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Assessing preferences for a mega shopping centre in theNetherlands: A conjoint measurement approachBorgers, A.W.J.; Vosters, C.
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Published: 01/01/2010
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Citation for published version (APA):Borgers, A. W. J., & Vosters, C. (2010). Assessing preferences for a mega shopping centre in the Netherlands:A conjoint measurement approach. In Proceedings of the European Institute of Retailing and Services Studiesconference (RASS) (pp. 21-). EIRASS.
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Assessing Preferences for a Mega Shopping Centre in the Netherlands:
A Conjoint Measurement Approach
Aloys Borgers
Eindhoven University of Technology
Urban Planning Group
Eindhoven, The Netherlands
a.w.j.borgers@tue.nl
Cindy Vosters
Advin BV
Oss, The Netherlands
Cindy.vosters@advin.nl
Abstract
In 2004, the Dutch central government decided to liberalise her restricted retail policy. This
stimulated some retail developers to prepare plans for mega shopping centres. As mega
shopping centres do not exist in the Netherlands, this study aims at eliciting consumers’
preferences for this kind of new developments. Consumers visiting a down town shopping
centre and one of the largest out-of-town shopping centres in the Netherlands were presented
descriptions of different hypothetical mega shopping centres, systematically varying on 10
attributes. The consumers were asked to select the centre they preferred most from sets of two
centres. The following attributes were used to define the mega shopping centres: accessibility by
car, accessibility by public transport, parking tariff, length of the main shopping streets, type of
shopping supply, type of anchor stores, type of traffic allowed in the shopping centre, design
style, scale of the shopping streets, and type of activities in the shopping centre.
Over 300 respondents completed the online questionnaire. Discrete choice models (both
multinomial and mixed logit) were estimated to assess the importance of each attribute. Overall,
the estimation results confirm expectations. Shoppers prefer well accessible shopping centres
and free parking. The preferred time needed to walk through the main streets of the shopping
centre is 45 minutes; 30 minutes is still acceptable, but 15 minutes is not preferred at all.
Shoppers do not prefer a shopping supply existing of small and medium sized (local) shops, and
specialised/exclusive shops are preferred over the well known national chains. Regarding
anchor stores, shoppers seem to dislike the very large electronics stores and traditional
department stores are preferred over flagship stores. Only pedestrians should be allowed to enter
the shopping centre. Other traffic modes like bicycles and especially motorized modes are not
preferred. The design style should be historically while a Disney style is detested. A modern
design style is somewhere in between. The preferred scale of the shopping streets is a mixture of
short/narrow and long/wide streets. Only long/wide scale shopping streets are not preferred.
Finally, the type of activities offered by the shopping centre should be a mixture of passive and
active activities. Shoppers seem to be less happy with active activities only. Although all
attributes have a significant impact on the preference for a shopping centre, parking fee and
design style appear to be the most important attributes. In addition to the overall effects,
significant differences between females and males, between younger and older respondents, and
between respondents recruited in the down town shopping centre and respondents recruited in
the large out-of-town shopping centre were found. Some interactions between attributes were
significant as well. The models perform very satisfactory.
1. Introduction
Until 2004, the Dutch central government pursued a restrictive retail policy. Shopping
centres had to be hierarchically organised with the down town shopping centre on top of
a city’s hierarchy. Out of town or peripheral shopping developments were restricted to
particular types of shops in selected cities (see also Gorter et al. 2003). In the 2004
policy document on spatial planning, the Dutch government delegated retail decisions to
the municipalities. Provinces were assigned to supervise and coordinate municipal
plans. This reversal in policy liberalised the Dutch retail system. Although some earlier
plans for mega shopping centres in the Netherlands failed, in September 2007, a plan for
a mega shopping mall in the medium sized city of Tilburg was announced. The 100.000
m2 mega shopping mall was planned at a former military area in the northern periphery
of the city. As may be expected from experiences in other countries (see e.g. Howard &
Davies, 1993; Marjanen, 1995; Williams, 1995), many objections rose against this plan.
Neighbouring municipalities worried about environmental aspects and the viability of
their retail facilities and established retailers expected decreasing turnover figures. The
municipality of Tilburg as well as other municipalities hired consultants to assess likely
economic and environmental impacts. After some years of political discussions, the plan
was cancelled by referendum in 2009. In the mean time, it was decided to investigate
potential customers’ preferences regarding a peripheral mega shopping centre. This
paper reports the approach and results of this investigation.
Because mega shopping centres like the one planned in Tilburg do not exist in
the Netherlands, preferences regarding mega shopping centres cannot be derived from
observed shopping centre patronage. Therefore, it was decided to use a conjoint choice
model to measure customers’ preferences. Conjoint preference or choice models
(Louviere et al. 2000) have been applied many times in the context of retailing. For
example, Oppewal et al. (1997) developed a conjoint choice model to measure the
effects of shopping centre size and marketing mix on customers’ choice behaviour;
Oppewal & Timmermans (1999) applied a stated preference model to measure the effect
of physical aspects of shopping centres on consumer perceptions; Borgers et al. (2006)
used a stated choice model to assess the impact of peripheral retail centres on traditional
urban shopping centres in a Dutch city; and Kim et al. (2009) used conjoint analysis to
design a novel suburban luxury brand outlet mall in S Korea.
Conjoint choice analysis involves a number of steps. First, attributes (or
characteristics) of the alternatives that are assumed relevant have to be identified, along
with their so-called attribute levels (section 2). Each combination of attribute levels
defines an alternative (a mega shopping centre). As the number of alternatives may
grow huge if the number of attributes and/or the number of attribute levels increases,
some experimental design is used to select a representative fraction from the complete
set of alternatives. Given the hypothetical mega shopping centres, experimentally
controlled choice situations must be created and presented to respondents (section 3).
To assess the preferences, data must be collected by asking respondents (potential
shoppers) to choose the mega shopping centre they prefer from the choice situations
created in section 3. This will be explained in section 4. Next, discrete choice models
have to be specified to estimate the effect of the attributes (and respondents’
characteristics) on the respondents’ preferences regarding mega shopping centres
(section 5). The results of the model estimation will be presented in section 6. Finally, in
section 7, conclusions will be drawn and implications for future development of mega
shopping centres will be discussed.
2. Selection of attributes
Developing large shopping centres involves many decisions. Location and accessibility
are very important decision variables to assure a sufficient number of potential
customers. In addition, the shopping centre should attract many shoppers. Supply of
retail and entertainment outlets, but also aspects related to design, layout, atmospherics,
et cetera are important. Although the plans for a mega shopping mall in Tilburg induced
this research project, the purpose is to investigate variables of special interest in the first
stages of designing a mega shopping centre somewhere in the Netherlands. Based on the
literature and opinions of industry experts, the selected variables are listed in Table 1.
For each variable (attribute), three levels were defined. The effects of these attribute
levels on shoppers’ preferences for mega shopping centres will be investigated.
As mega shopping centres are likely to be located in the periphery of urbanised
areas, accessibility by car and public transport should be guaranteed. Although
accessibility can be improved by means of infrastructural measures, it is of interest to
assess the importance of the positioning of the mega centre relative to the highway exit
and public transport stop. Accessibility by car represents the ease to reach the shopping
centre after leaving the highway. This is expressed by the number of obstacles between
the highway exit and the shopping centre. Examples of obstacles are traffic lights and
busy intersections. Accessibility by public transport is expressed by the time to walk
from the nearest public transport stop to the shopping centre and vice versa. Although
parking tariff is not a main decision variable in the beginning of the design process, it
was included in the list of variables as a kind of reference (or benchmark) variable. In
the Netherlands, many cities introduced paid parking at the parking facilities of the main
shopping centres (Van der Waerden et al. 2009). By including this attribute, the
importance of the other attributes can be related to the importance of parking costs. The
levels represent the range of commonly used tariffs at large, non down town, shopping
centres in the Netherlands. Note that the accessibility variables do not take into
consideration the time or distance to travel from home to the shopping centre. It is
assumed that before starting the design process, a suitable location for the mega
shopping centre already has been selected.
According to Reimers & Clolow (2004) consumers may be reluctant to walk
excessive distances in a shopping centre. Therefore, they advice creating compact
shopping environments. However, as shopping trips to mega shopping centres mainly
can be considered as recreative or hedonic shopping trips, consumers may be less
sensitive to walking distances. The length of the main shopping streets expresses the
time needed to traverse the main streets in the shopping centre. This does not include
the time to visit shops, window shopping or take a rest. Also related to the layout of a
shopping centre is the scale of the shopping streets. The shopping centre may consist of
a network of short and narrow shopping streets with narrow shop fronts. On the other
hand, the streets may be long and wide with wide shop fronts. The third level of this
attributes is defined by a mixture of both short/narrow and long/wide streets. Although
most large shopping centres in the Netherlands allow pedestrians only, it was
questioned whether shoppers would prefer (limited) access by bicycles or other
transportation modes as well in the case of extremely large shopping centres. Therefore,
the attribute type of traffic allowed was taken into consideration as well.
One of the long-run decisions regarding new shopping centres concerns the
selection of anchor stores. Finn & Louviere (1996) concluded from their research in the
Edmonton region that anchor stores have a dominant role on shopping centre image.
The types of anchor stores selected in this study are department stores, very large
electronics shops, or very large fashion shops. The latter type is also known as flagship
stores (Kozinets et al., 2002; Kent, 2002). In addition to anchor stores, the type of
shopping supply is considered relevant as well. As national (and international) chains
dominate many shopping centres, it was questioned whether consumers would prefer
other types of shopping supply in the mega shopping centre. Therefore, small to
medium sized and specialized/exclusive shops were considered as the main type of
shopping supply as well.
Table 1: Attributes and attribute levels
Attribute level description
Accessibility by car 1
2
3
1 obstacle between highway exit and shopping centre
3 obstacles between highway exit and shopping centre
5 obstacles between highway exit and shopping centre
Accessibility by public
transport
1
2
3
First PT-stop at 3 minutes walking from shopping centre
First PT-stop at 6 minutes walking from shopping centre
First PT-stop at 9 minutes walking from shopping centre
Parking tariff 1
2
3
Free parking
€1,00 per hour
€2.00 per hour
Length of main shopping
streets
1
2
3
15 minutes walking
30 minutes walking
45 minutes walking
Type of shopping supply 1
2
3
Well known national chains
Small to medium sized shops
Specialized and exclusive shops
Type of anchor stores 1
2
3
Department store
Mega electronics store
Flagship store (fashion)
Type of traffic allowed 1
2
3
Pedestrians only
Pedestrians and bicyclists
All transport modes
Design style 1
2
3
Historical
Modern
Disney style
Scale of shopping streets 1
2
3
Many short and narrow shopping streets
Some long and wide shopping streets
Mixture of both types
Type of activities 1
2
3
Passive, like a restaurant or a cinema
Active, like a fun-fair or a bowling alley
Mixture of both types of activities
Wakefield & Baker (1998) conclude that, amongst other things, overall architectural
design of the mall and entertainment outlets like a theatre or family recreation centre
may generate excitement and improve a mall’s competitive position. Also Sit et al.
(2003) conclude that entertainment is essential. However, Haynes & Talpade (1996)
warn that mall developers should use caution in developing a mall with an
entertainment centre. Teller & Reuttener (2008) found that entertainment does not
impact the evaluation of the attractiveness of a shopping centre. Although architectural
design and entertainment may be important, it is not clear which type of architectural
design is preferred and what kind of entertainment outlets should provide. Therefore,
design style and type of activities in the shopping centre are defined as relevant
attributes. Regarding the type of activities, a distinction is made between passive and
active activities. A mixture of both is considered as well. Regarding the architectural
design style, a historical, modern, and Disney style were chosen. In the case of a
historical style, the shopping centre consists of historical look-alike buildings. The
modern style represents a more present-day and technological character, while the
Disney style refers to a specific theme in a picturesque architecture.
By selecting one level for each attribute, a description of a hypothetical
shopping centre is generated. In total, 310
different hypothetical shopping centres can be
generated, which is an impractical high number of alternatives. However by taking an
orthogonal fraction of the full set of 310
alternatives, preferences can still be estimated.
Therefore, a fraction of 81 alternative shopping centres was selected. This selection
allows for the estimation of all main effects and the interaction effects between the first
five attributes listed in Table 1.
3. Choice tasks
One way to assess preferences regarding shopping centre attributes is to present
respondents choice sets and ask them to identify the most preferred alternative in each
choice set. In this study, the choice set is composed of different hypothetical shopping
centres. To keep the choice task simple, each choice set was composed of two
hypothetical shopping centres and a ‘no preference’ alternative which can be chosen if
the respondent has no preference regarding one of the two hypothetical centres in the
set. An example of a choice task is presented in Figure 1.
Information about the characteristics: IINNFFOO
Characteristic Shopping centre 1 Shopping centre 2
Accessibility by car 3 obstacles to the shopping
centre
5 obstacles to the shopping
centre
Accessibility by public transport First PT-stop at 6 minutes
walking
First PT-stop at 9 minutes
walking
Parking tariff €2.00 per hour €2.00 per hour
Length of main shopping streets 30 minutes walking 45 minutes walking
Type of shopping supply Specialized and exclusive Specialized and exclusive
Type of anchor stores Mega electronics store Department store
Type of traffic allowed All transport modes Pedestrians only
Architectural design style Disney style Disney style
Scale of shopping streets Many short and narrow shopping
streets
Mixture of short/narrow and
long/wide streets
Type of activities Mixture of both passive and
active activities
Mixture of both passive and
active activities
Which alternative do you prefer?
O shopping centre 1 O shopping centre 2 O no preference
Figure 1: Example of a choice task
Each respondent was presented 14 choice tasks. For each respondent, the 14 choice sets
were generated by randomly selecting two alternatives from the set of 81 alternatives.
The on-line questionnaire started with an introduction emphasizing that the context was
recreational shopping. So, a respondent had to imagine that the main purpose of visiting
the shopping centre is to enjoy his/her leisure time. It was explained that the respondent
would be presented 14 choice situations. The task was explained by an example choice
set which was shown prior to the 14 choice sets. The example was used to explain how
the two shopping centres are defined by the ten attributes and how the respondent can
identify his/her preference for one of the two shopping centres, or, if applicable, neither
shopping centre. Furthermore, it was explained that any time the respondent could ask
for an extensive description of the attributes by clicking the INFO-button on top of the
screen. These extensive descriptions showed some pictures for the attribute Type of
anchor stores, Design style, and Scale of the shopping streets. The respondents were
instructed to assume that the two mega shopping centres presented in each choice
situation only differ in terms of the listed characteristics. No information about the
location of the mega shopping centre was provided.
4. Data collection
Data was collected by means of an internet based questionnaire. After a short
introduction, the respondent was presented the 14 choice tasks as described in the
previous section. At the end of the questionnaire, the respondent was asked to provide
information regarding personal characteristics (gender and age), postal code, in which
shopping centre he/she was invited to participate in the research project, and the
preferred type of shopping centres for recreational shopping (down town shopping
centres, district shopping centres or other types of shopping centres). As five gift
coupons were raffled among the participants, the respondent was asked to provide
his/her email-address to notify whether a gift coupon was won.
It was decided to recruit respondents among customers in large shopping centres
such as down town shopping centres of Dutch cities and large peripheral shopping
centres. The down town shopping centre of Den Bosch and the peripheral shopping
centre Alexandrium, located in Rotterdam, were chosen to recruit respondents. Both
shopping centres attract customers from a wide region. The down town shopping centre
of Den Bosch is one of the most popular down town shopping centres in the
Netherlands. Alexandrium is one of the largest peripheral shopping centres in the
Netherlands (see also Gorter et al. 2003). In each shopping centre, respondents were
recruited during three days at the end of June and the beginning of July 2008. Weather
conditions were fine. As the Alexandrium is an indoor shopping centre and the down
town of Den Bosch is an open air shopping centre, rainy days might have reduced the
number of customers in Den Bosch. Customers were personally asked whether they
were willing to participate. If yes, their email-addresses were registered. Next, these
respondents were sent an email inviting them to visit the website containing the
questionnaire. Respondents not responding to the first invitation within two to three
weeks were sent a recall mail. To encourage participation, 5 gift coupons of €10,00 each
were raffled among the respondents.
In total, 667 usable email-addresses were collected in the two shopping centres.
Eventually, 312 (47%) respondents completed the online questionnaire. Table 2 lists
some characteristics of the respondents. Compared with national statistics regarding
recreational shopping in 2006/2007 (CBS / Statistics Netherlands), the male-female
ratio is approximately representative, but the age category of 15-24 is overrepresented
and the oldest category (over 65 years of age) is underrepresented. The number of
respondents recruited in Den Bosch is higher than in Rotterdam. This was expected as
relatively more shoppers in Den Bosch were willing to provide their email-address. All
pairs of characteristics (gender × age, gender × location, age × location) are independent
of each other according to the Chi2-test (if the 41-65 and >65 age categories are
merged). However, there is a significant difference between these subsamples in terms
of preferred type of shopping centre. For the Alexandrium-sample, the ratio down town
centre – district centre is approximately 50-50, while this ratio is about 85-15 for the
Den Bosch-sample. This may be attributed to the lack of attractive district or out of
town centres in the Den Bosch region.
Table 2: Respondents’ characteristics
# % CBS %1
Gender male 88 29 32
female 212 71 68
unknown 12 --
Age 15-24 years 101 34 17
25-40 years 74 25 34
41-65 years 115 38 33
older than 65 years 9 3 15
unknown 13 --
Location of Alexandrium Rotterdam 117 41
recruitment down town Den Bosch 167 59
unknown 28 -- 1) Note that the age category 0-15 was excluded as children are usually accompanied by adults
5. Model specification
The data collected from the choice situations were used to estimate a random utility
choice model. Each choice situation consisted of two hypothetical mega shopping
centres and a ‘no preference’ option. Thus, one of three choice alternatives has been
chosen. According to random utility theory (e.g. Train, 2003), each alternative i has a
utility (Ui). This utility consists of a structural (Vi) and a random (εi) component:
Ui = Vi + εi (1)
The structural component is assumed to be an additive function of the characteristics of
the alternative:
Vi = Σk βk Xik (2)
where Xik represents characteristic k of alternative i and βk is the parameter for
characteristic k. Note that the mega shopping centres are characterized by 10 attributes.
However, as each attribute consists of three levels (which can be considered as
characteristics), effect coding (see Table 3) was used to estimate the part-worth utility of
each characteristic. This means that 20 variables are needed to estimate all part-worth
utilities. The part-worth utility of the first level of the first attribute is equal to β1, of the
second level to β2, and of the third level to –(β1+β2), and so on. The utility of the ‘no
preference’ option is measured by a constant: β0.
Table 3: Effect coding
Attribute level Coding
1 1 0
2 0 1
3 -1 -1
If it is assumed that the random utility components are identically and independently
distributed, the multinomial logit model can be used to calculate the probability pi that
alternative i will be chosen. This model is defined as:
pi = exp(Vi) / Σj exp(Vj) (3)
The parameters are estimated by maximum likelihood estimation, which maximizes the
predicted probabilities of the chosen alternatives. Using the null-model (all parameters
are equal to 0.0) as a reference model, a goodness-of-fit measure Rho2 can be computed.
This measure ranges between 0.0 (no improvement compared with the null-model) to
1.0 (a perfect prediction of each observed choice). According to Hensher et al. (2005), a
Rho2 of 0.3 or higher represents a decent fit for a discrete choice model. However,
according to Louviere et al (2000) values between 0.2 and 0.4 can be considered to be
indicative of extremely good model fits.
The parameters β1 … β20 represent the main effects of the attributes. In fact, they
represent the preferences for the attribute levels. However, preferences may vary across
individuals’ characteristics. For example, Dholakia (1999) found that more married
women seem to enjoy going to the mall than married men and that the recreational and
expressive nature of shopping at the mall seems to appeal to the female shopper more
than to the male shopper. Ruiz et al. (2004) revealed four segments of shoppers:
recreational shoppers, full experience mall shoppers, traditional shoppers, and mission
shoppers. The first segment includes far more elderly people while the last segment
includes a higher proportion of young adults. Thus, it may be of interest to investigate
whether these personal characteristics affect the main effects of the attributes. In
addition to gender and age, the location of recruitment was taken into consideration as
well, because the respondents recruited in Rotterdam prefer other types of shopping
centres than the respondents recruited in Den Bosch.
By creating contrast variables, additional parameters can be estimated to test for
differences between subsamples (e.g. males and females). For the first subsample, all X-
variables should be copied into Z-variables, while for the second subsample, the
negative of the X-variables must be copied into the Z-variables. If values are estimated
for the β-parameters (related to the X-variables) and δ-parameters (related to the Z-
variables), the part-worth utility for variable k is equal to βk Xik + δk Zik, which is equal to
(βk +δk)Xik in the case of the first subsample and to (βk -δk)Xik in the case of the second
subsample. If δk is not significantly different from zero, the part-worth utility for both
subsamples is βkXik, meaning there are no differences between the subsamples. For each
X-variable (and also for the constant measuring the utility of the ‘no preference’ option)
a contrast variable is created for gender (males: Zik,gender = -Xik; females: Zik,gender = +Xik),
age (15-24 years: Zik,age = -Xik; 25-40 years: Zik,age = 0; over 40 years: Zik,age = +Xik), and
location of recruitment (Rotterdam Zik,location = -Xik; Den Bosch: Zik,location = +Xik). Note
that three subsamples were specified for age by joining the group aged 41-65 and the
small group aged over 65 years. The contrast effect for the middle age group is set to
zero, implying that a linear age effect is assumed. Contrast effects may also be referred
to as interaction effects between attributes and respondents’ characteristics, see e.g.
Alberini et al. (2003).
The experimental design that was used to generate the shopping centres allows
for the estimation of interaction effects between the first five attributes of Table 1. As
each attribute has three levels and consequently two indicator variables (see Table 3),
four variables define the interaction between the two attributes. For example, the first
two attributes are specified by variables X1, X2, X3, and X4. The interactions between
these attributes are equal to Ii1 = Xi1×Xi3, Ii2 = Xi1×Xi4, Ii3 = Xi2×Xi3, and Ii4 = Xi2×Xi4. In
total, 40 interaction variables (Ii1 … Ii40) must be specified to measure all possible first
order interaction effects between the first five attributes. Now, equation 2 can be
extended to:
Vi = β0 + Σk=1,20 βk Xik + Σk=1,20 δk,gender Zik,gender + Σk=1,20 δk,age Zik,age
+ Σk=1,20 δk,location Zik,location + Σk=1,40 θk Iik (4)
In this equation, β0 represents the utility of the ‘no preference’ option, the βk-parameters
measure the main effect of the attributes, the δk-parameters measure the contrast effects
between subsamples regarding gender, age, and location of recruitment, and the θk-
parameters represent the interaction effects between attributes.
The multinomial logit model assumes homogeneity (no taste variation among
respondents). To test for heterogeneity among the respondents, a mixed (or random
parameter) logit model (see e.g. Train, 2003) was estimated as well. Random parameter
models assume that respondents share the same kind of preference function, but vary in
terms of the weights they attach to the attributes. Such taste differentiation is captured
by estimating a distribution for the parameters of the utility function. For each βk-
parameter, a Normally distributed random component υk was added with mean 0.0 and
standard deviation σk. The equation for the structural utility then becomes:
Vi = (β0+υ0) + Σk=1,20 (βk+υk) Xik + Σk=1,20 δk,gender Zik,gender + Σk=1,20 δk,age Zik,age
+ Σk=1,20 δk,location Zik,location + Σk=1,40 θk Iik (5)
The standard deviation (σk) was estimated for each variable, in addition to the mean
value (βk). According to a mixed logit model, the choice probabilities are calculated by
repeatedly applying the multinomial logit. For each individual, random numbers are
drawn for the random variables and individual choice probabilities are calculated. This
is repeated R times for each individual and the probabilities for each alternative are
averaged across the R drawings. For a good performance, very large numbers of draws
are required. However, instead of a large number of random draws, a Halton sequence
of draws can be used (Bhat, 2001). Halton draws give a fairly even coverage over the
domain of the distributions and the draws for one observation tend to fill in the spaces
that were left empty by the previous observations. A Halton sequence of draws with
only one tenth the number of random draws is often equally effective. As per
respondent fourteen choices were observed, the random draws per variable were kept
constant for each respondent. If some of the standard deviations are significantly
different from zero, the assumption of homogeneity underlying the standard MNL
model is not valid.
Suárez et al. (2004) specified a random effects model as well. However, in their
model, heterogeneity was taken into consideration by two market segments
differentiating on the effects of the attributes. As in our model the influence of
respondents’ characteristics is already measured by means of contrast effects, an
additional random heterogeneity component for each main effect was considered
appropriate.
6. Estimation
Both the multinomial and the mixed logit model have been estimated. In the case of the
MNL model, the structural utility is defined by eq. 4, while this utility for the mixed
logit model is defined by eq. 5. The parameters of both choice models have been
estimated by Nlogit 4.0 (Greene, 2007). This was done stepwise. After the first run, all
variables with significance P[|Z|>z]| > 0.50 were removed from the model. This
criterion was gradually decreased until 0.10. Thus only parameters that are significant at
the significance level of 10% are included in the models. The estimated parameters
according to the models are presented in Tables 4 and 5. The significance of each
parameter is displayed between brackets. The column labelled ‘Overall’ represents the
estimates for all respondents (the β’s and the σ’s in the case of the mixed logit model),
regardless gender, age, and location of recruitment. The other columns show the
significant contrast effects for gender, age, and location (the δ’s). Remember that for
males, young respondents (aged 15-24) and respondents recruited in Rotterdam, the
contrast effects should be subtracted from the main effect, and for females, older
respondents (aged over 40 years) and respondents recruited in Den Bosch, the contrast
effects should be added to the main effects. As the part-worth utility of the third level of
an attribute has to be inferred from the corresponding parameters, it has been italicised
in the tables. For ease of interpretation, Figures 2 and 4 represent the part-worth utilities
for all attribute levels and all respondents in general. In addition, these figures represent
the effects for the gender, age, and location segments.
The interaction effects represent utility adjustments in the case of specific
combinations of attribute levels. The interaction effects (the θ‘s) between the attributes
are displayed in Figures 3 and 5 and will be discussed separately. The variable labelled
‘Con’ is equal to zero for the two shopping centre alternatives in each choice set, and
equal to one for the ‘no preference’ option. In fact, the parameter for this variable
measures the utility for the ‘no preference’ option.
The multinomial logit model
The multinomial logit model performs relatively well, rho2 is equal to 0.23. According
to Table 4, the utility of the ‘no preference’ option is on average -1.68. This rather
strong negative utility implies that in most cases, respondents made a choice between
one of the two shopping centres presented in the choice sets, supporting the estimation
of the attribute effects. Note that according to the contrast effects, younger respondents
have a higher tendency to choose the ‘no preference’ option than the older respondents.
Also respondents recruited at the Alexandrium shopping centre in Rotterdam have a
higher tendency to choose this option than respondents recruited in the down town
shopping centre of Den Bosch.
The effects of the first three attributes (accessibility by car, accessibility by
public transport, and parking tariff) are linear. The first level of each of these attributes
(one obstacle by car, 3 minutes walking to the public transport stop, free parking) is
positive, the last level (five obstacles by car, 9 minutes walking to the public transport
stop, €2,00 parking fee per hour) is negative, and the level in between has a zero utility.
Note that the parking tariff has a strong effect on the preference for an alternative.
Figure 2 shows that, according to males, the overall part-worth utility of the first level
of the first attribute increases and decreases for the third level. For females, the opposite
occurs. This means that males attach more weight to the accessibility by car than
women do. Males appreciate only one obstacle significantly more than women. Also
younger respondents appreciate only one obstacle significantly more than older
respondents. The gender effect on accessibility by public transport violates the overall
linearity of this attribute. Females still attach a positive value to 6 minutes walking, but
they are more negative about 9 minutes walking to the public transport stop. Males,
however, do not really differentiate between 6 and 9 minutes, they attach a small
negative value to both levels. Again, young respondents attach more weight to the
accessibility by public transport than older respondents. For the older respondents, the
difference between 3 minutes walking or 9 minutes walking to the public transport stop
is rather small. Regarding the parking fee, respondents recruited in Rotterdam attach a
negative utility to one euro per hour, while the respondents recruited in Den Bosch still
attach a positive utility to this parking fee. However, the latter group of respondents is
more discontent with the two euro per hour tariff than the respondents recruited in
Rotterdam. A possible explanation may be that respondents from the Rotterdam region
are used to the higher parking tariffs commonly applied in the denser urban areas of the
Dutch Randstad region.
Regarding the length of the main shopping streets in the shopping centre,
respondents do not appreciate a shopping centre that can be traversed in about a quarter
of an hour. Remember that the respondents were asked to choose the shopping centre
they prefer most in the context of a recreational shopping trip. In this context, a larger
shopping centre is preferred. The walking distances of half an hour and three quarters of
an hour are appreciated almost equally. This means that if we assume a rather slow
walking speed of 2 to 3 kilometres per hour, the total length of the main shopping
streets should be 1 to 2 kilometres. There are no significant contrast effects related to
gender, age, or location of recruitment regarding this attribute.
Overall, the respondents do not like a shopping centre with small or medium
sized (and possibly local) shops as the main type of shops. A shopping centre with
specialised and/or exclusive shops appears to be preferred. However, young respondents
prefer the national chains over the specialised and exclusive shops while the older
respondents appreciate the specialised and exclusive shops much more than average. A
similar effect appears for the location of recruitment: the respondents recruited in
Rotterdam prefer the national chains and the specialised/exclusive shops approximately
equally, while the respondents recruited in Den Bosch prefer the special and exclusive
shops more than the other types. The differences between the age groups however are
larger than between the location groups.
The overall preference for anchor stores is department stores, followed by
flagship stores. Mega electronics stores appear to be disliked. However, there are strong
differences between males and females. Females prefer department stores more and
mega electronics stores much less than the average respondent. In contrast, males do not
like the flagship stores and attach a positive utility to mega electronics stores.
Regarding traffic in the shopping centre, only pedestrians are preferred. In
general, allowing all transport modes (pedestrians, bicyclists, and motorized transport
modes) in the shopping centre is not preferred. The option of allowing pedestrians and
bicyclists is positioned in between. Young respondents attach less weight to this
attribute, the difference in utility between the first (pedestrians only) and third level (all
modes) is much smaller than on average. For older respondents, however, this
difference is much bigger, meaning that they attach more weight to this attribute. There
are also significant differences between respondents recruited in Rotterdam and
respondents recruited in Den Bosch. Those recruited in Rotterdam do not really
differentiate between the second (pedestrians and bicyclists) and third level (all modes),
while those recruited in Den Bosch attach an enlarged negative utility to the third level.
A historical design style is clearly preferred over the other design styles. In
general, respondents attach a negative utility to both the modern and Disney style, with
the latter being disliked most. However, there are a few exceptions. Females, young
respondents and (to a lesser extent) respondents recruited in Rotterdam are less distinct
than males, older respondents and respondents recruited in Den Bosch. In some cases
(females and young respondents), the difference between a modern style and a Disney
style disappears.
Another attribute related to design concerns the scale of the shopping streets.
Overall, a mixture of short/narrow and long/wide streets is preferred, with only
short/narrow streets in second position. Only long/wide streets are not preferred. For
young respondents, the utilities for only short/narrow and only long/wide are almost
equal and if respondents get older, the utility of short/narrow streets increases while the
utility for long/wide streets decreases. Older people attach more weight to this attribute
than young people. Something similar holds for the location of recruitment.
Respondents recruited in Rotterdam do not really differentiate between the short/narrow
and long/wide streets, while respondents recruited in Den Bosch dislike the long/wide
streets.
In general, respondents prefer a mixture of passive and active activities in the
shopping centre; only active activities are not preferred. Males care less about this
attribute than women. Respondents recruited in Den Bosch slightly prefer the passive
activities over the mixture of both passive and active activities.
The range between the highest and lowest utility of an attribute can be
considered as a measure of the impact of the attribute on shoppers’ preferences. This
range is largest for the parking fee attribute. This also holds for each subsample of
shoppers. Thus, it can be concluded that, according to the MNL model, parking fee is
the most important attribute from the list of attributes in Table 1. Remember that this
attribute was included as a kind of benchmark attribute. The next most important
attributes are design style and, at some distance, type of anchor stores. Regarding design
style, especially males, older shoppers, and shoppers recruited in Den Bosch like the
historical style and dislike the Disney style. Regarding type of anchors, especially the
female shoppers dislike the mega electronics shops. Accessibility by public transport
has the smallest range in utilities and thus can be considered the least important attribute
taken into consideration in this study. Probably, most people will travel by car to a mega
shopping centre. Type of shopping supply is the next least important attribute. However,
compared to the middle age segment, both the segment of young respondents and the
segment of older respondents attach more weight to this attribute, e.g. more than to the
type of activities attribute.
Table 4: Estimated parameters multinomial logit model (significance between brackets) Var. Attribute level Overall Gender Age Location
Con ‘no preference’ -1.683 (.000) -0.207 (.002) -0.148 (.011)
Accessibility by car
A1 1 obstacle 0.160 (.000) -0.054 (.097) -0.064 (.061)
A2 3 obstacles
A3 5 obstacles -0.160 0.054 0.064
Accessibility by public transport
B1 3 min. walking 0.093 (.001) -0.069 (.040)
B2 6 min. walking 0.057 (.056)
B3 9 min. walking -0.093 -0.057 0.069
Parking tariff
C1 Free 0.405 (.000)
C2 €1,00 per hour 0.076 (.012)
C3 €2,00 per hour -0.405 -0.076
Length of main shopping streets
D1 15 min. walking -0.169 (.000)
D2 30 min. walking 0.087 (.005)
D3 45 min. walking 0.082
Type of shopping supply
E1 National chains -0.127 (.000) -0.052 (.081)
E2 Small/medium -0.098 (.001)
E3 Special/exclusive 0.098 0.127 0.052
Type of anchor stores
F1 Dept stores 0.170 (.000) 0.105 (.003)
F2 Mega electro stores -0.183 (.000) -0.215 (.000)
F3 Flagship stores 0.013 0.110
Type of traffic allowed
G1 Pedestrians 0.151 (.000) 0.073 (.027)
G2 Peds + bicyclists 0.082 (.004)
G3 All modes -0.151 -0.073 -0.082
Design style
H1 Historical 0.317 (.000) -0.106 (.001) 0.122 (.000) 0.050 (.091)
H2 Modern -0.098 (.003)
H3 Disney style -0.219 0.106 -0.122 -0.050
Scale of shopping streets
I1 Short and narrow 0.062 (.100) 0.057 (.088)
I2 Long and wide -0.154 (.000) -0.082 (.039) -0.079 (.028)
I3 Mixed 0.154 0.020 0.022
Type of activities
J1 Passive 0.061 (.042)
J2 Active -0.108 (.000) -0.063 (.040)
J3 Mixed 0.108 0.063 -0.061
Interaction effects B2×C2 -0.067 (.053) B1×E2 -0.060 (.083)
Log-likelihood = -3650.161; Rho2
= 0.230; Rho2
adj = 0.228
A1
A2
A3
B1
B2
B3
C1
C2
C3
D1
D2
D3
E1
E2
E3
F1
F2
F3
G1
G2
G3
H1
H2
H3
I1
I2
I3
J1
J2
J3
Mean
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
Male
(bla
ck)
Fem
ale
(gre
y)
-0.4
-0.2
0.0
0.2
0.4
You
ng (
bla
ck)
Old
(gre
y)
-0.4
-0.2
0.0
0.2
0.4
R’d
am
(b
lack)
D. B
osch (
gre
y)
-0.4
-0.2
0.0
0.2
0.4
Figure 2: Part-worth utilities and contrast effects; MNL model
The interaction effect B2×C2 indicates that the combinations of the second and third
levels of the corresponding attributes generate special effects. The multiplication of the
B2- and C2-variable is different from zero in four cases. In the case of 6 minutes
walking to the public transport stop and the shopping centre (B2) and €2,00 parking
costs per hour (C2), the utility derived from both attributes decrease from 0.0 (main
effects only) to 0.067 (interaction effect). This also occurs in the case of 9 minutes
walking (B3) and €2,00 (C3). In the other cases (the combination of B2 and C3 or the
combination of B3 and C2), the interaction effect increases the utility by 0.067. The
interaction effect B1×E2 generates special effects for the combinations of 3 or 9
minutes walking from/to public transport stop and small/medium or special/exclusive
shopping supply. The effect is equal to a decrease (B1×E2, B3×E3) of 0.06 or an
increase (B1×E3, B3×E2) of 0.06. Note that in the case of 9 minutes walking time (B3),
both interaction effects (with parking costs and with type of shopping supply) have to be
taken into consideration. In Figure 3, the interaction effects are displayed. Although the
two interaction effects are significant at the 10% level, the effects appear to be rather
limited. Therefore, the multinomial logit model was re-estimated without the interaction
effects. The log-likelihood decreased from -3650.161 to -3653.398. According to the
likelihood ratio test, this difference is significant at the 5% level. Therefore, the
interaction effects should not be removed from the model.
E1
E2
E3
E
1
E2
E3
E
1
E2
E3
E
1
E2
E3
E
1
E2
E3
E
1
E2
E3
E1
E2
E3
E1
E2
E3
E
1
E2
E3
C1
C1
C1
C
2
C2
C2
C
3
C3
C3
C
1
C1
C1
C
2
C2
C2
C
3
C3
C3
C1
C1
C1
C2
C2
C2
C
3
C3
C3
B1
B1
B1
B
1
B1
B1
B
1
B1
B1
B
2
B2
B2
B
2
B2
B2
B
2
B2
B2
B3
B3
B3
B3
B3
B3
B
3
B3
B3
Withou
t in
tera
ction e
ffects
(bla
ck)
With inte
raction e
ffects
(gre
y)
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
Figure 3 Attributes B, C, and E, with and without interaction effects; MNL model
The mixed logit model
The mixed logit or random parameter model allows the weights attached to the
attributes to vary across individuals. For each β-parameter, the standard deviation of a
Normally distributed random component was estimated. The results of the estimation
are listed in Table 5. The number of Halton draws (R) was set to 1000, however, 500
draws produced almost the same parameter values. The random parameter (mixed) logit
model outperforms the multinomial logit model, rho2 is equal to 0.27. If applicable,
estimated significant standard deviations are printed below the corresponding mean
parameter values.
Table 5: Estimated parameters mixed logit model (significance between brackets)
Var. Attribute level Overall Gender Age Location
Con ‘no preference’ -2.447 (.000) -0.311 (.028) -0.234 (.059)
(st.dev.) 1.519 (.000)
Accessibility by car
A1 1 obstacle 0.153 (.000) -0.087 (.038)
A2 3 obstacles
A3 5 obstacles -0.153 0.087
Table 5: Estimated parameters mixed logit model (continued)
Var. Attribute level Overall Gender Age Location
Accessibility by public transport
B1 3 min. walking 0.109 (.002) -0.085 (.039)
B2 6 min. walking 0.063 (.083)
B3 9 min. walking -0.109 -0.063 0.085
Parking tariff
C1 Free 0.505 (.000)
(st.dev.) 0.404 (.000)
C2 €1,00 per hour 0.097 (.011)
C3 €2,00 per hour -0.505 -0.097
Length of main shopping streets
D1 15 min. walking -0.227 (.000)
(st.dev.) 0.531 (.000)
D2 30 min. walking 0.104 (.007)
D3 45 min. walking 0.123
Type of shopping supply
E1 National chains 0.000 -0.156 (.004) -0.082 (.085)
(st.dev.) 0.515 (.000)
E2 Small/medium -0.113 (.002)
E3 Special/exclusive 0.113 0.156 0.082
Type of anchor stores
F1 Dept stores 0.206 (.000) 0.139 (.001)
F2 Mega electro stores -0.236 (.000) -0.284 (.000)
(st.dev.) 0.444 (.000)
F3 Flagship stores 0.030 0.145
Type of traffic allowed
G1 Pedestrians 0.202 (.000) 0.095 (.026)
(st.dev.) 0.184 (.038)
G2 Peds + bicyclists 0.099 (.005)
G3 All modes -0.202 -0.095 -0.099
Design style
H1 Historical 0.404 (.000) -0.119 (.023) 0.204 (.000) 0.086 (.078)
(st.dev.) 0.514 (.000)
H2 Modern -0.134 (.003)
(st.dev.) 0.361 (.000)
H3 Disney style -0.270 0.119 -0.204 -0.086
Scale of shopping streets
I1 Short and narrow 0.103 (.026) 0.074 (.072)
I2 Long and wide -0.194 (.000) -0.113 (.018) -0.091 (.037)
I3 Mixed 0.194 0.010 0.017
Type of activities
J1 Passive 0.000 0.109 (.012)
(st.dev.) 0.357 (.000)
J2 Active -0.126 (.002) -0.091 (.026) -0.084 (.056)
(st.dev.) 0.176 (.070)
J3 Mixed 0.126 0.091 0.084 -0.109
Interaction effects A1×C1 -0.088 (.072) A1×C2 0.089 (.072) B2×C2 -0.073 (.088)
Log-likelihood = -3455.456; Rho2 = 0.271; Rho
2adj
=0.269
The results are also shown in Figure 4. For three attributes (accessibility by car
and public transport and the scale of the shopping streets) standard deviations were not
significantly different from zero. This means that there is not much random variation
across the respondents regarding these items. For the remaining attributes at least one
part-worth utility is represented by a random parameter with a standard deviation
significantly different from zero. Also the constant for the ‘no preference’ option has a
random component. For two attributes levels (shopping supply by national chains (E1)
and passive activities in the shopping centre (J1)), the standard deviation is significantly
different from zero, while the corresponding mean value is not. This suggests that
preferences regarding these attribute levels fluctuate around zero, cancelling out to
neutral mean values.
A1
A2
A3
B1
B2
B3
C1
C2
C3
D1
D2
D3
E1
E2
E3
F1
F2
F3
G1
G2
G3
H1
H2
H3
I1
I2
I3
J1
J2
J3
Mean
(bla
ck)
Sta
ndard
devia
tion (
gre
y)
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
Male
(bla
ck)
Fem
ale
(gre
y)
-0.4
-0.2
0.0
0.2
0.4
You
ng (
bla
ck)
Old
(gre
y)
-0.4
-0.2
0.0
0.2
0.4
R’d
am
(b
lack)
D. B
osch (
gre
y)
-0.4
-0.2
0.0
0.2
0.4
Figure 4: Part-worth utilities and contrast effects; ML model
In the upper part of Figure 4, it can be seen that the standard deviations are
rather large compared to the mean values. Although standard deviations are non-
negative by definition, the standard deviations in this figure were given the same
direction as the corresponding mean values to ease interpretation. Note that if both the
first and the second level of an attribute have significant standard deviations (Design
style and Type of activities), the standard deviation of the third level is equal to the root
of the sum of the squared standard deviations for the first and second level because the
mixed logit model specified in the study assumes uncorrelated random parameters.
Compared with the estimation results for the multinomial logit model, the
estimated parameters in general have (as expected) a higher (positive or negative) value.
Overall, however, the pattern of main effects and contrast effects is similar, apart from a
few exceptions. The gender contrast effect for accessibility by car is no longer
significant. On the other hand, the age effect on active activities has become significant
according to the random parameter model. Furthermore the B1×E2 interaction effect in
MNL model has been replaced by the A1×C1 and A1×C2 interaction effects, meaning
that all interactions are related to the accessibility variables. The interaction effects are
illustrated in Figure 5. If the interaction effects are omitted, the likelihood ratio statistic
is significant at the p=0.068 level. Thus, if one sticks to the 5% significance level, the
interaction effect may be deleted from the mixed logit model.
C1
C2
C3
C
1
C2
C3
C
1
C2
C3
C
1
C2
C3
C
1
C2
C3
C
1
C2
C3
C1
C2
C3
C1
C2
C3
C
1
C2
C3
B1
B1
B1
B
2
B2
B2
B
3
B3
B3
B
1
B1
B1
B
2
B2
B2
B
3
B3
B3
B1
B1
B1
B2
B2
B2
B
3
B3
B3
A1
A1
A1
A
1
A1
A1
A
1
A1
A1
A
2
A2
A2
A
2
A2
A2
A
2
A2
A2
A3
A3
A3
A3
A3
A3
A
3
A3
A3
Withou
t in
tera
ction e
ffects
(bla
ck)
With inte
raction e
ffects
(gre
y)
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
Figure 5: Attributes A, B, and C, with and without interaction effects; ML model
7. Conclusions and recommendations
Since the Dutch policy regarding mega shopping centre has become more liberal, some
retail developers have prepared plans to develop such a centre. However, as mega
shopping centres do not exist in the Netherlands yet, it is hard to assess customers’
preferences regarding these very large shopping facilities. Therefore, the purpose of this
paper was to investigate customers’ preferences regarding shopping centre attributes
that are considered relevant in the first stages of the design process of a mega shopping
centre. A stated choice approach was used to measure customers’ preferences regarding
accessibility, scale and length of shopping streets and traffic allowed in the streets, the
architectural design style, type of anchor stores, shopping supply, and type of activities
in the shopping centre. In addition, parking tariff was included as a benchmark. Contrast
effects for gender and age categories were included, as well as for the locations where
respondents were recruited. Furthermore, interaction effects between attributes were
also considered. Two types of discrete choice models were estimated, the standard
multinomial logit model and the random parameter model, a mixed logit model
allowing for taste heterogeneity. Respondents were asked to choose the most preferred
shopping centre from choice sets containing two shopping centres (and a ‘no
preference’ option) in the context of a recreational shopping trip.
According to both models, the parking tariff is the most important attribute
followed by the design attribute. Next, although at some distance, type of anchor stores,
accessibility by car, scale and total length of the shopping streets, type of traffic allowed
in the shopping centre, are the most important attributes. Finally, type of activities in the
shopping centre, type of shopping supply and accessibility by public transport appear to
be the least important attributes.
There are however some noteworthy exceptions regarding the segments of
respondents considered. For males, the anchor stores seem to be considerably less
important than for females, while males put much more weight on the design attribute.
Young respondents (15-24 years of age) attach relatively less weight to shopping supply
and architectural design than the older respondents. The differences between
respondents recruited in the Rotterdam Alexandrium shopping centre and the
respondents recruited in the down town shopping centre of Den Bosch are less distinct.
However, there are still significant differences between the two groups. It is unclear
whether these differences originate from the differences in residential areas (the
Rotterdam region versus the Den Bosch region) or from the difference in type of
shopping centre used to recruit respondents (a peripheral indoor shopping centre versus
an outdoor down town shopping centre).
Although structural differences between subgroups of customers have been
taken into consideration by means of contrast effects, there is still considerable random
variation in the main effect parameters. The random parameter model estimated
significant variation in the utilities regarding all attributes, except accessibility by car
and public transport and the scale of the streets in the shopping area. This means that
some customers prefer a particular attribute level much more and others much less than
average. The random parameter model also shows that in the case of some attribute
levels (shopping supply by national chains and passive activities) significant, but
opposing individual preferences exist, resulting in insignificant mean utility values.
Taking into consideration this heterogeneity improves the performance of the model
considerably. Rho2
adjusted for the multinomial logit model is equal to 0.228, and for the
random parameter equal to 0.269.
The number of mega shopping centres that can be realized in the Netherlands is limited.
If a developer is planning to build one, thorough investigation regarding consumer
preferences and shopping behaviour is important. This study provides some insights in
consumer preferences regarding a mega shopping centre in the Netherlands. According
to the main findings, it should be advised to implement a historical architectural design,
contract department stores as the main anchors, find a location near a highway, create
both long/wide and short/narrow shopping streets only allowing pedestrians and
providing one to two kilometres of walking distance, and offering a mixture of both
active and passive entertainment activities. Special and exclusive shops, as well as
shops from national chains should be provided. Finally, a good accessibility by public
transport may be advisable, although this is the least important attribute. The preference
for such a mega shopping centre can be considerably affected by manipulating (some
of) the attributes investigated in this study. However, it should be noted these attributes
are less important than the parking fee. A relatively high parking tariff may
considerably reduce the utility of a well designed shopping centre. Furthermore, it
should be stressed that preferences may vary extensively across consumers.
The findings from this study can also be used to determine the best selection of
attributes levels for a specific segment of shoppers. If a developer wants to develop a
shopping centre that is especially appreciated by young males or females, less
special/exclusive shops and more national chains should be supplied. Also the young
shoppers appreciate the historical architectural design style considerably less than the
older customers. For the young females, the difference between a historical design style
and a Disney design style is rather small. According to the mixed logit model, young
males slightly prefer active activities in the shopping centre while the other shoppers
prefer a mixture of both passive and active activities. Although a short walking distance
between public transport stop and shopping centre is hardly relevant for older shopper,
it may help attracting young shoppers.
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