CEF.UP - MaR Seminar | Webinar - Identifying Competitors in Geographical Markets Using the CSIS Method

Tuesday, April 2nd 2024 | 6:30 pm | room 631 | online

Identifying Competitors in Geographical Markets Using the CSIS Method

P. K. Kannan – Robert H. Smith School of Business at the University of Maryland (U.S.A.)
(joint work with Xian Gu)

“Identifying the most relevant competitors in a geographical market is an important and necessary requirement for businesses with a significant offline presence, such as hotels, restaurants, and retail stores. In particular, the specific location of a business and the geographical density of potential competitors can be critical factors in determining the competitive structure. However, this task can be quite difficult when the potential competitive set is large, and the competition is asymmetric. In this study, we apply the Conditional Sure Independence Screening (CSIS) method to a system of demand functions for competitor identification. This method offers significant computational efficiency because it estimates a marginal regression for each potential competitor instead of estimating a full model consisting of all potential competitors. To validate the effectiveness of our method and explore the boundary conditions of its performance, we conduct extensive simulation analyses under different spatial data generating processes. We show that our CSIS method’s performance is superior to multiple other variable selection approaches in identifying true competitors and is robust under spatial misspecifications. Then we apply our method to hotel competition in two U.S. geographical regions and show how the competitive structure varies across geographical densities and market segments. Finally, we highlight how managers can use the results strategically and outline the potential of the method for other non-geographical applications.”

Any questions please contact cefup.sec@fep.up.pt.

“Cef.up is financed by Portuguese public funds through FCT - Fundação para a Ciência e Tecnologia, I.P., in the framework of the project with reference UIDB/04105/2020”