Location of Franchise Networks in the United States: What Lessons for Networks Strategies?. Chaudey, M. & Bouzid, S. Applied Spatial Analysis and Policy, 2021. doi abstract bibtex This article focuses on the issue of location strategies for distribution networks. Our approach, essentially exploratory and empirical, is based on a cluster analysis from which we define and build an original indicator to study network location choice. First, we propose to identify clusters of franchise networks based on the endogenous characteristics of the networks. From these clusters, it is possible to identify standard location choices based on the location of the headend, the distance between the headend and its franchisees, and the spatial distance between the headend and the largest state in term of outlets. This method allows to estimate distances and construct an indicator, Network Linear Density, based on physical linear density. Our results, which confirm the lessons learned from the literature, show that franchise networks have specific location strategies, in line with the characteristics of the type of cluster in which they operate. We can foresee that if the characteristics of a network predispose it to belong to one of the three identified clusters, then we are also able to predict what the network’s location strategy might be. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.
@article{chaudey_location_2021,
title = {Location of {Franchise} {Networks} in the {United} {States}: {What} {Lessons} for {Networks} {Strategies}?},
issn = {1874-463X},
shorttitle = {Location of {Franchise} {Networks} in the {United} {States}},
doi = {10.1007/s12061-021-09375-6},
abstract = {This article focuses on the issue of location strategies for distribution networks. Our approach, essentially exploratory and empirical, is based on a cluster analysis from which we define and build an original indicator to study network location choice. First, we propose to identify clusters of franchise networks based on the endogenous characteristics of the networks. From these clusters, it is possible to identify standard location choices based on the location of the headend, the distance between the headend and its franchisees, and the spatial distance between the headend and the largest state in term of outlets. This method allows to estimate distances and construct an indicator, Network Linear Density, based on physical linear density. Our results, which confirm the lessons learned from the literature, show that franchise networks have specific location strategies, in line with the characteristics of the type of cluster in which they operate. We can foresee that if the characteristics of a network predispose it to belong to one of the three identified clusters, then we are also able to predict what the network’s location strategy might be. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.},
language = {en},
journal = {Applied Spatial Analysis and Policy},
author = {Chaudey, M. and Bouzid, S.},
year = {2021},
keywords = {ACL, Franchise, Location, Network, Spatial density},
}
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