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Table of Contents
Fundamental Analysis of Attractiveness of Shopping Street
Nobuhide TANAKA, Akira UCHINO, Tetsuma FURIHATA
Gakushuin University, Senshu University, Tokyo University of Science
School of Commerce, Senshu University
2-1-1 Higashimita Tamaku
Kawasaki JAPAN 214-8580
Tel. +81-44-900-7953 FAX +81-44-900-7849
uchino@isc.senshu-u.ac.jp
Abstract:
Previously, we have got some research results in order to explain how retailers
agglomerate in a city. We compared two simulation results, one condition in a uniform
distribution of population, the other radially populated so that we investigated how
population distribution affects the spatial structure of retailers’ accumulation in a city
(2003). These simulation models and other existing studies did not use the
attractiveness of shopping streets (areas) effectively in consumer choice because the
attractiveness of them includes many elements. Of course although some methods such
as Drezner and Drezner (1998) consider the attractiveness of shopping streets (areas),
these models did not explain dynamically change under time series. We have been
developing our research continuously, but in this presentation we show a new point of
view relevant to the attractiveness of shopping streets (areas) using SD models. Here we
show our new approach and basic model on shopping street or shopping district in
Japan. They are dynamic models of agglomerate retailers, shopping district, in which
the attractiveness is an important element.
1. Introduction
We show you simple SD models here. We need to explain why we show them, what
kind of significance they have. To begin with, we explain three points.
1. Existing related studies and necessity for a new point of view.
2. Current problems of classical shopping streets” in Japan.
3. Our approach and the meaning of our models.
1.1 Existing related studies and necessity for a new point of view
There are several theories on spatial distribution in intra-urban retail trade area”),
Central Place Theory”, Circulatory System Theory and Statistical Distribution
Theory. They partially explain how retailers are distributed or located in cities. Some
of them teach us that the current spatial distribution is quite rational. But they cannot
explain how retailers in a city develop dynamically.
There have been developed another series of theories, how consumers select a retail
shop when they go shopping. Theories on consumers’ spatial activity are Retail
Gravitation Model™, Intervening Opportunity Model, Network Model”. Especially
a Retail Drawing Power Model, a kind of Retail Gravitation Models originally came
from Huff’s, shows a suitable location of a new retail shop in a city. It is useful in
business. These theories can explain retailers’ development in a city in some extent.
We have got some research results in order to explain how retailers agglomerate in a
city. We compare two simulation results, one condition in a uniform distribution of
population, the other radially populated so that we investigate how population
distribution affects the spatial structure of retailers’ accumulation in a city (Furihata,
Uchino, et al., 2003). These simulation models and other existing studies did not use the
attractiveness of shopping streets (areas) effectively in consumer choice because the
attractiveness of them includes many elements. Of course although some methods such
as Drezner and Drezner(1998)” consider the attractiveness of shopping areas, these
models did not explain dynamically change under time series.
Existing studies above are effective in a certain extent to show the retailers’
development in a city. However, they cannot explain withdrawal of a retail shop, which
has lost sales. Once we open a shop, we are working hard to keep a shop going. When
sales are decreasing, we change goods selection, renovate the shop. If some retail shops
integrate their effort, they could extend the parking lot; make sheltered side walk; make
a bargain sale together. To what degree do we improve the attractiveness of shops when
we do these activities? Existing studies have no power at all to explain.
While we have developed a mathematical model using computer simulation that
explains how retailers in a city develop, we need another point of view and another
model that explains how a retailer practically succeeds in business and how a shop is
declining even though their efforts.
1.2 Current problems of classical shopping streets
You might have an image of Japan as highly developed industrial country. However,
Japan is an island country; there are four big islands and more than 3,900 of islands.
Most part of her land is mountainous. Mt. Fuji and Kyoto are typical tourist attractions
in Japan. But Japan is accumulation of attractions for sightseeing in a way. There are
volcanoes and earthquakes in Japan, so that are many hot springs. She has one side a lot
of tourist attractions; the other side big cities have a large population and density. Tokyo
metropolitan area has nearly 10 million; Yokohama has 3.5 million, Oosaka 2.7 million,
Nagoya 2.2 million, the other 9 cities are beyond | million.
These big cities are keeping good economic conditions, though they are not
developing rapidly because of long stagnation after the bubble bursting. But local small
cities are not keeping good conditions. There are a lot of classical retail streets or
districts in local small cities. They are suffering from economic stagnation, and
moreover most of them are facing slightly decreasing population, having new
competitors as large scale suburban shops or shopping centers. Most of typical classical
shopping streets or districts in small cities are struggling with these difficulties.
We now need policy making for surviving them, coexisting with new type of retailers,
competing fairly. We require new policy making model for classical shopping streets.
1.3 Our approach and the meaning of our models
(1) We have a mathematical model to resolve how an accumulation of retailers in a city
are developing and analyze it by simulation. We have considered how retail facilities
agglomerate in a city. We compare existing assumption that population distributes
evenly to our simulation results that population distribute more realistically. Our model
has been still unique and effective in the spatial hierarchy and distribution problem field.
We are improving our model from now on.
(2) But we are critically required to get another approach, another type of model. All
possible ways, have been thought out, are not so effective to help classical retail streets.
We should struggle to go another way.
(3) We have specific target of retail streets to apply our approach. Nigata has 530,000
populations, one of big size local city in Japan. There are two target retail streets or
districts there. One is Bundai District located very near the Nagoya Japan Rail Station.
Despite of adjacent to JR Station, the retailers in the district has been arranging large
size parking lots. They have been inviting worldwide brand shops. The shopping district
is attractive to not only nearby customers but also ones outside the city. They can go
shopping there by train and by car. The other is Furumachi District. This district has a
long history in Nigata, a little bit lack of freshness than Bandai District. There is not
much to choose between two districts for citizens in Nigata when they are shopping by
time and distance because two districts are close to each other. Accumulated efforts by
every shop and integrated effort by the district efforts are source of the attractiveness of
the district. The attractiveness of each district is changing by their effort dynamically.
We can compare two districts and we can focus on the attractiveness of shopping
districts.
We are studying (1) and (2) (3) separately. This presentation aims at (2) and (3). We
are making dynamic model of agglomerate retailers, shopping district, in which the
attractiveness is an important element. We can simulate it and get investigation for more
practical outcome. Our first trial is making two types of simple models.
The first model is for one shopping district, supposing a degree of the attractiveness,
declining attractiveness in process of time, and requiring effort to keep the
attractiveness. The second model is containing two competitive shopping districts.
We show a simplest first model using Stella by attached file. The second model will
be shown at the next time.
2. Shopping District Models
2.1 Single Shopping District Model
This is the first basic model of Single Shopping District Model. Initial variables are
based on Bandai District which we are searching for. We calculated retail drawing
power in whole shopping districts in Nigata Prefecture. We gave relative values in
Bandai District and Furumachi District as initial attractiveness. However this is
conceptual model in order to understand how we treat attractiveness in Sopping
Districts using system dynamics. And we want to understand how typical classical
shopping street or district works in the model.
In this model each shop are not making effort separately. Accumulated effort by
agglomerated shops shows investment in the model. It fluctuates in proportion to the
sales. Strictly, they start to invest in increasing the attractiveness of the district, when
they have recognized that the attractiveness of the district has dropped considerably.
While they continue to invest, they can keep the attractiveness of the district. The
attractiveness doesn’t fall down sharply when they have enough customers because the
increase of customer adds somewhat to the attractiveness. But investment on adding
attractiveness is stronger than increasing customers by words of mouth.
This simple model shows a lot of typical behaviors we see in a classical shopping
street or district in Japan. Most of the classical shopping streets are going downhill now.
A part of them stand still. Suburban big shopping centers and charming complex for
shopping at the center of big city are taking away the customers.
The most important problem is as follows. The retailers in the classical shopping
streets or districts do not make effort while they are satisfied with the original condition
as long as they feel that their shops are humming and that they keep the attractiveness.
Many a small shops, typically running by family business, apply to the case. Shopping
streets or agglomerated small shops shows the same tendency.
Figure 1. Customer & Attractiveness,
Two main stocks of the Shopping District Model
Sales > Customer
Facilities > Attractiveness
Figure 2. Single Shopping District Model
Increase Rate\of Customer Customer Decrease Rate) of Customer
Cy Increase of Customer ‘seres of Customer \
CLimit /
fe
On Rate of Attrbtiveness by ae omer Dpcroase Rato of Attract ivonoss
Sales \. me
x \ Attractiveness of shoppite Distr ict
Average Sales
dn
aot Sore
Increase Raté| cf \Attractivenes / \
Increase // Decrease
Investment Pe ©
Init attractiveness
Decison Point
2.2 Competitive Shopping Districts Model
Districts have initial variables based on Bandai District and Furumachi District. They
are close to each other, and distances from customers are almost same. The first version
of this model is that decreasing number of customers of one district is increasing
number of others. However, they don’t link directly. They are linking to each other
through calculations below. “_2” means other district.
+“Decrease_of_Customer_2” * “Attractiveness_of Shopping District”
*Increase_Rate_of Customer”
Customers are not limited to certain numbers. Two competitive districts can get
more customers within certain range.
Figure 2. Competitive Shopping Districts Model
Bandai District! ase
Customer 2
a a) ——
. i - "HY
Decrease mene Customer ——~ / Decrease Rate of Customer
a |
Ae: oon of fu Decrease Rate of
cel ee of -_
crease of Customer
Attrotiveness by my rr
Shopping as
/ Sales = eS
| Increase Ay fe of Attractivenes Increase
Average Sales
Investment Requirement of Investment
2)
-b0
Decison Point
wo Furumachi District Ae
Customer
O
Cy -
ty) Decrease Rate of Customer 2
Increase Rate of Customer’ 2
| Customer 2 vA
2)
a: Se of Cudtoner th of Cutomer 2
& \
sf \
\ Decrease Rate of
shi Ir of Attractiveness 2
ie 2
¥
Decrease of Customer cLinit
or Or
Increase Rate of ,
Attrotiveness by Ci tS aD | Increase 2 { ecrease
Average Sales 2 / yO -
AS Increase Rate of aD — »
Investment 2~Attractivenes 2
Te _Décison Point 2 Requirement of Investment 2
3. Conclusion and Future Research
In this paper we only show (1) Existing related studies and our research objectives
and (2) Two types of SD models. We are struggling to improve basic moles and
simulating them. We confirm one basic model of them and put it in the proceeding CD.
This is the first step study. If we can step up our models to apply practical decision
making, it will be a very fruitful research scheme.
Footnotes
(1) classical shopping streets, districts, intra-urban retail trade area
Retailer shops are commonly classified by the same technical terms. But the situation
is slightly different from country to country. There are a few facilities in Japan which
we call shopping mall in USA, or shopping center in UK. But classical shopping streets
or districts in this paper are slightly different, which sometimes are a mall or an arcade,
sometimes line with small shops, department stores, and super market. They expand
along the streets, or into a block or blocs in a city. We are discussing them in Japan. We
use shopping streets, shopping districts, and shopping or trade area replaceable in this
paper.
(2) Central Place Theory
Central place theory originated by Christaller(1966) has been developed in economic
geography. This theory was based on classical economic assumptions such as the
uniformity of consumers and travel without considering the attractiveness of shopping
areas in consumer choice. Nevertheless, it has been widely used to explain the retail
hierarchy: a town centre core radiating progressively further out with greater number of
district centers, neighborhood centers and finally local centers.
(3) Circulatory System Theory
Circulatory system theory is based on the relationship between transport system and
retail centers in urban areas. This theory shows that retail centers configure at the
intersection of main arteries, and along the main arteries.
(4) Statistical Distribution Theory
Rogers(1974) presented that spatial distribution of retail facilities(stores) in urban
areas conforms the statistical distribution such as a negative binomial distribution.
(5) Gravity Model
The origin of gravity models dates back to well-known ‘The law of gravitation’
proposed by Reilly(1929). A general equation of the models is follows:
AR;
YAR;
k=l
where P, is the probability that a consumer i purchases at a shopping destination j ,
ij >
A, is the attractiveness of a shopping destination (store) j, R, is the resistance
measure perceived from the consumer i’s shopping trip to the destination j such as
the distance and time. According to an above gravity model the probability that a
consumer i patronizes a shopping destination (store) j is proportional to its
attractiveness and inversely proportional to the resistance measure. By the definition
of the attractiveness and resistance measure on the above equation, many gravity
models are formulated. In the representative model proposed by Huff(1962), for
instance, the attractiveness and resistance measure substitute for the retail sales floor
area of shopping destination (store) and distance between the origin i and the
destination 7.
(6) Intervening Opportunity Model
Stouffer(1940) who is a sociologist proposed an intervening opportunity model.
The fundamental idea of this model is follows: the probability that a consumer chooses
a store proportional to the purchase opportunity served by its store and inversely
proportional to the total opportunity from the consumer’s origin to its store. This
model comes to the conclusion that the distance and time between the consumers’ origin
and the destination are not fundamental to explain for consumers’ behavior but the
spatial order of stores is a most important factor.
(7) Network Model
Network model developed by White and Ellis(1971) captures consumers’ behavior as
flows in network configured by arteries and public transportation. The network
consists of nodes such as consumers’ origin and the destination(stores) and arcs
connected the nodes. This model defines some assumptions to flows in the network,
and estimates the outflow(the number of consumers or total sales volume in each stores)
based on the inflow(the number of consumers or total expenditure in its consumers’
origin).
(8) Assessing the Attractiveness of Shopping Areas
Retail facilities attractiveness is one of the most important factors for models
analyzing competition among retail facilities. It is an essential component for
determining trade area, estimating market share, consumer behavior and spatial choice
modeling, location/allocation of retail facilities, renovation of retail facilities and
opening a new retail facility. Several models and approaches have been investigated
for determining retail facilities attractiveness. These methods assume that the
attractiveness of a retail facility is a composite index of many attributes. These
methods require listing and estimation of attributes, assigning weights and constructing
a composite attractiveness measure for each retail facility. Drezner and Drezner(1998)
proposed an assessing method that infer retail facility attractiveness from only
secondary data sources without the above undertaking. The required data sources are
consumers’ buying power, sales volumes for competing retail facilities and distances
from between consumers and retail facilities.
References
Christaller, W. J.(1933) Die zentralen Orte in Soddenutchland, (W.H. Woglom and W.F.
Stolper, Trans. Central Places in Southern Germany, Englewood Cliffs, New Jersey:
Prentice Hall, 1966).
Drezner, T. and Z. Drezner(1998) On Assessing the Attractiveness of Retail Facilities,
Communications of the Operations Research Society of Japan, Vol49, No.4, 2004.(in
Japanese)
Furihta, T., A. Uchino, Y. Takahashi and N. Tanaka (2003) Simulation for Configuration
of Retail Agglomerations Considering the Interaction between Retail Facilities and the
Distribution of Population, Journal of Japan Society of Business Mathematics, Vol.25,
No.2, pp.151—163 in Japanese.
Huff, D.L.(1962) Determination of Intra-Urban Retail Trade Areas, Publication of Retail
Estate Research Program, Graduate School of Business Administration, Division of
Research.
Reilly, W.J.(1929) Methods for the Study of Retail Relationships, University of Texas
Bulletin, No.2944.
Rogers, A.(1974) Statistical Analysis of Spatial Distribution, London:Prion Ltd.
Stouffer, S.A.(1940) Intervening Opportunities; a Theory Relating Mobility and
Distance, American Sociological Review, Vol.5, 845—867.
White, L.A. and J.B. Ellis(1971) A System Construct for Evaluating Retail Market
Locations, Journal of Marketing Research, Vol.8, pp.43—46.
Supplemental Information:
Our first model for Basic Single Shopping District contains in the CD Proceedings. The
model was made by Stella Version 5.11J. The CD contains stm file by Stella Version
7.03.
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