Assessing preferences for a mega shopping centre in theNetherlands: A conjoint measurement approachBorgers, A.W.J.; Vosters, C.

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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

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-0.4

-0.2

0.0

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0.6

0.8

Male

(bla

ck)

Fem

ale

(gre

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-0.4

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0.0

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ng (

bla

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Old

(gre

y)

-0.4

-0.2

0.0

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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

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With inte

raction e

ffects

(gre

y)

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

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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

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0.0

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0.6

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Male

(bla

ck)

Fem

ale

(gre

y)

-0.4

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You

ng (

bla

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Old

(gre

y)

-0.4

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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

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C

1

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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

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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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