Learning in Retail Entry

International Journal of Research in Marketing,Forthcoming

51 Pages Posted: 24 Nov 2011 Last revised: 28 Sep 2019

Date Written: September 26, 2019

Abstract

Retailers may face uncertainty about the profitability of local markets, which provide opportunities for learning when making entry decisions. To quantify these informational benefits, I develop an empirical framework for studying dynamic retail entry with uncertainty and learning (from others). Using novel data about fast food chains, I estimate the model with a forward simulation estimation approach augmented with particle filtering as a way to flexibly account for unobserved firm beliefs about market profitability. The estimates confirm the presence of uncertainty and learning. Most importantly, simulations using the estimated model demonstrate that learning from others may indeed help mitigate some of the uncertainty.

Keywords: Bayesian Learning; Dynamic Discrete Choice; Location Intelligence; Market Structure; Retail Strategy; Social Learning; Unobserved Heterogeneity

JEL Classification: C73, D83, L13, L66, L81, R00

Suggested Citation

Yang, Nathan, Learning in Retail Entry (September 26, 2019). International Journal of Research in Marketing,Forthcoming, Available at SSRN: https://ssrn.com/abstract=1957992 or http://dx.doi.org/10.2139/ssrn.1957992

Nathan Yang (Contact Author)

Cornell University ( email )

Dyson School, Warren Hall
360E
Ithaca, NY 14853-6201
United States
6072551590 (Phone)

HOME PAGE: http://dyson.cornell.edu/faculty-research/faculty/ncy6/

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