I am an Assistant Professor of Business Analytics at University of Maryland, College Park. I am excited about the potential of new business models for smarter cities, be it bike-share systems, ride-hailing platforms, or delivery based models. In my research work I integrate consumer behavior, location and availability related operational concerns and their interrelation with environmental aspects. I have an interest in applying structural empirical models as well as advancing the methodology.
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.
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.
Draft available upon request
Achieving scale is key to the efficacy, survival and eventual domination of peer- to-peer marketplaces. Marketplace operators often run aggressive promotions and incentive schemes to attract new users or increase the usage of existing users. This study quantifies and compares the effect of incentives given to the “buyer” side and “seller” side of the marketplace. Specifically, using data from one of the leading ride-hailing marketplace, we estimate the effect of passenger incentives and driver incentives on number of trips arranged through the marketplace. Further, the incentives can be designed in different formats, i.e. they could be given for every use (linear incentives) or could be given only upon a certain level of use (threshold incentives). We build a structural model to accurately capture the driver and passenger response to incentives, and the nature of incentives. We take into account the effect of service levels on passenger and driver behavior and their endogenous realization in the system, as well as the cross externalities and economies of scale effects. Driver effort on the platform is unobserved, for which we devise a novel local matching model based imputation method. We find that in short-term (current week) passenger incentives are more effective than similar driver incentives. In long-term (next 3 months), the opposite is true; driver incentives are more effective than passenger incentives. This change of effects is determined by the differential stickiness of passengers and drivers to the platform, as well as differential response to evolving service levels. When structuring driver incentives, it is more effective to use threshold incentives compared to linear incentives. The marketplace exhibits substantial economies of scale. For every doubling of passenger and driver numbers, the number of trips more than doubles, it increases by further 25%.
Draft available upon request
This study addresses the challenge of matching demand and supply in the product introduction phase. We model the effects of prepositioning strategies designed to increase responsiveness and adopt a dynamic approach which permits linking to management of products that have reached steady state. Empirical calibra- tion and evaluation of this strategy is made possible using data from a major cosmetics company marketing their own products in the Middle East and where the approach has been implemented.