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Why Do People Choose the Shopping Malls? The Attraction Theory Revisited

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Journal of International Consumer Marketing
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The goal of this study is to examine the factors which influence the consumers' attraction to shopping malls, and attempt to predict the consumers' behavior in choosing a shopping mall for their retail needs.The gravitational approaches and linear regression models, which are commonly used in this research area, cannot sufficiently explain the impact of factors on the choice of shopping centers; therefore, a review of these approaches and an empirical application of them is considered. The purpose is to construct a model of consumer attraction to malls, through distance and retail image, using a Conditional Logit Model.
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TÍTULO “Why Do People Choose the Shopping Malls? The Attraction
Theory Revisited: A Spanish Case”
EDITORIAL Journal of International Consumer Marketing
FECHA DE PUBLICACIÓN Volume 17, Number 1, 2004.
AUTOR/COAUTOR De Juan, Maria D.
ISSN 1528-7068
Why Do People Choose the Shopping Malls? The Attraction
Theory Revisited
María Dolores De Juan Vigaray
Lecturer of Business Administration,
Dpt. Finance, Accounting and Marketing
Faculty of Business Administration
University of Alicante
E-03080 Alicante
Spain
Email: mayo@ua.es
Tel: + 00 34 (9) 6 590 34 00 (ext. 3167)
+ 00 34 (9) 6 526 98 84
Fax: + 00 34 (9) 6 512 61 20
Category: Consumer Behavior & Retailing
February, 2002.
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Why do people choose the shopping malls? The attraction
theory revisited
Abstract (100 words)
The goal of this study is to examine the influence of factors that explain the consumers
choice and attraction to shopping centers in order to predict the consumer´s behavior
when choosing a shopping center for their purchases.
Having reviewed that the gravitational approaches and the linear regression models,
which are commonly used in this research area, can not sufficiently explain the impact of
factors on the choice of shopping centers, a review of those and an empirical application
is considered. The purpose is to model the consumer´s attraction to malls, through
distance and retail image, using a conditional logit model.
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INTRODUCTION
Nowadays, shopping centers display a wide choice for consumers: from stores
specialized in fashion, electric appliances or gifts to enormous shopping malls with
multipurpose stores, cinemas or restaurants. Consumers are familiarized with this
combination of products, services and leisure, but neither all the shopping malls nor all
the consumers are homogeneous. In this article one more step is walked in the way of
knowing why do people choose the shopping malls.
The present research investigates one of the most significant aspects of consumer
behavior: commercial attraction and offers an interesting perspective on shopping mall
patronage behavior. A knowledge of consumer behavior influences the spatial
organization of commercial distribution, and in turn has an impact on diverse aspects of
economic and social life. It is enough to tip that, besides for manufacturers and
distributors, the configuration of the market areas is an excellent challenge for the
companies competition, as well as a crucial subject for the citizens opportunities of
purchase homogenization and, consequently, for their welfare. Interest on this subject
thus arises from the generated necessity in the retail sector to take efficient commercial
decisions in order to satisfy the consumers, and cope with an increasing competition.
The classical models that appear in the consumer´s decision process literature (Andreasen
1965; Berman and Evans 1989; Engel, Blackwell and Miniard 1986; Engel, Kollat and
Blackwell 1978; Engel Kollat and Blackwell 1978; Howard and Sheth 1969; Nicosia
1966; and Van Raaij 1988) do not especially take into consideration store choice, even
though the product purchase decision is interdependent on the placement of the buying
location. This second decision can be even more important than the first one since, in
some cases, it can precede the decision of product or brand choice (Darden 1979).
Nevertheless, as opposed to the abundance of products and brand choice investigations,
the studies in the area of store and shopping centers choice are relatively little. This is
why this study decides to investigate and contribute to this issue.
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The relevance of this research for several business problems is notorious: if consumer´s
behavior towards different kinds of shopping centers is known, managers will be able to
better satisfy consumer´s needs and will focus their strategies on specific objectives.
Thus, the topic of this paper is an extremely important issue to both academicians and
practitioners.
In recent times this investigation field has become increasingly relevant for marketing
researchers (Baker, Levy and Grewal 1992; Bettman et al. 1998; Finn and Louviere 1990;
Howell and Rogers 1980; Swait and Sweeney 2000, Darian et al. 2001, Yavas 2001,
among others) and of maximum importance for the retailing managers, since it allows to
determine crucial factors that yield consumer attraction (Bell, Ho and Tang 1998). Not all
shopping malls have the necessary assortment and facilities to completely satisfy the
consumers needs. For that reason, consumers are attracted by other stores. As a result an
“expense shift” takes place between geographically disparate stores, depending on the
several attraction factors (Stassen, Mittelstaedt and Mittelstaedt 1999). This paper
contributes to the existing literature to better understand consumers from a perspective
that deals with their behavior towards planned and unplanned shopping centers and
provides an illustration of a model combining consumer evaluations on preferences for
shopping center with travel time in examining shopping center choice.
Therefore, the main purpose of this study is to examine the influence of factors which
explain the consumer choice among alternative shopping centers. At the same time, the
consumer behavior is predicted when a shopping center to buy is chosen. An example of
such a model is the empirical application that was carried out in the suburban shopping
context of Alicante (Spain), adding a welcomed international twist.
With all of this in mind, the author structures the paper as follows. First of all, a review of
the spatial consumer´s behavior literature related to shopping malls is presented.
Subsequently, based on a review of the empirical research work, models most used in the
shopping centers attraction setting are also examined. Moreover, the relationship between
store choice, preference and patronage, as well as store image is considered. Afterwards,
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a brief overview of the problems derived from the empirical design of a study about
consumers attraction to shopping centers is given, and in relation to those and pursuing to
avoid them, the hypotheses are formulated. Following, the methodology to predict the
consumer´s purchase behavior is presented and the results are shown. Further, the
conclusion section reports a summary of the findings and managerial implications.
Finally, limitations of the study and directions for future research are indicated.
REVIEW OF SPATIAL CONSUMER BEHAVIOR
According to Craig et al. (1984), the spatial behavior models can be classified in three
groups. Firstly, the models based on normative hypotheses about the transfer´s behavior
of consumers. The most simplified store choice model is the one of the nearest center,
hypothesis postulated by the classical theoreticians who plead for the “central place
theory” (Christaller 1933 and Lösch 1954). Afterwards, several works based on the
physics were developed, resulted in what is known as the “retail gravitation law” based
on attraction and dissuasion variables (Converse 1949; Reilly 1931).
These first models were denominated gravitational, and were described at an aggregated
and deterministic level without considering the probabilistics´ risks (Montgomery and
Urban 1977). Nevertheless, this approach has been criticized from the fields of
psychology (Luce 1959; Thurstone 1945) and economy (McFadden 1981) since, often,
the individual is not sure about the alternative to choose, not even about if she or he
would make the same decision under apparently identical conditions.
Secondly, there are models that use revealed information gathered through a study of the
consumer´s behavior in the past. These methods improve considerably the predictive
capacity of the previous store choice models. The most employed model to define the
retail attraction is proposed by Huff (1964) who establishes a disaggregate and
probabilistic commercial attraction model, giving rise to the birth of a new generation of
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models called the “stocastic models” (Huff and Rust 1984). Generalizing the previous
model, there arises the multiplicative competitive interaction models (MCI) that open a
new stream of investigation (Nakanishi and Cooper 1974). These contributions are the
origin of the denominated “spatial interaction models”.
Thirdly, one finds models that evaluate the consumer´s utility functions using conjoint
methods. Instead of observing the choices made in the past, these methods use the
evaluations that consumers make about the descriptions of hypothetical establishments to
calculate a utility function. In opinion of Anderson, De Palma and Thisse (1992) it is
equivalent to both first discrete choice approaches when they fulfill certain properties.
Authors that pursue this multinomial logit approach are Agrawal and Schorling (1996),
Arnold, Roth and Tigert (1980), Gönül and Srinivasan (1993), Koelemeijer and Oppewal
(1999), Louviere and Woodworth (1983), Miller and Lerman (1981) and Oppewal,
Louviere and Timmermans (1993). The advantage of these experimental procedures can
be seen in the fact that they do not trust on passed choices to reveal the utility function.
Models of spatial consumer behavior & shopping centers
Nowadays, few retail establishments exist as isolated entities. The synergies produced by
the proximity of numerous shops, the legal restrictions on possible locations for retailers,
and the limited availability of attractive areas, tend to encourage the grouping of retail
outlets in relatively compact shopping centers. The existence of intra-urban shopping
areas, understood as intermediate level retail entities that lie somewhere between the
individual establishment and the city, implies another level of decisions about retail
attraction for the consumer (Bucklin 1967): that of a shopping center and that of a
specific store from the shopping mall.
The term “shopping center” may mean either a coherent, planned and controlled group of
establishments, with its own management and control of competition, or rather, the
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concentration of retail establishments, each one individually owned by without any
overall coordination (McGoldrick 1992). The latter is also known as a “commercial
district” (Dawson and Lord 1985) which, in turn, may be formalized by the local
authorities, forming revitalized areas (Medaway et al. 1999; 2000). Berman and Evans
(1989) and Levy and Weitz (1992), present an extended shopping centers definition,
distinguishing between unplanned business district and planned shopping center.
The different studies that use, at an empirical level, the shopping centers as a retail
formula to make their investigation (Bellenger, Robertson and Greenberg 1977; Evans,
Christiansen and Gill 1996; Gentry and Burns 1977-78; Hauser and Koppelman 1979;
Howell and Rogers 1980; McGoldrick 1992; Stoltman, Gentry and Anglin 1991;
Westbrook and Black 1985 and Wee 1985; among others) employ the shopping centers
definitions in different ways. Being specific, authors reference a concrete type of
shopping center and explain if it is a planned or an unplanned shopping center (Gautschi
1981; Nevin and Houston 1980) or in a more general form, without indicating the type of
concrete shopping center considered in the study (Meoli, Feinberg and Westgate 1991;
Wee and Pearce 1985).
Taking the above into consideration, the studies that analyze the commercial attraction of
the different formats of shopping centers are reviewed. It needs to be mentioned that
shopping malls have specific characteristics that distinguish them from single stores and,
obviously, models should be different.
Traditionally, as stated before, research on retail attraction has been developed around the
so-called probabilistic models (Huff 1964), defined according to the size of the shopping
center and its distance from the consumer. Otherwise, it is assumed that such
establishments are similar. The main criticism of these models lies in the fact that the
modeling approach is unsuitable when consumers perceive differences among centers by
considering other aspects. In this sense, the first approach proposes the modification of
the Huff model (1964) considering only the set of shopping centers evoked by
consumers. Wee and Pearce (1985) affirm that a model of this type is superior to Huffs
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and allows to identify real competing shopping centers from the consumer´s point of
view.
A second approach examines the shopping centers attraction with different extensions to
Huff´s model, including the characteristics of the establishments and the means of
transportation to them (Gautschi 1981). The basic characteristic of this model is that
analyses the homogeneity between the centers, meaning it considers in the same choice
set the planned and the unplanned shopping centers.
An alternative stream incorporates the shopping center´s image as an extension to the
Huff´s model. It includes the studies of McGoldrick (1992), Nevin and Houston (1980)
and Wee (1986) among others. This orientation tries, on the one hand, to determine the
image dimensions and, on the other, to identify the relationship between the image and
the purchase behavior. For the attainment of the first objective, all the authors agree in
using first the factorial analysis and, for the second step, the linear regression analysis.
Another approach proposes to indicate the retail attraction according to a two-stage
modeling, based on the consumer´s preference towards the shopping centers, in addition
to the distance and the cognitive image dimensions (Hauser and Koppelman 1979 and
Howell and Rogers 1980).
Finally, it is found an approach that analyses the consumers´ motivational and
demographic aspects in order to explain the consumers attraction towards the shopping
centers. Within this approach there are studies that continue in the line with Huff´s such
as Meoli, Feinberg and Westgate (1991) and Stoltman, Gentry and Anglin (1991).
Summing up, the different models that, through the different studies, have been
developed to discover the determinant variables that cause consumers to be attracted by
the shopping centers, range from the simplest such as the gravitational models, to the
complex discrete choice models and conjoint analysis. It has been checked that the
majority of the authors apply step wise linear regressions to explain the shopping centers
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choice by the consumers. The factors used are usually obtained by previous factorial
analyses. Several studies coincide in the image determinants.
REVIEW OF STORE CHOICE AND ATTRACTION MODELLING
The retailing sector has experienced deep changes in last decades (Eastlick and Lotz
1999). On one hand, it has been arisen to the substantial transformations in the
environment, particularly in the market and in the consumer. On the other hand, it has
been due to changes taking place when arising different retail management formulas
(Davies and Rogers 1984).
When it comes to planning retail development strategies, the commercial attraction
becomes a basic management tool because of the changes produced by the increase in the
number of retail options in which a consumer counts to choose where to acquire the
desire products. The store develops an attraction power on the buyer by employing a mix
of stimuli (store image) which take part of a buyer´s mental process and lead him or her
to a certain action. In this process, multiple causes exist (relative to the establishment and
to the consumer) and one consequence: the behavior of a particular individual when
confronted with such stimuli. This existing relationship between the buyer and the store is
the underpinning question of this study.
Store Choice
The store choice is a consumer´s behavior answer of binary nature. The consumer
chooses whether to buy or not in a store, after searching for information and evaluating
the stores that represent options (Spiggle and Sewall 1987). When reviewing the
investigations related to the store choice process four approaches are found (McGoldrick
1992), as it is depicted in Table 1.
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INSERT TABLE 1
This review on store choice is limited to probabilistic models of store choice. Although
any categorization is somewhat arbitrary, store choice models can be classified into two
groups (Ahn and Ghosh 1989): (1) market share models, and (2) logit type models based
on random utility theory. While the two types of models share many common features,
they differ in one important respect. Market share models are calibrated on aggregated
data on the proportion of shopping trips made to different stores, whereas logit models
are based on the discrete choices made by consumers on individual shopping trips.
Although market share models are based on assumptions regarding individual choice
behavior, only aggregate data are used to estimate the parameters. In practice the study
area is divided into a number of zones and information on the proportion of trips made
from each zone to the different stores is obtained through surveys or travel diaries. These
zone probabilities are then used to calibrate the model. A number of researchers (see, for
instance, Arnold et al. 1980; Louviere and Woodworth 1983; Miller and Lerman 1980)
have suggested the use of the multinomial logit (MNL) model for modeling consumer
store choice. The focus of the MNL model is on discrete choices made by consumers on
individual shopping trips rather than on the aggregate proportion of trips made from each
residential area. The mathematical formulation of the MNL model is presented in the next
section.
In their review of store choice models Craig, Ghosh and McLafferty (1984) point out that
there exists considerable evidence that logit models provide good approximation to
aggregate choice data, from actual market information or simulated experiments. The
authors argued for the use of logit-type models for understanding store choice behavior.
Logit models offer powerful tools for analyzing store choice in that they provide
information about the way consumers evaluate the attributes of stores, predictions of
choice probabilities and, consequently, estimates of market shares of alternative stores.
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Preference and Patronage
There are several factors that suggest that the establishment attraction and choice as
opposed to brand choice, can be the variables of interest in a consumer behavior model
(Darden, Erdem and Darden 1983). Following the contributions of Spiggle and Sewall
(1987), the preference refers to the positive influence state of a consumer in respect to a
given store, that can succeed or not in the store choice or patronage. Brunner and Mason
(1968), Green, Mahajan, Goldberg and Kedia (1984) and Bearden (1977) are examples of
studies that analyze the preference.
Generally, the patronage construct is defined in terms of visiting frequency (Korgaonkar,
Lund and Price 1985) and, therefore, it is described in percentages and not as a binary
result. What forms the patronage behavior is the habit or the search for variety (Spiggle
and Sewall 1987). Assortment overlap and interstore distance are determinants of shared
patronage (Stassen, Mittelstaedt 1999). Some of the most recognized studies on this
model are Arnold, Handelman and Tigert (1996), Evans, Christiansen and Gill (1996),
Gautschi (1981), Houston and Nevin (1980), Korgaonkar, Lund and Price (1985), Meoli,
Feinberg and Westgate (1991), Ring (1979) and Stanley and Sewall (1976).
In patronage literature there exist many approaches which analyze these concepts. Sheth
(1983) integrates preference and patronage concepts, whereas the model that Spiggle and
Sewall (1987) propose integrates those and choice. Swan and Trawick (1983), when it
comes to explaining patronage, focus their attention on a satisfaction model that considers
consumer expectations, level of performance and its disagreement.
Donovan et al. (1994) suggest that in-store pleasantness moderates arousal´s impact: for
pleasant environments, arousal is positively related to patronage behaviors; yet, for
unpleasant environments, arousal is inversely related to patronage. Complementing the
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picture, Mano (1999) stresses the importance of pre-purchase arousal moderator as an
important influencing store patronage.
The latest approaches suggested by Bloch, Ridgway and Dawson (1994), Evans,
Christiansen and Gill (1996), Feinberg, Sheffler, Meoli and Rummel (1989), Thompson,
Locander and Pollio (1990), among others, based on the theories of Tauber (1972), focus
on the study of the impact of the social factors on patronage, indicating that these aspects
(to meet with friends, to spend time) are critical when explaining the shopping behavior.
Store Image
The most emphasized relationship by the researchers in this field talks about the link
between the store characteristics and the subjectivity of these: image. Image constitutes
one of the most important elements of the store choice process and can be considered
from the consumers perspective (Baker, Grewal and Parasuraman 1994; Darley and Lim
1999; Joyce and Lambert 1996; Nevin and Houston 1980), from a management point of
view (Finn and Louviere 1996) and from both perspectives (Pontier 1988). At the same
time, image can be related to the store patronage phenomenon (Arnold, Handelman and
Tigert 1996; Evans, Christiansen and Gill 1996, Peterson and Kerin 1983; among others)
and can also affect the individual´s preference for a specific store, as it is deduced in
Bearden (1977) and Pessemier (1983) works. The store image literature also stresses
merchandise and service quality as key variables that influence store image (Mazursky
and Jacoby 1986).
Over the years different authors have distinguished different store attributes or
characteristics that are part of the overall image towards the store (the so-called retail
mix). Martineau (1958) first introduced the concept of store image and he further
identified four determinants of store image: layout and architecture; symbols and colors;
advertising; and sales personnel, placing the greatest emphasis on the latter. Lindquist
(1974-75), in his study on the store image literature, combined models from 19 studies
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and came up with nine different elements: merchandise, service, clientele, physical
facilities, comfort, promotion, store atmosphere, institutional and post-transaction
satisfaction. Bearden (1977) suggested the following characteristics: price, quality of the
merchandise, assortment, atmosphere, location, parking facilities and friendly personnel.
Store image is also supposed to be composed of the different elements of the retail
marketing mix as introduced by Ghosh (1990). These eight elements are: location,
merchandise, store atmosphere, customer service, price, advertising, personal selling and
sales incentive programs. Furthermore, Table 2 shows some of the studies that consider
the image attributes to measure the commercial attraction.
INSERT TABLE 2
For each retail store a distinct image may exist within consumers´ minds. This is based on
the salient elements of the retail mix. According to Ghosh (1990), the merchandise of a
retailer is its most important retail mix element. A retailer has to make sure that he/she
offers those products to his/her customers that they expect him/her to offer. Nevertheless,
other non-functional elements also have to be in line with the expectations of the
customer in order for a customer to become store loyal.
In any case, it can be argued that the image of a shopping area may be more complex than
that of a single store, since a shopping area is a conglomerate of different kinds of stores
that offer a wide variety of products and services. In fact, in his study, Wee (1985) found
that the factor analytic structure of the image scale for a shopping area differed with
regard to recent effects and the size of the shopping area. Thus, the need for the
consumer to be familiar with the shopping area is crucial to any assessment of its image.
Finally, the relationship between shopping area image and patronage behavior as Wee
(1986) already stated needs to be better established. The findings by Nevin and Houston
(1980) should not be taken as conclusive in that they included a variable on preferred
store, measured in a dichotomous basis. To the extent that the image of a shopping center
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is correlated with the image of that store (whether or not shopping center image actually
exists), the significance of shopping center image will lessen. Thus, it is possible for a
shopping center image to exist and help determine shopping behavior, yet emerge as
insignificant from Nevin and Houston´s analysis.
COMMERCIAL ATTRACTION: HYPOTHESIS
Theoretical Underpinnings
As a result of the empirical review about the studies related to the determination of the
consumer´s attraction to shopping centers, the approaches differ from one to another in
the formats of retail entities studied, the variables selected and the meaning given to each
of them, and in the methodologies applied. As a result, the conclusions obtained also
differ. It is therefore difficult to decide on the consequences of the determinant factors of
the attraction of the centers. Following, these questions are presented in brief, allowing to
show an empirical application that overcomes the problems identified. The empirical
problems that can arise and that need to be taken into account are the following ones: 1)
the type of products chosen; 2) the variables chosen; and 3) level of model aggregation.
It needs to be pointed out that knowing the advantages and limitations of the different
studies allows to present an alternative study that overcomes the stated limitations and
offers a more appropriate methodology.
Type of products chosen
The studies reviewed show that the authors do not always indicate the type of product or
the specific buying situation that the consumer choose at the time of going to the
shopping centers (see Table 3).
INSERT TABLE 3
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The complexity of the consumer´s purchase decisions reveals that it makes no sense to
consider of general consumers´ preference for the centers without diminishing the
importance of this preference based on the products to acquire (Simonson 1999). The
type of product appears as a good variable in the type of shopping center selected choice.
That it is the case because there are some formats with very deep lines and others lack
supply of certain types of products.
In agreement with the above and with the affirmation that indicates that the products of
lasting consumption are those by which the consumer makes an extra effort to obtain
them and according to the studies of Gautschi (1981) the typology of products considered
in this work included specialty goods: clothing, home furnishings and electrical
appliances.
Hypotheses Development
The different authors propose different alternatives in relation to the determinant
variables for the consumer´s patronage. There is not consensus about the way of
evaluating or measuring them. This diversity of interpretations is found either in the
dependent or in the independent variables. As a result, considering the studies that use the
discrete choice methodology, the variables and its interpretations are discussed.
Patronage
When it comes to measuring the dependent variable patronage still there is no agreement
in the literature, being used dimensions such as choice, preference, frequency of trips,
motivation, affection or buying behavior (see Table 4). According to the results obtained
from the different empirical approaches, the relative trip frequency measured by the
number of trips during last year can be used as a good individual or combined measure
(Gautschi 1981).
INSERT TABLE 4
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Distance
Distance (or proximity) to the shopping center variable has been measured and
understood from diverse points of view (see Table 5).
INSERT TABLE 5
As shows Table 5, the more accepted way in the literature to measure the distance
variable is through driving time. The different authors and results point out that this
procedure becomes the more appropriate. Using miles, either from the consumer´s
residence to the shopping center or from his or her work place, does not take into account
aspects such as traffic jams, parking or traffic lights that, for the same distance could
cause “different” and ambiguous results. Then, it is likely that difficulties in the
accessibility to shopping centers imply less patronage to those because of the time
invested in driving (Le and Young 1998; Timmermans et al. 1984, 1991). Therefore, the
following three hypothesis being tested are:
Hypothesis 1: There is less patronage to the shopping centers if the centers are
far away from the consumer´s residence, when those decide to buy specialty
products.
Hypothesis 2: There is less patronage to the shopping centers if consumers find
difficult to park when they go to buy specialty products.
Hypothesis 3: There is more patronage to the shopping centers if it turns out
comfortable to go from store to store, when consumers buy specialty products.
Commercial attraction
When measuring the attraction to the shopping centers there are several options to be
considered. The most recommended measure by the literature turns out to be the square
meters unit (see Table 6). This variable reflects best the shopping center´s offer and it is
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also the first unit considered in various empirical studies of this type (Bucklin 1971; Huff
1962-63-64; Nevin and Houston 1980; Stanley and Sewall 1976; Wee and Pearce 1985;).
INSERT TABLE 6
Assortment, at the same time, can also be a good measure although it is more difficult to
apply (Gaustchi, 1981). This happens with the preference as well, since the
interpretations can be various and some times proxi variables are needed for a correct
application (Hauser and Koppelman 1979; Nevin and Houston 1980). Taken this issue
into consideration and the attraction measures previously reviewed, consumers will visit
more the shopping centers if they find a good variety of specialty products. Then, it is
stated that:
Hypothesis 4: There is more patronage to the shopping centers if there is a varied
assortment of products, when consumers buy specialty products.
Image
When interpreting the image variable there are two significant problems: 1) how to
measure the interaction between the consumer´s mind in relation to the multiple shopping
centers attributes; and 2) how to determine which are the “important” and the
“determinant” attributes. This conflict situation becomes more problematic when the
image studied at an individual level cannot be extended to a shopping center.
The conclusions obtained from the literature review show that the majority of the studies
(Gaustchi 1981; Gentry and Burns 1977-78; Howell and Rogers 1980, among others)
include the dimensions suggested by Lindquist (1974-1975) as the best predictors of the
center´s attractiveness. If the authors identify different dimensions, these are likely based
on the Lindquist ones (McGoldrick 1992; Stoltman, Gentry and Anglin 1991). Table 2
showed the different image attributes reflecting that there exist several attributes
measuring it.
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It is very important to examine all the variables that could influence the shopping center´s
image. At the same time, the researcher needs to make sure that all the consumers in the
sample know and have lately visited the planned or unplanned shopping centers studied.
The factorial analyses applied to the variables in Table 2 reduce the attributes to a set of
factors that “properly” define the shopping center´s image (see Table 7).
INSERT TABLE 7
Following Table 7, as the different authors propose, the factors that are frequently
employed in the different studies refer to the “assortment”, the “quality”, the “parking”
and the center´s “facilities” (Gaustchi 1981; Gentry and Burns 1977-78; Howell and
Rogers 1980; Lindquist 1974-1975). The attributes less considered are the “opening
hours” and “the possibility of driving a car or taking the bus” to have access to the
commercial center. In this context, there are several hypothesis that derived from the
image dimensions to be analyzed. Those are the following ones:
Hypothesis 5a: There is more patronage to the shopping centers if the prices of
specialty products are low in relation to the offered quality.
Hypothesis 5b: There is more patronage to the shopping centers if the consumers
dress informally at the time of going shopping for specialty products.
Hypothesis 5c: There is more patronage to the shopping centers if the salesmen
are professional, when consumers buy specialty products.
Hypothesis 5d: There is more patronage to the shopping centers if the consumers
find that those are clean when buying specialty products.
Hypothesis 5e: There is more patronage to the shopping centers if it is possible to
buy specialty products in the afternoons and at the weekends.
Hypothesis 5f: There is more patronage to the shopping centers if exists
tranquility in the specialty products buying process.
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Thus, when determining the shopping center´s image there are several issues that need to
be taken into consideration: 1) considering if the single stores image can be applied to the
shopping centers. In this sense, Nevin and Houston (1980) suggest to develop a scale
based on a review of the store´s image literature and a discussion with managers.
However, they do not consider if the items are appropriate for the consumers or not; 2) it
is very important that, at least, there must exist familiarity between centers and
consumers. Acito and Anderson (1979) find that the image, referred to a specific
establishment, is a differentiated variable for those consumers that had recently bought.
Then, if a research study is based on consumer´s evaluation and not on his or her
familiarity with the establishment, the results obtained will probably not be useful for
managers.
METHODOLOGY
The goal of this empirical application is to model the consumers´ attraction to shopping
centers patronage, through the distance and the retail image, reflected through the
shopping centers characteristics. For this application a conditional logit model, due to its
advantages with respect to other methodologies, is used. This analysis will allow to avoid
the over simplification of the original probabilistics models and will try to explain the
determinant variables of those.
The model considered is a multiple discrete choice model. The individuals decide
whether to go the shopping centers that are familiar to them, and their choice depends on
the center´s characteristics and the distance from the center to their residence. In other
words, it is intended to determine the probability that an individual, facing a set of
characteristics that reflect the shopping center image and the distance, makes a particular
choice instead of another alternative. This model does provide estimated probabilities
considered within the interval 0,1, which is the most important critic according to the
regression models widely used by different authors.
20
The application of a logit model implies the fulfillment of the assumptions suggested by
Greene (1993). The Multinomial Logit Model (MNL), is a multiple discrete choice model
in which the dependent variable is discrete with not-ordered alternatives. The hypotheses
of independence and identical distribution is fulfilled. Another important restriction is
that the model assumes that the explanatory factors that influence in the probabilities are
the same for all consumers, and that the explanatory factors do not change either with
respect to the different options to choose from.
When the data consist of specific “attribute´s” choices instead of specific individual´s
characteristics, the model is known as the Conditional Logit Model (Ben-Akiva and
Lerman 1985); excepting this, it is essentially the MNL. With this model one gains in
interpretation, not in calculation, since the computed results are exactly the same as those
in the MNL, but it changes the model´s “spirit”.
The decision to choose between several purchasing options represented by shopping
centers assumes a “consumer discrete choice” and, therefore, the models to apply in this
case would have to be models with the discrete and noncontinuous dependent variable, as
it happens in the regression models. The multinomial logit model takes the following
form (Malhotra 1984; McFadden 1974):
Pe
e
ij
X
h
m
X
h
m
k
n
hij hij
hik hik
1
1
1
where:
Pij= Probability that the consumer i chooses the purchase alternative j,( j= 1,..., n).
Xhij,, Xhik = Any i consumer´s attribute, from h = 1..., m alternatives j and k,
respectively.
= Parameter that determines the effect of each attribute in the selected
probability.
21
Several authors favor the MNL arguing that it represents a really powerful tool to analyze
the establishments choice by the consumers and, as a result of it, the determination of the
establishments´ market areas (Arnold, Roth and Tigert 1980; Louviere and Woodworth
1983; Punj and Staelin 1978; Swait and Louviere 1993). These are models that allow to
find a consumer´s profile patronage and, in addition, to calculate that behavior
probability. Moreover, these models are specially recommended when the dependent
variable is discrete and, therefore, a regression application is unsuitable.
Selected shopping malls
In order to undertake a study such as the present one, given the ample existing variety of
shopping centers, it is essential to specify the concrete type of those, with the objective to
know if they are homogeneous to each other.
The four centers consider separate the central areas of the city from those located on the
outskirts as in Alzubaidi, Vignali, Davies and Schmidt (1997). More precisely, a CBD, a
SBD and two strings are considered (Levy and Weitz 1992). In particular the “Heart of
Alicante” is a revitalized central district, made up of a large number of specialized shops
which have formed an association in order to defend their own interests. On the other
hand, “Maisonnave-Oscar Esplá” is located in another central urban area, though it
consists of two department stores, and a large number of specialized shops. Finally, the
areas of “San Vicente String” and “San Juan String” represent two centers of attraction
which are easily accessible by road, both of which include a hipermarket and several
category killers and other shops of occasional consumption products.
The four shopping centers are unplanned centers, meaning they do not count on a
common management nor on a previous planning as far as the location of the
establishments or the variety of such in each zone.
22
Consequently, to deepen in the knowledge of consumers´ places of purchase, as well as in
the criteria that take part in the selection of such, the individuals indicated the zone or
commercial zones to which they habitually went to buy specialty or attribute-based
shopping products. Consumers also evaluated the characteristics and services that in his
or her opinion these shopping centers grant. With this statement two outstanding
limitations of the commercial attraction literature are avoided: all the consumers know
the shopping centers targeted by the interview, and his or her purchasing trip is for
acquiring a type of concrete product.
Level of aggregation
In the attraction models, the parameter´s estimation, can be aggregated or non-aggregated
(see Table 8). Different studies such as Howell and Rogers (1980) prefer an aggregated
statement, with the advantages of interpreting it in “aggregated demand” terms. Table 8
shows the level of aggregation of the studies reviewed.
INSERT TABLE 8
The empirical review allows to indicate that the aggregated estimation is preferable when
the relative parameters to different variables considered in the model are statistically
equal for all the shopping centers chosen for the study.
If the relative parameters to the preference and patronage prediction are (statistically)
equal for all the shopping centers, then it is preferable to use an aggregated model. That is
why an increase in the number of observations used to estimate each parameter will
imply estimators with smaller variance (Howell and Rogers 1980). Nevertheless, an
unsuitable level of aggregation can give rise to biased parameters of the estimators.
Following these authors, the approach taken in this study is an aggregated approach.
23
Sample, Data Collection and Variables
The basic information was obtained from a door-to-door survey, with a structured
questionnaire, aimed at a sample of 177 individuals who participate actively in family
decisions regarding shopping in a Spanish city, Alicante, during November 1994. The
sample population consisted of member of both sexes, eighteen years or older, 294.718
(IVE 1994). The sampling was random poly-stage, and the interviewees were selected by
means of a random routes procedure.
In order to avoid the route overlap, geographic limits in the different zones were marked.
The coordinators of each area followed up the field work in two ways: (1) by examination
of the routes, invalidating the null ones and repeating the process again, so that when
finalizing the maximum information could be gathered; (2) by telephone, checking
randomly with thirty percent of the sample to corroborate the participants data.
The size of the sample guarantees a sample error of +-7,5 percent with a level of
confidence of 95.45 percent (in the worst case p=q=0,5). Likewise, the sample chosen is
homogeneous in relation to socio-economic status, which would primarily solve problems
of heteroscedasticity due to income, and would restrict the scope of the study to the
preferences towards certain shopping malls and to the income group included.
A detailed information inspection obtained allows various errors in certain interviews to
be detected, and therefore they were eliminated from the analysis. The number of
interviews in the analysis was consequently reduced. However, examining the
characteristics of the remaining sample led to the conclusion that is was reasonably
representative of the objective population. In this sense, and with an identical level of
confidence, the sample error was of 7,5 percent, instead of 6 percent, as expected.
24
Measurement of the variables
The dependent variable in any choice model is a measurement of past shopping behavior
in each center. Specifically, it reflects the number of times that the respondent has
shopped, for occasional consumption products in each of the centers, over the past twelve
months. These absolute frequencies become relative from the total number of shopping
trips of each individual during the preceding year (Gautschi 1981). A “shopping trip” is
considered to be any trip made to look for specialty products, regardless of whether they
are purchased or not.
The independent variables were divided in two groups and are explained below:
1) Variables regarding shopping centers. A typical problem that arises in the research
into preference and choice of shopping malls is the evaluation of their image
components. That is, we cannot ensure that the factors used in the image studies of
individual establishments are appropriate for shopping centers. This is because some
attributes of shops can only be applied to shopping centers if they have achieved
success by creating a generally consistent and cohesive image. Likewise, the
shopping center has certain attributes of its own which are quite different from those
of sales points, yet common to the whole area.
Taking these issues into account, the following variables based in Lindquist (1974-
1975) studies and supported later on by Gaustchi (1981) and Howell and Rogers
(1980), among others, are used to define the image of shopping centers: “informally
consumer´s dressing”, “product variety or ” (size or center importance proxi), “sales
assistant professionalism”, “calm of the buying process”, “cleanliness”, “ease of
communication between establishments”, “prices”, “parking facilities”, “opening
hours” (in the evenings and at weekends). These variables are specified by means of a
series of affirmations with which the individual identifies him or herself based on
five-point scale, in which a score of five points reflects “total agreement” and a score
of one point the opposite.
25
2) Variable regarding distance. This variable is measured by the average time spent
travelling, as perceived by the interviewee, from his or her place of residence to the
shopping mall using different means of transportation. The choice of this particular
way of measuring the distance variable was made following that proposed by Howell
and Rogers (1980), McGoldrick (1992), Nevin and Houston (1980) and Stoltman,
Gentry and Anglin (1991).
RESULTS
Firstly, the results that show the media and the standard deviation for each variable
considered in the model are displayed in Table 9. Furthermore, the results obtained
referred to the estimations of a conditional logit model are displayed in Table 10. The
maximum likelihood method (from Newton method) is the one employed to estimate the
population parameters. In the first estimation the four shopping and the ten explanatory
variables were considered (nine that reflected the “characteristics of the shopping
centers” and the “distance” variable). The results are depicted in the shadowed column in
Table 10.
INSERT TABLE 9
INSERT TABLE 10
Based on previous predictions, all the coefficients, with the exception of the
corresponding to the variables X1 (“informal dressing”), X5 (“cleanliness”) and X7
(“parking”) present the expected sign. All except X1, X3 (“salespeople professionalism”),
X4 (“calm”), X5, X7 (“prices”) and X10 (“distance”) are statistically significant. It is
observed that X2 (“variety of products”), X6 (“communication from store to store”) and X9
(“opening hours”) increase the attractiveness of the shopping centers, whereas X8
(“prices”) reduces the attractiveness to go to these centers.
26
In relation to hypothesis one, the negative coefficient of the “distance” variable is not
surprising by its sign, although it could be the case by its nonsignificativity in the model.
Attending these results, as Nevin and Houston (1980) pointed out, the “distance” variable
does not improve the model (see
2 in the estimated model without the distance). In the
second estimation (without considering the “distance”) there appear the same significance
coefficients with the same signs as in model one.
Concerning hypothesis two, the expected coefficient´s signs do not totally match the final
ones obtained by the model. It is strange to find in the results the underpinning idea that
the easiest to park for consumers, the less that they visit the centers. It was predicted that
the consumers would visit the centers more if parking would be easy and if the shopping
center were close from their place of residence. Since the consumers decide whether to go
to the shopping centers to buy occasional products, it turns out interesting to verify that
the variety of products more than the parking facility that the centers offer it is very
important for them and, this is not surprising, given the nature of this type of products.
As stated hypothesis three, it was hoped that the consumers would visit the centers more
if it was comfortable to go out of one store and into the next one. Again, the type of
products chosen is related to the need of comparison between them, and it is maybe for
this reason that consumers focus its importance in the possibility of visiting comfortably
different shops. Probably, if they would have gone to buy convenience products they
would have been very happy just visiting one store.
Considering hypothesis four and five (a-f), it was expected that the consumers would go
more to the shopping centers if they find more product´s variety, if they can dress more
informally, and if the salespeople are more professional, as well as the cleaner the centers
are. Also it was hoped that they would visited it more if prices were low and if the
shopping hours were ample.
27
In summary, the results indicate that the consumers are willing to visit the shopping
centers instead of not visiting them if the variety that is offered to them in such is, in their
opinion, ample enough. On the other hand, the consumers also value positively the
easiness of being able “to go shopping”, as well as the amplitude in the opening hours.
Finally, it should be underlined that the model and the results that appear in Table 10,
although reasonable, must be considered guiding more than conclusive for diverse
reasons that are exposed next.
CONCLUSIONS
This study has attempted to deepen in one of the fields of the consumer behavior and the
commercial distribution in the discipline of marketing: the consumer´s attraction towards
the shopping centers. A review of this field of research is presented and an empirical
contribution on this aspect is the analysis that reflects the attraction that four commercial
centers in the city of Alicante exert on the consumers of this city when they decide to buy
products of nonoccasional consumption.
If it is understood how and why the consumers choose to go to the shopping centers, the
managers gain knowledge in the reasons of success or failure of a commercial centers.
The study of the commercial attraction by shopping centers on the consumers, has been
considered in the literature through five lines of investigation. Taking as it bases one of
those directions, the one that incorporates the image of the centers as an extension of the
Huff´s model, this analysis is presented. The technique used is a conditional logit model
overcoming the limitations presented by previous studies.
The conclusions obtained suggest that it is verified that the gravitational approaches and
the linear regression models widely used in this field of investigation do not properly
explain the influence of the determining factors in the decision to go to the shopping
centers. It can be argued that the first ones are too simple models in their conception, and
28
the seconds, do not consider the qualitative character of the dependent variable, reaching
therefore biased results.
Secondly, it is corroborated that the complexity of the purchase decisions that the
consumers must make reveal that it makes no sense to speak of general preference on the
consumers part of one or another type of center without diminishing the importance of
this preference on the nature of the products to acquire. That is to say, the type of product
is an excellent variable in the selection of the shopping center that cannot be neglected.
Thirdly, of the results of the model´s application arises the fact that there is an important
issue to be considered: no support was found for hypothesis suggesting that there are
variables such as the informality of dressing, the salesmen´s professionalism, the
tranquillity that can be enjoyed during the purchase, the cleanliness of the centers, the
prices offered by these centers and, finally, the distance from the buyer´s residence to the
shopping centers, have not been considered by the consumers as crucial when deciding
which shopping centers to visit. However, the data revealed relationships between certain
hypothesis: the consumers take into consideration the possibility of parking their cars and
the opening hours, as well as the fact of shopping in a comfortable way from store to
store.
Based on the above discussion it is suggested for practitioners, specially those of
unplanned centers, that design the retailing mix should be targeted with appropriate
information paying attention to the parking facilities, security and the establishments
opening hours, which seem to be a competitive advantage for the commercial centers that
offer them to their consumers.
To conclude, an effort has been made to carry out a work of synthesis in the extensive
field of the commercial attraction including two points of view, the consumer´s behavior
and the commercial distribution, making an empirical contribution hoping to contribute
with new conclusions in order to explain this phenomenon, while avoiding with this
29
approach some outstanding limitations of a big portion of the commercial attraction
literature.
LIMITATIONS AND DIRECTIONS FOR FUTURE RESEARCH
As far as the limitations of the methodology of this work, the hypothesis of utility
maximization can be too restrictive and not adapt to consumers who can use other
decision-making rules or that use a selection process in several stages (selection-
evaluation-selection). In any case, the concept of utility as a latent variable and choice
probability is sufficiently supported in literature (Luce 1959 and McFadden 1981). The
model also rests on a very restrictive assumption: independence between irrelevant
alternatives. Due to this problem, when two or more of the alternatives are highly
substitute, the logit model can not produce reasonable results.
A limitation that could be important needs to be mentioned explicitly. This is the lack of
information regarding the variable “consumer motivation when visiting shopping
centers”, that does not allow to determine this variable can condition, along with the
shopping centers characteristics” variables, the consumer´s buying decision process.
The directions for future research suggested by the author indicate that it would be of
great interest to deepen into research of the effects of other explanatory variables, such as
the dimensions related to the own characteristics of the consumers instead of those
specific only to the stores. The design of this model could be also carried out with other
market segments to discover which would be the estimations and thus, to be able to
compare between the different segments of consumers. An alternative study could consist
on verifying if a specific store exists, or several, within the shopping center, that are
really the determinants of the consumer purchase decision in a commercial center.
30
Finally, it would be possible to design a multinomial nested logit model to know if the
consumers consider it an option to visit the shopping centers in two stages; taking into
consideration first the location in the suburban or, vice versa, in the inner city, and later,
the type of concrete center within that previously established category.
31
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Table 1. Store´s Choice Process Approaches
Approach Contributions
Explanation of how and why a buyer decides to visit a certain
number of commercial establishment and not another one. Bearden, Crockett and Teel (1983); Huff (1963-
64); Sheth (1983); Spiggle and Sewall (1987).
Investigations based on the buyers´ characteristics that
examine how variables such as the income, sex and age
influence the store choice.
Pessemier (1983); Tauber (1972); Westbrook and
Black (1985); Woodside and Moore (1983).
Studies that examine the impact in a certain market area, as
much on the retailers as on the consumers.
Shopping mall. Nowell and Stanley
(1991).
Outside the shopping mall. O´Neill and Hawkins
(1980).
Predictive-probabilistics models referred to the psychological
state of the buyers and those referred to the influence of the
establishment.
Brown (1989); Craig et al. (1984); Evans,
Christiansen and Gill (1996); Wrigley (1988).
Source: Adapted from McGoldrick (1992).
42
Table 2. Image Attributes to Measure the Commercial Attraction
IMAGE ATTRIBUTES AUTHORS
(1) variety of products; (2) professionalism of sales assistant; (3) formality or informality
of dress required by the shoppers; (4) tranquility of the buying process; (5) cleanliness;
(6) ease of communication between establishments; (7) parking facilities; (8) value for
money; and (9) opening hours (in the evenings and at weekends).
Gaustchi
(1981)
1) quality of the stores; 2) variety of establishments; 3) merchandise quality; 4) product
assortment; 5) general price level; 6) special discounts/ promotions; 7) layout of the area;
8) parking facilities; 9)availability of lunch/refreshments; 10) comfort zones; 11) special
events/exhibits; 12) center´s atmosphere; 13) personnel; 14) childcare; 15) great place to
spend a few hours; 16) conservative center.
Nevin and
Houston
(1980).
1) interesting place to buy; 2) it has everything that I need; 3) it is not old nor neglected;
4) possibility to find fashionable merchandise; 5) I can go to buy with children; 6) it
offers a great variety of stores; 7) good restaurants; 8) good advertising; 9) good lighting;
10) well planned; 11) well clean and maintained; 12) pretty and pleasant surroundings;
13) sufficient parking space; 14) public telephones; 15) good places of entertainment; 16)
department store or other establishments of that type that exert the pull; 17) easy to arrive
by bus; 18) safe place; 19) easy to arrive at by car; 20) it has public restrooms; 21)
different from other areas; 22) friendly salesmen; 23) ample opening hours; 24) parking
has reasonable price; 25) stores are not too far away from each other; 26) I can go
shopping even when the weather is bad; 27) the stores have a good product assortment;
28) easy to park; 29) good directories and indicators; 30) I go shopping when I have short
time; and 31) prices are lower than in other centers.
Wee
(1986).
1) cleanliness; 2) quality of the establishments; 3) illumination; 4) space amplitude; 5)
only one place to go shopping with multitude of establishments; 6) possibility of seating;
7) selection of department stores; 8) opening schedule; 9) place to spend time; 10) bad
weather does not influence to go; 11) restaurants and bars; 12) general decoration; 13)
cordiality in the atmosphere; 14) security; 15) facility to park; 16) restrooms; 17)
assortment in the store; 18) disposition of stores; 19) variety of establishments; 20) air
conditioning; 21) attention of the salesmen; 22) place to take children; 23) access by bus;
24) access by car; 25) agglomeration of visitors; 26) general level of prices; and 27)
nondesirable characteristics.
McGoldrick
(1992).
1) attractiveness; 2) quality of products; 3) atmosphere; 4) cleanliness; 5) attractiveness of
the buildings and the surroundings; 6) helpfulness of personnel; 7) client´s attention; 8)
knowledge of personnel; 9) fashion availability; 10) variety of styles; 11) possibility of
comparison between stores; 12) quantity; 13) quality; 14) advertising information; 15)
comfort in general; 16) store hours; 17) parking; and 18) traffic.
Hauser and
Koppelman
(1979).
1) proximity to home; 2) availability of parking; 3) variety of products; 4) cleanliness of
stores; 5) prices; 6) traffic congestion; 7) friendly sales personnel; 8) buildings and
landscaping; 9) store hours; 10) free parking available; 11) advertising; 12) quality of
stores; 13) variety of stores; 14) comparative shopping; 15) reputation of stores; 16) type
of customers ; and 17) value for price.
Gentry and
Burns
(1977-78).
A) Nevin and Houston (1980).
B) Howell and Rogers (1980).
Stoltman,
Gentry and
Anglin
(1991).
1) attractiveness; 2) quality of merchandise; 3) atmosphere; 4) cleanliness of stores; 5)
attractiveness of the buildings and the surroundings; 6) courtesy of salespeople; 7)
helpfulness of salespeople; 8) knowledge of the salespeople; 9) fashion availability; 10)
variety of styles; 11) comparison shopping; 12) advertising; 13) quality of the advertising;
14) advertising information; 15) comfort in general; 16) store hours; 17) parking; and 18)
traffic.
Howell and
Rogers
(1980).
43
Table 3. Type of Products
TYPE OF PRODUCT/BUYING
SITUATION AUTHORS
Non specified. Bellenger, Robertson and Greenberg (1977); Gentry and Burns (1977-78);
Hauser and Koppelman (1979); McGoldrick (1992); Nevin and Houston
(1980); Wee (1986); Weisbrod, Parcells and Kern (1984).
Non basic products. Wee and Pearce (1985).
Clothing. Stoltman, Gentry and Anglin (1991).
Women clothing. Howell and Rogers (1980).
Furniture and electric appliances. Gautschi (1981).
44
Table 4. Patronage Dimensions
VARIABLE AUTHORS
Relative trip frequency measured as a ratio of the total number of
trips to the evoked shopping centers to the per-visit expense. Wee and Pearce (1985); Wee (1986).
Number of times that the respondent has shopped in each of the
centers over the past twelve months. Gautschi (1981).
A combination of three levels of drawing power: endearment,
behavioral intentions and actual behavior. Nevin and Houston (1980).
Visit frequency, measured through amount spent and visit duration. McGoldrick (1992).
Trip frequency as a function of preferences and accessibility
(distance in kilometers) during the past twelve months.
Hauser and Koppelman (1979); Howell and
Rogers (1980); McGoldrick (1992); Stoltman,
Gentry and Anglin (1991).
Shopping frequency (6 items scale) and shopping intentions (4
items scale). Stoltman, Gentry and Anglin (1991).
Shopping trips made by members of households during the week
before the surveys. Weisbrod, Parcells and Kern (1984).
Relative shopping trips measured by the number of trips, duration,
consumer motivation and demographic characteristics in the last
three years. Finn, McQuitty and Rigby (1994).
A combination of shopping trips, amount spent and number of
weeks since the last purchase. Howell and Rogers (1980).
45
Table 5. Distance Dimensions
DISTANCE AUTHORS
Distance measured in miles. Wee and Pearce (1985); Hauser and Koppelman (1979).
Real distance measured in driving time (special
measurement system or driving time).
Gautschi (1981); Howell and Rogers (1980); Nevin and
Houston (1980); Stoltman, Gentry and Anglin (1991);
Weisbrod, Parcells and Kern, (1984).
Multiple index: perceptual distance (minutes) and
objective distance (building blocks). Howell and Rogers (1980).
46
Table 6. Attraction to Shopping Center Dimensions
ATTRACTION AUTHORS
Store surface in square meters. Bucklin (1971); Huff (1962-63-64); Nevin and Houston
(1980); Stanley and Sewall (1976); Wee and Pearce (1985);.
Preferred store in the shopping center. Nevin and Houston (1980).
Number of establishments in the shopping center
and “Byrne reinforcement model (1971)”. Meoli, Feinberg and Westgate (1991).
Shopping center assortment. Gaustchi (1981).
Preference. Hauser and Koppelman (1979); Nevin and Houston (1980).
47
Table 7. Image Representative Factors to Measure Attraction
FACTORS OBTAINED FROM THE IMAGE ATTRIBUTES AUTHOR
1) benefits offered by the market area; 2) facilities; 3) positioning. Nevin and Houston (1980).
1) assortment; 2) facilities; 3) maintenance; 4) effectiveness. Wee (1986).
1)variety; 2) quality and satisfaction; 3) value; 4) parking. Hauser and Koppelman (1979).
1) leisure experience; 2) client´s service; 3) establishments; 4)
atmosphere´s quality; 5) accessibility by car; 6) crowds; 7) accessibility by
bus; 8) prices. McGoldrick (1992).
1) atmosphere; 2) personnel; 3) fashion; 4) advertising; 5) accessibility. Howell and Rogers (1980).
A) 1) assortment; 2) leisure; 3) atmosphere; 4) economicity.
B) 1) variety; 2) sales; 3) facilities; 4) atmosphere; 5) convenience; 6)
public relations. Stoltman, Gentry and Anglin (1991).
1) offer; 2) parking; 3) establishments; 4) opening hours. Gentry and Burns (1977-78).
48
Table 8. Level of Aggregation
LEVEL OF AGGREGATION AUTHORS
Non aggregated. Hauser and Koppelman (1979); McGoldrick (1992); Nevin and Houston
(1980); Stoltman, Gentry and Anglin (1991).
Aggregated. Gautschi (1981); Wee (1986); Wee and Pearce (1985); Weisbrod, Parcells
and Kern (1984).
49
Table 9. Media and Standard Deviation
VARIABLES Media Standard
deviation
X1 = consumer´s informally dressing. 3.3990
3.4984
3.3269
2.8381
3.1715
3.3141
2.7676
2.7163
3.7019
128.28
1.8722
X2 = products assortment. 1.7926
X3 = knowledge of salespeople. 1.7534
X4 = calm in the buying process. 1.7384
X5 = center´s cleanliness. 1.7319
X6 = communication between establishments. 1.8989
X7 = parking. 1.6728
X8 = low prices in relation to quality. 1.9259
X9 = opening hours (evenings and weekend).
X10 =average distance from the consumer´s residence to the shopping center. 1.9023
131.82
50
Table 10. Conditional Logit Model Estimated Coefficients
VARIABLES With the distance
variable Without the distance
variable
Coefficients t-ratio Coefficient
s t-ratio
X1 = consumer´s informality dressing. -0,01182 -0,098 -0,00799 -0,066
X2 = products assortment. 0,4193 3,440 0,4169 3,493
X3 = salespeople professionalism. 0,1288 1,037 0,1350 1,085
X4 = calm in the buying process. -0,0018 -0,021 0,0057 0,069
X5 = center´s cleanliness. -0,0384 -0,327 -0,5133 -0,433
X6 = communication from shop to shop. 0,1874 2,174 0,1783 2,079
X7 = parking. 0,1492 1,509 0,1457 1,475
X8 = low prices in relation to the offered quality. -0,1959 - 2,304 -0,17036 -2,074
X9 = opening hours (evenings and weekend).
X10 =average distance from the consumer´s
residence to the shopping center.
0,3087
-0,0011
2,337
-1,209
0,3053
___ 2,277
___
Ln likelihood with
= 0. -216,26 -216,26
Ln maximum likelihood. -180,32 -181,07
2 0,16 0,16
% correctly predicted 44% 44%
2 10; 0,05
2 9
;
0
,
05 71,87
70,37
p < 0,05
51
INFORMATION ABOUT THE AUTHOR: Mª DOLORES DE JUAN VIGARAY
(84 words)
Mª Dolores De Juan Vigaray is a Business Administration Professor from Alicante
University (Spain), where she lectures since 1991.
De Juan Vigaray has collaborated as lecturer in University of Florida (USA) and in
Southampton Institute (United Kingdom), as well as in several Masters and Spanish
Business Schools. She is the author of books “Shopping Centre Attraction Towards
Consumers” and “Commercial Distribution: Channels and Retailing”, as well as different
articles about distribution and consumer behaviour. She focuses her researching on
“Commercial Attraction” and “Consumer Behaviour”.
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Least squares estimation techniques are developed for a special multiplicative model based on the Luce choice axiom whose potential usefulness in marketing applications justifies estimation techniques which can be easily implemented.
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This important study shows that an understanding of product differentiation is crucial to understanding how modern market economies function and that differentiated markets can be analyzed using discrete choice models of consumer behavior. Product differentiation - in quality, packaging, design, color, and style - has an important impact on consumer choice. It also provides a rich source of data that has been largely unexplored because there has been no generally accepted way to model the information available. This important study shows that an understanding of product differentiation is crucial to understanding how modern market economies function and that differentiated markets can be analyzed using discrete choice models of consumer behavior. It provides a valuable synthesis of existing, often highly technical work in both differentiated markets and discrete choice models and extends this work to establish a coherent theoretical underpinning for research in imperfect competition.The discrete choice approach provides an ideal framework for describing the demands for differentiated products and can be used for studying most product differentiation models in the literature. By introducing extra dimensions of product heterogeneity, the framework also provides richer models of firm location. Discrete Choice Theory of Product Differentiation introduces students and researchers to the field, starting at the beginning and moving through to frontier research. The first four chapters detail the consumer-theoretic foundations underlying choice probability systems (including an overview of the main models used in the psychological theory of choice), while the next four chapters apply the probabilistic choice approach to oligopoly models of product differentiation, product selection, and location choice. The final chapter suggests various extensions of the models presented as well topics for further research.
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