Examining the Impact of Ridehailing Services on Public Transit Use

Informations Systems and Operations Management

Speaker: Gordon Burtch
Assistant Professor of Information Systems & Decision Sciences (Carlson School of Management - Minnesota University)

6 October 2017 - in Room Bernard Ramanantsoa (Building V) - From 2:00 am to 3:30 am


We examine the impact that ridehailing services (e.g., Uber, Lyft) have had on the use of various modes of public transportation in the United States, via a city-level analysis. We first evaluate these effects by exploiting the temporally and spatially staggered entry of Uber across the United States. Recognizing that the timing and location of Uber’s entry is likely to be endogenous with respect to dynamic variables that reflect a local economic environment, we introduce a novel time-series matching procedure that can deliver plausible identification under a difference-in-differences estimation framework. Subsequently, we re-evaluate the effects by exploiting a natural experiment in which the Google Maps application incorporated Uber and Lyft services into its transit / direction recommendations. Under both identification strategies we find consistent results. Our estimates indicate that ridehailing service entry has lead to significant reductions in the utilization of road-based, short-haul public transit services (e.g., bus), yet increased utilization of rail-based and long-haul transit services (e.g., subway, commuter rail). Finally, we show that resulting cannibalization and complementarity effects are attenuated and amplified, respectively, by transit agencies’ quality of service.

Short Bio:

Gord is an Assistant Professor of Information & Decision Sciences and Jim & Mary Lawrence Fellow at the University of Minnesota's Carlson School of Management, as well as a Consulting Researcher with Microsoft Research NYC. His research, which focuses on the economic evaluation of information systems, employs empirical analyses rooted in econometrics and field experimentation to identify and quantify the drivers of individual participation in online social contexts. His work has been published in a variety of top tier outlets, including Management Science, Information Systems Research, MIS Quarterly. In 2014, Gord won the ISR best paper award, and in 2016 he won the ISR best reviewer award. He holds a PhD from Temple University’s Fox School of Business, as well as Bachelor of Engineering and MBA degrees from McMaster University’s DeGroote School of Business.

Heterogeneity of Reference Effects in the Competitive Newsvendor Problem

Informations Systems and Operations Management

Speaker: Anton Ovchinnikov
Associate Professor & Distinguished Professor of Management Science & Operations Management , Smith School of Business, Queen‘s University

24 March 2017 - Room Bernard Ramanantsoa (Building V) - From 11:00 am to 12:30 pm

​This paper examines two recently-proposed reference effect formulations for the newsvendor problem and extends them to a competitive setting. The analysis of the resultant game shows that the heterogeneity of newsvendors’ reference effects can explain multiple regularities observed in recent experimental studies of newsvendor competition. Specifically, the observations that a behavioral newsvendor may effectively ignore the orders of the competitor, receive a significantly smaller profit, and over-order when there is no expected demand overflow can all be attributed to the heterogeneous reference effects in our model’s equilibrium. ​

Improving Environment, Health and Safety in Supply Chains: Some Preliminary Studies

Informations Systems and Operations Management

Speaker: Christopher S. Tang
Carter Professor of Business Administration, , UCLA Anderson School

17 March 2017 - Room Bernard Ramanantsoa - From 11:00 am to 12:30 pm

Many factories in developing countries have serious Environment, Health and Safety (EHS) issues. Due to inconsistent law enforcement, limited progress has been made. What can be done? This is an open research topic that operations management and supply chain researchers should explore. I plan to share some of my preliminary studies in this presentation.

Consumer Choice Under Limited Attention and Implications on Firm Decisions

Informations Systems and Operations Management

Speaker: Tamer Boyaci
Professor of Management Science, the Michael Diekmann Chair in Management Science, and Director of Research, , ESMT Berlin

3 March 2017 - Room Bernard Ramanantsoa - From 11:00 am to 12:30 pm

Facing an abundance of product choices and related information, but with only limited time and attention to evaluate them, consumers have to come to grips with how much and what type of information to pay attention to (and what to ignore), and make product choice and purchase decisions based on this partial information.
Evidently, it is often times easier to obtain information about some products then others (by the very nature of the product or simply because it is offered in an assortment and readily observable, among other reasons). At the same time there may be similarities (i.e., correlations) among products such that as the customer learns about a particular product, he/she may do so about another one.

Utilizing rational inattention theory, we present a general discrete choice model that describes the choice behavior of customers who optimally acquire information about available options with ex-ante uncertain values through potentially different channels with different costs. Customers trade-off the benefits of better information obtained by asking questions (and receiving informative signals) with the associated cost. We quantify acquired information and its cost through a novel function based on (Shannon) mutual information. Solving the consumer’s choice problem, we analytically characterize the resulting optimal choice behaviour. Some special cases of this model (including the generalized multinomial choice) are analyzed to illustrate key properties.

We then turn our attention to the applications of this choice model to business operations. We study assortment decisions of a seller as well as pricing decisions, demonstrating the implications of salient factors such as limited attention, cost of information, and correlations among products. Finally, we show how limited time and attention shapes the learning behaviour of the seller and its ordering strategies in a newsvendor setting.

A Markovian Approach to Choice Modeling and Assortment Optimization

Informations Systems and Operations Management

Speaker: Antoine Desir
5th year PhD candidate - Industrial Engineering and Operations Research , (Columbia University, USA)

20 February 2017 - Room Bernard Ramanantsoa (Building V) - From 10:30 am to 12:00 pm

Which set of products should be offered to arriving consumers to maximize expected revenue? This is a core revenue management problem known as assortment optimization which applies to a wide variety of settings. Discrete choice model theory offers a way to mathematically model the substitution behavior of consumers and provide a key ingredient for this problem. Many choice models have been proposed in the literature, introducing a fundamental tradeoff between model expressiveness and computational complexity. In particular, the assortment optimization problem is notoriously hard for general choice models. In this talk, we look at a new framework which tries to strike a good balance between expressiveness and tractability. In particular, the substitution behavior of consumers is modeled as transitions in a Markov chain. By doing so, this new model helps alleviate the Independence of Irrelevant Alternatives (IIA) property, a well-known limitation of the popular multinomial logit model. Moreover, it provides a good approximation to the class of random utility models. We show that not only this model has a great predictive power, it is also tractable from a computational perspective. In particular, we give a algorithm framework to derive efficient algorithm for different variants of the assortment optimization problem.