Business and Industry | Ethnic Consumer Patterns
How strongly does ethnicity influence consumption? We ask this question in the context
of car purchase decisions. In particular, we examine the concept of "ethnocentrism"
here, which postulates that consumers normally consider goods from their own country/region
superior to others. Given data availability, we focus on Asians and their car purchasing
decisions in Southern California. The analysis below will not only allow us to answer
this question, but it also demonstrates how GIS analysis can be used for car companies
or dealers to reconsider locations. In the light of most recent reorganizations
in the car industry and expected changes in the future, this type of analysis may
prove especially helpful as a spatial reorganization of dealerships may occur.
To investigate our question of interest, we categorize brands by region of origin
(e.g. Toyota and Honda as Asian, BMW as European, Ford and GM as American) and class
(roughly corresponding to luxury, middle-class and economy). We then perform a simple
spatial cluster analysis to identify dealership clusters, where a dealership cluster
can be thought of as a set of dealers that are located close together and far from
others. We construct trade areas around those dealership clusters, and calculate,
for example, the share of Asian population in the trade area. We compare this share
of Asian population with the share of Asian brand car sales in the cluster. We find
that in most cases higher shares of Asian population imply higher shares of Asian
car sales. However, only low income Asians really have a relatively strong affinity
to Asian brands, while middle income Asians are undistinguishable from American
buyers, and high income Asians seem to rather buy American brands.
How many clusters of car dealerships exist in Southern California? Unfortunately,
this question turned out to be hard to answer precisely. The reason is that it depends
under which conditions a certain dealership should be included in a cluster. For
example, should a dealership be included if it is quite far from all the others
in a cluster, but offers a different brand? Or should a dealership be excluded from
a cluster if it is close by but another dealer already carries the same brand?
We decided to allow a statistical program to answer this question for us. All that
we required was that not too many clusters would carry a brand more than once, that
the maximum distance between dealers in the largest cluster would not be too large
and that there would not be too many clusters that only had one or two dealers.
We learned that all three criteria were very hard to fulfill simultaneously. We
consequently chose a solution that provided us with reasonable values for most of
the clusters. We ended up with 200 clusters, with 13% of clusters having more than
one dealership carrying a particular brand, a 36 mile distance for the largest cluster
(which is bearable since it is located in a thinly populated area) and about 5%
of individual dealers being listed as their own cluster. Note that visual inspection
of resulting clusters (see any of the maps provided) would have suggested to combine
certain clusters. However, we deliberately did not change clusters based on visual
analysis to investigate the quality of this automated procedure.