a microsimulation model for e-services in cultural heritage tourism

Tourism Economics 2010 16 (2) 361ndash384

A microsimulation model for e-services incultural heritage tourism

EVELINE VAN LEEUWEN AND PETER NIJKAMP

Department of Spatial Economics VU University De Boelelaan 1105 1081 HVAmsterdam The Netherlands E-mail pnijkampfewebvunl

Tourism is on a rising curve from both policy and researchperspectives This paper presents new research advances on individualtourist behaviour and motives with particular reference to the roleof e-services in cultural heritage tourism An innovative tool adoptedhere is (spatial) microsimulation modelling (MSM) This method isused to offer a micro-based picture of the motives and behaviour ofthe total tourist and resident population concerned including theirpreferences and personal characteristics MSM is a novel but hithertolittle used scientific tool in the behavioural analysis of culturalheritage tourism mainly because of the lack of detailed andconsistent (spatial) information on tourist flows and their character-istics at an urban scale MSM is a powerful tool one of its advantagesis its ability to link existing databases and information so as toprovide new behavioural insights at the meso-level of research Totrace the motives preferences and spatial behaviour of touristsempirically advanced micro-based research techniques are needed Inthis empirical application to tourist flows in the city of Amsterdamthe authors use factor analysis and ordered logit models as thefoundation stones for the design of MSM The empirical model isthen applied to the use of e-services by tourists in Amsterdam whowish to enjoy the cultural heritage of the city

Keywords microsimulation cultural heritage e-services discrete choiceanalysis

The trend towards a leisure society where rises in productivity and welfare haveshaped the conditions for a significant increase in expenditures and in flexibletime for discretionary consumption has been pervasive in the Western worldin the past few decades Recreation culture and tourism are prominent examples ofnew lifestyles in our modern age The growing flows of tourists to remote andperipheral areas indicate that both places of origin and of destination are

This paper is a result of the EU FP7 project ISAAC The authors wish to thank the various ISAACparticipants for their help in collecting the information used in this paper The authors are alsograteful to two anonymous reviewers for their constructive comments

TOURISM ECONOMICS362

affected by this mega-trend (see Fusco Girard and Nijkamp 2009) This drasticchange in spatial behaviour is caused not only by economic prosperity and ourwelfare and leisure society but also by the use of the modern information andcommunication (ICT) sector which offers (i) more direct information oninteresting places to visit (ii) efficient technological tools to organize and bookleisure trips and (iii) techniques for communication with friends and relativesthrough which physical mobility will be enhanced (see also March 2009)

A main challenge of modern regional policy is to market ndash through the useof ICT ndash the attractiveness of a certain region in order to generate growth intourist visits and expenditures An important contribution of cultural heritagelies in the support of the destination image generation This means that forexample cultural heritage is not only a source of historical information affectingthe image of the attraction itself but also the broader destination imageConsequently information provided to (potential) visitors has an impact on thedestination image Thus ICT has become one of the competitive tools inregional tourist policy (see Goeldner and Ritchie 2006)

Amsterdam for instance has a dedicated policy that aims to strengthen itscultural profile The city is known for its interesting links between the (urban)past and the future which can be experienced in a lively cosmopolitanatmosphere Clearly cities like Barcelona Rome Lisbon and Prague areimportant competitors Each destination offers a variety of products and servicesto attract visitors and each tourist has the opportunity to choose from a setof destinations (Crompton 1992) Therefore it is very important to know whatthe unique selling point of a particular city is and how tourists can be(virtually) attracted It is equally important to know how important culturalheritage is for the tourism sector and for the city as a whole at the individual(micro) level This calls for due insight into the motives and preferences oftourists for different elements of cultural heritage as well as for the sites theyactually plan to visit

The present paper highlights the importance of ICT facilities for enhancingthe tourist profile of a given city in our case Amsterdam We will address inparticular the critical importance of cultural heritage as a promising spearheadfor the tourist attractiveness of host cities In our analysis we will focus largelyon the individual (micro) preferences and attitudes of visitors To this end wewill employ as a technical tool for decision support ndash in addition to orderedlogit models and factor analysis ndash microsimulation models (MSM) in order toinvestigate the driving forces which encourage visitors and residents to usee-services in the tourist sector with regard to cultural heritage In recent yearssimulation models and especially microanalytic simulation models have beenapplied increasingly in quantitative analyses of economic and social policyproblems In general MSM enables the researcher to investigate individualbehaviour for planning and policy analyses (Merz 1991) In this article MSMtechniques are applied to obtain a detailed picture of the tourist populationwhich is necessary to define future target groups of tourists interested incultural heritage in Amsterdam The paper is organized as follows In the nextsection we describe a few essential elements of cultural heritage in the contextof tourism Then in the following section we highlight the use of ICT in thetourist sector Subsequent sections deal respectively with the use of e-servicesin enhancing the benefits to visitors of cultural heritage amenities the research

363E-services in cultural heritage tourism

framework of the paper an analysis of tourist preferences with the applicationof a factor analysis in combination with the use of an ordered logit model andour MSM experiments which are presented and complemented with asensitivity analysis The penultimate section offers a target group analysis andfinally we summarize our conclusions

Cultural heritage as a tourist asset

Tourism in our modern world appears in many different guises but a significantpart of tourism is due to the attractiveness of cultural capital (for examplecultural heritage) in destination cities This has become a major economic assetin modern tourism (Ark and Richards 2006 Correia et al 2007 Barros et al2008)

An important contribution to the destination image generation of cities doesindeed originate from the attractiveness of cultural heritage This means thatcultural heritage is not only a source of historical information or place identityaffecting the image of the attraction itself but also influences the broaderdestination image of the city

A significant part of the cultural history of our world is reflected in man-made assets that still regain from the past and which have a unique social valueoften referred to as lsquocultural heritagersquo (Fusco Girard et al 2008) Culturalheritage is generally defined as the legacy of physical artefacts and intangibleattributes of a group or society that are inherited from past generationsmaintained in the present and bestowed for the benefit of future generationsOften though what is considered cultural heritage by one generation may berejected by the next generation only to be revived by a succeeding generationCunnell and Prentice (2000) make a distinction between tangible andintangible features of cultural heritage Physical or lsquotangiblersquo cultural heritageincludes buildings and historic places monuments artefacts etc that areconsidered worthy of preservation for the future These include objectssignificant to the archaeology architecture science or technology of a specificculture lsquoNatural heritagersquo is also an important part of a culture encompassingthe countryside and the natural environment including flora and fauna Thesekinds of heritage sites often serve as an important factor in a countryrsquos touristindustry attracting many visitors from abroad as well as locally The lsquointangiblecultural heritagersquo includes social values and traditions customs and practicesaesthetic and spiritual beliefs artistic expression language and other aspects ofhuman activity Naturally intangible cultural heritage is more difficult topreserve than physical objects

The role of ICT

Tourism is no longer a technology-poor or low-tech activity Nowadays tourismis highly dependent on modern technological advances (see for example Giaoutziand Nijkamp 2006 Cooper et al 2008) It plays a critical role in localeconomic development in many countries and is an important constituent ofthe emerging global network society which is in turn stimulated by the


modern ICT sector The Internet plays an indispensable role in internationaland national tourism and will most likely become the critical tool for tourismin the future The introduction of ICT in recent decades has created newopportunities for the tourist attractiveness of remote and peripheral areas whichthemselves also now have a virtual access to major centres of tourist origin Thishas also led to service competition between tourist facilities in areas ofdestination where increasingly firms are involved in global competition (evenwhen they belong to the SME sector)

The introduction of the various ICT applications related to the tourism sectorfocuses inter alia on

bull The promotion of tourist destinations through advertising the touristproduct in the context of multimedia applications

bull Interactive communication between interested parties (tourist destinationand the tourist)

bull Online transactions between the tourist destination and the tourist such asbooking payment etc

bull Teleworking applications which provide the opportunity to combine workwith vacations and thus eventually lengthen the duration of leisure time

bull Telemedicine applications which encourage elderly people to enjoy them-selves away from home

bull Transport telematics which aim at the more efficient management of touristflows etc

It is obvious that the ICT sector has changed the tourist market drasticallyMany potential visitors already derive much pleasure from the fact that almostall tourist destinations can be seen on the PC screen They also have becomemore critical of the type of facilities offered while at the same time a largeshare of tourism bookings (hotels flights etc) is done over the Internet Andmore recently we see a new ICT facility where visitors can receive on-the-spotreal-time information on forthcoming events The tourist has become anemancipated visitor through the use of ICT Here the related services providedby ICT will be called lsquoe-servicesrsquo

E-services and cultural heritage

The structure of the tourist industry is rather complex and encapsulates inter-twined links between travel agencies tour operators airlines railwaycompanies firms hotel and restaurant chains tourist bureaus and the popularmedia Local governments that want to develop a favourable tourism profile fortheir city have to generate a variety of public resources to pay for the necessaryinvestments in tourism (see for example Barros and Rodriquez 2008) Sincethe tourist industry has many specialized market niches it is clear that tourismmarketing for which ICT is an important tool has become a critical successfactor (Giaoutzi and Nijkamp 2006) E-services can have different interpreta-tions in different subject areas (business ICT etc) With respect to culturalheritage e-service is defined as lsquothe provision of services based on an interactiveinformation exchange over an electronic networkrsquo (Baida et al 2004) Accordingto Riganti et al (2007) a shift from traditional ways of consuming culturalheritage to modern ways is likely to happen This new way of experiencing


cultural resources is often linked to ICT Until now ICT has been appliedmostly in the digitalization of cultural goods but there is also a move toe-heritage digital environments are created which make cultural heritage moreaccessible (Riganti 2007) Examples are virtual tours and e-forums A majorquestion now is whether the provision and use of e-services will lead to a risein tourist attractiveness and visits

An important advantage of e-services is first of all that they can enhanceand widen the access to cultural heritage Via the Internet people can easilyfind information with respect to both the cultural heritage of places they wantto visit and cultural objects they do not yet know (Scavarda et al 2001) Inother words e-services provide the most effective way to communicate with thetarget market (Riganti et al 2007) Because suppliers of cultural heritage cantrace more information about the customer by checking his or her onlinehistory demand can also increase because of the increased potential to providepersonalized information (Scavarda et al 2001) Secondly e-services make thecomparison between different cultural sites more easy for the consumer (Rigantiet al 2007) Information asymmetry on the side of the consumer is reducedand the consumer can make better decisions about which cultural heritage heor she wants to visit A third advantage is the better availability of informationabout cultural heritage It appears that local residents in particular are moreaware of the importance of cultural heritage and therefore want to preserve itmore (Azjen and Fishbein 1980) However there are also some potentialdisadvantages to providing e-services Firstly since it is likely that there is morepersonalized and individually tailored information provided there can be aconflict between the needs and access conditions of different consumer groupsSecondly because of the improved accessibility of information there will bemore competition between suppliers of cultural heritage This could even implythat cultural heritage sites that do not use e-services may be neglected orconsumers who do not have access to such e-services will be socially excluded(Rayman-Bacchus and Molina 2001)

Research framework

In our empirical research framework we are interested in analysing thepreferences of visitors for various types of cultural heritage Using microsurveydata we will employ an ordered logit model to identify the drivers of thesepreferences in the context of the provision and use of e-services Given themultidimensional nature of the data factor analysis will be used to structurethe data and to arrive at a systematic typology of visitors This approach formsthe basis for the final and critical step in our analysis that is the design ofan MSM Figure 1 shows the structure of data handling in our approach

Important components for developing a microsimulation model are theavailability of a micropopulation with a large number of relevant characteristicsas well as the availability of statistics about the subject and location underresearch In our applied research in Amsterdam there is micropopulationinformation available from local choice experiments The detail in dataavailability from local sources is however also very important in particular tobe able to choose the best constraint variables (see later) This is only possible


Figure 1 Data analysis framework

when there is statistical information available at the municipality level (or atan even lower scale) concerning all relevant variables Fortunately for our casestudy of Amsterdam sufficient information was available By performing amicrosimulation we were able to develop a detailed picture of the total touristpopulation of Amsterdam with their relevant characteristics allowing us topinpoint which kinds of tourists were present already and which ones could beconsidered as future target groups

E-services have a broad meaning Apart from having general e-servicesAmsterdam is moving towards delivering e-services on mobile devices that canprovide information during the visit and towards enabling visitors toexperience the cityrsquos attractions both before and after the visit The general ideais to provide an e-service in which a visitor can take a virtual walk throughAmsterdam and learn more about the architecture monuments and history ofthe city The tour is made with state-of-the-art content and techniques suchas 360-degree photographs and Google-maps The tour fits with the need ofthe city of Amsterdam to enhance its information on cultural heritage forvisitors and citizens The tour aims to reach a wide target group people whohave some interest in architectural history and want to be inspired to visitAmsterdam in a virtual way as well as people with more than a general interestin architectural history who use the application as an extra possibility forexploring the history of the city The virtual tour is combined with an inter-active map Interactive maps booking services journey planners andpersonalized information can all be found on the Amsterdam Website Further-more there is a Web shop available on this site

Tourist preferences

Which kinds of tourists areinterested in cultural heritage and e-services

Promotiontool

Data

Database of statistical informationon tourist characteristics and totalsof tourists in Amsterdam

Micro-informationabout all tourists

visiting Amsterdam

Microsimulation

Strategic goal

Which kinds of tourists are already present which kinds are missing and should be attracted


Tourist preferences

There have been several studies on the preferences of travellers these studiesoften used conjoint analysis (a stated preference method) which has beenapplied successfully in tourism as a technique to describe and forecast touristchoice behaviour (Suh and McAvoy 2005 Riganti and Nijkamp 2008)Important factors that influence peoplersquos choice of destination are age incomegender personality education cost distance nationality risk and motivationetc (Kozak 2002 Hsu et al 2009) In addition information sources andprevious experiences also affect the destination choice of visitors

The data used for this analysis were collected by user surveys carried out inthe city of Amsterdam between August and November 2007 These surveysinvolved extensive field data collection by interview teams who were hired andprofessionally trained The questionnaires used both online and face-to-faceinterview modes (stand-alone computer versions or paper versions) In totalaround 650 tourists each filled in a questionnaire

In the survey respondents were asked to value several cultural heritagecharacteristics (among others the presence of museums architecture andcultural festivities in Amsterdam) and e-services (such as online booking virtualtours journey planner etc) Since these valuations of cultural heritagecharacteristics and e-services are captured into discrete (in contrast tocontinuous) dependent variables ndash ranging from lsquonot importantrsquo to lsquoveryimportantrsquo in five categories ndash standard regression tools are not applicableFortunately appropriate discrete choice models are available to study how theindividual characteristics of respondents influence the valuation of culturalheritage In this section we use an ordered logit model an econometric toolfrequently used in applied behavioural research (Hensher et al 2005) Theordered probability model is an extension of the binary probabilitymodel whereby the dependent (qualitative) variable has a limited numberof ordered outcomes The requirement of ordering is necessary and this ispresent in the cultural heritage survey the level of importance indicated bythe respondents is a clear example of a discrete ordered (ranked) dependentvariable (see Appendix 1 for a more formal description of the ordered logitmodel)

Because the choice models are applied to estimate the preferences of touristsfor eight different components of cultural heritage and because a relatively largenumber of characteristics are used as independent variables it is difficult todraw clear-cut conclusions about for example future target groups Thereforewe will use in addition a factor analysis approach Factor analysis is a multivariatestatistical approach that can be used to analyse interrelationships between alarge number of variables and to explain these variables in terms of theircommon underlying dimensions The underlying assumption is that there existsa number of unobserved latent lsquofactorsrsquo that account for the correlations amongobserved variables The main purpose of factor analytic techniques is to reducethe number of mutually correlated variables andor to detect underlyingpatterns or a structure in the relationships between variables In our casehowever the aim is not particularly to condense the number of variables butwith a limited number of factors it is easier to identify significant differencesbetween groups of tourists Here we use specifically a principal component


analysis with a varimax rotation The factors extracted by this method are bydefinition uncorrelated and can be arranged in order of decreasing variance Tointerpret the factors easily we focus on components with a loading higher than04 although variables with a loading equal to or greater than 035 may stillbe meaningful in order to decrease the probability of misclassification (Hair etal 1995)

Preferences for cultural heritage

In this section we investigate distinct classes of tourists and their preferencesfor different kinds of cultural heritage The ordered logit models provide insightinto which combinations of the characteristics of the tourists affect theirpreferences We analyse which personal characteristics correlate with eightdifferent types of cultural heritage (see Table 1) Besides the interpretation andthe significance levels of the estimated coefficients the overall performance ofthe model can be evaluated by looking at the McFadden pseudo-R2 Largervalues of the McFadden pseudo-R2 indicate a better-specified model

The tourist characteristics that we included as explanatory variables in ourmodels were dummies for the use of e-services age income level educationlevel gender and for being employed or unemployed Furthermore country-of-origin dummies were added to correct for country-specific characteristics

A first important variable for tourists visiting Amsterdam is the use ofe-services for planning leisure activities Table 1 shows that tourists who do usee-services often have a higher preference for all kinds of cultural heritage Andwomen tend to value cultural heritage higher than men

The age variable has a significant and positive influence on the valuation oftangible cultural heritage (such as architecture monuments and the urbanlandscape) and a negative influence on the intangible cultural heritage valuationof cultural events traditions customs and knowledge Education follows thesame pattern lower-educated tourists have a higher preference for intangiblecultural heritage In addition the country of residence particularly affects thepreference for intangible kinds of cultural heritage almost all country (ofresidence) dummies are significant for the cultural events traditions customsand knowledge models For the last three models being a non-Dutch visitorincreases the chance that he or she values these three cultural amenities moreFurthermore Dutch tourists more often prefer cultural events

As noted above a factor analysis is carried out next as a second stepperformed in order to extract groups of tourists with more or less the samepreferences for related cultural heritage elements The factor analysis that dealswith the preferences and plans of tourists visiting Amsterdam extracts fivefactors which explain 58 of the variance (see Appendix 2 for a presentationof the factor loadings)1

(1) Intangible cultural heritage enthusiasts people who like all kinds of culturalheritage but in particular the intangible ones such as traditions customsand knowledge They also plan to visit one or more museums

(2) Nightlife enjoyers tourists who are not interested in cultural heritageespecially not in architecture and museums and so on instead they cometo enjoy the cityrsquos nightlife and atmosphere


Tab

le 1

C

oeff

icie

nts

of

the

ord

ered

logi

t m

odel

s es

tim

atin

g th

e p

refe

ren

ces

of t

ouri

sts

in A

mst

erd

am f

or d

iffe

ren

t k

ind

s of

cu

ltu

ral h

erit

age

Arc

hit

ectu

reM

onu

men

tsM

use

um

sU

rban

lan

dsc

ape

Cu

ltu

ral e

ven

tsT

rad

itio

ns

Cu

stom

sK

now

led

ge

E-s

ervi

ce0

205

031

9

058

1

033

4

026

20

257

018

00

014

(01

60)

(01

52)

(01

54)

(01

55)

(01

51)

(01

60)

(01

54)

(01

57)

Age

019

30

392

0

338

0

213

ndash0

351

ndash03

62

ndash0

197

0

062

(01

13)

(01

08)

(01

06)

(01

01)

(01

07)

(01

19)

(01

20)

(01

15)

Edu

cati

on0

229

0

090

010

30

187

ndash0

108

ndash0

098

ndash0

121

ndash0

132

(0

068

)(0

065

)(0

067

)(0

063

)(0

059

)(0

059

)0

056

(00

59)

Gen

der

025

50

269

0

310

0

528

0

109

048

1

038

0

024

8(0

147

)(0

141

)(0

146

)(0

144

)(0

140

)(0

143

)0

141

(01

43)

USA

023

30

254

ndash00

76ndash0

039

ndash07

47

1

790

1

503

1

199

(0

221

)(0

217

)(0

217

)(0

214

)(0

224

)(0

252

)0

240

(02

45)

UK

021

0ndash0

004

ndash02

71ndash0

260

ndash07

85

1

915

1

518

1

588

(0

228

)(0

247

)(0

253

)(0

234

)(0

254

)(0

244

)0

255

(02

71)

Ger

man

y0

554

ndash0

494

ndash0

030

ndash03

08ndash1

338

056

8

022

70

270

(02

52)

(02

55)

(02

49)

(02

51)

(02

49)

(02

22)

022

8(0

227

)R

est

of E

urop

e0

492

ndash0

298

ndash02

18ndash0

160

ndash07

96

1

237

1

189

1

032

(0

219

)(0

223

)(0

222

)(0

225

)(0

224

)(0

234

)(0

225

)(0

212

)R

est

of t

he w

orld

030

5ndash0

075

039

0ndash0

224

ndash07

75

1

597

1

301

0

951

(0

323

)(0

304

)(0

291

)(0

307

)(0

286

)(0

323

)(0

283

)(0

294

)

Obs

erva

tion

s37

136

436

137

236

336

735

335

7M

cFad

den

pseu

do-R

20

032

001

50

012

001

00

029

001

80

009

001

3

Not

eSi

gnif

ican

t at

the

00

1

0

05 a

nd

01

0 le

vels


(3) Tangible cultural heritage fans tourists who are not interested in intangiblecultural heritage but who plan to visit architecture museums and the urbanlandscape

(4) Cultural events visitors tourists who are specifically interested in culturalevents and who also plan to visit such an event

(5) Shopping addicts tourists who come to shop in Amsterdam

With the help of factor loadings we can see which personal characteristics arerelated to these five groups of tourists It appears that the cultural heritageenthusiast is often a younger female visiting Amsterdam on a holiday trip andwho is familiar with e-services In addition it is less likely that he or she isDutch or from Germany and more likely that he or she is from the UK therest of Europe or the rest of the world

The nightlife enjoyers are generally young males with a lower education andlower income They are in Amsterdam for holiday reasons Furthermore theyare generally not from the Netherlands but from neighbouring countries orfrom the USA The tourists who are particularly interested in tangible culturalheritage are often the older tourists with a higher education They do not comefor business reasons but for pleasure They often come from Germany or fromthe rest of Europe

The cultural events fans are often younger male tourists who are not inAmsterdam for holiday reasons (but probably to visit friends) who do usee-services and who generally come from the Netherlands Finally the shoppersthey can be typified as female tourists with a higher income and they usuallycome from the UK

Preferences for e-services

Apart from the preferences of tourists for cultural heritage we are also interested inthe appreciation of different types of e-services to get a better marketing insightinto the way the different groups of tourists might be reached Not verysurprisingly it appears that tourists who already use e-services have in generala higher appreciation for different types of e-services (see Table 2) Especiallythe appreciation of an online booking service increases with familiarity withe-services Furthermore we observe that education has a mixed effect Ingeneral when the coefficient of this variable is significant education has anegative impact on the appreciation of different e-services This variable is notsignificant for more or less lsquotraditionalrsquo e-services E-forums virtual tourspersonalized information and interactive games are more lsquomodernrsquo and lsquotrendyrsquoforms of e-services and are appreciated more by less-educated tourists

Gender only has a statistical impact on the appreciation of virtual tours andinteractive games It appears that men value these e-services more highly thanwomen do In addition younger tourists also favour these kinds of e-servicesMore generally younger people tend to find e-services more important thanolder people Possibly older tourists are less familiar with e-services such ase-forums and interactive games

Concerning the country of residence it appears that tourists from the USAor Canada value some e-services (respectively interactive maps personalizedinformation and booking services) more highly than tourists from the


Tab

le 2

C

oeff

icie

nts

of

the

ord

ered

logi

t m

odel

s es

tim

atin

g th

e p

refe

ren

ces

of t

ouri

sts

in A

mst

erd

am f

or d

iffe

ren

t ty

pes

of

e-se

rvic

es

Inte

ract

ive

map

Per

son

aliz

ed in

form

atio

nB

ook

ing

serv

ice

Jou

rney

pla

nn

erE

-for

um

Vir

tual

tou

rsIn

tera

ctiv

e ga

mes

E-s

ervi

ce0

497

0

343

1

156

0

373

0

362

0

193

009

1(0

156

)(0

153

)(0

164

)(0

157

)(0

147

)(0

152

)(0

168

)E

duca

tion

007

4ndash0

205

005

50

026

ndash02

10

)ndash0

124

ndash0

339

(00

62)

(00

65)

(00

65)

(00

59)

(00

63)

(00

65)

(00

70)

Gen

der

008

2ndash0

070

ndash00

650

158

ndash00

94ndash0

294

ndash0

423

(01

47)

(01

45)

(01

48)

(01

44)

(01

42)

(01

43)

(01

61)

Age

ndash01

89

ndash01

82ndash0

248

ndash0

103

ndash04

70

0

011

ndash04

34

(0

114

)(0

117

)(0

116

)(0

107

)(0

107

)(0

114

)(0

123

)E

mpl

oyed

031

7

ndash00

260

098

016

70

177

018

6ndash0

054

(01

60)

(01

63)

(01

57)

(01

52)

(01

52)

(01

52)

(01

78)

USA

081

4

070

6

075

5

ndash02

940

261

034

1ndash0

171

(02

17)

(02

35)

(02

41)

(02

14)

(02

15)

(02

28)

(02

52)

UK

059

4

060

1

068

0

036

80

254

045

80

675

(0

245

)(0

262

)(0

234

)(0

244

)(0

251

)(0

235

)(0

259

)G

erm

any

032

80

247

ndash00

47ndash0

808

ndash00

700

052

013

1(0

233

)(0

234

)(0

243

)(0

232

)(0

232

)(0

231

)(0

273

)R

est

of E

urop

e0

894

0

792

0

593

ndash0

500

0

460

0

396

028

6(0

217

)(0

225

)(0

221

)(0

219

)(0

230

)(0

214

)(0

248

)R

est

of t

he w

orld

082

3

102

1

131

5

ndash04

710

735

0

283

035

1(0

331

)(0

278

)(0

291

)(0

303

)(0

260

)(0

272

)(0

315

)

Obs

erva

tion

s65

065

165

165

165

165

165

0M

cFad

den

pseu

do-R

20

026

002

20

055

001

80

027

001

00

047

Not

eSi

gnif

ican

t at

the

00

1

0

05 a

nd

01

0 le

vels


Netherlands It is possible that these e-services are (already) more common inthe USA or Canada

The factor analysis dealing with the preferences of tourists in Amsterdam fordifferent kinds of e-services results in two factors which together explain 57of the variance (see Appendix 3 for the exact loadings)

(1) E-services enthusiasts people who appreciate e-services in general(2) Fans of interactive games tourists who prefer interactive games but who have

no preference for online booking

The personal characteristics of the tourists who can be labelled as enthusiastsof e-services are younger tourists males and persons with a lower educationThey use e-services and are also interested in many kinds of cultural heritageThere is a positive relation with tourists who visit Amsterdam for pleasure fromthe UK or the rest of Europe

The tourists who are interested in online games and not in an online bookingsystem are also often younger tourists male and with a lower education andincome A strange result is that they do not use e-services when planning theirleisure time However that would explain why they are not interested in anonline booking system but more in online games They are not interested somuch in tangible cultural heritage but more in cultural events They oftencome from Germany

Microsimulation

Microsimulation (MSM) is a technique that aims to model the likely behaviourof individual persons households or individual firms and combines communi-cative qualities together with more analytical qualities In simulation modellingthe analyst is interested in information relating to the joint distribution ofattributes over a population (Clarke and Holm 1987) In these models agentsrepresent members of a population for the purpose of studying how individual(that is micro-) behaviour generates aggregate (that is macro-) regularitiesfrom a bottom-up approach (for example Epstein 1999) This results in anatural instrument to anticipate trends in the environment by means ofmonitoring and early warning as well as to predict and value the short-termand long-term consequences of implementing certain policy measures (Saarloos2006) Increasingly MSM is used in quantitative analyses of economic and socialpolicy problems such as by Hanaoka and Clarke (2007) who use MSM for retailmarket analysis or van Sonsbeek and Gradus (2006) who simulate thebudgetary impact of the 2006 regime change in the Dutch disability schemeMSMs can be developed in different ways the choice between thesecharacteristics relates on the one hand to the problem or situation to beanalysed and on the other hand to data availability (see also Ballas et al2005b) Three ways to classify MSMs are staticdynamic deterministicprobabilistic and spatialnon-spatial

First of all models can simulate developments in the short run withoutallowing the households to change (for instance by getting older) This is calleda lsquostatic MSMrsquo The agents do not change but for example their actualbehaviour can change or the distribution of benefits over the agents may


change When a model takes into account longer-term developments with anexplicit consideration of time it is called a lsquodynamic MSMrsquo In this case theagents do change over the years they get older start relationships or havechildren etc It is obvious that dynamic models are more complex and ingeneral need more data input

The rules which determine the characteristics of the agents (in both staticand dynamic models) can be deterministic or probabilistic In a deterministicmodel the relationships are fully determined by the parameters defined withinthe model therefore in a real deterministic model the patterns of outcomeswill always be stable Often national data is reweighted to fit small-areadescriptions Obviously the total number of households or the total numberof families with children in a small area should be the same every time Aprobabilistic (or stochastic) model incorporates random processes for exampleby using Monte Carlo simulations either to reflect the random nature ofunderlying relationships or to account for random influences Often acombination of deterministic and probabilistic processes is used (Zaidi andRake 2001)

A major advantage of MSM concerns the ability to address a series ofimportant policy questions Microsimulation is particularly suitable for systemswhere the decision making occurs at the individual unit level and where theinteractions within the system are complex When the consequences are verydifferent for different groups and thus difficult to predict MSMs are wellsuited to estimate and analyse the distributional impacts of policy changes asthey are concerned with the behaviour of micro-units (Merz 1991)

Using a microsimulation approach in simulating visitor flows

In the tourism literature it is often mentioned that measuring demand isobstructed by the lack of suitable data and that the number of studies aimedat modelling tourism behaviour is limited One notable exception is the studyof Lundgren (2004) in which information from the Swedish Tourism Databaseis integrated into the spatial MSM model SVERIGE by means of a separatemodule This tourism module consists of socio-economic attributes which arealso used in SVERIGE Changes in population characteristics can be fed in thismanner into the tourism module The creation of this linkage enables thesimulation of the effects of changes in the Swedish population on the size anddirection of tourism flows Furthermore the adjusted SVERIGE MSM modelallows for the analysis of possible adjustments in the direction of tourism flowsby changes in the environment with respect to the location of tourismattractions

In this article in order to simulate tourism behaviour in relevant places wewill develop a spatial deterministic MSM model This MSM model can beenhanced by including a behaviour module describing and predicting the choiceof (individual) tourists for a tourist or cultural heritage attraction in the cityconcerned (see also SIMtown van Leeuwen 2010) In this research we use theresults of the MSM leading to a detailed picture of the total tourist populationin Amsterdam in order to define future target groups of tourists interested incultural heritage that could be reached by e-services


To construct the MSM model information about the total number of visitorsin the cities together with certain characteristics describing these visitors isnecessary Another important input is the results produced by the choiceexperiment already conducted in the ISAAC project Report 14 (2007) and theresults of the application of the Tourist Satisfaction System

For the development of our MSM model the static deterministicmicrosimulation techniques applied by Ballas et al (2005a) and enhanced bySmith et al (2007) are used This deterministic method used to create themicropopulation of tourists is a proportional fitting technique Using thisdeterministic reweighting methodology tourists from the questionnaires thatbest fit chosen personal (constraint) characteristics (for example goals age levelof education and country of residence) available from secondary data sources arelsquoclonedrsquo until the tourist population in Amsterdam is simulated The reliabilityof these synthetic populations can be validated against other known variablesto ensure that this synthetic population resembles the actual population to themaximum degree possible

The procedure is repeated until each tourist has been reweighted At the endthere may appear a few lsquoclonesrsquo of one specific tourist and many copies ofanother tourist in the synthetic database The criterion is simply how well eachtourist matches the constraints from the secondary data sets The tourists willbe simulated at the municipality level Therefore we use four constraintvariables (see next section) We only look at visitors who stay for at leastone night which results in a total tourist population of around 49 milliontourists

Constraint variables

Constraint variables are used to fit the microdata to the real situation Theyare (the most) important characteristics abstracted from the literature reviewand the behavioural models Each of the constraints must be present in boththe base survey (microdata set) and in other databases in this case severalsources from Statistics Netherlands (2007) ATCB (2008) and O+S Amsterdam(2008)

The choice of which variables to use is very important as it affects theoutcomes In some models the order of constraints in the model as well asthe number of classes distinguished also has an effect on the resultsUnfortunately there are only a few publications which deal with these issues(for example Smith et al 2007) Furthermore the best variables to use as aconstraint are not always available In our case more detailed information aboutthe characteristics of tourists in Amsterdam is rather limited However bycoupling different data sets we could derive information for four importantvariables which are described below

Goal The first constraint variable is the goal of the visitor The purpose of thetrip can be either enjoying a holiday or doing business Although the visitinggoal does not affect a touristrsquos interest in cultural heritage (therefore it is notincluded in the ordered logit models) it does affect the possibility that theperson will actually visit cultural heritage in the city2 According to O+S


Amsterdam (2008) the share of business visitors is 39 This is also the figurewe use for the simulation

Age From the logit models it appears that age is an important variable forestimating a certain interest in cultural heritage The ISAAC database used fiveage classes but because the last class (older than 74) included only a fewrespondents we merged it with the age group of 55ndash74 years The ATCBresearch also shows the visitors per age category Because the age groups wereslightly different we had to recalculate the results from ATCB to fit the ISAACage groups Therefore we assumed that the number of tourists was distributedequally over the number of years in the class For example when the age classincluded 16ndash25 years we assumed that 10 of the people in this class were16

Education Finding reliable information about the employment situation oreducation level of tourists turned out to be very difficult We focused oneducation because this variable was often mentioned as being important in theliterature and it was significant in the logit models The NBTC (theNetherlands Board of Tourism and Conventions) in a report about foreigntourists in the Netherlands (2006) shows the average education level of touristsaccording to their country of residence We assume that this distribution issimilar for the city of Amsterdam We distinguish between a primary secondaryand higher education level

Country The final constraint variable we use is the country of residence Wedistinguish between the Netherlands Germany the UK and Ireland the restof Europe the USA Canada and Australia and the rest of the world For thetotal number of tourists per country visiting Amsterdam we used data fromO+S Amsterdam (2008)

When we compare the sample with the actual situation (from external statistics) itappears that there are some discrepancies In particular the share of businesstourists is unrepresented in our sample the share of younger tourists (18ndash34)is rather large and the share of lower-educated tourists very modest Theseinconsistencies will be eliminated in our microsimulation However the shareof higher-educated tourists and the share of tourists according to their countryof residence is very similar to the actual situation

Sensitivity analysis tourists

A technical disadvantage of MSM was the difficulty of validating the outcomessince it estimated distributions of variables which were previously unknownOne way of validating the results is to reaggregate estimated data sets to thelevel at which observed data exist and compare the estimated to the observeddistributions

Another challenge in MSM is that when simulating the effect of a certainevent on the behaviour of households usually a (behavioural) model is requiredDifferent kinds of models are suitable but nevertheless the results depend on


these differences It is important that the model is robust However when itis working often a wide range of effects can be simulated

Our MSM approach is affected necessarily by some statistical assumptionsBecause of a lack of (clear) additional information about tourists it is difficultto evaluate the results of the MSM with known statistics However we willtry to make an evaluation of the outcomes of the simulation model using thestandardized absolute error measure (SAE) as described by Voas and Williamson(2001) The measure sums the discrepancies (TAE = total absolute error)divided by the number of expected tourists

TAE = ΣkTk ndash Ek

SAE = TAEN

in which Tk is the observed count of cell k (for example number of touristsfrom Germany) Ek the expected count for cell k and N the total expected countfor the whole table (total number of tourists in Amsterdam) Of course it isalso necessary to have an error threshold Clarke and Madden (2001) use an errorthreshold of at least 80 of the areas with less than 20 error (SAE lt 020)In a medical study Smith et al (2007) work with a model that simulates peoplewith diabetes which is a relatively rare disease and therefore use an errorthreshold of less than 10 error (SAE lt 010) in 90 of the output areas

Amsterdam tourists are simulated at the municipality level (see Table 3)When looking at the SAE values it appears that the simulation results are quiterobust In the total tourist population we have a small underestimation of thenumber of business visitors However this is not a real problem becausedifferent sources give different shares of business visitors ATCB (2008) givesa share of 30 of business visitors [instead of the 39 we used from O+SAmsterdam (2008)]

The age groups appear to be very well simulated and the education groupsalso seem quite robust The only weaker part of the simulation is theoverestimation of Dutch visitors by around 9 This is something we have tokeep in mind although there has been a recent tendency for the share of Dutchtourists to grow

It is always relevant to compare the indirect results from the MSM withexisting results these are called the control variables A first control variablecould be the distribution of male and female tourists No information can befound about the gender of national and international tourists in Amsterdam orthe Netherlands However Statistics Netherlands (CBS) (2007) indicates thatof all Dutch people who go on holiday half are male and half female Oursimulated tourist population contains 51 female tourists which is a very goodresult

Another control variable could be the number of nights of stay From themicrosimulation we can derive the average number of nights that visitors fromdifferent countries stay in Amsterdam Because the questionnaires asked peoplethe number of days they intended to stay we first subtracted one day to getan estimation of the number of nights This resulted in an average of 29 nightsof stay for all tourists (see Table 4) Dutch tourists stay on average a littlelonger than one night tourists from the UK or Germany almost three days andtourists from countries further away stay on average almost four days When


Table 3 Standardized error measure (SAE) for the four constraint variables simulatingtourists in Amsterdam

Constraint Class SAE

Goal Holiday 004Business ndash004

Age lt 18 00018ndash34 00135ndash54 001gt 55 ndash002

Education Primary ndash004Secondary 006High ndash002

Country Netherlands 009Germany ndash001UK ndash001Rest of EU ndash005USA ndash001Canada and Australia ndash001Rest of the world ndash003

Table 4 Average number of nights of stay of tourists from different countries accordingto our simulation the Amsterdam City Department of Research and Statistics (O+S) andthe Amsterdam Tourism and Convention Board (ATCB)

Holland USA UK Germany Rest of EU Rest of world Total

Simulation 13 27 28 32 37 40 29O+S 16 18 19 18 18 19 18ATCB 24 46 33 37 44 ndash 41

Note Average of France Italy and Spain

we compare this with results from O+S Amsterdam (estimated by dividing thenumber of nights spent by guests from a specific country over the number ofguests from that same country)3 or from the ATCB (from the 2008 visitorsprofile) it appears that the simulation results fall in between the results of thosetwo sources Only the average stay of Dutch tourists is relatively low Thismeans that we could use these results keeping in mind that Dutch touristsprobably stay longer

Target group tourists

The next research issue is to define which tourists might be seen as potentialvisitors of cultural heritage sites in Amsterdam and how they could be reached


Table 5 Amsterdam visitors (very) interested in three types of cultural heritage

Kind of CH Number of tourists Share of total tourists ()

Tangible No visit 355333 7Visit 782272 16

Intangible 619823 13Cultural events No visit 606073 12

Visit 365221 7

Total 2728722 55

by e-services In this section analytical insights from the behavioural models(in particular the factor analysis) are linked to the detailed simulatedmicropopulation

From the factor analysis applied to the data on the Amsterdam tourists threegroups of tourists can be distinguished tangible cultural heritage fansintangible cultural heritage fans and tourists that particularly like culturalevents From the (simulated) micropopulation of the Amsterdam tourists weselected those persons who more than the average tourist favour one of thosethree groups of cultural heritage and who do use e-services to plan their leisuretime (Table 5) These people can be seen as the easiest ones to reach with apromotion tool so we call them the target group

On average on a scale of 1ndash5 the appreciation for tangible cultural heritage(architecture monuments museums and urban landscape) is around 4 forintangible cultural heritage (traditions customs and knowledge) around 35 andfor cultural events around 35 Therefore we selected those people who diduse e-services and who valued tangible cultural heritage higher than 45intangible cultural heritage higher than 45 and cultural events higher than 45

For the tourists we also made the distinction between those who plannedto visit any (tangible) cultural heritage site or cultural event and those whodid not (this was asked in the questionnaire) For those who preferred intangiblecultural heritage it was not possible to distinguish between visitors and non-visitors

In total 55 of the visitors to Amsterdam can be labelled as part of a targetgroup Interestingly 7 of the tourist population is (very) interested in tangiblecultural heritage but has not planned to visit any point of interest Those peoplealso use the Internet to plan their trip Another 12 of the tourist populationis very interested in cultural events but also has not planned a visit Althoughthe reason for not having planned a visit could be quite different for these twogroups (if one likes cultural events the right kind of event must be availableat the right time to be able to visit it while tangible cultural heritage is usuallyavailable the whole year-round) they clearly consist of potential visitors ofcultural heritage

When looking at the preferences of the five target groups for e-services itis very interesting to see that the values of those people who did plan a visitand those who did not are very different (see Table 6) First of all the touristswho visited either a cultural heritage site or a cultural event showed higher


Table 6 Preferences for e-services of tourists (very) interested in cultural heritage

Inter- Personal Online Journey E-forum Virtual Inter-active inform- booking planner tours activemap ation games

Low value (1 + 2)Tangible No visit 35 39 12 8 53 28 87

Visit 13 16 20 30 50 21 77Intangible 19 18 10 10 29 23 70Cultural events No visit 37 36 12 13 53 32 82

Visit 5 4 34 39 47 6 79

High value (4 + 5)Tangible No visit 65 35 88 80 18 48 13


Visit 92 73 61 51 26 75 13

preferences for an interactive map than the ones who did not visit anythingthis also holds for personalized information and virtual tours At the same timethe tourists who indicated they did not plan to visit anything perhaps couldnot find the services they required because they had higher preferences for usingan online booking system and a journey planner

From these results it appears that the target groups of tourists interestedin cultural heritage who do use e-services are relatively large A usefuldistinction to make should be related not so much to the kind of culturalheritage they prefer but much more to if they have already planned to visitanything The preferences for different kinds of e-services appear to dependstrongly on this background factor

Conclusions

Modern tourism is increasingly becoming a high-tech sector even in areaswhich are perceived traditionally as low-tech domains such as cultural heritageE-services are becoming an important tool in a competitive global touristsystem This also calls for due insight into the motives and preferences ofvisitors The tourism strategy of Amsterdam aims to change the image of thecity by attracting a different mix of visitors and to stimulate them to broadentheir horizons by visiting more sites of interest moving outwards from theimmediate overcrowded city centre Therefore the tourist policy in the cityaims to promote new aspects of the cityrsquos cultural heritage such as Amsterdamas a cultural city or a city of events In addition smaller attractions should alsobe integrated into Amsterdamrsquos positioning strategy through the use of themessuch as the 2008 Hidden Treasures theme4

From the ordered logit models and the factor analysis carried out oninformation regarding Amsterdam tourists three groups of tourists who areinterested in cultural heritage could be distinguished intangible cultural


heritage enthusiasts tangible cultural heritage fans and tourists whoparticularly like cultural events The intangible cultural heritage enthusiasts aregenerally younger people and women both of whom already use e-services toplan their trips They are often international tourists Tangible cultural heritagefans are often older international tourists with a higher education The culturalevents fans are often younger people and men who do use e-services andgenerally come from the Netherlands This information calls for fit-for-purposetourism strategies from the side of the city of Amsterdam In our study amicrosimulation model (MSM) has been developed to obtain a detailed pictureof the total tourist population of Amsterdam with their relevant characteristicsallowing us to define which groups of tourists interested in cultural heritagecould be considered as (future) target groups to be reached by e-servicesTherefore the tourists from the questionnaires (including their preferences forcultural heritage e-services and their plans to visit a site) have been reweightedto fit the total tourist population of Amsterdam using relevant constraintvariables (goals age education and country of residence) Those constraintvariables resulted from a literature review and the logit and factor analysisapplied here

This micropopulation of Amsterdam tourists the simulated database with49 million tourists allowed the detection of those people that particularlyfavoured one of the three groups of cultural heritage and who did use e-servicesto plan their leisure time These people can be seen as the easiest ones to reachwith the promotional tool Therefore we consider these people as target groupsFurthermore a distinction was also made between those who planned to visitany (tangible) cultural heritage site or cultural event and those who did not

It appears that 23 of the tourist population is (very) interested in tangiblecultural heritage of which 7 has not planned to visit any point of interestThese people are familiar with using the Internet to plan their trip Another19 of the tourist population is very interested in cultural events of which12 has not planned a visit either Although the reason for not having planneda visit could be quite different for these two groups they clearly consist ofpotential (additional) cultural heritage visitors In addition we found that thepreferences of the people who did plan a visit and the ones who did not werevery different First of all the tourists who visited either a cultural heritagesite or a cultural event showed higher preferences for an interactive map thanthe ones who did not visit anything this also held for personalized informationand virtual tours Tourists who appreciate intangible cultural heritage attacha higher value to an e-forum and to interactive games It is obvious that theabove-mentioned information is of strategic importance for the development ofa promotion tool different e-services can attract different users depending onwhether or not they have already decided to visit a cultural heritage site

Endnotes

1 The factor loading is the Pearson correlation between a factor and a variable Factor scorecoefficients can be calculated in several ways the simplest way is the regression method Thismeans that the factor loadings are adjusted to take account of the initial correlations betweenvariables

2 Besides business and tourists purposes the ISAAC database also distinguishes visiting familyand friends We assigned those persons to the tourists as was also done in the ATCB 2008 report


3 The results from O+S Amsterdam comprise all visitors both business and leisure travellers TheATCB and simulation figures include tourists only However according to the ATCB businesstravellers stay a little longer than leisure travellers This means that this cannot explain thedifference in outcomes

4 A wealth of culture was revealed in the 2008 lsquoAmsterdam Hidden Treasuresrsquo theme year Itenabled visitors from the Netherlands and abroad to discover the lesser-known attractions ofAmsterdam

References

Ark LA van der and Richards G (2006) lsquoAttractiveness of cultural activities in European citiesa latent class approachrsquo Tourism Management Vol 27 No 6 pp 1408ndash1413

ATCB (2008) Amsterdam Visitor Profile Visitor Research Amsterdam 2008 Amsterdam Tourism andConvention Board Amsterdam

Azjen I and Fishbein M (1980) Understanding Attitudes and Predicting Social Behaviour PrenticeHall NJ

Baida Z Gordijn J and Omelayenko B (2004) lsquoA shared service terminology for online serviceprovisionrsquo in Janssen M Sol HG and Wagenaar RW eds Proceedings of the 6th InternationalConference on Electronic Commerce ICEC ACM New York

Ballas D Clarke GP and Wiemers E (2005a) lsquoBuilding a dynamic spatial microsimulationmodel for Irelandrsquo Population Space and Place Vol 11 pp 157ndash172

Ballas D Rossiter D Thomas B Clarke GP and Dorling D (2005b) Geography MattersSimulating the Local Impacts of National Social Policies Joseph Rowntree Foundation Leeds

Barros CP and Rodriguez PJ (2008) lsquoA revenue-neutral tax reform to increase demand for publictransport servicersquo Transport Research Part A Vol 42 No 4 pp 659ndash672

Barros CP Butler R and Correia A (2008) lsquoHeterogeneity in destination choice tourism inAfricarsquo Journal of Travel Research Vol 27 pp 785ndash804

Clarke GP and Madden M eds (2001) Regional Science in Business Springer BerlinClarke M and Holm E (1987) lsquoMicrosimulation methods in spatial analysis and planningrsquo

Geografiska Annaler Vol 69B No 2 pp 145ndash164Cooper C Fletcher J Fyall A Gilbert D and Wanhill S (2008) Tourism Principles and Practice

4th edition Prentice Hall NJCorreia A Santos CM and Barros CP (2007) lsquoTourism in Latin America a choice analysisrsquo

Annals of Tourism Research Vol 34 No 3 pp 610ndash629Crompton JL (1992) lsquoStructure of vacation destination choice setsrsquo Annals of Tourism Research Vol

19 pp 420ndash434Cunnell D and Prentice R (2000) lsquoTouristsrsquo recollections of quality in museums a service space

without peoplersquo Museum Management and Curatorship Vol 18 No 4 pp 369ndash390Epstein JM (1999) lsquoAgent-based computational models and generative social sciencersquo Complexity

Vol 4 No 5 pp 41ndash60Fusco Girard L and Nijkamp P eds (2009) Cultural Tourism and Sustainable Local Development

Ashgate AldershotFusco Girard L De Montis A and Nijkamp P (2008) Editorial International Journal of Services

Technology and Management Vol 10 No 1 pp 1ndash7Giaoutzi M and Nijkamp P eds (2006) Tourism and Regional Development Ashgate AldershotGoeldner CR and Ritchie B (2006) Tourism Principles Practices Philosophies John Wiley New

YorkHair JF Anderson RE Tathamand RL and Black WC (1995) Multivariate Data Analysis with

Readings 4th edition Prentice Hall NJHanaoka K and Clarke GP (2007) lsquoSpatial microsimulation modelling for retail market analysis

at the small-area levelrsquo Computers Environment and Urban Systems Vol 31 No 2 pp 162ndash187Hensher DA Rose JM and Green WH (2005) Applied Choice Analysis A Primer Cambridge

University Press CambridgeHsu T Tsai Y and Wu H (2009) lsquoThe preference analysis for tourist choice of destination a

case study of Taiwanrsquo Tourism Management Vol 30 No 2 pp 288ndash297ISAAC D14 (2007) lsquoReport on users requirements for ISAAC e-services using conjoint analysisrsquo

ISAAC deliverable 14 (httpwwwisaac-projecteupublicationsasp)Kozak M (2002) lsquoComparative analysis of tourist motivations by nationality and destinationsrsquo

Tourism Management Vol 23 pp 221ndash232


Lundgren A (2004) lsquoMicro-simulation modelling of domestic tourism travel patterns in SwedenrsquoPaper presented at 7th International Forum on Tourism Statistics Stockholm Sweden 9ndash11 June2004

March R (2009) Tourism Behaviour Travellersrsquo Decisions and Actions CAB International WallingfordMerz J (1991) lsquoMicrosimulation ndash a survey of principles developments and applicationrsquo Interna-

tional Journal of Forecasting Vol 7 pp 77ndash104NBTC (Netherlands Board of Tourism and Conventions) (2006) Destinatie Holland de buitenlandse

toerist nader bekeken NBTC LeidschendamO+S Amsterdam (2008) Amsterdam in cijfers 2008 Gemeente Amsterdam AmsterdamRayman-Bacchus L and Molina A (2001) lsquoInternet based tourism services business issues and

trendsrsquo Futures Vol 33 pp 587ndash605Riganti P (2007) lsquoFrom cultural tourism to cultural e-tourism issues and challenges to economic

valuation in the information erarsquo Paper presented at the 47th Congress of the European RegionalScience Association 29 August to 2 September 2007 Paris

Riganti P and Nijkamp P (2008) lsquoCongestion in popular tourist areas a multi-attribute experi-mental choice analysis of willingness-to-wait in Amsterdamrsquo Tourism Economics Vol 14 No 1pp 25ndash44

Riganti P Strielkowski W and Jing W (2007) lsquoCultural tourism and e-services using in depthinterviews to assess potential consumersrsquo preferencesrsquo Paper presented at the 47th Congress ofthe European Regional Science Association 29 August to 2 September 2007 Paris

Saarloos DJM (2006) lsquoA framework for a multi-agent planning support systemrsquo PhD thesisEindhoven University Press Facilities Eindhoven

Scavarda AJ Lustosa LJ and Teixeira JP (2001) lsquoThe e-tourism and the virtual enterprisersquoProceedings 12th Conference of the Production and Operations Management Society POM-2001Orlando FL

Smith DM Harland K and Clarke GP (2007) lsquoSimHealth estimating small area populationsusing deterministic spatial microsimulation in Leeds and Bradfordrsquo Working Paper 0706University of Leeds Leeds

Statistics Netherlands (2007) Neighborhood Statistics 2007 Statistics Netherlands The HagueSuh K and McAvoy L (2005) lsquoPreferences and trip expenditures A conjoint analysis of visitors

to Seoul Korearsquo Tourism Management Vol 26 pp 325ndash333Van Leeuwen ES (2010) UrbanndashRural Interactions Towns as Focus Points in Rural Development

Contributions to Economics Physica-Verlag HeidelbergVan Sonsbeek JM and Gradus RHJM (2006) lsquoA microsimulation analysis of the 2006 regime

change in the Dutch disability schemersquo Economic Modelling Vol 23 No 3 pp 427ndash456Voas D and Williamson P (2001) lsquoEvaluating goodness-of-fit measures for synthetic microdatarsquo

Geographical and Environmental Modelling Vol 5 No 2 pp 177ndash200Zaidi A and Rake K (2001) lsquoDynamic microsimulation models a review and some lessons for

SAGErsquo SAGE Discussion Paper 2 London School of Economics London


Appendix 1

Ordered probability modelMore formally the ordered probability model with individual data can be written asfollows

yi = β1X1 + β2X2 + + βnXn + εi

The dependent variable yi is the so-called latent variable and depends linearly on

the explanatory variables Xi with i = 1 n The error terms εi complete the modelthese error terms are assumed to be independent and identically distributed randomvariables The latent variable yi

is measured using the observed values of yi and thefollowing censoring mechanism

yi = 0 if yi le micro0

= 1 if micro0 lt yi le micro1


=

= J if microjndash1 lt yi le microj

The micro values which can be interpreted as boundaries are estimated within the modelWithin the ordered logit model the error terms are assumed to have an independentand identical Gumbel distribution

Appendix 2

Table A1 Factor analysis for tourists in Amsterdam with respect to cultural heritage

Factors1 2 3 4 5

Variance explained 20 13 11 8 6PreferenceArchitecture 0478 ndash0547 0334 ndash0015 0103Monuments 0459 ndash0520 0202 0148 0194Museums 0390 ndash0462 0292 0286 ndash0025Urban landscape 0397 ndash0505 0194 ndash0099 0031Cultural events 0336 ndash0248 ndash0520 0503 ndash0146Traditions 0708 0118 ndash0419 ndash0230 ndash0005Customs 0730 0058 ndash0380 ndash0294 ndash0015Knowledge 0687 0015 ndash0374 ndash0289 0046Planning to visitArchitecture 0453 0297 0434 0050 ndash0206Museums 0317 0300 0518 0014 0093Urban landscape 0378 0265 0452 ndash0223 ndash0262Cultural events 0340 0293 ndash0123 0684 ndash0241Shopping 0160 0337 0057 0177 0799Nightlife 0306 0474 ndash0046 0167 0284Atmosphere 0389 0514 0201 0048 ndash0204

Note The definitions of factors 1ndash5 are given in the section lsquoPreferences for cultural heritagersquo Thefigures in bold show the most meaningful variables with the highest loadings


Appendix 3

Table A2 Factor analysis of tourist preferences with respect to e-services

Factors1 2

Variance explained 42 15PreferenceInteractive map 0633 ndash0254Personal information 0633 ndash0035Online booking 0693 ndash0524Journey planner 0613 ndash0316E-forum 0633 0291Virtual tours 0713 0207Interactive games 0723 0679

Note The definitions of factors 1 and 2 are given in the section lsquoPreferences for e-servicesrsquo The figuresin bold show the most meaningful variables with the highest loadings


affected by this mega-trend (see Fusco Girard and Nijkamp 2009) This drasticchange in spatial behaviour is caused not only by economic prosperity and ourwelfare and leisure society but also by the use of the modern information andcommunication (ICT) sector which offers (i) more direct information oninteresting places to visit (ii) efficient technological tools to organize and bookleisure trips and (iii) techniques for communication with friends and relativesthrough which physical mobility will be enhanced (see also March 2009)

A main challenge of modern regional policy is to market ndash through the useof ICT ndash the attractiveness of a certain region in order to generate growth intourist visits and expenditures An important contribution of cultural heritagelies in the support of the destination image generation This means that forexample cultural heritage is not only a source of historical information affectingthe image of the attraction itself but also the broader destination imageConsequently information provided to (potential) visitors has an impact on thedestination image Thus ICT has become one of the competitive tools inregional tourist policy (see Goeldner and Ritchie 2006)

Amsterdam for instance has a dedicated policy that aims to strengthen itscultural profile The city is known for its interesting links between the (urban)past and the future which can be experienced in a lively cosmopolitanatmosphere Clearly cities like Barcelona Rome Lisbon and Prague areimportant competitors Each destination offers a variety of products and servicesto attract visitors and each tourist has the opportunity to choose from a setof destinations (Crompton 1992) Therefore it is very important to know whatthe unique selling point of a particular city is and how tourists can be(virtually) attracted It is equally important to know how important culturalheritage is for the tourism sector and for the city as a whole at the individual(micro) level This calls for due insight into the motives and preferences oftourists for different elements of cultural heritage as well as for the sites theyactually plan to visit

The present paper highlights the importance of ICT facilities for enhancingthe tourist profile of a given city in our case Amsterdam We will address inparticular the critical importance of cultural heritage as a promising spearheadfor the tourist attractiveness of host cities In our analysis we will focus largelyon the individual (micro) preferences and attitudes of visitors To this end wewill employ as a technical tool for decision support ndash in addition to orderedlogit models and factor analysis ndash microsimulation models (MSM) in order toinvestigate the driving forces which encourage visitors and residents to usee-services in the tourist sector with regard to cultural heritage In recent yearssimulation models and especially microanalytic simulation models have beenapplied increasingly in quantitative analyses of economic and social policyproblems In general MSM enables the researcher to investigate individualbehaviour for planning and policy analyses (Merz 1991) In this article MSMtechniques are applied to obtain a detailed picture of the tourist populationwhich is necessary to define future target groups of tourists interested incultural heritage in Amsterdam The paper is organized as follows In the nextsection we describe a few essential elements of cultural heritage in the contextof tourism Then in the following section we highlight the use of ICT in thetourist sector Subsequent sections deal respectively with the use of e-servicesin enhancing the benefits to visitors of cultural heritage amenities the research







The role of ICT

















Research framework







Tourist preferences


Promotiontool

Data



visiting Amsterdam

Microsimulation

Strategic goal



Tourist preferences
















Tab

le 1

C

oeff

icie

nts

of

the

ord

ered

logi

t m

odel

s es

tim

atin

g th

e p

refe

ren

ces

of t

ouri

sts

in A

mst

erd

am f

or d

iffe

ren

t k

ind

s of

cu

ltu

ral h

erit

age

Arc

hit

ectu

reM

onu

men

tsM

use

um

sU

rban

lan

dsc

ape

Cu

ltu

ral e

ven

tsT

rad

itio

ns

Cu

stom

sK

now

led

ge

E-s

ervi

ce0

205

031

9

058

1

033

4

026

20

257

018

00

014

(01

60)

(01

52)

(01

54)

(01

55)

(01

51)

(01

60)

(01

54)

(01

57)

Age

019

30

392

0

338

0

213

ndash0

351

ndash03

62

ndash0

197

0

062

(01

13)

(01

08)

(01

06)

(01

01)

(01

07)

(01

19)

(01

20)

(01

15)

Edu

cati

on0

229

0

090

010

30

187

ndash0

108

ndash0

098

ndash0

121

ndash0

132

(0

068

)(0

065

)(0

067

)(0

063

)(0

059

)(0

059

)0

056

(00

59)

Gen

der

025

50

269

0

310

0

528

0

109

048

1

038

0

024

8(0

147

)(0

141

)(0

146

)(0

144

)(0

140

)(0

143

)0

141

(01

43)

USA

023

30

254

ndash00

76ndash0

039

ndash07

47

1

790

1

503

1

199

(0

221

)(0

217

)(0

217

)(0

214

)(0

224

)(0

252

)0

240

(02

45)

UK

021

0ndash0

004

ndash02

71ndash0

260

ndash07

85

1

915

1

518

1

588

(0

228

)(0

247

)(0

253

)(0

234

)(0

254

)(0

244

)0

255

(02

71)

Ger

man

y0

554

ndash0

494

ndash0

030

ndash03

08ndash1

338

056

8

022

70

270

(02

52)

(02

55)

(02

49)

(02

51)

(02

49)

(02

22)

022

8(0

227

)R

est

of E

urop

e0

492

ndash0

298

ndash02

18ndash0

160

ndash07

96

1

237

1

189

1

032

(0

219

)(0

223

)(0

222

)(0

225

)(0

224

)(0

234

)(0

225

)(0

212

)R

est

of t

he w

orld

030

5ndash0

075

039

0ndash0

224

ndash07

75

1

597

1

301

0

951

(0

323

)(0

304

)(0

291

)(0

307

)(0

286

)(0

323

)(0

283

)(0

294

)

Obs

erva

tion

s37

136

436

137

236

336

735

335

7M

cFad

den

pseu

do-R

20

032

001

50

012

001

00

029

001

80

009

001

3

Not

eSi

gnif

ican

t at

the

00

1

0

05 a

nd

01

0 le

vels













Tab

le 2

C

oeff

icie

nts

of

the

ord

ered

logi

t m

odel

s es

tim

atin

g th

e p

refe

ren

ces

of t

ouri

sts

in A

mst

erd

am f

or d

iffe

ren

t ty

pes

of

e-se

rvic

es

Inte

ract

ive

map

Per

son

aliz

ed in

form

atio

nB

ook

ing

serv

ice

Jou

rney

pla

nn

erE

-for

um

Vir

tual

tou

rsIn

tera

ctiv

e ga

mes

E-s

ervi

ce0

497

0

343

1

156

0

373

0

362

0

193

009

1(0

156

)(0

153

)(0

164

)(0

157

)(0

147

)(0

152

)(0

168

)E

duca

tion

007

4ndash0

205

005

50

026

ndash02

10

)ndash0

124

ndash0

339

(00

62)

(00

65)

(00

65)

(00

59)

(00

63)

(00

65)

(00

70)

Gen

der

008

2ndash0

070

ndash00

650

158

ndash00

94ndash0

294

ndash0

423

(01

47)

(01

45)

(01

48)

(01

44)

(01

42)

(01

43)

(01

61)

Age

ndash01

89

ndash01

82ndash0

248

ndash0

103

ndash04

70

0

011

ndash04

34

(0

114

)(0

117

)(0

116

)(0

107

)(0

107

)(0

114

)(0

123

)E

mpl

oyed

031

7

ndash00

260

098

016

70

177

018

6ndash0

054

(01

60)

(01

63)

(01

57)

(01

52)

(01

52)

(01

52)

(01

78)

USA

081

4

070

6

075

5

ndash02

940

261

034

1ndash0

171

(02

17)

(02

35)

(02

41)

(02

14)

(02

15)

(02

28)

(02

52)

UK

059

4

060

1

068

0

036

80

254

045

80

675

(0

245

)(0

262

)(0

234

)(0

244

)(0

251

)(0

235

)(0

259

)G

erm

any

032

80

247

ndash00

47ndash0

808

ndash00

700

052

013

1(0

233

)(0

234

)(0

243

)(0

232

)(0

232

)(0

231

)(0

273

)R

est

of E

urop

e0

894

0

792

0

593

ndash0

500

0

460

0

396

028

6(0

217

)(0

225

)(0

221

)(0

219

)(0

230

)(0

214

)(0

248

)R

est

of t

he w

orld

082

3

102

1

131

5

ndash04

710

735

0

283

035

1(0

331

)(0

278

)(0

291

)(0

303

)(0

260

)(0

272

)(0

315

)

Obs

erva

tion

s65

065

165

165

165

165

165

0M

cFad

den

pseu

do-R

20

026

002

20

055

001

80

027

001

00

047

Not

eSi

gnif

ican

t at

the

00

1

0

05 a

nd

01

0 le

vels








Microsimulation































SAE = TAEN

























Visit 365221 7

Total 2728722 55












Visit 5 4 34 39 47 6 79



Visit 92 73 61 51 26 75 13



Conclusions







Endnotes






References












































Appendix 1









=



Appendix 2


Factors1 2 3 4 5




Appendix 3


Factors1 2









The role of ICT

















Research framework







Tourist preferences


Promotiontool

Data



visiting Amsterdam

Microsimulation

Strategic goal



Tourist preferences
















Tab

le 1

C

oeff

icie

nts

of

the

ord

ered

logi

t m

odel

s es

tim

atin

g th

e p

refe

ren

ces

of t

ouri

sts

in A

mst

erd

am f

or d

iffe

ren

t k

ind

s of

cu

ltu

ral h

erit

age

Arc

hit

ectu

reM

onu

men

tsM

use

um

sU

rban

lan

dsc

ape

Cu

ltu

ral e

ven

tsT

rad

itio

ns

Cu

stom

sK

now

led

ge

E-s

ervi

ce0

205

031

9

058

1

033

4

026

20

257

018

00

014

(01

60)

(01

52)

(01

54)

(01

55)

(01

51)

(01

60)

(01

54)

(01

57)

Age

019

30

392

0

338

0

213

ndash0

351

ndash03

62

ndash0

197

0

062

(01

13)

(01

08)

(01

06)

(01

01)

(01

07)

(01

19)

(01

20)

(01

15)

Edu

cati

on0

229

0

090

010

30

187

ndash0

108

ndash0

098

ndash0

121

ndash0

132

(0

068

)(0

065

)(0

067

)(0

063

)(0

059

)(0

059

)0

056

(00

59)

Gen

der

025

50

269

0

310

0

528

0

109

048

1

038

0

024

8(0

147

)(0

141

)(0

146

)(0

144

)(0

140

)(0

143

)0

141

(01

43)

USA

023

30

254

ndash00

76ndash0

039

ndash07

47

1

790

1

503

1

199

(0

221

)(0

217

)(0

217

)(0

214

)(0

224

)(0

252

)0

240

(02

45)

UK

021

0ndash0

004

ndash02

71ndash0

260

ndash07

85

1

915

1

518

1

588

(0

228

)(0

247

)(0

253

)(0

234

)(0

254

)(0

244

)0

255

(02

71)

Ger

man

y0

554

ndash0

494

ndash0

030

ndash03

08ndash1

338

056

8

022

70

270

(02

52)

(02

55)

(02

49)

(02

51)

(02

49)

(02

22)

022

8(0

227

)R

est

of E

urop

e0

492

ndash0

298

ndash02

18ndash0

160

ndash07

96

1

237

1

189

1

032

(0

219

)(0

223

)(0

222

)(0

225

)(0

224

)(0

234

)(0

225

)(0

212

)R

est

of t

he w

orld

030

5ndash0

075

039

0ndash0

224

ndash07

75

1

597

1

301

0

951

(0

323

)(0

304

)(0

291

)(0

307

)(0

286

)(0

323

)(0

283

)(0

294

)

Obs

erva

tion

s37

136

436

137

236

336

735

335

7M

cFad

den

pseu

do-R

20

032

001

50

012

001

00

029

001

80

009

001

3

Not

eSi

gnif

ican

t at

the

00

1

0

05 a

nd

01

0 le

vels













Tab

le 2

C

oeff

icie

nts

of

the

ord

ered

logi

t m

odel

s es

tim

atin

g th

e p

refe

ren

ces

of t

ouri

sts

in A

mst

erd

am f

or d

iffe

ren

t ty

pes

of

e-se

rvic

es

Inte

ract

ive

map

Per

son

aliz

ed in

form

atio

nB

ook

ing

serv

ice

Jou

rney

pla

nn

erE

-for

um

Vir

tual

tou

rsIn

tera

ctiv

e ga

mes

E-s

ervi

ce0

497

0

343

1

156

0

373

0

362

0

193

009

1(0

156

)(0

153

)(0

164

)(0

157

)(0

147

)(0

152

)(0

168

)E

duca

tion

007

4ndash0

205

005

50

026

ndash02

10

)ndash0

124

ndash0

339

(00

62)

(00

65)

(00

65)

(00

59)

(00

63)

(00

65)

(00

70)

Gen

der

008

2ndash0

070

ndash00

650

158

ndash00

94ndash0

294

ndash0

423

(01

47)

(01

45)

(01

48)

(01

44)

(01

42)

(01

43)

(01

61)

Age

ndash01

89

ndash01

82ndash0

248

ndash0

103

ndash04

70

0

011

ndash04

34

(0

114

)(0

117

)(0

116

)(0

107

)(0

107

)(0

114

)(0

123

)E

mpl

oyed

031

7

ndash00

260

098

016

70

177

018

6ndash0

054

(01

60)

(01

63)

(01

57)

(01

52)

(01

52)

(01

52)

(01

78)

USA

081

4

070

6

075

5

ndash02

940

261

034

1ndash0

171

(02

17)

(02

35)

(02

41)

(02

14)

(02

15)

(02

28)

(02

52)

UK

059

4

060

1

068

0

036

80

254

045

80

675

(0

245

)(0

262

)(0

234

)(0

244

)(0

251

)(0

235

)(0

259

)G

erm

any

032

80

247

ndash00

47ndash0

808

ndash00

700

052

013

1(0

233

)(0

234

)(0

243

)(0

232

)(0

232

)(0

231

)(0

273

)R

est

of E

urop

e0

894

0

792

0

593

ndash0

500

0

460

0

396

028

6(0

217

)(0

225

)(0

221

)(0

219

)(0

230

)(0

214

)(0

248

)R

est

of t

he w

orld

082

3

102

1

131

5

ndash04

710

735

0

283

035

1(0

331

)(0

278

)(0

291

)(0

303

)(0

260

)(0

272

)(0

315

)

Obs

erva

tion

s65

065

165

165

165

165

165

0M

cFad

den

pseu

do-R

20

026

002

20

055

001

80

027

001

00

047

Not

eSi

gnif

ican

t at

the

00

1

0

05 a

nd

01

0 le

vels








Microsimulation































SAE = TAEN

























Visit 365221 7

Total 2728722 55












Visit 5 4 34 39 47 6 79



Visit 92 73 61 51 26 75 13



Conclusions







Endnotes






References












































Appendix 1









=



Appendix 2


Factors1 2 3 4 5




Appendix 3


Factors1 2


















Research framework







Tourist preferences


Promotiontool

Data



visiting Amsterdam

Microsimulation

Strategic goal



Tourist preferences
















Tab

le 1

C

oeff

icie

nts

of

the

ord

ered

logi

t m

odel

s es

tim

atin

g th

e p

refe

ren

ces

of t

ouri

sts

in A

mst

erd

am f

or d

iffe

ren

t k

ind

s of

cu

ltu

ral h

erit

age

Arc

hit

ectu

reM

onu

men

tsM

use

um

sU

rban

lan

dsc

ape

Cu

ltu

ral e

ven

tsT

rad

itio

ns

Cu

stom

sK

now

led

ge

E-s

ervi

ce0

205

031

9

058

1

033

4

026

20

257

018

00

014

(01

60)

(01

52)

(01

54)

(01

55)

(01

51)

(01

60)

(01

54)

(01

57)

Age

019

30

392

0

338

0

213

ndash0

351

ndash03

62

ndash0

197

0

062

(01

13)

(01

08)

(01

06)

(01

01)

(01

07)

(01

19)

(01

20)

(01

15)

Edu

cati

on0

229

0

090

010

30

187

ndash0

108

ndash0

098

ndash0

121

ndash0

132

(0

068

)(0

065

)(0

067

)(0

063

)(0

059

)(0

059

)0

056

(00

59)

Gen

der

025

50

269

0

310

0

528

0

109

048

1

038

0

024

8(0

147

)(0

141

)(0

146

)(0

144

)(0

140

)(0

143

)0

141

(01

43)

USA

023

30

254

ndash00

76ndash0

039

ndash07

47

1

790

1

503

1

199

(0

221

)(0

217

)(0

217

)(0

214

)(0

224

)(0

252

)0

240

(02

45)

UK

021

0ndash0

004

ndash02

71ndash0

260

ndash07

85

1

915

1

518

1

588

(0

228

)(0

247

)(0

253

)(0

234

)(0

254

)(0

244

)0

255

(02

71)

Ger

man

y0

554

ndash0

494

ndash0

030

ndash03

08ndash1

338

056

8

022

70

270

(02

52)

(02

55)

(02

49)

(02

51)

(02

49)

(02

22)

022

8(0

227

)R

est

of E

urop

e0

492

ndash0

298

ndash02

18ndash0

160

ndash07

96

1

237

1

189

1

032

(0

219

)(0

223

)(0

222

)(0

225

)(0

224

)(0

234

)(0

225

)(0

212

)R

est

of t

he w

orld

030

5ndash0

075

039

0ndash0

224

ndash07

75

1

597

1

301

0

951

(0

323

)(0

304

)(0

291

)(0

307

)(0

286

)(0

323

)(0

283

)(0

294

)

Obs

erva

tion

s37

136

436

137

236

336

735

335

7M

cFad

den

pseu

do-R

20

032

001

50

012

001

00

029

001

80

009

001

3

Not

eSi

gnif

ican

t at

the

00

1

0

05 a

nd

01

0 le

vels













Tab

le 2

C

oeff

icie

nts

of

the

ord

ered

logi

t m

odel

s es

tim

atin

g th

e p

refe

ren

ces

of t

ouri

sts

in A

mst

erd

am f

or d

iffe

ren

t ty

pes

of

e-se

rvic

es

Inte

ract

ive

map

Per

son

aliz

ed in

form

atio

nB

ook

ing

serv

ice

Jou

rney

pla

nn

erE

-for

um

Vir

tual

tou

rsIn

tera

ctiv

e ga

mes

E-s

ervi

ce0

497

0

343

1

156

0

373

0

362

0

193

009

1(0

156

)(0

153

)(0

164

)(0

157

)(0

147

)(0

152

)(0

168

)E

duca

tion

007

4ndash0

205

005

50

026

ndash02

10

)ndash0

124

ndash0

339

(00

62)

(00

65)

(00

65)

(00

59)

(00

63)

(00

65)

(00

70)

Gen

der

008

2ndash0

070

ndash00

650

158

ndash00

94ndash0

294

ndash0

423

(01

47)

(01

45)

(01

48)

(01

44)

(01

42)

(01

43)

(01

61)

Age

ndash01

89

ndash01

82ndash0

248

ndash0

103

ndash04

70

0

011

ndash04

34

(0

114

)(0

117

)(0

116

)(0

107

)(0

107

)(0

114

)(0

123

)E

mpl

oyed

031

7

ndash00

260

098

016

70

177

018

6ndash0

054

(01

60)

(01

63)

(01

57)

(01

52)

(01

52)

(01

52)

(01

78)

USA

081

4

070

6

075

5

ndash02

940

261

034

1ndash0

171

(02

17)

(02

35)

(02

41)

(02

14)

(02

15)

(02

28)

(02

52)

UK

059

4

060

1

068

0

036

80

254

045

80

675

(0

245

)(0

262

)(0

234

)(0

244

)(0

251

)(0

235

)(0

259

)G

erm

any

032

80

247

ndash00

47ndash0

808

ndash00

700

052

013

1(0

233

)(0

234

)(0

243

)(0

232

)(0

232

)(0

231

)(0

273

)R

est

of E

urop

e0

894

0

792

0

593

ndash0

500

0

460

0

396

028

6(0

217

)(0

225

)(0

221

)(0

219

)(0

230

)(0

214

)(0

248

)R

est

of t

he w

orld

082

3

102

1

131

5

ndash04

710

735

0

283

035

1(0

331

)(0

278

)(0

291

)(0

303

)(0

260

)(0

272

)(0

315

)

Obs

erva

tion

s65

065

165

165

165

165

165

0M

cFad

den

pseu

do-R

20

026

002

20

055

001

80

027

001

00

047

Not

eSi

gnif

ican

t at

the

00

1

0

05 a

nd

01

0 le

vels








Microsimulation































SAE = TAEN

























Visit 365221 7

Total 2728722 55












Visit 5 4 34 39 47 6 79



Visit 92 73 61 51 26 75 13



Conclusions







Endnotes






References












































Appendix 1









=



Appendix 2


Factors1 2 3 4 5




Appendix 3


Factors1 2







Tourist preferences


Promotiontool

Data



visiting Amsterdam

Microsimulation

Strategic goal



Tourist preferences
















Tab

le 1

C

oeff

icie

nts

of

the

ord

ered

logi

t m

odel

s es

tim

atin

g th

e p

refe

ren

ces

of t

ouri

sts

in A

mst

erd

am f

or d

iffe

ren

t k

ind

s of

cu

ltu

ral h

erit

age

Arc

hit

ectu

reM

onu

men

tsM

use

um

sU

rban

lan

dsc

ape

Cu

ltu

ral e

ven

tsT

rad

itio

ns

Cu

stom

sK

now

led

ge

E-s

ervi

ce0

205

031

9

058

1

033

4

026

20

257

018

00

014

(01

60)

(01

52)

(01

54)

(01

55)

(01

51)

(01

60)

(01

54)

(01

57)

Age

019

30

392

0

338

0

213

ndash0

351

ndash03

62

ndash0

197

0

062

(01

13)

(01

08)

(01

06)

(01

01)

(01

07)

(01

19)

(01

20)

(01

15)

Edu

cati

on0

229

0

090

010

30

187

ndash0

108

ndash0

098

ndash0

121

ndash0

132

(0

068

)(0

065

)(0

067

)(0

063

)(0

059

)(0

059

)0

056

(00

59)

Gen

der

025

50

269

0

310

0

528

0

109

048

1

038

0

024

8(0

147

)(0

141

)(0

146

)(0

144

)(0

140

)(0

143

)0

141

(01

43)

USA

023

30

254

ndash00

76ndash0

039

ndash07

47

1

790

1

503

1

199

(0

221

)(0

217

)(0

217

)(0

214

)(0

224

)(0

252

)0

240

(02

45)

UK

021

0ndash0

004

ndash02

71ndash0

260

ndash07

85

1

915

1

518

1

588

(0

228

)(0

247

)(0

253

)(0

234

)(0

254

)(0

244

)0

255

(02

71)

Ger

man

y0

554

ndash0

494

ndash0

030

ndash03

08ndash1

338

056

8

022

70

270

(02

52)

(02

55)

(02

49)

(02

51)

(02

49)

(02

22)

022

8(0

227

)R

est

of E

urop

e0

492

ndash0

298

ndash02

18ndash0

160

ndash07

96

1

237

1

189

1

032

(0

219

)(0

223

)(0

222

)(0

225

)(0

224

)(0

234

)(0

225

)(0

212

)R

est

of t

he w

orld

030

5ndash0

075

039

0ndash0

224

ndash07

75

1

597

1

301

0

951

(0

323

)(0

304

)(0

291

)(0

307

)(0

286

)(0

323

)(0

283

)(0

294

)

Obs

erva

tion

s37

136

436

137

236

336

735

335

7M

cFad

den

pseu

do-R

20

032

001

50

012

001

00

029

001

80

009

001

3

Not

eSi

gnif

ican

t at

the

00

1

0

05 a

nd

01

0 le

vels













Tab

le 2

C

oeff

icie

nts

of

the

ord

ered

logi

t m

odel

s es

tim

atin

g th

e p

refe

ren

ces

of t

ouri

sts

in A

mst

erd

am f

or d

iffe

ren

t ty

pes

of

e-se

rvic

es

Inte

ract

ive

map

Per

son

aliz

ed in

form

atio

nB

ook

ing

serv

ice

Jou

rney

pla

nn

erE

-for

um

Vir

tual

tou

rsIn

tera

ctiv

e ga

mes

E-s

ervi

ce0

497

0

343

1

156

0

373

0

362

0

193

009

1(0

156

)(0

153

)(0

164

)(0

157

)(0

147

)(0

152

)(0

168

)E

duca

tion

007

4ndash0

205

005

50

026

ndash02

10

)ndash0

124

ndash0

339

(00

62)

(00

65)

(00

65)

(00

59)

(00

63)

(00

65)

(00

70)

Gen

der

008

2ndash0

070

ndash00

650

158

ndash00

94ndash0

294

ndash0

423

(01

47)

(01

45)

(01

48)

(01

44)

(01

42)

(01

43)

(01

61)

Age

ndash01

89

ndash01

82ndash0

248

ndash0

103

ndash04

70

0

011

ndash04

34

(0

114

)(0

117

)(0

116

)(0

107

)(0

107

)(0

114

)(0

123

)E

mpl

oyed

031

7

ndash00

260

098

016

70

177

018

6ndash0

054

(01

60)

(01

63)

(01

57)

(01

52)

(01

52)

(01

52)

(01

78)

USA

081

4

070

6

075

5

ndash02

940

261

034

1ndash0

171

(02

17)

(02

35)

(02

41)

(02

14)

(02

15)

(02

28)

(02

52)

UK

059

4

060

1

068

0

036

80

254

045

80

675

(0

245

)(0

262

)(0

234

)(0

244

)(0

251

)(0

235

)(0

259

)G

erm

any

032

80

247

ndash00

47ndash0

808

ndash00

700

052

013

1(0

233

)(0

234

)(0

243

)(0

232

)(0

232

)(0

231

)(0

273

)R

est

of E

urop

e0

894

0

792

0

593

ndash0

500

0

460

0

396

028

6(0

217

)(0

225

)(0

221

)(0

219

)(0

230

)(0

214

)(0

248

)R

est

of t

he w

orld

082

3

102

1

131

5

ndash04

710

735

0

283

035

1(0

331

)(0

278

)(0

291

)(0

303

)(0

260

)(0

272

)(0

315

)

Obs

erva

tion

s65

065

165

165

165

165

165

0M

cFad

den

pseu

do-R

20

026

002

20

055

001

80

027

001

00

047

Not

eSi

gnif

ican

t at

the

00

1

0

05 a

nd

01

0 le

vels








Microsimulation































SAE = TAEN

























Visit 365221 7

Total 2728722 55












Visit 5 4 34 39 47 6 79



Visit 92 73 61 51 26 75 13



Conclusions







Endnotes






References












































Appendix 1









=



Appendix 2


Factors1 2 3 4 5




Appendix 3


Factors1 2




Tourist preferences
















Tab

le 1

C

oeff

icie

nts

of

the

ord

ered

logi

t m

odel

s es

tim

atin

g th

e p

refe

ren

ces

of t

ouri

sts

in A

mst

erd

am f

or d

iffe

ren

t k

ind

s of

cu

ltu

ral h

erit

age

Arc

hit

ectu

reM

onu

men

tsM

use

um

sU

rban

lan

dsc

ape

Cu

ltu

ral e

ven

tsT

rad

itio

ns

Cu

stom

sK

now

led

ge

E-s

ervi

ce0

205

031

9

058

1

033

4

026

20

257

018

00

014

(01

60)

(01

52)

(01

54)

(01

55)

(01

51)

(01

60)

(01

54)

(01

57)

Age

019

30

392

0

338

0

213

ndash0

351

ndash03

62

ndash0

197

0

062

(01

13)

(01

08)

(01

06)

(01

01)

(01

07)

(01

19)

(01

20)

(01

15)

Edu

cati

on0

229

0

090

010

30

187

ndash0

108

ndash0

098

ndash0

121

ndash0

132

(0

068

)(0

065

)(0

067

)(0

063

)(0

059

)(0

059

)0

056

(00

59)

Gen

der

025

50

269

0

310

0

528

0

109

048

1

038

0

024

8(0

147

)(0

141

)(0

146

)(0

144

)(0

140

)(0

143

)0

141

(01

43)

USA

023

30

254

ndash00

76ndash0

039

ndash07

47

1

790

1

503

1

199

(0

221

)(0

217

)(0

217

)(0

214

)(0

224

)(0

252

)0

240

(02

45)

UK

021

0ndash0

004

ndash02

71ndash0

260

ndash07

85

1

915

1

518

1

588

(0

228

)(0

247

)(0

253

)(0

234

)(0

254

)(0

244

)0

255

(02

71)

Ger

man

y0

554

ndash0

494

ndash0

030

ndash03

08ndash1

338

056

8

022

70

270

(02

52)

(02

55)

(02

49)

(02

51)

(02

49)

(02

22)

022

8(0

227

)R

est

of E

urop

e0

492

ndash0

298

ndash02

18ndash0

160

ndash07

96

1

237

1

189

1

032

(0

219

)(0

223

)(0

222

)(0

225

)(0

224

)(0

234

)(0

225

)(0

212

)R

est

of t

he w

orld

030

5ndash0

075

039

0ndash0

224

ndash07

75

1

597

1

301

0

951

(0

323

)(0

304

)(0

291

)(0

307

)(0

286

)(0

323

)(0

283

)(0

294

)

Obs

erva

tion

s37

136

436

137

236

336

735

335

7M

cFad

den

pseu

do-R

20

032

001

50

012

001

00

029

001

80

009

001

3

Not

eSi

gnif

ican

t at

the

00

1

0

05 a

nd

01

0 le

vels













Tab

le 2

C

oeff

icie

nts

of

the

ord

ered

logi

t m

odel

s es

tim

atin

g th

e p

refe

ren

ces

of t

ouri

sts

in A

mst

erd

am f

or d

iffe

ren

t ty

pes

of

e-se

rvic

es

Inte

ract

ive

map

Per

son

aliz

ed in

form

atio

nB

ook

ing

serv

ice

Jou

rney

pla

nn

erE

-for

um

Vir

tual

tou

rsIn

tera

ctiv

e ga

mes

E-s

ervi

ce0

497

0

343

1

156

0

373

0

362

0

193

009

1(0

156

)(0

153

)(0

164

)(0

157

)(0

147

)(0

152

)(0

168

)E

duca

tion

007

4ndash0

205

005

50

026

ndash02

10

)ndash0

124

ndash0

339

(00

62)

(00

65)

(00

65)

(00

59)

(00

63)

(00

65)

(00

70)

Gen

der

008

2ndash0

070

ndash00

650

158

ndash00

94ndash0

294

ndash0

423

(01

47)

(01

45)

(01

48)

(01

44)

(01

42)

(01

43)

(01

61)

Age

ndash01

89

ndash01

82ndash0

248

ndash0

103

ndash04

70

0

011

ndash04

34

(0

114

)(0

117

)(0

116

)(0

107

)(0

107

)(0

114

)(0

123

)E

mpl

oyed

031

7

ndash00

260

098

016

70

177

018

6ndash0

054

(01

60)

(01

63)

(01

57)

(01

52)

(01

52)

(01

52)

(01

78)

USA

081

4

070

6

075

5

ndash02

940

261

034

1ndash0

171

(02

17)

(02

35)

(02

41)

(02

14)

(02

15)

(02

28)

(02

52)

UK

059

4

060

1

068

0

036

80

254

045

80

675

(0

245

)(0

262

)(0

234

)(0

244

)(0

251

)(0

235

)(0

259

)G

erm

any

032

80

247

ndash00

47ndash0

808

ndash00

700

052

013

1(0

233

)(0

234

)(0

243

)(0

232

)(0

232

)(0

231

)(0

273

)R

est

of E

urop

e0

894

0

792

0

593

ndash0

500

0

460

0

396

028

6(0

217

)(0

225

)(0

221

)(0

219

)(0

230

)(0

214

)(0

248

)R

est

of t

he w

orld

082

3

102

1

131

5

ndash04

710

735

0

283

035

1(0

331

)(0

278

)(0

291

)(0

303

)(0

260

)(0

272

)(0

315

)

Obs

erva

tion

s65

065

165

165

165

165

165

0M

cFad

den

pseu

do-R

20

026

002

20

055

001

80

027

001

00

047

Not

eSi

gnif

ican

t at

the

00

1

0

05 a

nd

01

0 le

vels








Microsimulation































SAE = TAEN

























Visit 365221 7

Total 2728722 55












Visit 5 4 34 39 47 6 79



Visit 92 73 61 51 26 75 13



Conclusions







Endnotes






References












































Appendix 1









=



Appendix 2


Factors1 2 3 4 5




Appendix 3


Factors1 2














Tab

le 1

C

oeff

icie

nts

of

the

ord

ered

logi

t m

odel

s es

tim

atin

g th

e p

refe

ren

ces

of t

ouri

sts

in A

mst

erd

am f

or d

iffe

ren

t k

ind

s of

cu

ltu

ral h

erit

age

Arc

hit

ectu

reM

onu

men

tsM

use

um

sU

rban

lan

dsc

ape

Cu

ltu

ral e

ven

tsT

rad

itio

ns

Cu

stom

sK

now

led

ge

E-s

ervi

ce0

205

031

9

058

1

033

4

026

20

257

018

00

014

(01

60)

(01

52)

(01

54)

(01

55)

(01

51)

(01

60)

(01

54)

(01

57)

Age

019

30

392

0

338

0

213

ndash0

351

ndash03

62

ndash0

197

0

062

(01

13)

(01

08)

(01

06)

(01

01)

(01

07)

(01

19)

(01

20)

(01

15)

Edu

cati

on0

229

0

090

010

30

187

ndash0

108

ndash0

098

ndash0

121

ndash0

132

(0

068

)(0

065

)(0

067

)(0

063

)(0

059

)(0

059

)0

056

(00

59)

Gen

der

025

50

269

0

310

0

528

0

109

048

1

038

0

024

8(0

147

)(0

141

)(0

146

)(0

144

)(0

140

)(0

143

)0

141

(01

43)

USA

023

30

254

ndash00

76ndash0

039

ndash07

47

1

790

1

503

1

199

(0

221

)(0

217

)(0

217

)(0

214

)(0

224

)(0

252

)0

240

(02

45)

UK

021

0ndash0

004

ndash02

71ndash0

260

ndash07

85

1

915

1

518

1

588

(0

228

)(0

247

)(0

253

)(0

234

)(0

254

)(0

244

)0

255

(02

71)

Ger

man

y0

554

ndash0

494

ndash0

030

ndash03

08ndash1

338

056

8

022

70

270

(02

52)

(02

55)

(02

49)

(02

51)

(02

49)

(02

22)

022

8(0

227

)R

est

of E

urop

e0

492

ndash0

298

ndash02

18ndash0

160

ndash07

96

1

237

1

189

1

032

(0

219

)(0

223

)(0

222

)(0

225

)(0

224

)(0

234

)(0

225

)(0

212

)R

est

of t

he w

orld

030

5ndash0

075

039

0ndash0

224

ndash07

75

1

597

1

301

0

951

(0

323

)(0

304

)(0

291

)(0

307

)(0

286

)(0

323

)(0

283

)(0

294

)

Obs

erva

tion

s37

136

436

137

236

336

735

335

7M

cFad

den

pseu

do-R

20

032

001

50

012

001

00

029

001

80

009

001

3

Not

eSi

gnif

ican

t at

the

00

1

0

05 a

nd

01

0 le

vels













Tab

le 2

C

oeff

icie

nts

of

the

ord

ered

logi

t m

odel

s es

tim

atin

g th

e p

refe

ren

ces

of t

ouri

sts

in A

mst

erd

am f

or d

iffe

ren

t ty

pes

of

e-se

rvic

es

Inte

ract

ive

map

Per

son

aliz

ed in

form

atio

nB

ook

ing

serv

ice

Jou

rney

pla

nn

erE

-for

um

Vir

tual

tou

rsIn

tera

ctiv

e ga

mes

E-s

ervi

ce0

497

0

343

1

156

0

373

0

362

0

193

009

1(0

156

)(0

153

)(0

164

)(0

157

)(0

147

)(0

152

)(0

168

)E

duca

tion

007

4ndash0

205

005

50

026

ndash02

10

)ndash0

124

ndash0

339

(00

62)

(00

65)

(00

65)

(00

59)

(00

63)

(00

65)

(00

70)

Gen

der

008

2ndash0

070

ndash00

650

158

ndash00

94ndash0

294

ndash0

423

(01

47)

(01

45)

(01

48)

(01

44)

(01

42)

(01

43)

(01

61)

Age

ndash01

89

ndash01

82ndash0

248

ndash0

103

ndash04

70

0

011

ndash04

34

(0

114

)(0

117

)(0

116

)(0

107

)(0

107

)(0

114

)(0

123

)E

mpl

oyed

031

7

ndash00

260

098

016

70

177

018

6ndash0

054

(01

60)

(01

63)

(01

57)

(01

52)

(01

52)

(01

52)

(01

78)

USA

081

4

070

6

075

5

ndash02

940

261

034

1ndash0

171

(02

17)

(02

35)

(02

41)

(02

14)

(02

15)

(02

28)

(02

52)

UK

059

4

060

1

068

0

036

80

254

045

80

675

(0

245

)(0

262

)(0

234

)(0

244

)(0

251

)(0

235

)(0

259

)G

erm

any

032

80

247

ndash00

47ndash0

808

ndash00

700

052

013

1(0

233

)(0

234

)(0

243

)(0

232

)(0

232

)(0

231

)(0

273

)R

est

of E

urop

e0

894

0

792

0

593

ndash0

500

0

460

0

396

028

6(0

217

)(0

225

)(0

221

)(0

219

)(0

230

)(0

214

)(0

248

)R

est

of t

he w

orld

082

3

102

1

131

5

ndash04

710

735

0

283

035

1(0

331

)(0

278

)(0

291

)(0

303

)(0

260

)(0

272

)(0

315

)

Obs

erva

tion

s65

065

165

165

165

165

165

0M

cFad

den

pseu

do-R

20

026

002

20

055

001

80

027

001

00

047

Not

eSi

gnif

ican

t at

the

00

1

0

05 a

nd

01

0 le

vels








Microsimulation































SAE = TAEN

























Visit 365221 7

Total 2728722 55












Visit 5 4 34 39 47 6 79



Visit 92 73 61 51 26 75 13



Conclusions







Endnotes






References












































Appendix 1









=



Appendix 2


Factors1 2 3 4 5




Appendix 3


Factors1 2















Tab

le 2

C

oeff

icie

nts

of

the

ord

ered

logi

t m

odel

s es

tim

atin

g th

e p

refe

ren

ces

of t

ouri

sts

in A

mst

erd

am f

or d

iffe

ren

t ty

pes

of

e-se

rvic

es

Inte

ract

ive

map

Per

son

aliz

ed in

form

atio

nB

ook

ing

serv

ice

Jou

rney

pla

nn

erE

-for

um

Vir

tual

tou

rsIn

tera

ctiv

e ga

mes

E-s

ervi

ce0

497

0

343

1

156

0

373

0

362

0

193

009

1(0

156

)(0

153

)(0

164

)(0

157

)(0

147

)(0

152

)(0

168

)E

duca

tion

007

4ndash0

205

005

50

026

ndash02

10

)ndash0

124

ndash0

339

(00

62)

(00

65)

(00

65)

(00

59)

(00

63)

(00

65)

(00

70)

Gen

der

008

2ndash0

070

ndash00

650

158

ndash00

94ndash0

294

ndash0

423

(01

47)

(01

45)

(01

48)

(01

44)

(01

42)

(01

43)

(01

61)

Age

ndash01

89

ndash01

82ndash0

248

ndash0

103

ndash04

70

0

011

ndash04

34

(0

114

)(0

117

)(0

116

)(0

107

)(0

107

)(0

114

)(0

123

)E

mpl

oyed

031

7

ndash00

260

098

016

70

177

018

6ndash0

054

(01

60)

(01

63)

(01

57)

(01

52)

(01

52)

(01

52)

(01

78)

USA

081

4

070

6

075

5

ndash02

940

261

034

1ndash0

171

(02

17)

(02

35)

(02

41)

(02

14)

(02

15)

(02

28)

(02

52)

UK

059

4

060

1

068

0

036

80

254

045

80

675

(0

245

)(0

262

)(0

234

)(0

244

)(0

251

)(0

235

)(0

259

)G

erm

any

032

80

247

ndash00

47ndash0

808

ndash00

700

052

013

1(0

233

)(0

234

)(0

243

)(0

232

)(0

232

)(0

231

)(0

273

)R

est

of E

urop

e0

894

0

792

0

593

ndash0

500

0

460

0

396

028

6(0

217

)(0

225

)(0

221

)(0

219

)(0

230

)(0

214

)(0

248

)R

est

of t

he w

orld

082

3

102

1

131

5

ndash04

710

735

0

283

035

1(0

331

)(0

278

)(0

291

)(0

303

)(0

260

)(0

272

)(0

315

)

Obs

erva

tion

s65

065

165

165

165

165

165

0M

cFad

den

pseu

do-R

20

026

002

20

055

001

80

027

001

00

047

Not

eSi

gnif

ican

t at

the

00

1

0

05 a

nd

01

0 le

vels








Microsimulation































SAE = TAEN

























Visit 365221 7

Total 2728722 55












Visit 5 4 34 39 47 6 79



Visit 92 73 61 51 26 75 13



Conclusions







Endnotes






References












































Appendix 1









=



Appendix 2


Factors1 2 3 4 5




Appendix 3


Factors1 2










Microsimulation































SAE = TAEN

























Visit 365221 7

Total 2728722 55












Visit 5 4 34 39 47 6 79



Visit 92 73 61 51 26 75 13



Conclusions







Endnotes






References












































Appendix 1









=



Appendix 2


Factors1 2 3 4 5




Appendix 3


Factors1 2































SAE = TAEN

























Visit 365221 7

Total 2728722 55












Visit 5 4 34 39 47 6 79



Visit 92 73 61 51 26 75 13



Conclusions







Endnotes






References












































Appendix 1









=



Appendix 2


Factors1 2 3 4 5




Appendix 3


Factors1 2






















Visit 365221 7

Total 2728722 55












Visit 5 4 34 39 47 6 79



Visit 92 73 61 51 26 75 13



Conclusions







Endnotes






References












































Appendix 1









=



Appendix 2


Factors1 2 3 4 5




Appendix 3


Factors1 2








Visit 5 4 34 39 47 6 79



Visit 92 73 61 51 26 75 13



Conclusions







Endnotes






References












































Appendix 1









=



Appendix 2


Factors1 2 3 4 5




Appendix 3


Factors1 2







Endnotes






References












































Appendix 1









=



Appendix 2


Factors1 2 3 4 5




Appendix 3


Factors1 2






References












































Appendix 1









=



Appendix 2


Factors1 2 3 4 5




Appendix 3


Factors1 2






















Appendix 1









=



Appendix 2


Factors1 2 3 4 5




Appendix 3


Factors1 2




Appendix 3


Factors1 2



a microsimulation model for e-services in cultural heritage tourism

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