jang morrisono leary2004jttm

A Procedure for Target Market Selection in Tourism

SooCheong (Shawn) JangAlastair M Morrison

Joseph T OrsquoLeary

ABSTRACT The primary objective of this research was to evaluate the attractiveness of travelactivity segments to assist with target market selection Prior studies evaluating segment attractive-ness have used ranking systems to determine the best markets However due to a lack of precisionin these ranking procedures they have not effectively reflected the degree to which one segment ismore attractive than the others This research attempted to provide more quantifiable and sophisti-cated evaluation criteria Using mean expenditure expenditure risk segment size and segmentsize risk as the evaluation criteria the resulting segments were assessed and compared Threerisk-adjusted indexes were also introduced to simultaneously consider expenditure level marketsegment size market potential and risk in the process of segment attractiveness evaluation Thetarget market selection procedure suggested should be helpful to marketers who are most con-cerned with the market and economic potential of available travel segments [Article copies avail-able for a fee from The Haworth Document Delivery Service 1-800-HAWORTH E-mail addressltdocdeliveryhaworthpresscomgt Website lthttpwwwHaworthPresscomgt copy 2004 by The Haworth PressInc All rights reserved]

KEYWORDS Target market selection segment attractiveness activity segmentation travelexpenditure cluster analysis

INTRODUCTION

Target market selection is an important stepin establishing a marketing strategy A targetmarket in tourism is a market segment that atravel organization decides to serve and it con-sists of travelers who share common charac-teristics (Kotler Bowen amp Makens 1999)Rarely if ever can one tourism destination ororganization succeed in being all things to all

travelers The main objectives in selecting tar-get markets are to maximize the effectivenessof marketing programs to more efficientlyutilize limited marketing resources and bud-gets and to produce the greatest economicbenefits

Target marketing is becoming more crucialin tourism today With higher disposable in-comes and more time coupled with a muchgreater variety of tourism offerings travelers

SooCheong (Shawn) Jang is Assistant Professor Department of Hotel Restaurant Institutional Managementand Dietetics Kansas State University Alastair M Morrison is Professor Department of Hospitality and TourismManagement Purdue University Joseph T OrsquoLeary is Professor and Head Department of Recreation Park ampTourism Sciences Texas AampM University

Address correspondence to SooCheong (Shawn) Jang Department of Hotel Restaurant Institutional Manage-ment and Dietetics Kansas State University Manhattan KS 66506-1404 (E-mail jangsksuedu)

The authors express their appreciation to the Canadian Tourism Commission (CTC) for providing the 1998French Pleasure Travel Markets to North America data set Price Waterhouse Coopers collected the data used in thisstudy Neither the collector of the original data nor the CTC are responsible for the interpretations reported here

Journal of Travel amp Tourism Marketing Vol 16(1) 2004httpwwwhaworthpresscomwebJTTM

2004 by The Haworth Press Inc All rights reservedDigital Object Identifier 101300J073v16n01_02 17

now demand a much wider range of travel ex-periences The immediate challenge for tour-ism organizations is how to meet these diversi-fied traveler needs Target marketing requires astrong focus on specific traveler groups andtailor-making products to meet their uniquedemands By targeting tourism marketers withlimited resources increase their probability ofmarketing success and are more likely toachieve their marketing objectives Howeveras travel markets continue to splinter and be-come more complex one of the greatest chal-lenges is to select the best set of market seg-ments This research addressed this challengeby providing a procedure for evaluating the at-tractiveness of individual travel market seg-ments

The process leading to target market selectionshould involve five sequential steps (1) deter-mining segmentation variable or variables(2) conducting market segmentation (3) pro-filing identified segments (4) evaluating seg-ment attractiveness and (5) selecting targetmarket(s) (Figure 1)

Many variables have been recommended asviable segmentation bases but researchersseem to agree that there is no single ideal seg-mentation base that fits in every situation(Morrison 2002) However several research-ers have suggested that travel activity segmen-tation is one of the best segmentation bases fortourism (Choi and Tsang 1999 Hsieh OrsquoLearyand Morrison 1992 Rao Thomas and Javalgi1992) They argue that activity segmentationhelps with the bundling of travel activities intopackages with greater market appeal Thebundles or packages of preferred activities canbe considered sub-aggregates of the total travelmarket (Romsa 1973) Another major claimabout activity-based segmentation is thattravel activities can be connected with the eco-nomic benefits to the destination For exam-ple the expenditures of people with shoppingand bird watching as travel activities can be

compared A study by Spotts and Mahoney(1993) supported the proposition that there is aclose relationship between travel activitiesand expenditures As such it may be possiblefor tourism marketers to develop appropriateproducts for their selected target markets andto estimate the economic benefits of eachproduct through activity segmentation Forthese reasons this research employed travelactivities as the segmentation base to analyzea specific travel origin market (France)

Middleton and Clarke (2001) define marketsegmentation as the process of dividing a totalmarket such as all visitors or a market sectorsuch as holiday travel into sub-groups for mar-keting management purposes The resultingsegments are assumed to have homogeneoustravel behaviors The other requirement of mar-ket segmentation is to find meaningful differ-ences among the segments within a total mar-ket This process provides tourism marketerswith a greater understanding of individual mar-kets and more precise ideas for product devel-opment One of the common ways to identifythe differences is to profile the segments of thetotal market Profiling helps by distinguishingthe attitudes behaviors socio-demographicstravel planning patterns and trip-related char-acteristics of travel market segments

The evaluation of market segments is thestep before target market selection and is criti-cal to the potential success of a marketingstrategy The key issue is which segments aremost likely to lead to the achievement of mar-keting goals and objectives The answer dif-fers based upon the criteria used to evaluatethe relative merits of each market segmentThe criteria for judging segment attractivenessinclude (1) market potential (2) competitionand segment structural attractiveness (3) mar-keting organizationrsquos vision goals and objec-tives (4) serviceability and (5) costs (Heath ampWall 1992 Kotler 1991 Kotler Bowen ampMakens 1999 McKercher 1995)

18 JOURNAL OF TRAVEL amp TOURISM MARKETING

DeterminingSegmentation

Variable(s)

ConductingMarket

Segmentation

ProfilingIdentifiedSegments

EvaluatingSegment

Attractiveness

SelectingTarget

Market(s)

FIGURE 1 Steps in Target Market Selection

Note Adapted from Pride and Ferrell (2000)

This research applied the first criterionmarket potential A target market must satisfythe condition of substantiality meaning that itmust be large enough to be economically via-ble (Kotler et al 1999) The underlying ratio-nale here is that a market with greater marketpotential is more attractive Thus for a traveldestination the market potential of the targetmarket in terms of expenditures should beviewed as one of the most important selectioncriteria In addition to market potential risk isa second factor that should be evaluated sincerisk negatively influences the level of ex-pected expenditures as frequently noted in fi-nance research (Board and Sutcliffe 1991Cardozo and Wind 1985) Risk in this casemeans level of uncertainty as to whether or nota destination can have a certain level of travelexpenditure That is if the probability of at-tracting travel expenditures is low or the levelof the expenditures drastically varies within ayear or between years due to fluctuating de-mand a market segment is not as attractive aswhen it has a high probability and stable ex-penditures Therefore it is suggested that mar-ket potential and risk of travel market seg-ments should occupy a central position inevaluating segment attractiveness and select-ing the most appropriate target markets (stepsfour and five in Figure 1)

The French International Travel Market

Despite the importance of target marketingonly a limited number of prior empirical stud-ies were found that dealt with target market se-lection (Jang Morrison and OrsquoLeary 2002Loker and Perdue 1992 McQueen and Miller1985) Additionally a perusal of the literaturerevealed that there has been no research on theevaluation of the market potential and risk oftravel market segments To fill this researchgap while providing a useful activity segmen-tation of an important international travel mar-ket this research examined French outboundtravelers As one of the major economic pow-ers in the world France has played an impor-tant role through its economic contributions toworld tourism The international travel expen-ditures excluding transportation by Frenchoutbound travelers amounted to $177 billionin 1999 putting France on the worldrsquos fifth

position (WTO 2001) This research is ex-pected to provide useful insights into theplanning development and marketing for in-ternational travel planners and destination mar-keters by identifying activity segments ofFrench outbound travelers and then evaluatingthe resulting segments using the market poten-tial and risk concepts

Research Objectives

The main objectives of this research wereto (1) identify the activity segments of Frenchoutbound travelers (2) profile the activity seg-ments (3) determine if there were statisticaldifferences across the segments in terms ofsocio-demographic and trip-related character-istics (4) evaluate the activity segments on thebasis of market potential and the risk and(5) recommend the activity segment with thegreatest market potential bearing in mind theirrisk

REVIEW OF RELATED LITERATURE

Activity Segmentation in Tourism

The use of travel activity as a segmentationbase is a relatively recent development in tour-ism research Using factor analysis Bryantand Morrison (1980) identified vacation activ-ity preferences by six distinct traveler typesyoung sports outdoorsman hunter winterwa-ter resort type sightseer and nightlife activi-ties To implement new marketing strategiesin Michigan the researchers analyzed the eco-nomic impact of these activity segments usingtravel expenditures and evaluated the past ad-vertising and promotional efforts Rao et al(1992) focused on the activity preferences andtravel-planning behaviors of US outboundpleasure travelers Concluding that activitytypes might be associated with destinationchoices the authors suggested that activity-based segments provide destination marketerswith valuable information on the best businessopportunities and the most appropriate activi-ties to include in product development Usingactivity segmentation Hsieh et al (1992) clus-tered Hong Kong international pleasure trav-elers into five groups visiting friends and rela-

Jang Morrison and OrsquoLeary 19

tives outdoor sports sightseeing full-house activityand entertainment Significant statistical dif-ferences were found across the groups insocio-demographic and trip-related variablessuch as age education occupation and partysize The results suggested that activity seg-ments have unique socio-demographic andtrip-related characteristics indicating the exis-tence of distinct sub-markets Choi and Tsang(1999) completed a more recent study and theresulting segmentation scheme closely resem-bled the activity clusters of Hong Kong plea-sure travelers found by Hsieh et al (1992) Theresearchers also used cluster analysis and theyfound four activity segments sightseeing out-door sports entertainment and outdoor activi-ties and visiting friends and relatives As inthe Hsieh et al study statistically significantdifferences were found among the activitysegments in terms of socio-demographic andtrip-related variables The results showed thatmost of Hong Kongrsquos private housing travel-ers were young and had fairly high educationlevels

In relation to economic contribution to des-tinations a few studies using expenditure lev-els as their segmentation base have been con-ducted (Jang Ismail and Ham 2002 Pizamand Reichel 1979 Spotts and Mahoney 1991)Pizam and Reichel (1979) first identified de-mographic and socioeconomic variables thatdifferentiate between big and small spenderson domestic travel in the US and found thatseveral variables including education maritalstatus market value of owned home and num-ber of cars helped to discriminate the spend-ersrsquo segments Spotts and Mahoney (1991) at-tempted to group visitors to Michiganrsquos UpperPeninsula into three groups and discoveredthat heavy spenders were distinguishable insome variables such as party size length oftravel level of involvement in recreation ac-tivities and use of information A recent studyby Jang Ismail and Ham (2002) investigatedthe expenditure level of Japanese outboundpleasure travelers The results showed a fewinteresting points that Japanese travelers to theUS mainland Canada Europe and Oceaniashowed greater propensity to spend whencompared to those to Asian countries Hawaiiand Guam and that honeymooners and the

travelers for combined business and pleasurepurposes were the big spender segment

These groups of studies show striking simi-larities in research methods statistical analy-ses and even interpretation and implicationsThis past research on activity and expendituresegmentation has provided a solid basis for fu-ture research and marketing but there is aneed for further innovative approaches to ex-tend the value of this approach Another studyseems to contribute to developing a morepractical understanding of activity segmentsand to showing how to approach target marketselection from the economic value perspec-tive Spotts and Mahoney (1993) investigatedwhether the characteristics of fall tourists dif-fered from those of summer tourists Theysegmented fall tourists based on the combina-tions of recreation activity participation andestimated the average per-trip and per-dayspending by activity market segment Thestudy showed that it was possible to estimatethe segmentsrsquo sizes and spending levels andthereby to calculate the potential economiccontributions of each segment As Morrison(2002) pointed out the application of activitysegmentation in vacation package develop-ment and marketing may improve profitabilityby enhancing the appeal to specific target seg-ments

Evaluation of Market SegmentAttractiveness

Little previous research has been conductedon the evaluation of travel segment attractive-ness to support target market selection Asmentioned earlier Bryant and Morrison (1980)utilized travel expenditures to evaluate thelevels of economic value of different activitytypes in Michigan They suggested that expen-ditures should function as a key barometer ofthe level of economic contributions to a desti-nation To determine segment attractivenessMcQueen and Miller (1985) considered theprofitability variability and accessibility ofsegments Profitability was calculated as therelative weighted population size times themean expenditures of each group The proba-bility of revisiting the destination representedvariability The researchers attempted to de-scribe a systematic approach for selecting tar-


get markets Using vacation benefits soughtLoker and Perdue (1992) applied three evalua-tion criteria for target market selection profit-ability accessibility and reachability Therewere three measures of profitability for thenon-resident summer travel market in NorthCarolina the percentages of total expendituresrelated to percentages of respondents for eachof the identified segments the percentages oftotal person-nights and average expendituresper person per night Each segment was rankedon its relative performance on all three evalua-tion criteria the lowest ranking was assigned avalue of 1 and the highest the same value asthe number of segments The overall rankingfor each segment was determined by summingthe scores across the criteria

The main limitation of these previous stud-ies was in the lack of precision in the rankingprocedure With these ranking systems it wasdifficult to determine the degree to which onesegment was superior over another The devel-opment of a more precise quantitative methodfor evaluating market segments was stillneeded A most recent research study by Janget al (2002) provided a breakthrough in thisrespect These researchers introduced the prof-itability and risk concepts that have been welldeveloped in the finance field and attemptedto simultaneously analyze segment profitabil-ity and risk in evaluating travel market seg-ment attractiveness Mean expenditures wereused as a proxy for profitability and the stan-dard deviation of a segmentrsquos mean expendi-tures was employed as the risk variable TheRisk-adjusted Profitability Index (RPI themean expenditure divided by the standard de-viation times one hundred) and Relative Seg-ment Size (RSS mean expenditure multipliedby the probability of the occurrence of a spe-cific segment) were applied for the overallevaluation of market segments and for targetmarket selection Despite the freshness of thisidea the weakness of the approach was thatthe risk concept had very limited scope anddid not address one the most serious risks intourism seasonal risk or seasonality This re-search is intended to advance the earlier re-search by addressing this weakness and creat-ing an easier-to-use procedure for evaluatingtravel market segments from an economicviewpoint

METHODOLOGY

Data Set and Sample Selection

This research used data from the PleasureTravel Markets Survey for France collectedby the Coopers amp Lybrand Consulting Groupin 1998 under the joint sponsorship of the Ca-nadian Tourism Commission and the Interna-tional Trade Administration-Tourism Indus-tries of the US With random sampling usingthe birth date method a total of 1221 personalinterviews in French households were con-ducted All respondents were 18 years or olderand had taken overseas vacations of fournights or more by plane outside of Europe andthe Mediterranean region in the past threeyears or were planning to take such a trip in thenext two years This comprehensive surveycollected information on socio-demographiccharacteristics (eg age gender marital sta-tus education occupation income) trip-re-lated characteristics (eg travel expenditurestravel activity participation travel regionsmonth of travel) benefits sought travel phi-losophies and levels of trip satisfaction

The sample used in this research was Frenchpleasure travelers who took non-package tripsThe reason for this choice was the assumptionthat expenditures associated with packagetours would not be a good indicator of themore general expenditure behavior of pleasuretravelers Given the nature of a package tourmost of the expenditures are incurred as pre-payments for pre-determined itineraries re-sulting in package travelers having differentexpenditure patterns than non-package travel-ers (Sung Morrison Hong and OrsquoLeary 2001)Of the 1221 interviews conducted 984 re-spondents reported expenditures for theirtrips and 475 respondents with package ex-penditures were eliminated from consider-ation In addition length of travel was checkedfor outlier detection and 13 respondents fall-ing outside the four standard deviations (morethan 90 days) were taken out of the data set(Hair Anderson Tatham and Black 1998pp 65) A total of 496 respondents were usedfor the analysis of French non-package over-seas travelers in this research


Analysis Methods

The data analysis procedures followed thesteps shown in Figure 1 leading to target mar-ket selection First after travel activity partici-pation was selected as the segmentation vari-able based on the objective of this researchtwo different types of cluster analysis were ap-plied for market segmentation The data wereinitially analyzed using a hierarchical cluster-ing procedure with squared Euclidean dis-tance as the similarity measure between casesWardrsquos Method was used to maximize within-cluster homogeneity and the number of clus-ters was determined based on an agglomera-tion schedule and dendrogram The K-Meansclustering technique as a nonhierarchical pro-cedure was then employed to fine-tune the re-sults even further by utilizing the hierarchicalresults as a basis for the cluster seed pointsAccording to Hair et al (1998 pp 498) nonhier-archical methods have advantages and gainincreased acceptability in that the results areless susceptible to the outliers and the dis-tance measure used The advantages can berealized only with the use of specified initialseed points where clusters are built around thesepoints Second cross tabulations were used toprofile the socio-demographic and trip-relatedcharacteristics of the resulting clusters Chi-square analyses and Analysis of Variance(ANOVA) determined whether statisticallysignificant differences existed among the clus-ters Third the market potential and risk levelsof the clusters were calculated and then usedto determine segment attractiveness and to se-lect activity segments with the greatest poten-tial


The market potential and risks of activitysegments were used as the measures of seg-ment attractiveness Travelersrsquo expenditurelevels within a destination are directly relatedto the revenues that each segment can gener-ate In other words the higher the travelersrsquoexpenditures the greater the business reve-nues that could be generated from that seg-ment and the greater economic potential themarket segment has for the destination As ex-

penditure levels have a direct relationshipwith market potential it appears reasonable touse a segmentrsquos mean expenditures as a proxyfor the segmentrsquos market potential More ana-lytically as suggested by Kotler (1991) theequation of market potential is Q = nqp whereQ is total market potential n is number of buy-ers q is quantity purchased by average buyerand p is average unit price The equation canbe rewritten as Q = nR where R is averagerevenue per buyer or average expenditure Ifthe equation is applied to tourism the R or av-erage expenditure per visitor or travel partyfunctions as a determinant factor for travelmarket potential Q However the average ex-penditure alone does not effectively representthe market potential of a segment and the seg-ment size is another critical indicator as shownin Kotlerrsquos equation where the number ofbuyers n is the segment size Thus the mar-ket potential of a travel segment was definedin this research as the mean expenditure pertravel party times segment size (Figure 2) Thenumber of respondents in each segment wasused as the indicator of segment size

Risk is another important measure for eval-uating segment attractiveness Risk usuallymeasures the probability that something unfa-vorable will occur The risk concept has beenextensively applied in the field of finance andin the most basic sense risk is the chance of afinancial loss Projects having greater chancesof loss are viewed as more risky than thosewith lesser chances In the marketing fieldrisk may represent the likelihood that a seg-ment may have less market potential than themean According to Brigham and Gapenski(1988) a popular measure of risk is the stan-


FIGURE 2 Formula for the Evaluation of SegmentAttractiveness

Name Formula

1 Market Potential of a Seg-ment

Mean Expenditures yen SegmentSize

2 Risk (1) Expenditure Risk(2) Segment Size Risk

3 Risk-adjusted Expenditure In-dex (REI)

(Mean ExpendituresExpendi-ture Risk) yen 100

4 Risk-adjusted Segment SizeIndex (RSSI)

(Segment SizeSegment SizeRisk) yen 100

5 Risk-adjusted Market Poten-tial Index (RMPI)

(REI yen RSSI)100

dard deviation which means the tightness ofthe probability or frequency distribution Thetighter the distribution the smaller the stan-dard deviation and accordingly the lower therisk of a business loss Two types of risk wereemployed in this research expenditure riskand segment size risk Expenditure risk mea-sured how far a segmentrsquos expenditures werefrom their mean If the observations wereclose to the mean so that the expendituredistribution was tight it indicated a highprobability that the mean expenditures wouldbe attained The number of travelers in eachsegment represented segment size Monthlyfrequencies were used to compute the segmentsize risk of the activity segments in this re-search If a segmentrsquos travelers were concen-trated in only a few months of a year this in-troduced greater uncertainty about the meanmonthly frequency of these visitors Seasonal-ity in a tourism destination causes economicand social problems including instability ofemployment income and tax revenue Thebest scenario is to have a segment with consis-tent month-to-month demand throughout theyear In this case segment size risk becomesminimal

To better evaluate segment attractiveness itwas necessary to simultaneously consider theexpenditure and risk concepts and two in-dexes developed in the Jang et al (2002) studywere adopted the Risk-adjusted ExpenditureIndex (REI) and Risk-adjusted Segment SizeIndex (RSSI) REI represented the mean ex-penditure divided by the standard deviationtimes one hundred and indicated the relativeexpenditure level per unit of risk This pro-vided a more meaningful basis for multiplecomparisons when the risk levels of segmentsinvolved were not the same The RSSI was thesegment size divided by the standard devia-tion times one hundred representing the rela-tive seasonal risk-adjusted frequency of eachsegment Finally to consider market potentialand its risk simultaneously multiplying REItimes RSSI and then dividing by one hundredproduced the Risk-adjusted Market PotentialIndex (RMPI) This represented the segmentrsquosmarket potential after adjusting for both ex-penditure and segment size risks Thus thehigher the RMPI the better the segment per-formed in terms of both market potential and

risk REI RSSI and RMPI were used for theoverall evaluation of market segments and fortarget market decision The formulas for theevaluation of segment attractiveness are sum-marized in Figure 2

ACTIVITY SEGMENTATION

Clustering and Labelingof Market Segments

The French travelers were grouped into ac-tivity participation segments using the sur-veyrsquos 44 activities as the clustering variablesBecause the variables that are multicollinearare weighted more heavily in clustering pro-cess multicollinearity was checked with Vari-ance Inflation Factor (VIF) The results of VIFrange from 1111 to 2048 which are well be-low a usual threshold of 100 (Hair et al 1998pp 220) Thus it is reasonable to decide thatthe clustering variables were not seriously af-fected by multicollinearity The four clustersconsisting of 140 (282) 125 (252) 86(174) and 145 (292) respondents wereidentified To label the four clusters activityparticipation rates were computed (Table 1)The labels were determined based on the mostpopular activities within each cluster

Cluster 1 Beach and Sunshine Lovers

The travelers in this cluster enjoyed swim-ming (886) sunbathing or other beach ac-tivities (857) together with sampling localfoods (907) They preferred informal or ca-sual dining with table service (736) for mealsand had a high participation rate in shopping(836)

Cluster 2 City Sightseers

This group had high proportions of sight-seeing in cities (808) seeing big moderncities (848) and sampling local foods (880)Both informal or casual dining with table ser-vice (840) and dining in fast food restau-rants or cafeterias (776) were important fortravelers in this segment



TABLE 1 Travel Activity Participation Rates of Clusters

(UnitPercentage)

Activity Participation C1 C2 C3 C4 Overall

Sampling local foods 907 880 954 552 804

Swimming 886 216 663 179 472

Sunbathing or other beach activities 857 72 628 165 417

Shopping 836 736 837 503 714

Getting to know local people 736 784 965 414 694

Informal or casual dining with table service 736 840 826 448 694

Walking tours 736 568 884 366 611

Visiting small towns and villages 729 664 954 214 601

Seeing local crafts and handiwork 721 632 895 255 593

Sightseeing in cities 621 808 919 359 643

Enjoying ethnic cultureevents 543 664 767 331 551

Seeing people from different ethnic background 536 568 767 241 498

Visiting friends or relatives 524 512 488 593 534

Visiting night clubs 493 464 500 317 436

Visiting remote coastal attractions 479 176 674 55 313

Outdoor activities such as climbing hiking etc 379 192 674 83 296

Visiting national state or provincial parks and forests 343 432 826 214 411

Dining in fine restaurants 336 384 500 317 371

Observing wildlifebird watching 336 256 733 48 300

Visits to appreciate natural ecological sites 300 296 767 152 337

Dining in fast food restaurants or cafeterias 286 776 465 324 452

Taking a cruise for a day or less 271 56 267 62 155

Divingsurfing 257 32 221 28 127

Visiting arts and cultural attractions 257 440 535 179 329

Water sports 257 32 221 28 139

Seeing big modern cities 250 848 709 379 518

Visiting protected landsareas 243 160 640 69 240

Visiting mountainous areas 207 232 721 110 274

Visiting places with religious significance 207 408 686 144 323

Visiting museumsgalleries 193 672 779 317 452

Huntingfishing 172 08 105 35 79

Seeing unique aboriginal or native groups 171 192 477 48 194

Taking a nature andor science learning trip 150 184 523 89 206

Visiting places of historical interest 150 528 861 200 383

Visiting scenic landmarks 150 672 861 172 411

Short guided excursionstours 143 328 570 124 258

Golfingtennis 107 40 93 21 63

Attending local festivalsfairs 86 152 500 76 171

Visiting sites commemorating people 79 328 547 110 232

Visiting casinos and other gambling 71 104 116 97 95

Visiting theme parks or amusement parks 71 176 372 207 189

Attending spectator sporting events 64 112 174 14 81

Bicycle riding 64 96 151 48 83

Visiting places of archaeological interest

Average Number of Activity (Unit Activity)

36

161

104

169

535

263

55

87

145

159

Note 1 Multiple responses2 Rows are arrayed in descending order according to the participation rates in Cluster 13 The bold-faced number means the highest percentage among the clusters

Cluster 3 Culture and Nature Enthusiasts

The people in this segment showed thegreatest interest in participating in a broadrange of activities especially in cultural andnature activities Their highest participationrates were for getting to know local people(965) sampling local foods (954) andvisiting small towns and villages (954)Compared to the other three segments thesetravelers had higher participation in nature-based activities such as visiting national stateor provincial parks and forests (826) Thisgroup had the most active travelers participat-ing on the average in 263 of the 44 specifiedactivities

Cluster 4 Visiting Friends and Relatives

This was the largest (292 of the totalmarket) of the four groups and had the highestparticipation rate for visiting friends or rela-tives The French travelers in this group werethe least active in terms of participation ratesfor most of the travel activities Only a few ac-tivities such as sampling local foods (552)and shopping (503) were over the 50 par-ticipation rate On the average they only par-ticipated in 87 of the 44 activities

Socio-Demographic and Trip-RelatedProfiles of Activity Clusters

The profile of the clustersrsquo socio-demogra-phic and trip-related characteristics providesuseful information for destination marketerson the members of each segment and howthey behave when traveling overseas Theprofiles were generated using cross-tabulationanalyses Chi-square analyses or ANOVAswere conducted to determine whether signifi-cant differences existed among the segmentsThese analyses showed that marital status andoccupation differed significantly across thefour clusters while age gender educationand household income were not significantlydifferent

Table 2 shows that all four clusters had thehighest proportions in the 30s suggesting thatFrench travelers 30-year age group are the larg-est outbound travel market The age and gender

distributions of the segments were similar andhad no statistical differences The majority ofpeople in most of the clusters were married orliving together but almost half of Cluster 2 (CitySightseers) were single Cluster 4 (VisitingFriends and Relatives) included more marriedtravelers (379) Most of travelers had sec-ondary school educations or higher suggestingthat these non-package French outbound trav-elers were a well-educated group Moreoverover 40 of all four clusters had received a col-lege education or above the proportion wasparticularly high in Cluster 2 (536) Thelargest occupational group for all four activityclusters was white-collar workers

Cluster 3 had the highest proportion in thenon-working housewiferetired category whichmay partially explain why these travelers wereinvolved in the greatest number of activitiesand spent the most nights away from home(274 nights) as shown in Table 3 The highestproportion of blue-collar workers (114)was in Cluster 1 (Beach and Sunshine Lovers)This may be due to the nature of labor-ori-ented jobs where physical fatigue at worktends to require rest and relaxation on vaca-tion About 50 or more of respondents in allfour clusters had monthly incomes ranging be-tween 6500 French Francs (FF) and 20000FF Less than 10 of the travelers in each ofthe four segments reported monthly incomesof 30000 FF or more

There were statistically significant differ-ences at the 01 level for all of the trip-relatedvariables (Table 3) As mentioned earlier Clus-ter 3 spent the greatest number of nights (274nights) away from home demonstrating theirdistinctiveness as active travelers whereasCluster 4 took the shortest trips (19 nights)All the clusters except for Cluster 2 had thehighest proportion in traveling with wifehus-bandboy or girl friend and an especially highpercentage (57) was found in Cluster 3Compared with the other three groups the mem-bers of Cluster 2 were more likely to travelalone or travel with friends Travelers in allfour clusters tended to travel more in the sum-mer and less in the fall Cluster 1 showed themost consistent travel patterns across the fourseasons Just over 50 of the persons in Clus-ter 2 were summer vacationers and this may berelated to their travel destinations The major


destinations of Cluster 1 included the West In-diesCaribbean and Other Africa where sun-bathing and swimming are possible irrespec-tive of season Canada and US were the maindestinations for Cluster 2 where many travelactivities are limited due to weather condi-tions in winter These two North Americancountries were also popular in Cluster 4 Thetravel regions for Cluster 3 were relativelyevenly distributed but Asian countries at-tracted the largest percentage from this group(254)

SEGMENT ATTRACTIVENESSAND TARGET MARKET SELECTION

Mean Expenditure and Expenditure Risk

As presented in Table 4 the expenditureson international travel were divided into inter-national air transportation cost and within-destination expenses The within-destinationmean expenditures per party were used in thisresearch as one of the key measures of marketpotential With the highest mean expenditures


TABLE 2 Socio-Demographic Characteristics of Clusters

Characteristics

C1(Beach andSunshineLovers)

C2(City

Sightseers)

C3(Culture

and NatureEnthusiasts)

C4(VisitingFriends

and Relatives)

2

Age18-1920-2930-3940-4950-5960 or older

1422132123615750

3224037696

144112

23209302186163116

00172303228186110

151

GenderMaleFemale

521479

456544

512488

441559

24

Marital StatusSingleMarriedLiving TogetherDivorcedWidowedOther

307286271136

47225617696

349314233105

276379193145

196

EducationPrimary SchoolHigh School Without DiplomaHigh School or EquivalentCollege or Above

57257250421

56160232536

47244302407

90179303428

143

OccupationUniversityCollege StudentWhite-Collar WorkerBlue-Collar WorkerAdministratorManagerSelf-EmployedFreelancerNon-Working HousewifeRetiredUnemployedOther

5735711410022110743

1043682480

22416040

8140723

12812621223

5532414

17221415169

435

Household IncomeLess than 6500 FF6500-9999 FF10000-12999 FF13000-15999 FF16000-19999 FF20000-24999 FF25000-29999 FF30000 or moreRefused

93171143129114869343

129

12813616013672803280

176

701401161281401059381

128

6913812413197

1175569

200

226

Note 1 p lt 01 p lt 005 p lt 0012 C1-C4 means Cluster 1-Cluster 43 FF stands for French Franc French Francs was the French currency at the time of the data collection

(19396 FF) Cluster 3 was considered to bethe segment with the greatest economic con-tribution while Cluster 4 made the least con-tribution (13633 FF) The members of Cluster3 also spent more money on international airtransportation The lowest spending group forair fares was Cluster 2 which may be relatedwith the main travel regions of that groupCanada and the US

Although Cluster 3 appeared to be the bestin terms of the within-destination expendi-

tures risk needed to be considered to check forpotential variability of the expenditures Riskwas measured by the standard deviation ofmean expenditures within destinations Clus-ter 4 emerged as the least risky segment andCluster 2 had the highest expenditure risk Todetermine the best segment from an expendi-ture-risk viewpoint it was necessary to com-bine mean expenditures and expenditure riskThus the Risk-adjusted Expenditure Index (REI)was calculated and the results are presented in


TABLE 3 Travel-Related Characteristics of Clusters

Characteristics

C1(Beach

and SunshineLovers)

C2(City

Sightseers)

C3(Culture


C4(VisitingFriends

and Relatives)

2 or F

No of People in Travel Party 174 162 197 166 F 25

No of Nights Away from Home 239 243 274 190 F 53

Travel CompanionAloneWifeHusbandBoy or Girl FriendFatherMotherChildrenOther RelativesFriendsOrganized GroupOther

3574862921

10007

4002808824

18424

2335702312

15112

38643441418314

c2 417

Season of TravelSpringSummerFallWinter

279279207236

200504112184

174395140291

283317131269

c2 517

Travel RegionCanadaUSMexicoCentral and South AmericaThe West IndiesCaribbeanSouth AfricaOther AfricaOceaniaAsia

579385

32186

21428

116

408328563208561696

18693

163174474736

254

23432476

124146934

125

c2 1969

Note 1 p lt 01 p lt 005 p lt 0012 C1-C4 means Cluster 1-Cluster 4

TABLE 4 Mean Expenditures and Expenditure Risk of Clusters

Expenditures and Risk C1(Beach

and Sunshine Lovers)

C2(City

Sightseers)

C3(Culture amp Nature

Enthusiasts)

C4(Visiting Friendsand Relatives)

Mean ExpendituresTravel Party 14320 FF 14740 FF 19396 FF 13633 FF

International Air Transportation 6797 FF 5821 FF 8586 FF 6521 FF

Mean Expenditure within Destination 7523 FF 8919 FF 10810 FF 7112 FF

Expenditure Risk 6021 8841 7995 5979

REI 125 101 135 119

Note 1 Mean expendituretravel party was divided into two components International air transportation and mean expenditure within destinations2 Expenditure Risk is the standard deviation of expenditures within destinations3 REI was calculated as mean expenditure within destinations divided by expenditure risk times one hundred4 C1-C4 means Cluster 1-Cluster 4

Table 4 Representing the best market seg-ment among the four from an expenditurelevel standpoint Cluster 3 also had the highestREI at 135 whereas Cluster 2 had the lowestREI (at 101) and was the least attractive seg-ment Therefore Cluster 3 was judged to con-tribute the greatest economic benefits to traveldestinations

Segment Size and Segment Size Risk

Another important consideration in segmentselection was the market size Cluster 3 was thesegment with the highest expenditure levelHowever even though expenditure levels maybe high the actual scale of economic benefitswill not be as great if the segment size is smallThus it was necessary to consider the segmentsize together with expenditure level The monthlymean frequency counts for each cluster wereused as the measure of segment size Table 5shows that Cluster 4 had the largest market size(121 per month) while Cluster 3 had the small-est number (72) As explained earlier the un-certainty or risk associated with the market sizeis seasonality and this is one of the most criticalissues in tourism The standard deviation in themonthly frequency distributions was employedas the segment size risk The results showedthat Cluster 2 was the most risky market and

Cluster 1 had the lowest risk To derive asimultaneous measure of segment size andsegment size risk the Risk-adjusted SegmentSize Index (RSSI) was created by dividingsegment size by the segment size risk timesone hundred As shown in Table 5 Cluster 1clearly emerged as the best market segmentafter adjusting for the effect of seasonalitywith an RSSI of 329 Cluster 4 also had a goodmarket size index (RSSI = 273) but bothCluster 3 and Cluster 2 did not perform well inthis respect

Market Potential of Activity Segments

The REI and RSSI results provided usefulinformation for identifying potentially valuablemarket segments To better visualize the attrac-tiveness of the four activity segments in termsof REI and RSSI Z-standardized REIs andRSSIs were plotted on an X-Y axis as presentedin Figure 3 Clusters in the first (top-right)quadrant represented relatively attractive mar-kets for both mean expenditures and marketsize after risk adjustment Cluster 1 seemed tobe the most appealing to marketers Indicatingthe least desirable segment Cluster 2 was posi-tioned in the third (bottom-left) quadrant whereboth the mean expenditures and the market sizewere small Despite a high expenditure level


TABLE 5 Segment Size and the Risk of Clusters

C1(Beach


C2(City

Sightseers)

C3(Culture and Nature

Enthusiasts)


Monthly FrequencyJanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember

101514187

1212157

12108

75799

162324752

11

81052858

215437

131711141614151763

109

Segment Size 117 104 72 121

Segment Size Risk 35 70 49 44

RSSI 329 148 145 273

Note 1 Segment Size is the monthly mean frequency of cluster2 Segment Size Risk is the standard deviation in monthly frequency distribution of cluster3 RSSI was calculated as Segment Size divided by Segment Size Risk times one hundred4 C1-C4 means Cluster 1-Cluster 4

Cluster 3 was a relatively small segment sizepositioned in the second (top-left) quadrantThe information generated from the REIs andRSSIs can be applied in different ways accord-ing to the marketerrsquos situation If the organiza-tion or destination is large and wants a majormarket share Cluster 1 would be the most at-tractive segment However if travel marketersare planning a niche marketing strategy Clus-ter 3 may be a better target

Due to the diverse nature of different organi-zationsrsquo marketing objectives it is difficult todetermine which segment is the best for everytourism marketer As a combined evaluation ofthe market segmentation Risk-adjusted MarketPotential Index (RMPI) provides a useful quan-titative tool to determine the overall levels ofattractiveness (Table 6) Using RMPI Cluster1 again appeared to be the most appealing seg-ment with the highest risk-adjusted market po-tential (RMPI = 423) among the four segmentsThe least attractive segment was Cluster 2(RMPI = 150) Although the final target mar-ket selection usually requires some subjectivedecision criteria the process described anddemonstrated in this study should help deci-sion-makers with target market selection

DISCUSSION AND CONCLUSION

The main objectives of this research were toidentify distinct segments of French outboundpleasure travelers based upon activity partici-pation and to evaluate the attractiveness of the

activity segments to assist with target marketselection Four distinct groups of French travel-ers were found Cluster 1 (Beach and SunshineLovers) Cluster 2 (City Sightseers) Cluster 3(Culture and Nature Enthusiasts) and Cluster 4(Visiting Friends and Relatives) Since activ-ity-based segmentation information indicateswhat French travelers want to do on their over-seas trips different positioning messages canbe used to individually appeal to each of thesegments For example promoting the beautyof a beach would be appropriate to Beach andSunshine Lovers However when communi-cating to Culture and Nature Enthusiasts whowant to pursue new experiences in local cultureand nature appreciation the promotional con-tent should focus on new knowledge and theadventure orientation of trips Significant dif-ferences were found among the four marketsegments for marital status occupation travelparty size number of nights away from hometravel companions season of travel and travelregions These differences in socio-demo-graphic and trip-related characteristics can helpmarketers decide on how each segment can beapproached and served The findings of this re-search indicated direction to destination mar-keters in formulating marketing strategy to-wards French outbound travelers

Using mean expenditure expenditure risksegment size and segment size risk as theevaluation criteria the Beach and SunshineLovers group emerged as the most attractiveof the four segments and City Sightseersgroup was the least attractive However thestudyrsquos outcomes can be interpreted in differ-ent ways according to what each destinationhas to offer and the marketersrsquo objectives Ma-ture beach and sun destinations with a priorityon volume markets may be most attracted bythe Beach and Sunshine Lovers group Otherdestinations especially those with an empha-sis on nature- or culture-based niche marketswould find the Culture and Nature Enthusiastsgroup to be a better fit with their objectives

Prior studies evaluating market segment at-tractiveness have used ranking systems to de-termine the best markets However due to alack of precision in these ranking proceduresthey have not effectively gauged the degree towhich one segment is more attractive than theothers This research is expected to contribute


II High ExpendituresSmall Market Size

I High ExpendituresLarge Market Size

III Low ExpendituresSmall Market Size

IV Low ExpendituresLarge Market Size

Cluster 3

20 1005

10

15Cluster 2

20

15

10

05

00

00 10 20R

EI

Cluster 4

Cluster 1

RSSI

FIGURE 3 Cluster Positions

Note REI (Risk-adjusted Expenditure Index) = (Mean ExpendituresExpenditureRisk) x100RSSI (Risk-adjusted Segment Size Index) = (Segment SizeSegment SizeRisk) x 100

to the literature by suggesting more quantifi-able and sophisticated evaluation criteria Thisresearch also provided an illustration as tohow to visualize the attractiveness of the can-didate segments and how to determine targetmarkets in travel destinations with new pro-posed criteria and suggested strategic impli-cations for destination marketers The targetmarket selection procedure described shouldbe especially helpful to marketers who aremost concerned with the economic potentialof the available market segments It is recom-mended that destination marketers continu-ously explore new target markets on a regularbasis in order to gain a competitive edge sincetravelersrsquo taste of activity participation canmove in a short cycle

There are some limitations to the researchOne of the most critical limitations was the recallbias of travel expenditures that might be embed-ded in the data set Also even though the activitysegmentation approach provided helpful in-sights for travel marketing strategies it cannotbe concluded that activity participation is themost effective base for market segmentationFuture research on travel market segmentationshould integrate other important bases such assatisfaction motivation and philosophy To fur-ther test the target market selection procedurefuture research should explore internationaltravelers of different nationalities or other marketsegments to specific destinations In addition themeasures of segment attractiveness used in thisresearch can be individually operationalized forfuture research For instance the Segment SizeRisk (SSR) concept can be utilized in discoveringoptimal segment mixes for smoothing seasonaldemand fluctuations in tourism by repeatedly com-bining the identified travel segments and checkingthe SSR levels of the mixes

REFERENCES

Board J and C Sutcliffe (1991) Risk and incometradeoffs in regional policy A portfolio theoretic ap-proach Journal of Regional Science 31(2) 191-210

Brigham E and L Gapenski (1988) Financial Man-agement Theory and Practice (5th ed) New YorkNY The Dryden Press

Bryant B and A Morrison (1980) Travel Market Seg-mentation and the Implementation of Market Strat-egies Journal of Travel Research 18(3) 2-8

Cardozo R N and J Wind (1985) Risk return ap-proach to product portfolio strategy Long RangePlanning 18(2) 77-85

Choi W M and C K L Tsang (1999) Activity BasedSegmentation on Pleasure Travel Market of HongKong Private Housing Residents Journal of Travel ampTourism Marketing 8(2) 75-97

Hair J Anderson R Tatham R and W Black(1998) Multivariate Data Analysis (5th ed) UpperSaddle River NJ Prentice-Hall Inc

Heath E and G Wall (1992) Marketing Tourism Des-tinations A Strategic Planning Approach NewYork NY John Wiley amp Sons Inc

Hsieh S OrsquoLeary J T and A M Morrison (1992)Segmenting the International Travel Market by Ac-tivity Tourism Management 13(2) 209-223

Jang S Ismail J A and S Ham (2002) Heavy spend-ers medium spenders and light spenders of Japaneseoutbound pleasure travelers Journal of Hospitalityamp Leisure Marketing 9(34) 83-106

Jang S Morrison A M and J T OrsquoLeary (2002)Benefit Segmentation of Japanese Pleasure Trav-elers to the USA and Canada Selecting Target Mar-kets Based on the Profitability and Risk of IndividualMarket Segments Tourism Management 23(4)367-378

Kotler P (1991) Marketing management AnalysisPlanning Implementation amp Control (7th ed)Englewood Cliffs NJ Prentice Hall Inc

Kotler P Bowen J and J Makens (1999) Marketingfor Hospitality and Tourism (2nd ed) Upper SaddleRiver NJ Prentice Hall


TABLE 6 Relative Market Potential Index (RMPI) of Clusters and Overall Ranking

C1(Beach


C2(City

Sightseers)


Enthusiasts)


REI 125 101 135 119

RSSI 339 148 145 273

RMPI 423 150 196 325

Ranking 1 4 3 2

Note1 RMPI was calculated as REI times RSSI divided by one hundred2 C1-C4 means Cluster 1-Cluster 4

Loker L E and R R Perdue (1992) A Benefit-BasedSegmentation of a Nonresident Summer MarketJournal of Travel Research 31(1) 30-35

McKercher R (1995) The Destination-Market MatrixA Tourism Market Portfolio Analysis Model Jour-nal of Travel amp Tourism Marketing 4(2) 23-40

McQueen J and K Miller (1985) Target Market Selec-tion of Tourists A Comparison of Approaches Jour-nal of Travel Research 24(1) 2-6

Middleton V T and J Clarke (2001) Marketing inTravel and Tourism (3rd ed) Oxford UK Butter-worth Heinemann

Morrison A M (2002) Hospitality and Travel Mar-keting (3rd ed) Albany NY Delmar ThomsonLearning

Pizam A and A Reichel (1979) Big spenders and lit-tle spenders in US tourism Journal of Travel Re-search 18(1) 42-43

Rao S R Thomas E G and R G Javalgi (1992) Ac-tivity Preferences and Trip-Planning Behavior of theUS Outbound Pleasure Travel Market Journal ofTravel Research 30(3) 3-12

Romsa G H (1973) A Method of Deriving OutdoorRecreational Activity Packages Journal of LeisureResearch 5(Summer) 34-46

Spotts D M and E M Mahoney (1991) Segmentingvisitors to a destination region based on the volumeon their expenditures Journal of Travel Research29(4) 24-31

Spotts D M and E M Mahoney (1993) Understand-ing the Fall Tourism Market Journal of Travel Re-search 32(2) 3-15

Sung H H Morrison A M Hong G and J TOrsquoLeary (2001) The Effects of Household and TripCharacteristics on Trip Types A Consumer Behav-ior Approach for Segmenting the US Domestic Lei-sure Travel Market Journal of Hospitality amp TourismResearch 25(1) 46-68

World Tourism Organization (2001) Facts amp FiguresAvailable httpwwwworld-tourismorgframesetframe_market_datahtm

SUBMITTED 112202FIRST REVISION SUBMITTED 041203SECOND REVISION SUBMITTED 050503

ACCEPTED 060103REFEREED ANONYMOUSLY


now demand a much wider range of travel ex-periences The immediate challenge for tour-ism organizations is how to meet these diversi-fied traveler needs Target marketing requires astrong focus on specific traveler groups andtailor-making products to meet their uniquedemands By targeting tourism marketers withlimited resources increase their probability ofmarketing success and are more likely toachieve their marketing objectives Howeveras travel markets continue to splinter and be-come more complex one of the greatest chal-lenges is to select the best set of market seg-ments This research addressed this challengeby providing a procedure for evaluating the at-tractiveness of individual travel market seg-ments

The process leading to target market selectionshould involve five sequential steps (1) deter-mining segmentation variable or variables(2) conducting market segmentation (3) pro-filing identified segments (4) evaluating seg-ment attractiveness and (5) selecting targetmarket(s) (Figure 1)

Many variables have been recommended asviable segmentation bases but researchersseem to agree that there is no single ideal seg-mentation base that fits in every situation(Morrison 2002) However several research-ers have suggested that travel activity segmen-tation is one of the best segmentation bases fortourism (Choi and Tsang 1999 Hsieh OrsquoLearyand Morrison 1992 Rao Thomas and Javalgi1992) They argue that activity segmentationhelps with the bundling of travel activities intopackages with greater market appeal Thebundles or packages of preferred activities canbe considered sub-aggregates of the total travelmarket (Romsa 1973) Another major claimabout activity-based segmentation is thattravel activities can be connected with the eco-nomic benefits to the destination For exam-ple the expenditures of people with shoppingand bird watching as travel activities can be

compared A study by Spotts and Mahoney(1993) supported the proposition that there is aclose relationship between travel activitiesand expenditures As such it may be possiblefor tourism marketers to develop appropriateproducts for their selected target markets andto estimate the economic benefits of eachproduct through activity segmentation Forthese reasons this research employed travelactivities as the segmentation base to analyzea specific travel origin market (France)

Middleton and Clarke (2001) define marketsegmentation as the process of dividing a totalmarket such as all visitors or a market sectorsuch as holiday travel into sub-groups for mar-keting management purposes The resultingsegments are assumed to have homogeneoustravel behaviors The other requirement of mar-ket segmentation is to find meaningful differ-ences among the segments within a total mar-ket This process provides tourism marketerswith a greater understanding of individual mar-kets and more precise ideas for product devel-opment One of the common ways to identifythe differences is to profile the segments of thetotal market Profiling helps by distinguishingthe attitudes behaviors socio-demographicstravel planning patterns and trip-related char-acteristics of travel market segments

The evaluation of market segments is thestep before target market selection and is criti-cal to the potential success of a marketingstrategy The key issue is which segments aremost likely to lead to the achievement of mar-keting goals and objectives The answer dif-fers based upon the criteria used to evaluatethe relative merits of each market segmentThe criteria for judging segment attractivenessinclude (1) market potential (2) competitionand segment structural attractiveness (3) mar-keting organizationrsquos vision goals and objec-tives (4) serviceability and (5) costs (Heath ampWall 1992 Kotler 1991 Kotler Bowen ampMakens 1999 McKercher 1995)


DeterminingSegmentation

Variable(s)

ConductingMarket

Segmentation

ProfilingIdentifiedSegments

EvaluatingSegment

Attractiveness

SelectingTarget

Market(s)

FIGURE 1 Steps in Target Market Selection

Note Adapted from Pride and Ferrell (2000)





Research Objectives















METHODOLOGY





Analysis Methods








Name Formula









(REI yen RSSI)100














(UnitPercentage)



Swimming 886 216 663 179 472


Shopping 836 736 837 503 714



Walking tours 736 568 884 366 611


















Water sports 257 32 221 28 139





















36

161

104

169

535

263

55

87

145

159



















Characteristics


C2(City

Sightseers)

C3(Culture


C4(VisitingFriends

and Relatives)

2

Age18-1920-2930-3940-4950-5960 or older

1422132123615750

3224037696

144112

23209302186163116

00172303228186110

151

GenderMaleFemale

521479

456544

512488

441559

24


307286271136

47225617696

349314233105

276379193145

196


57257250421

56160232536

47244302407

90179303428

143


5735711410022110743

1043682480

22416040

8140723

12812621223

5532414

17221415169

435


93171143129114869343

129

12813616013672803280

176

701401161281401059381

128

6913812413197

1175569

200

226







Characteristics

C1(Beach

and SunshineLovers)

C2(City

Sightseers)

C3(Culture


C4(VisitingFriends

and Relatives)

2 or F




3574862921

10007

4002808824

18424

2335702312

15112

38643441418314

c2 417


279279207236

200504112184

174395140291

283317131269

c2 517


579385

32186

21428

116

408328563208561696

18693

163174474736

254

23432476

124146934

125

c2 1969





C2(City

Sightseers)


Enthusiasts)






REI 125 101 135 119










C1(Beach


C2(City

Sightseers)


Enthusiasts)



101514187

1212157

12108

75799

162324752

11

81052858

215437

131711141614151763

109



RSSI 329 148 145 273














Cluster 3

20 1005

10

15Cluster 2

20

15

10

05

00

00 10 20R

EI

Cluster 4

Cluster 1

RSSI





REFERENCES















C1(Beach


C2(City

Sightseers)


Enthusiasts)


REI 125 101 135 119

RSSI 339 148 145 273

RMPI 423 150 196 325

Ranking 1 4 3 2





















Research Objectives















METHODOLOGY





Analysis Methods








Name Formula









(REI yen RSSI)100














(UnitPercentage)



Swimming 886 216 663 179 472


Shopping 836 736 837 503 714



Walking tours 736 568 884 366 611


















Water sports 257 32 221 28 139





















36

161

104

169

535

263

55

87

145

159



















Characteristics


C2(City

Sightseers)

C3(Culture


C4(VisitingFriends

and Relatives)

2

Age18-1920-2930-3940-4950-5960 or older

1422132123615750

3224037696

144112

23209302186163116

00172303228186110

151

GenderMaleFemale

521479

456544

512488

441559

24


307286271136

47225617696

349314233105

276379193145

196


57257250421

56160232536

47244302407

90179303428

143


5735711410022110743

1043682480

22416040

8140723

12812621223

5532414

17221415169

435


93171143129114869343

129

12813616013672803280

176

701401161281401059381

128

6913812413197

1175569

200

226







Characteristics

C1(Beach

and SunshineLovers)

C2(City

Sightseers)

C3(Culture


C4(VisitingFriends

and Relatives)

2 or F




3574862921

10007

4002808824

18424

2335702312

15112

38643441418314

c2 417


279279207236

200504112184

174395140291

283317131269

c2 517


579385

32186

21428

116

408328563208561696

18693

163174474736

254

23432476

124146934

125

c2 1969





C2(City

Sightseers)


Enthusiasts)






REI 125 101 135 119










C1(Beach


C2(City

Sightseers)


Enthusiasts)



101514187

1212157

12108

75799

162324752

11

81052858

215437

131711141614151763

109



RSSI 329 148 145 273














Cluster 3

20 1005

10

15Cluster 2

20

15

10

05

00

00 10 20R

EI

Cluster 4

Cluster 1

RSSI





REFERENCES















C1(Beach


C2(City

Sightseers)


Enthusiasts)


REI 125 101 135 119

RSSI 339 148 145 273

RMPI 423 150 196 325

Ranking 1 4 3 2


























METHODOLOGY





Analysis Methods








Name Formula









(REI yen RSSI)100














(UnitPercentage)



Swimming 886 216 663 179 472


Shopping 836 736 837 503 714



Walking tours 736 568 884 366 611


















Water sports 257 32 221 28 139





















36

161

104

169

535

263

55

87

145

159



















Characteristics


C2(City

Sightseers)

C3(Culture


C4(VisitingFriends

and Relatives)

2

Age18-1920-2930-3940-4950-5960 or older

1422132123615750

3224037696

144112

23209302186163116

00172303228186110

151

GenderMaleFemale

521479

456544

512488

441559

24


307286271136

47225617696

349314233105

276379193145

196


57257250421

56160232536

47244302407

90179303428

143


5735711410022110743

1043682480

22416040

8140723

12812621223

5532414

17221415169

435


93171143129114869343

129

12813616013672803280

176

701401161281401059381

128

6913812413197

1175569

200

226







Characteristics

C1(Beach

and SunshineLovers)

C2(City

Sightseers)

C3(Culture


C4(VisitingFriends

and Relatives)

2 or F




3574862921

10007

4002808824

18424

2335702312

15112

38643441418314

c2 417


279279207236

200504112184

174395140291

283317131269

c2 517


579385

32186

21428

116

408328563208561696

18693

163174474736

254

23432476

124146934

125

c2 1969





C2(City

Sightseers)


Enthusiasts)






REI 125 101 135 119










C1(Beach


C2(City

Sightseers)


Enthusiasts)



101514187

1212157

12108

75799

162324752

11

81052858

215437

131711141614151763

109



RSSI 329 148 145 273














Cluster 3

20 1005

10

15Cluster 2

20

15

10

05

00

00 10 20R

EI

Cluster 4

Cluster 1

RSSI





REFERENCES















C1(Beach


C2(City

Sightseers)


Enthusiasts)


REI 125 101 135 119

RSSI 339 148 145 273

RMPI 423 150 196 325

Ranking 1 4 3 2

















Analysis Methods








Name Formula









(REI yen RSSI)100














(UnitPercentage)



Swimming 886 216 663 179 472


Shopping 836 736 837 503 714



Walking tours 736 568 884 366 611


















Water sports 257 32 221 28 139





















36

161

104

169

535

263

55

87

145

159



















Characteristics


C2(City

Sightseers)

C3(Culture


C4(VisitingFriends

and Relatives)

2

Age18-1920-2930-3940-4950-5960 or older

1422132123615750

3224037696

144112

23209302186163116

00172303228186110

151

GenderMaleFemale

521479

456544

512488

441559

24


307286271136

47225617696

349314233105

276379193145

196


57257250421

56160232536

47244302407

90179303428

143


5735711410022110743

1043682480

22416040

8140723

12812621223

5532414

17221415169

435


93171143129114869343

129

12813616013672803280

176

701401161281401059381

128

6913812413197

1175569

200

226







Characteristics

C1(Beach

and SunshineLovers)

C2(City

Sightseers)

C3(Culture


C4(VisitingFriends

and Relatives)

2 or F




3574862921

10007

4002808824

18424

2335702312

15112

38643441418314

c2 417


279279207236

200504112184

174395140291

283317131269

c2 517


579385

32186

21428

116

408328563208561696

18693

163174474736

254

23432476

124146934

125

c2 1969





C2(City

Sightseers)


Enthusiasts)






REI 125 101 135 119










C1(Beach


C2(City

Sightseers)


Enthusiasts)



101514187

1212157

12108

75799

162324752

11

81052858

215437

131711141614151763

109



RSSI 329 148 145 273














Cluster 3

20 1005

10

15Cluster 2

20

15

10

05

00

00 10 20R

EI

Cluster 4

Cluster 1

RSSI





REFERENCES















C1(Beach


C2(City

Sightseers)


Enthusiasts)


REI 125 101 135 119

RSSI 339 148 145 273

RMPI 423 150 196 325

Ranking 1 4 3 2






























(UnitPercentage)



Swimming 886 216 663 179 472


Shopping 836 736 837 503 714



Walking tours 736 568 884 366 611


















Water sports 257 32 221 28 139





















36

161

104

169

535

263

55

87

145

159



















Characteristics


C2(City

Sightseers)

C3(Culture


C4(VisitingFriends

and Relatives)

2

Age18-1920-2930-3940-4950-5960 or older

1422132123615750

3224037696

144112

23209302186163116

00172303228186110

151

GenderMaleFemale

521479

456544

512488

441559

24


307286271136

47225617696

349314233105

276379193145

196


57257250421

56160232536

47244302407

90179303428

143


5735711410022110743

1043682480

22416040

8140723

12812621223

5532414

17221415169

435


93171143129114869343

129

12813616013672803280

176

701401161281401059381

128

6913812413197

1175569

200

226







Characteristics

C1(Beach

and SunshineLovers)

C2(City

Sightseers)

C3(Culture


C4(VisitingFriends

and Relatives)

2 or F




3574862921

10007

4002808824

18424

2335702312

15112

38643441418314

c2 417


279279207236

200504112184

174395140291

283317131269

c2 517


579385

32186

21428

116

408328563208561696

18693

163174474736

254

23432476

124146934

125

c2 1969





C2(City

Sightseers)


Enthusiasts)






REI 125 101 135 119










C1(Beach


C2(City

Sightseers)


Enthusiasts)



101514187

1212157

12108

75799

162324752

11

81052858

215437

131711141614151763

109



RSSI 329 148 145 273














Cluster 3

20 1005

10

15Cluster 2

20

15

10

05

00

00 10 20R

EI

Cluster 4

Cluster 1

RSSI





REFERENCES















C1(Beach


C2(City

Sightseers)


Enthusiasts)


REI 125 101 135 119

RSSI 339 148 145 273

RMPI 423 150 196 325

Ranking 1 4 3 2


































Characteristics


C2(City

Sightseers)

C3(Culture


C4(VisitingFriends

and Relatives)

2

Age18-1920-2930-3940-4950-5960 or older

1422132123615750

3224037696

144112

23209302186163116

00172303228186110

151

GenderMaleFemale

521479

456544

512488

441559

24


307286271136

47225617696

349314233105

276379193145

196


57257250421

56160232536

47244302407

90179303428

143


5735711410022110743

1043682480

22416040

8140723

12812621223

5532414

17221415169

435


93171143129114869343

129

12813616013672803280

176

701401161281401059381

128

6913812413197

1175569

200

226







Characteristics

C1(Beach

and SunshineLovers)

C2(City

Sightseers)

C3(Culture


C4(VisitingFriends

and Relatives)

2 or F




3574862921

10007

4002808824

18424

2335702312

15112

38643441418314

c2 417


279279207236

200504112184

174395140291

283317131269

c2 517


579385

32186

21428

116

408328563208561696

18693

163174474736

254

23432476

124146934

125

c2 1969





C2(City

Sightseers)


Enthusiasts)






REI 125 101 135 119










C1(Beach


C2(City

Sightseers)


Enthusiasts)



101514187

1212157

12108

75799

162324752

11

81052858

215437

131711141614151763

109



RSSI 329 148 145 273














Cluster 3

20 1005

10

15Cluster 2

20

15

10

05

00

00 10 20R

EI

Cluster 4

Cluster 1

RSSI





REFERENCES















C1(Beach


C2(City

Sightseers)


Enthusiasts)


REI 125 101 135 119

RSSI 339 148 145 273

RMPI 423 150 196 325

Ranking 1 4 3 2






















Characteristics

C1(Beach

and SunshineLovers)

C2(City

Sightseers)

C3(Culture


C4(VisitingFriends

and Relatives)

2 or F




3574862921

10007

4002808824

18424

2335702312

15112

38643441418314

c2 417


279279207236

200504112184

174395140291

283317131269

c2 517


579385

32186

21428

116

408328563208561696

18693

163174474736

254

23432476

124146934

125

c2 1969





C2(City

Sightseers)


Enthusiasts)






REI 125 101 135 119










C1(Beach


C2(City

Sightseers)


Enthusiasts)



101514187

1212157

12108

75799

162324752

11

81052858

215437

131711141614151763

109



RSSI 329 148 145 273














Cluster 3

20 1005

10

15Cluster 2

20

15

10

05

00

00 10 20R

EI

Cluster 4

Cluster 1

RSSI





REFERENCES















C1(Beach


C2(City

Sightseers)


Enthusiasts)


REI 125 101 135 119

RSSI 339 148 145 273

RMPI 423 150 196 325

Ranking 1 4 3 2

























C1(Beach


C2(City

Sightseers)


Enthusiasts)



101514187

1212157

12108

75799

162324752

11

81052858

215437

131711141614151763

109



RSSI 329 148 145 273














Cluster 3

20 1005

10

15Cluster 2

20

15

10

05

00

00 10 20R

EI

Cluster 4

Cluster 1

RSSI





REFERENCES















C1(Beach


C2(City

Sightseers)


Enthusiasts)


REI 125 101 135 119

RSSI 339 148 145 273

RMPI 423 150 196 325

Ranking 1 4 3 2





























Cluster 3

20 1005

10

15Cluster 2

20

15

10

05

00

00 10 20R

EI

Cluster 4

Cluster 1

RSSI





REFERENCES















C1(Beach


C2(City

Sightseers)


Enthusiasts)


REI 125 101 135 119

RSSI 339 148 145 273

RMPI 423 150 196 325

Ranking 1 4 3 2



















REFERENCES















C1(Beach


C2(City

Sightseers)


Enthusiasts)


REI 125 101 135 119

RSSI 339 148 145 273

RMPI 423 150 196 325

Ranking 1 4 3 2















