproportionately favoring unbranded sellers of neighboring quality, and (ii) the unbranded seller’s ability to adjust their supply quantities. Our findings have implications on marquee sellers’ adoption strategies for marketplaces where sellers strategically set their supply quantity (a key feature of several marketplaces, including many gig-economy marketplaces). The choice of marquee sellers, examined through the lens of their externality on unbranded sellers, can foster or undermine the platform’s long-term growth objectives.