ARTICLE IN PRESS

0261-5177/$ - se

doi:10.1016/j.to

�CorrespondE-mail addr

due.edu (A.M.

Tourism Management 27 (2006) 576–588

www.elsevier.com/locate/tourman

The economic value portfolio matrix: A target market selection toolfor destination marketing organizations

Gyehee Leea,�, Alastair M. Morrisonb, Joseph T. O’Learyc

aDepartment of Tourism Management, College of Business, Keimyung University, 1000 Shindang-dong, Dalseogu, Daegu 704-701, KoreabDepartment of Hospitality & Tourism Management, Purdue University, Room 111A, Stone Hall, 700 W. State Street, West Lafayette,

IN 47907-2059, USAcDepartment of Recreation, Park & Tourism Sciences, Texas A & M University, 2261 TAMU, College Station, TX 77843-2261, USA

Received 19 January 2005; accepted 18 February 2005

Abstract

The main goal of the study was to propose a practical evaluation tool for destination marketers to evaluate travel market

segments in terms of the expected economic return on each identified segment. An Economic Value Portfolio Matrix based on the

Stay-Spend Index (SSI) and market share was developed. French travelers to Canada were segmented based on benefits sought.

Benefit segmentation has been extensively used in travel research in the past 20 years and has helped us better understand the

dynamic global tourism market. The results showed that the Economic Value Portfolio Matrix approach may be a useful quantifiable

and objective evaluation tool for destination marketing organizations and that benefits sought clearly differentiated the French long-

haul pleasure travel market.

r 2005 Elsevier Ltd. All rights reserved.

Keywords: Benefit segment; French travellers; Economic value portfolio; Market segmentation; Stay-spend index (SSI)

1. Background

French travelers represent the world’s third largesttourism-generating market in absolute terms, after onlyGermany and the United Kingdom (UK), recording 23million international departures in 1999, about 65% ofwhich were for pleasure travel (World Tourism Orga-nization (WTO), 2000). Despite the significant volumegenerated by French travelers in international tourismto all destinations, there is limited research on thismarket, with a few exceptions (Sussmann & Rashcov-sky, 1997; Richardson & Crompton, 1988; Qiu &Zhang, 1995).

The French market is of great importance to theCanadian tourism industry. Among overseas travelers

e front matter r 2005 Elsevier Ltd. All rights reserved.

urman.2005.02.002

ing author. Tel.: +8253 580 6401; fax: +82 53 6364.

esses: ghlee@kmu.ac.kr (G. Lee), alastair@cfs.pur-

Morrison), joleary@rpts.tamu.edu (J.T. O’Leary).

(excluding US residents), the French market rankednumber three after the UK and Japan, with an averageannual growth rate above 3.0% (World TourismOrganization (WTO), 2000). The growth rate amongFrench outbound travelers was in the double digitsthroughout the early 1990s, but began to rapidlydecrease late in the decade (Canadian Tourism Commis-sion (CTC), 2002). In response to the situation,PriceWaterhouseCoopers (PWC) was hired by theCTC to study the French market to discover the reasonsbehind this decline and to better understand how torecapture the double-digit growth. A survey conductedin 1998 by PWC, on behalf of CTC (1999), divided theFrench long-haul pleasure travel market four ways, bysocio-demographics, activities, motivational segments,and travel arrangements. Although much was gleanedfrom this study, it did not offer a sufficiently quantita-tive measure for identifying which of the pleasuremarket segments were the most profitable to pursue.

ARTICLE IN PRESSG. Lee et al. / Tourism Management 27 (2006) 576–588 577

Hence, the strategy to recapture the market was notfully identified. The demand for more effective tools toselect the most profitable target segment is urgent. Thesetools should help destination marketers to identify thesegments that produce the highest return on dollarsinvested, and to focus on key marketing strategies withrespect to reaching and communicating with targetmarkets and providing services and facilities demandedby the markets.

2. Study goal and objectives

This study attempted to fill this gap with a methodcalled the Economic Value Portfolio Matrix, whichestimates profit-generating efficiency based on lengthof stay factored into total expenditure per person,known as the Stay-Spend Index (SSI). Applying benefitssought by French travelers to Canada as a segmentationbasis, this study aimed to provide Canadian DMOs andother destination marketers with a useful tool for theassessment of the economic value of market segments sothat they can be quantified and objectively evaluated interms of profitability.

The following three specific research objectives wereidentified:

(1)

To develop a market segmentation approach basedon French travelers to Canada in terms of theirbenefits sought.

(2)

To incorporate key trip related behaviors, includingtravel mode, satisfaction, revisit intention, andvacation activities, with benefit sought.

(3)

To evaluate the value of each segment in terms ofprofitability using the Economic Value PortfolioMatrix and to recommend the most viable segmentsfor Canadian tourism.

3. Literature review

3.1. Background of benefit segmentation

Travel researchers have excelled in the study ofdestinations, and market segmentation analyses havebeen especially powerful in identifying segments deser-ving different levels of marketing treatment and devel-oping strategies to target the identified markets. Theyhave made extensive use of various segmentation tools;both a priori segmentation approaches, such as trippurpose, demographic, and geographic segmentation,and a posteriori segmentation, including the psycho-graphic, behavioral, and benefit segmentation techni-ques. In tourism research, benefit segmentation has beenemployed for just over 20 years. Its origin, however,

dates back further to 1968 when Haley (1968) developedthe technique as a method of predicting customers’purchasing behavior more effectively than did tradi-tional segmentation methods, which tended to useconsumers’ demographic, socio-economic, or geo-graphic characteristics. Haley argued that purchasingbehavior was mostly directed by the benefits sought inthe product, and therefore that ‘‘benefits which peopleare seeking in consuming a given product are the basicreasons for the existence of true market segments’’(Haley, 1968, p. 31). More recently, Haley (1999)suggested that the new era of mass customization andthe increase in the variety of promotional supports hasincreased the necessity and usefulness of the benefitsegmentation approach to meet the increasingly diversi-fied customer demand. Focusing specifically on traveland tourism marketing, Frochot and Morrison (2000)concluded that benefit segmentation is most helpful indesigning and modifying facilities and attractions,vacation packaging, activity programming, and servicequality measurement.

3.2. Benefit segmentation in tourism

The research on benefits sought seems to havedeveloped in three directions in the field of tourism.First, tourism researchers considered the possibility ofusing benefits to explain decision-making processes inrelation to destination marketing (Woodside & Pitts,1976), consumer preferences for destination attributes(Ryan & Glendon, 1998), and travel planning time(Schul & Crompton, 1983). Vacation behaviors such asdestination choice, length of stay, and activities pursuedwere linked to the benefits that tourists seek (Gitelson &Kerstetter, 1990; Moscardo, Morrison, Pearce, Lang, &O’Leary, 1996). These studies identified benefits asa key factor in tourist decision-making, thus providingknowledge critical to strategies for targeting markets.One of the earliest applications was by Goodrich (1977,1980), who conducted an extensive study of AmericanExpress travelers based on destinations’ attributes,including both destination-based attributes and psycho-logical benefits. Goodrich’s study showed that themarket could successfully be divided into threesegments, passive-entertainment, sports, and out-door, leading to recommendations in terms of advertis-ing, travel brochures, and packaged tours for eachsegment.

Showing the usefulness of benefit segmentation fordestination marketing, Gitelson and Kerstetter (1990)examined the relationship between socio-demographicvariables, benefits sought, and subsequent behavior.They identified four benefits sought by North Carolinavisitors: relaxation, excitement, social opportunities, andexploration. They also found that each benefit segmentshowed distinctive behavioral patterns in terms of trip

ARTICLE IN PRESSG. Lee et al. / Tourism Management 27 (2006) 576–588578

party, length of stay, activity participation, and seasonof visit, while there were mixed results for socio-demographic variables. In their study of Canadian, USand Japanese travelers to Hawaii, Woodside and Jacobs(1985) suggested that benefit segmentation could beeffectively used for designing advertising messages andpackages, and improving tourism facilities, beyond justdividing and describing existing markets.

The second focus in benefit studies has been ondifferentiating specific travel markets and facilitatingmarketing strategy development, including advertisingcampaigns, designing, packaging and distributing pro-ducts, and evaluating satisfaction. For example,Shoemaker (1994) illustrated the usefulness of usingbenefit segmentation for the senior travel market,and Andereck, Caldwell, and Debbage (1991) appliedbenefit segmentation to zoo visitors. Similarly, Tian,Crompton, and Witt (1996) applied the technique tomuseum patrons, and McCool and Reilly (1993) used itfor state park visitors. These research studies indicatedthat benefit segments merit separate marketing treat-ment.

Davies and Prentice (1995) argued that benefitsegmentation, which distinguishes homogeneous sub-groups of potential customers by their wants and needswithin a heterogeneous market, is a key to potentialmarket development by attractions. Benefit segmenta-tion guides message differentiation, capitalizing on adeep understanding of markets, and makes it possible toreach the target market. Benefit segmentation therebypotentially enables managers to fine-tune their products.The identification of such intrinsic-terminal benefits orexperiences sought from tourism and leisure offers thepotential of redefining or re-promoting tourism pro-ducts to meet these motivations, and converting demandinto actual needs.

Investigating European business travelers, Mason andGray (1996) stated that customers seek benefits from theproduct that relate to both personal and organizationalobjectives, and used a benefit segmentation model tocreate a marketing strategy for the short-haul Europeanbusiness travel market. In their model, the market wassegmented based on the benefits sought from theproduct, using a stakeholder model of organizationaldecision processes. This approach drew on the advan-tages of the two most appropriate segmentation basesavailable: benefit and buyer center segmentation. Goingfurther by distinguishing subgroups within a givenmarket, Prentice, Witt, and Hamer (1998) focused onthe premise that the core product of tourism is thebeneficial experiences gained. Their benefits-based man-agement approach described these experience-basedmanagement outputs more explicitly as improvedconditions. Inherent in such an approach is the benefitchain of causality, linking activities, setting, experiences,and benefits in a sequence. Here activities are under-

taken in settings to gain experiences that are regarded asbeneficial.

These studies underscore how important it is tounderstand the benefits sought by travelers for aneffective market segmentation strategy in tourism. Thecomplexity of these studies and the practicality of theinformation gleaned from them have greatly increasedover time, yet those studies lack the ability to quantifythe most profitable segments and this remains theprimary weakness of the benefit segmentation approach.Hence, the third concern of researchers is the issue ofhow to select the most profitable segments.

3.3. Benefit segment evaluation criteria

Kotler and Armstrong (2003, pp. 250–251) suggestedthat market segments should meet five selection criteriato be viable. They need to be: (1) measurable, (2)accessible, (3) sustainable, (4) differentiable, and (5)actionable. In addition to Kotler and Armstrong’s list,Morrison (2002) added five more criteria for effectivesegmentation; homogeneity, defensibility, competitive-ness, durability, and compatibility. These theoreticallyfundamental criteria provide marketers with usefulguidelines for targeting markets; however, they lackmeasurability by not clearly operationalizing quantifi-able and objective measures for each criterion. Anadditional disadvantage is they do not incorporate themost important quality of a segment as a target market,namely profitability.

Recently, tourism researchers have tried to identifycomprehensive and objective evaluation criteria forselecting the best possible target markets. However, todate only a limited number of such studies areto be found in the tourism and hospitality literature(McQueen & Miller, 1985; Loker & Perdue, 1992;Kastenholz, Davis, & Paul, 1999; Jang, Morrison, &O’Leary, 2000). Furthermore, limited attention has beengiven to the evaluation of the economic value of asegment as a key selection criterion for benefit segments.Once benefit segments are defined, marketers do notcurrently have an effective tool to determine whichsegments they should pursue to maximize the return onmarketing dollars spent targeting these markets.

3.4. Target market profitability

It is clearly counter-intuitive to market to a targetgroup without any consideration of how much revenuethe target market can generate. Bock and Uncles (2002)suggested that, when preparing a segmentation strategy,profitability must be considered as one of the mainselection criteria. Where one customer segment providesgreater profit to an organization than another, or wherethere is potential for this, profitability exists in thatcustomer segment. Consequently, the profitability of

ARTICLE IN PRESSG. Lee et al. / Tourism Management 27 (2006) 576–588 579

potential segmentation provides a way to determine thevalue of future research for understanding differencesamong customers. Here, the profitability of the segmentsin a given market should be a cornerstone upon whichmarketing strategies should be laid, provided that thesesegments also meet other prerequisites such as accessi-bility, substantiality, and actionability.

There are a few marketing research examples thataddress the profitability issue as a key basis for targetmarket selection. For example, McQueen and Miller(1985) recommended the assessment of market attrac-tiveness based upon profitability, viability, and accessi-bility. Similarly, Loker and Perdue (1992) proposed asystematic approach to evaluating segments using aranking procedure. They assessed segment attractivenessin terms of profitability, accessibility, and reachabilityby ranking each segment on its relative performance onthe three evaluation criteria. Profitability was measuredby the percentages of total expenditure related to thepercentage of respondents, percentage of person-nights,and average expenditures per person-night. Kastenholzet al. (1999) conducted a study of rural tourism inPortugal using a composite index of segment attractive-ness from a revenue-generating perspective. Eventhough these studies suggested profitability as a keysegment selection criterion, the measures were weakwith respect to comprehensiveness and objectivity.These criteria were applied rather subjectively due to alack of objective and quantifiable measures for eachcriterion. The measures of reachability and accessibilitywere especially subjective. Addressing this issue, Jang etal. (2000) incorporated the profitability and riskconcepts in evaluating segment attractiveness as morequantifiable and comprehensive profitability measures.

The usefulness and viability of benefit segmentation intourism are well supported (Gitelson & Kerstetter, 1990;Loker & Perdue, 1992; Moscardo et al., 1996; Jang etal., 2000). Benefit segmentation as an approach tounderstanding and developing segment structures isdemonstrably superior to traditional methods such asdemographic and geographic segmentation. However,once benefit segments are developed, marketers do nothave an effective tool to determine which segmentsthey need to pursue in order to maximize profit andreturn on their marketing dollars spent for targetingthose segments. A simple and effective tool toevaluate the effectiveness of benefit segments as a finalstep in segmentation is much needed. The three-stageapproach to segmentation suggested by Morrison,Hsieh, and O’Leary (1994) was to divide, differentiate,and describe segments; the current study goesfurther still in providing a functional model of thebenefit segment market, which enables destinationmarketers to accurately target the most valuable sub-groups based on their expected economic returns atrelatively low costs.

4. Methodology

4.1. Data source

The data used in the current study were gatheredunder the program called the Pleasure Travel MarketStudy to North America surveys (PTAMS), which wasdeveloped jointly by the International Trade Adminis-tration—Tourism Industries (formerly the United StatesTravel and Tourism Administration) and the CanadianTourism Commission (formerly Tourism Canada),under a Memorandum of Understanding (MOU) in1986, for 5 years of cooperative research, which wasrenewed in 1991 for an additional 5 years. Thesenational household surveys on long-haul travel provideinformation on the size of potential markets, travelbenefits sought, travel philosophies, expenditures, per-ceived strengths and weaknesses of North Americandestinations, media habits of potential travelers, andsocio-demographics. The samples were representative ofall households with listed telephone numbers accordingto stratified probability samples. Prior to in-homeinterviewing, telephone-screening interviews were con-ducted to indicate the size of the target market withinthe total population and to determine whether respon-dents were eligible for in-home interviews.

Approximately 1200 personal in-home interviewsaveraging 50min in length were conducted in Francein 1998 among those who were 18 years or older and hadtaken a vacation trip of four nights or longer outsideEurope and the Mediterranean area in the previous 3years or intended to take such a trip in the next 2 years.In selecting the interviewees, the closest birthdaymethod was employed. The sample for this studyconsisted of 307 respondents from French householdswhere members had traveled to Canada for pleasurepurposes in the previous 3 years.

4.2. Data analysis and construction of the economic value

portfolio matrix

Five analytical steps were followed in this study(Table 1). In segmenting the French market to Canada,the benefits sought in terms of both psychologicalbenefits and destination attributes were used as thesegmentation base characteristics. In the first step, 39benefit sought items by individual respondents measuredon a four-point rating scale were factor analyzed toderive the underlying construct of the benefit dimensionsand to condense the data for ease of interpretation(Table 2). These factors were then used for a clusteranalysis of 307 respondents in the second step of theanalysis.

For the cluster analysis, first, Ward’s method was usedto determine the optimal number of clusters based onthree criteria (the cubic clustering criterion (CCC), the

ARTICLE IN PRESS

Table 1

Analytical steps and methods

Analytical

steps

Analytical method

Step 1: Develop a benefit scale: Factor analysis of benefit items

Step 2: Divide the market into segments: Cluster analysis (Ward’s and K-means techniques) of the cases based on benefit sought factors

Step 3: Describe and test heterogeneity of segments: ANOVA and Chi-square tests for comparisons across clusters in terms of demographic

characteristics and trip behaviors

Step 4: Develop profit criteria: Economic Value Portfolio Matrix based on Stay-Spend Index (SSI)

Step 5: Evaluate and select target markets: Economic value evaluation of each benefit sought cluster based on the market mix of SSI

G. Lee et al. / Tourism Management 27 (2006) 576–588580

pseudo F statistic, and the pseudo t2 statistic). In furtherfine-tuning the clusters, a K-means cluster analysis wasapplied based on the cluster solution from Ward’sminimum variance method. Even though Ward’s cluster-ing method has been credited with being the mostpopular technique, outlying cases may distort thesolution (Hair, Anderson, Tatham, & Black, 1995). Anon-hierarchical method is resistant to outliers; however,this technique has the drawback that an a priori decisionon the exact number of clusters must be made by theresearcher (Hair et al., 1995). Therefore, it is desirable tocombine these two clustering techniques to obtain morereliable cluster solutions.

After identifying clusters, the cluster pattern was crossvalidated using the Analysis of Variance (ANOVA)procedure. Further, in order to incorporate key triprelated behaviors (e.g., travel mode, satisfaction, revisitintention, expenditure patterns, and vacation activities)with benefit sought, an in-depth analysis was conductedapplying ANOVA and w2 tests (see Table 1).

In the final analytical step, the economic value of eachbenefit cluster was evaluated by means of the EconomicValue Portfolio Matrix. The EVPM was comprised offour quadrants, each of which indicated a stay-spendindex (SSI), a combination of the two factors mostrelevant to estimating the clusters’ profitability to atourism destination, namely trip expenditures perperson per day (Exp. PPPD) and average length ofstay. By applying the median value of each variable(e.g., median expenditure PPPD: 714.28FF and 12 daysfor length of stay), the Economic Value Portfolio Matrixwas created (Fig. 1). Utilizing the median instead ofmean was appropriate because the distribution of thosevariables was not normal. Thus, the SSI generated fourcategories: low Total Exp. PPPD and short length ofstay group (coded as 1), low Total Exp. PPPD and longstay group (coded as 2), high Total Exp. PPPD andshort stay group (coded as 3), and high Total Exp.PPPD and long stay group of travelers (coded as 4).Thus, the respondents in all clusters were assigned anSSI index score.

The respondents coded as ‘‘3’’ comprised the prioritymarket, since these travelers generated tourism revenues

most efficiently for DMOs, with their Total Exp PPPDbeing much higher than that of any other SSI group andthe costs to service them relatively low due to their shortstays at the destination. Efficiency in generating profitcan be achieved when visitors spend intensely with ahigh level of spending propensity within relatively shorttrip durations at minimum service costs to a destination.For example, if a traveler A stays at a destination for 20days he may reduce his daily expenses over this ratherlengthy trip duration, while consuming travel infra-structure and municipal services, which are not necessa-rily directly paid for by this traveler. Meanwhile,traveler B stays in a destination for 15 days but shemay spend her travel expenses more intensely, andconsume less traveler service/support services thantraveler A. However, the final determination of thevalue of the priority market when compared to thefavorable market largely depends on the situation andstrategy of the individual destination marketing organi-zation.

In general, travelers in low-value markets tendto spend much less than average and also takeshorter trips. Therefore, they generate smaller yieldand Total Exp. PPPD. Back-ups spend less butstay longer, and thus generate a good yield but withlower efficiency. Favorable markets usually have thehighest levels of yield, and their revenue efficiency isrelatively good. In this way, the Economic ValuePortfolio can be developed for each identified benefitsegment.

The value of each benefit cluster was then assessed byits Economic Value Portfolio and market size. Forexample, if a cluster included more of the prioritymarket, then the economic value of the cluster wasevaluated as high. Most of the previous studiescompared the aggregated mean value of each variableper benefit segment instead of analyzing the structure ofthe segment. The portfolio approach has two majorbenefits. First, this approach enables destination mar-keters to analyze the actual economic value profile ofeach segment. Second, it helps them avoid misunder-standing the true profitability of each segment. It is notunusual for destination marketers to evaluate the

ARTICLE IN PRESS

Table 2

Factor analysis of benefits sought by french long-haul pleasure travelers to Canada

Factors and Items (% of total variance explained by each factor) Loading Eigenvalue Reliability alpha

Factor 1: Convenience and deal seeking (31.4%) 3.864 0.8474

Taking advantage of the currency exchange rate 0.730

The best deal I could get 0.711

Good public transportation 0.569

Availability of comprehensive tourist information 0.544

Destination that provides value for my holiday money 0.508

Convenience and frequency of flights to the destination 0.471

Visiting a place I can talk about when I get back home 0.430

Factor 2: Novelty seeking (5.64%) 3.324 0.8246

Going place I have not visited before 0.740

Opportunity to increase one’s knowledge about places, people and things 0.710

Historical buildings and sites 0.630

Interesting rural countryside 0.605

Factor 3: Seeking escape (5.12%) 3.142 0.7988

Getting away from the demands of home 0.725

Getting a change from a busy job 0.679

Escape from the ordinary 0.678

Having fun being entertained 0.582

Finding thrills and excitement 0.565

Factor 4: Seeking environmental quality and safety (4.87%) 3.047 0.8339

Environmental quality and air, water, and soil 0.808

High standards of hygiene and cleanliness 0.723

Personal safety even when traveling alone 0.676

Nice weather 0.449

Outstanding scenery 0.422

Factor 5: Seeking differences (4.14%) 2.916 0.8066

Experiencing a new and different lifestyle 0.674

See people from different ethnic backgrounds 0.647

Opportunity to see or experience unique aboriginal groups 0.602

Trying new foods 0.543

Meeting new and different people 0.527

Factor 6: Roughing it and coping (3.62%) 2.814

Roughing it/wilderness and adventure 0.773

Ease of driving on my own in the destination 0.624

Outdoor activities such as hiking, climbing, 0.573

Being able to practice a foreign language 0.528

Factor 7: Shopping and art/culture (3.22%) 2.032 0.6205

Shopping 0.669

Arts and cultural attractions 0.539

Indulging in luxury 0.504

Just relax 0.493

Factor 8: Seeking activities for the entire family (2.79%) 2.015 0.7924

Activities for the entire family 0.841

Being together as a family 0.823

Factor 9: Visiting friends and relatives (2.67%) 1.608 �0.0284

Visiting friends and relatives �0.695

Visits to appreciate natural ecological sites 0.444

Note: Extraction method: Principal Components Analysis. Rotation method: Varimax with Kaiser Normalization. Total variance explained: 63.5%.

Cronbach’s a for the overall scale: 0.938.

G. Lee et al. / Tourism Management 27 (2006) 576–588 581

economic value of segments simply by the arithmeticmean of each segment’s total travel expenditures, whichmay mislead them into an inappropriate marketingbudget allocation or ineffective advertising efforts. This

simple matrix provides a very useful tool for marketvalue assessment and gives marketers a clear directionfor effective budgeting of marketing dollars andstrategic market targeting.

ARTICLE IN PRESS

Average Length of StayShort

Average Length of StayLong

Total Expenditure *PPPD:High Priority market Favorable market

Total Expenditure PPPD: Low Low value market Back-ups

Fig. 1. Economic value portfolio matrix based on the stay-spend index

(SSI) *PPPD ¼ Per person per day.

G. Lee et al. / Tourism Management 27 (2006) 576–588582

5. Results

5.1. Factor-cluster analysis: defining benefit sought

market segments

Nine factors were derived using a principal compo-nents method for initial factor extraction; a Varimaxrotation was then applied. An Eigenvalue criterion(factors with an Eigenvalue of greater than 1.0) was usedin determining the number of factors. It appeared thatthe factors fell into two broad categories: psychologicalbenefit factors, including novelty, escape, seekingdifferences, and roughing it and coping; and factorsderived from destination attributes, such as environ-mental quality and safety, convenience and deals,shopping and art/cultural experiences, activities for theentire family, and visiting friends and relatives. Overall,the scale had a high level of internal consistency,showing a reliability a of 0.94. Most of the factors,except for factor 7, had relatively high reliability, aboveor close to 0.8. The nine factors explained 63.5% of thetotal variance. The last factor, namely VFR, wasexcluded from further analysis due to low reliability(Table 2).

Based on the eight benefit sought factors, therespondents clustered into five distinctive groups whenanalyzed by Ward’s and K-means cluster analyses. Thefirst cluster was the family oriented (n ¼ 98, 32% of therespondents), which had the highest rating on factor 1(convenience and deal seeking) and also rated high onshopping and art/culture (factor 7) and seeking escape(factor 3). The family oriented group was the mostdifferent from Cluster 4 (roughing it and coping).Cluster 2 (n ¼ 46, 15%), the environmental qualityconscious group, gave the highest importance ratings toenvironmental quality, personal safety, weather, andoutstanding scenery (factor 4). They also ranked thehighest on factor 5 (seeking differences), and wererelatively high on factors 2 (novelty seeking) and factor1 (convenience and deal seeking). The Cluster 2respondents were markedly different from those inCluster 3, the culture and luxury indulgent. Cluster 3(n ¼ 71, 23%) showed the highest interest in shoppingand art and cultural experiences (factor 7), and alsoconsidered novelty (factor 2) to be an important benefit

that they sought from a trip to Canada. Finally, Cluster4 comprised those who sought opportunities for rough-ing it and coping (factor 5). They also rated factor 2(novelty seeking) relatively high. One of the initialclusters was eliminated from the further analysis due tothe small number of cases it contained (n ¼ 25). Therespondents in this cluster rated low on all benefitsought factors. Therefore, the remaining analyses werebased on four clusters (Table 3).

5.2. Tests for group differences

The next step of the analysis was to investigate if theseclusters had significantly different socio-demographicand behavioral characteristics applying w2 analysis andANOVA procedure. There were statistically significantdifferences across clusters in terms of age, occupation,education, and marital status. Cluster 2 contained theoldest travelers (53 years), while the youngest groupamong the four clusters was Cluster 3, with an averageage of approximately 38 years. There seemed to be morefemale than male travelers to Canada; in particularClusters 1 and 2 had more females than males, beingcomposed of 62.2% and 63.0% women respectively. Themajority of French travelers to Canada had monthlyhousehold incomes between 13,000FF and 15,999FF.Cluster 1 was the highest income group (almost 68%had either middle or high income levels) and Cluster 2the lowest (64.7% were in the lowest income range). Theother two clusters, 3 and 4, showed similar incomedistributions. More than half of each of Clusters 1, 2and 3 were employed in white-collar administrative ormanagerial fields. However, the second largest occupa-tional group was non-working class (i.e., housewives,retired, students, and other); in particular, the majorityof Cluster 2 (61%) belonged to this category. The levelsof education appeared to be correlated with age. Theyoungest cluster (Cluster 3) were better educated thanthe other clusters (52% had a college or universityeducation), and especially contrasted with Cluster 2, theoldest group (of which 22.7% had only primaryeducation). Overall, the majority of French travelers toCanada had either a high school or college education.

5.3. Behavioral difference among benefit segments

AVOVA and w2 tests were applied to the groupdifference among cluster. There was a significantdifference (p ¼ 0:000) across the four clusters in termsof travel arrangements. The French tourists werecategorized based on travel mode (package vs. non-package) and duration and frequency of trips. Thisyielded three travel arrangement groups: packagetravelers, long-stay independent travelers, and frequentshort-stay independent travelers. There were relativelymore package travelers in Clusters 1 and 2 (53.1% and

ARTICLE IN PRESS

Table 3

Benefits sought segments of French long-haul pleasure travelers to Canada

Benefit sought variables Cluster 1:

family

oriented

Cluster 2:

environment

& safety

conscious

Cluster 3:

culture &

luxury

indulgent

Cluster 4:

roughing it

and coping

*Test statistic

(n ¼ 98, 32%) (n ¼ 46, 15%) (n ¼ 71; 23%) (n ¼ 67, 22%) (F test)

Factor 1: Convenience and deal seeking 0.227 0.225 �0.252 0.0026 3.812

Factor 2: Novelty seeking �0.07 0.326 0.392 0.279 6.733

Factor 3: Seeking escape 0.327 �0.418 0.097 �0.022 6.833

Factor 4: Seeking environmental quality

and safety

0.155 0.726 �0.571 0.130 21.138

Factor 5: Seeking differences 0.065 0.462 �0.497 0.130 13.165

Factor 6: Roughing it and coping �0.084 �1.15 0.274 0.794 58.027

Factor 7: Shopping and art/culture 0.506 �0.161 0.525 �0.984 63.181

Factor 8: Seeking activities for the entire

family

0.836 �0.708 �0.721 0.120 72.165

Note: Factors scores were standardized by Z-scores. *Test statistic indicated po0.001.

Table 4

Travel behaviors of benefit sought segments of French long-haul pleasure travelers to Canada

Trip behavior variables Cluster 1:

family

oriented

Cluster 2:

environment

& safety

Conscious

Cluster 3: culture

& luxury

indulgent

Cluster 4:

roughing &

coping

Test statistic p Value

(n ¼ 98,

32%)

(n ¼ 46,

15%)

(n ¼ 71, 23% ) (n ¼ 67,

22%)(w2 or F)

Number in travel party 2.02 (1.17) 1.80 (1.07) 1.56(1.23) 1.93 (1.47) F ¼ 1:571 0.755

Number of previous visits to Canada 1.34 (1.11) 1.35 (0.95) 1.37 (1.40) 2.04 (2.61) F ¼ 3:561 0.014

Total number of nights stayed in

Canada

12.41 (6.04) 12.78 (9.56) 19.75 (25.5) 20.06(19.70) F ¼ 4:499 0.004

Satisfaction 3.72 (0.47) 3.57 (0.62) 3.80 (0.40) 3.69 (.50) F ¼ 2:276 0.080

Value for the money evaluation 8.42 (1.26) 8.43 (1.07) 8.17 (1.23) 8.16 (1.08) F ¼ 1:112 0.344

Intention to revisit in next 3 years 2.82 (1.13) 2.96 (0.84) 2.91 (0.75) 2.83 (0.95) F ¼ 1:476 0.221

Level of language proficiency:

Speaking English 1.96 (0.99) 0.59 (0.83) 2.56 (0.94) 2.37 (0.93) F ¼ 12:658 0.000

Reading English 1.97 (1.03) 1.57 (0.86) 2.58 (0.95) 2.39 (0.95) F ¼ 12:747 0.000

Travel Arrangements:

Package travelers 53.1% 71.7% 31.0% 31.3%

Long-stay independent travelers 24.5% 19.6% 29.6% 31.3%

Frequent short-stay independent

travelers

22.4% 8.7% 39.4% 29.4% w2 ¼ 28:874 0.000

Note: Numbers in parentheses are standard deviations.

G. Lee et al. / Tourism Management 27 (2006) 576–588 583

71.7% respectively), while members of Clusters 3 and 4tended to be more independent in their travel arrange-ments (Table 4). The total number of previous visits toCanada was significantly different across clusters(p ¼ 0:014). Cluster 4 (roughing it and coping) hadpreviously visited Canada more than the other clusters,having made just over two previous visits. The overalltotal number of nights stayed in Canada was 16.1.Cluster 4 had the longest stay, spending 20.1 days onaverage in Canada, followed by Cluster 3 (19.8 days).Cluster 1 travelers stayed the shortest length of time inCanada, averaging 12.4 days.

The respondents were asked to rate their abilities tocommunicate in English on a four-point scale, 1 beingnot at all, 2 not very well, 3 quite well, and 4 very well.This question should be particularly useful for commu-nications strategies. Cluster 3 reported the highest levelsin both speaking and reading ability, rating themselveson average 2.56 and 2.58 respectively. These were in theyoungest cluster. Naturally, the oldest cluster (Cluster 2)rated their level of English proficiency the lowest, 1.59 inspeaking and 1.57 in reading. There were no significantdifferences across clusters in the other variables,including trip party size (overall respondents, 2.13),

ARTICLE IN PRESSG. Lee et al. / Tourism Management 27 (2006) 576–588584

satisfaction (3.71 on a four-point rating scale), perceivedvalue for money (8.3 on a ten-point scale), andconversion intention in the next 3 years (2.9 on a four-point scale). In conclusion, French travelers to Canadashowed a high level of overall destination satisfactionand value for the holiday money but not particularlyhigh conversion intention.

5.4. Vacation activity participation patterns

Vacation activity is usually closely related to thebenefits the travelers seek. Fifty-four dichotomous itemsof vacation activities based on multiple responses werecategorized into six activity groups: shopping anddining, cultural activities, experience of nature andecology, sports and watching sports events, sightseeingand touring, and visiting friends and relatives (Table 5).Then the average amount of participation in eachactivity category and total number of activities partici-pated in were calculated for a statistical comparison(ANOVA) of groups. There were significant differences(p ¼ 0:05) across clusters in three activity categories,namely shopping and dining, cultural activities, andsports activities and watching sports events. Cluster 1enjoyed shopping and dining more than any othercluster (2.43). Cluster 2, the oldest female-dominantgroup, participated in cultural activities more than anyother cluster (6.02), whereas Cluster 4 (roughing it andcoping) pursued nature and ecological experiences moreenthusiastically than the others (2.79). The respondentsin Cluster 4 engaged in sports activities more than theother clusters, showing 1.31 activities participated in forthis category. As for sightseeing and touring, Cluster 2had the highest participation among the four clusters(5.78). Overall, Clusters 2 and 4 had the highest levels ofparticipation in total vacation activities, with 17.32 and17.29, whereas Cluster 3 had the lowest level of activityparticipation, at 15.79.

Table 5

Most popular activities of benefit sought segments of French long-haul plea

Vacation activity participation Cluster 1:

family

oriented

(n ¼ 98,

32%)

Cluster 2:

environment

and safety

conscious

(n ¼ 46,

15%)

Shopping and dining 2.43 (1.10) 2.15 (1.11)

Cultural activity 5.29 (3.02) 6.02 (3.24)

Experience of nature and ecology 2.46 (2.02) 2.30 (1.86)

Sports activities and watching sports

events

1.13 (1.61) 0.52 (.98)

Sightseeing and touring 5.21 (2.36) 5.78 (2.47)

Visiting friends/relatives 0.28 (0.45) 0.24 (0.43)

Total number of activities 16.11 (6.99) 17.32 (7.32)

Note: Numbers in parentheses are standard deviations.

For all French travelers to Canada, cultural experi-ences such as sampling local food (80% of allrespondents participated), visiting museums and gal-leries (60%), and seeing local crafts and handiwork(60%) were the most popular activities, along withsightseeing and touring (78%) and visiting friends andrelatives (30%). Sports activities including golf (3.6%),fishing/hunting (4.6%), bicycling (8.8%), and watersports (5.2%) were the least popular activities.

5.5. Expenditure patterns

In comparing expenditure patterns across the fourbenefit clusters, three variables were investigated in thisstudy using ANOVA procedure: total expenditures,total expenditures per capita (yield), and total exp.PPPD. Overall, French travelers spent 14,729.32 FF onaverage for their trips to Canada (Table 6). As for thetotal expenditures, respondents in Cluster 1 were the topspenders with an average of 17,337.66 FF, with Clusters4 and 3 a distant second and third (14,626.62 and14,304.29 respectively). The total expenditures forCluster 2, at 11,022 FF, were far below the overallaverage expenditure. When comparing total expendi-tures per capita, the travelers in Cluster 3 were the topspenders.

Another key variable in evaluating the level ofexpenditure is based on total exp. PPPD, which is thetotal expenditure per capita (yield) divided by totalnights of stay. Interestingly, Cluster 1 showed a higherlevel of total exp. PPPD than Cluster 3, once the lengthof stay was taken into account. Cluster 4, despite havingthe second-largest total trip expenditures among thefour clusters due to long stays, had the smallest amountof total exp. PPPD, 659.94 FF, which was far below theoverall average of 793.26 FF.

In addition to the variation in amounts spent, the fourclusters demonstrated different spending patterns,

sure travelers to Canada

Cluster 3: culture

and luxury

indulgent (n ¼ 71,

23%)

Cluster 4:

roughing it

and coping

(n ¼ 67,

22%)

Test statistic

(F)

p Value

2.25 (1.11) 2.24 (1.18) F ¼ 2:731 0.049

5.14 (2.34) 5.55 (2.73) F ¼ 2:983 0.023

2.21 (1.88) 2.79 (2.07) F ¼ 1:471 0.122

0.90 (1.28) 1.31 (1.67) F ¼ 3:024 0.036

4.86 (2.03) 5.01 (2.20) F ¼ 1:721 0.094

0.25 (0.44) 0.45 (0.50) F ¼ 2:836 0.039

15.79 (5.25) 17.29 (6.63) F ¼ 2:965 0.033

ARTICLE IN PRESS

Table 6

Travel expenditures of benefit sought segments of French long-haul pleasure travelers to Canada

Expenditures (aFF) Cluster 1: family

oriented (n ¼ 93)

Cluster 2:

environment &

safety conscious

(n ¼ 46)

Cluster 3: culture &

luxury indulgent

(n ¼ 70)

Cluster 4: roughing

it & coping (n ¼ 65)

Test statistic

(F)

p Value

Total amount spent on trip to Canada 17,337.66 (11,525.39) 11,022.61 (5344.33) 14,304.29 (8679.97) 14,626.62 (11,673) F ¼ 4:194 0.009

Packages/organized tours 8530.00 (9506.59) 6258.26 (2582.00) 5074.29 (7746.02) 4706.15 (7421.95) F ¼ 3:698 0.024

Meals 1135.11 (2142.19) 475.00 (930. 64) 1367.14 (1670.78) 1407.08 (2199.65) F ¼ 2:667 0.050

Shopping 2393.62 (1722.14) 1671.74 (1036.37) 1912.86 (1653.71) 1807.69 (1608.21) F ¼ 2:985 0.027

Total expenditures per capita 8854.55 (3284.20) 7715.74 (3190.16) 10,895.89 (7694.39) 8754.30 (4238.57) F ¼ 3:118 0.016

Total expenditures per day per capita 841.36 (451.72) 747.34 (323.32) 810.15 (555.73) 659.94 (444.55) F ¼ 2:765 0.028

Note: Numbers in Parentheses are standard deviations.a1 FF (French Franc) is about 0.2USD in 2004.

Table 7

Market shares and expenditure comparison by SSI code

Expenditure (in FF) Low value

market

(SSI ¼ 1)

Back-ups

(SSI ¼ 2)

Priority market

(SSI ¼ 3)

Favorable

market

(SSI ¼ 4)

Test statistic (F) p Value

Market share (volume) 10.8% 39.9% 38.5% 10.8% 100%

Yield (total exp per capita) 4811.29 8313.60 9489.69 15,579.97 F ¼ 32:711 0.000

Total exp. PPPD 555.84 412.06 1198.70 993.08 F ¼ 80:425 0.000

Note: n ¼ 288.

G. Lee et al. / Tourism Management 27 (2006) 576–588 585

showing significant differences in the expenditurecategories of package tours, meals, and shopping. It isnoteworthy that Cluster 3, who valued art/culture andindulging in luxury, spent a significantly larger amounton accommodations than the other clusters (1,138.57 FFcompared with the overall average of 785.77 FF). Apartfrom package tours and transportation, French travelersspent the largest portion of their travel budgets onshopping (1980 FF, overall) (Table 6).

5.6. Economic value evaluation

In assessing the economic value of each benefitsegment, the two most effective variables, expenditureand length of stay, were incorporated to generate theEconomic Value Portfolio Matrix, as presented in Fig. 1.The French benefit segments were then analyzed basedon the matrix. The profitability of each benefit segmentwas evaluated in terms of efficiency of generating traveland high economic impact, measured by the composi-tion of SSI. Yield is believed to be an efficient indicatorof the economic value of a market. As revealed in thestudy, however, yield alone without consideration of thelength of stay does not disclose the real economic valueof a market in terms of efficiency in generating profit fora destination. A more analytical tool is the SSI, whichfacilitates a clearer and more direct comparison amongbenefit segments (Table 7).

Those who stayed in Canada 12 days or less and spentmore than 714.30FF per day per person, and were thusassigned an SSI value of 3 (the priority market), werebelieved to have a comparatively high economic value toDMOs. Benefit Cluster 1, family oriented, seemed to bea strong target market for Canadian tourism, beingcomposed mainly of the priority market (41%) and thefavorable market (12%), and capturing the largestmarket share (32%). Cluster 2 also had a healthyeconomic value portfolio. This segment, however,garnered only 15% of the overall market; therefore, itsoverall value was not significant. A good targetingopportunity seemed to lie in the second largest segment,the culture and luxury indulgent (Cluster 3). Althoughthere were a good number of back-ups (41%), thepriority and favorable markets together made up morethan half of the segment with a considerable marketshare of 23%. Cluster 4, the roughing it and copinggroup, who sought wilderness and adventure, enjoyinghiking and climbing and driving around Canada,seemed unattractive to Canadian tourism marketers,with a rather poor economic value structure; a majorityof this group were back-ups (55%), which had low profitefficiency.

In summary, Canadian tourism marketers may bebest off targeting those who seek family togetherness astheir major benefit, and French travelers who seekculture and luxury experiences may also generate arewarding business (Table 8).

ARTICLE IN PRESS

Table 8

Economic value assessment of benefit sought segments of French long-haul pleasure travelers to Canada

SSI index variable Cluster 1: family

oriented (n ¼ 93, 32%)

Cluster 2: environment

and safety conscious

(n ¼ 41, 15%)

Cluster 3: culture and luxury

indulgent (n ¼ 71, 23% )

Cluster 4: roughing it and

coping (n ¼ 65, 22%)

Low value market 10.8% 15.6% 7.2% 12.3%

Back-ups 33.3% 33.3% 40.6% 55.4%

Priority market 41.4% 42.2% 34.8% 26.2%

Favorable market 11.8% 8.9% 17.4% 6.2%

Note: The index variables in the four categories were created from variables of the average length of stay and the total expenditure per person per day

(PPPD) spent in Canada based on the median values of each variable.

Economic Value Index was computed by multiplying market share of overall market (100) by proportion of priority market (percentage of each

segment).

G. Lee et al. / Tourism Management 27 (2006) 576–588586

6. Conclusions and marketing implications

The main goal of the study was to propose a practicaltool for evaluating travel market segments in terms ofthe expected economic return on each identifiedsegment. This was achieved by developing a compre-hensive and objective measure of the economic valueportfolio of the various benefit segments among Frenchlong-haul pleasure travelers to Canada. In addition, byconnecting the key variables (e.g., travel mode andarrangement, satisfaction, perceived value, revisit inten-tion, and vacation activities) with the benefits sought,the current study aimed to provide DMOs withintegrated information on each segment. This approachbridges the gap in the benefit literature, as suggested byseveral researchers (Jang et al., 2000; Morrison, 2002).Ultimately this approach may help the DMOs gaininsight into product design and communication strate-gies.

In an extremely competitive market environment withincreasing pressure for return on marketing dollars,adopting a segmentation strategy for high efficiency ingenerating revenue is a critical task for all destinationmarketers. The current study indicates that benefit-based market segmentation is a viable and useful toolfor segmenting the French leisure travel market toCanada. As to the usefulness of benefit as a marketsegmentation basis, the result is consistent with previousstudies (Jang et al., 2000; Yannopoulos & Rotenberg,1999; Frochot & Morrison, 2000; Morrison et al., 1996;Moscardo et al., 1996; Woodside & Jacobs, 1985;Goodrich, 1976). Several researchers have shown thesuperiority of benefit as a predictor for tourist destina-tion choice, compared to other psychographicsand behavior variables (Johar & Sirgy, 1995). Theseresearch examples, like the current study, have con-firmed the usefulness of the benefit segmentationapproach as a strategic marketing tool for the industrypractitioners.

To date, however, few studies (e.g., Jang et al., 2000)have suggested objective and quantitative criteria for the

segments with the most economic value to the touristdestinations. Accordingly, the demand for more effec-tive tools to select the most profitable target segmentseems to be urgent. These tools should help destinationmarketers to identify the segments that produce thehighest return on dollars invested, and thus to focus onkey marketing strategies with respect to reaching andcommunicating with target markets and providingservices and facilities demanded by the markets.

Addressing this need, several researchers have sug-gested marketing target selection criteria to help DMOsbased on profitability and economic return (McQueen &Miller, 1985; Loker & Perdue, 1992; Kastenholz et al.,1999). However, these methods had three main draw-backs: complexity, subjectivity, and lack of comprehen-siveness. Therefore, instead of a complex and costlyprocedure, a method that is easily applicable at arelatively low cost was suggested here for destinationmarketers who are striving to evaluate and select themost favorable target segments.

As an advance in this much-needed area, the currentstudy suggested a simple and practical technique toassess the economic value of segments for targetselection in light of efficiency in generating profit andmarket size. Applying an in-depth scale of 39 itemscovering both psychological and destination attribute-based benefits, four distinctive benefit segments wereidentified among French leisure travelers to Canada: thefamily oriented, the environment and safety conscious,the culture and luxury indulgent, and the roughing itand coping. These four benefit segments demonstratedsharp contrasts not only in their benefit sought but alsoin their travel behaviors. The family oriented (Cluster 1)seemed to be the most viable target market for Canadiantourism, and targeting the culture and luxury indulgent(Cluster 3) benefit segment might be rewarding as well.These two benefit segments were relatively substantial(32% and 23% of the market share, respectively) andcomposed largely of priority and favorable markets byvirtue of which they had higher profit-generatingefficiency.

ARTICLE IN PRESSG. Lee et al. / Tourism Management 27 (2006) 576–588 587

Several marketing implications were derived from theanalyses in this study. Overall, the status of the Frenchmarket for Canadian tourism seems to be quitefavorable. First, the priority and favorable marketsconstituted almost half of the market, while only 11% ofFrench travelers were from the low value market.Second, even though back-ups do not generateexpenditures as efficiently, there is a great opportunityfor Canada to capitalize on this segment, especiallyduring off-seasons and weak economic periods.Back-ups made up the largest share at 40% of theFrench travel market to Canada. It is highly recom-mended that Canadian tourism officials carefullyidentify tactics to turn this low efficiency market into amore lucrative one.

In addition, there seems to be good growth potentialfor Canadian tourism in targeting Clusters 2 and 4.First, Cluster 2 had a positive profit efficiency structure.However, the market size was relatively small. There-fore, it is recommended that Canadian DMOs identify astrategy to enlarge this segment. Based on the informa-tion collected in this study, members of this group weretypically female, older, non-working, with relatively loweducation and a lower level of proficiency in bothspeaking and reading English. Cluster 4, roughing it andcoping, had the highest proportion of back-ups. It issuggested that Canadian tourism develop marketingstrategies to induce more spending from the back-ups byproviding more opportunities for them to spend, andredesigning services and facilities to better meet theirneeds and desires. Connected to this analysis, Cluster 4,whose average length of stay was 35 days, were thetravelers most experienced with Canada, engaging insports activities, enjoying hiking and climbing, seekingwilderness and adventure, and visiting friends andrelatives while in Canada. This segment may be betterapproached with a VFR marketing strategy. Further-more, these travelers seem to seek what Canada offersbest (i.e., wilderness and nature), so this segment has ahigh compatibility. Hence, the best strategy for thissegment may be to induce the back-ups’ spending and toenlarge short-staying heavy spender market (prioritymarket).

Incorporating useful information on travel behaviorsand psychological benefits sought, the Economic ValuePortfolio Matrix technique can help DMOs focus ontheir strongest market segments, while also addressingmarket weaknesses. The ultimate goal of this researchwas to assist destination marketers in their targetselection by proposing a tool to evaluate at low costalternative market segments in terms of profitabilitywith maximum efficiency. However, the best results canbe achieved when all market information is integratedbased on consideration of the destination’s uniquemarket situation and compatibility with the existingmarket position.

Disclaimer Note: The data utilized in this research wasmade available by the Canadian Tourism Commission(CTC). The data were originally gathered by PriceWa-terhouseCoopers under arrangement with the CTC.Neither the collector of the original data nor the CTCbear any responsibility for the analysis or interpretationspresented here.

References

Andereck, K. L., Caldwell, L. L., & Debbage, K. (1991). A market

segmentation analysis of zoo visitors. In Travel and Tourism

Research Association 22nd Annual Conference (pp. 359–372). Salt

Lake City, Utah.

Bock, T., & Uncles, M. (2002). A taxonomy of differences between

consumers for market segmentation. International Journal of

Research in Marketing, 19, 216–219.

Canadian Tourism Commission (2002). Canadian Tourism Facts &

Figures 2001. Retrieved March 27, 2003 from http://www.canada

tourism.com/ctxUploads/en_publications/Tourism2001.pdf.

Canadian Tourism Commission (1999). France strategic segmentation

study. Communique, retrieved on June 12, 2003 from http://

www.canadatourism.com/en/ctc/ctx/ctx-news/general/articledetails.

cfm?articleID=10022&language=english

Davies, A., & Prentice, R. (1995). Conceptualizing the latent visitor to

heritage attractions. Tourism Management, 17(7), 491–500.

Frochot, I., & Morrison, A. M. (2000). Benefit segmentation: A review

of its applications to travel and tourism research. Journal of Travel

& Tourism Marketing, 9(4), 21–45.

Gitelson, R. J., & Kerstetter, D. L. (1990). The relationship between

sociodemographic variables, benefits sought and subsequent

vacation behavior: A case study. Journal of Travel Research,

28(3), 24–29.

Goodrich, J. N. (1976). An investigation of consumer perceptions of,

and preferences for, selected tourist destinations: A multidimen-

sional scaling approach, Unpublished Ph.D. dissertation, State

University of New York at Buffalo.

Goodrich, J. N. (1977). Benefit bundle analysis: An empirical study of

international travelers. Journal of Travel Research, 16(Fall), 6–9.

Goodrich, J. N. (1980). Benefit segmentation of US international

travelers: An empirical study with American Express. In D.

Hawkins, E. Shafer, & J. Rovelstad (Eds.), Tourism Marketing

and Management Issues (pp. 133–147). Washington, DC: George

Washington University.

Haley, R. (1968). Benefit segmentation: A decision-orientated research

tool. Journal of Marketing, 32(July), 30–35.

Haley, R. (1999). Benefit segmentation—thoughts on its past and its

future. Journal of Segmentation in Marketing, 3(1), 5–11.

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1995).

Multivariate data analysis (4th ed.). NJ: Prentice Hall.

Jang, S. C., Morrison, A. M., & O’Leary, J. T. (2000). Benefit

segmentation of Japanese pleasure travellers to the USA and

Canada: Selecting target markets based on the profitability and

risk of individual market segments. Tourism Management, 23,

367–378.

Johar, J. S., & Sirgy, M. J. (1995). Using segment congruence analysis

to determine actionability of travel/tourism segments. Journal of

Travel & Tourism Marketing, 4(3), 1–17.

Kastenholz, E., Davis, D., & Paul, G. (1999). Segmenting tourism in

rural areas: The case of north and central Portugal. Journal of

Travel Research, 37(4), 353–363.

Kotler, P., & Armstrong, G. (2003). Principles of marketing (10th ed.).

Upper Saddle River, NJ: Prentice-Hall.

ARTICLE IN PRESSG. Lee et al. / Tourism Management 27 (2006) 576–588588

Loker, L., & Perdue, R. (1992). A benefit-based segmentation of a

nonresident summer travel market. Journal of Travel Research,

31(1), 30–35.

Mason, K. J., & Gray, R. (1996). Short haul business travel in the

European Union: a segmentation profile. Journal of Air Transport

Management, 2(3/4), 197–198.

McCool, S. F., & Reilly, M. (1993). Benefit segmentation analysis of

state park visitor stating preference and behavior. Journal of Park

and Recreation Administration, 11(4), 1–14.

McQueen, J., & Miller, K. (1985). Target market selection of tourists:

A comparison of approaches. Journal of Travel Research, 24(1),

2–6.

Morrison, A. M. (2002). Hospitality and travel marketing. Albany,

New York: Delmar Thomson Learning.

Morrison, A. M., Hsieh, S., & O’Leary, J. T. (1994). Segmenting the

Australian domestic travel market by holiday activity participa-

tion. Journal of Tourism Studies, 5(1), 39–56.

Moscardo, G. M., Morrison, A. M., Pearce, P. L., Lang, C. T., &

O’Leary, J. T. (1996). Understanding vacation destination choice

through travel motivation and activities. Journal of Vacation

Marketing, 2(2), 109–122.

Prentice, R. C., Witt, S. F., & Hamer, C. (1998). Tourism as

experience: The case of heritage parks. Annals of Tourism Research,

25(1), 1–2.

Qiu, H., & Zhang, J. (1995). Determinants of tourist arrivals and

expenditures in Canada. Journal of Travel Research, 34(2), 43–49.

Richardson, S. L., & Crompton, J. (1988). Vacation patterns of French

and English Canadians. Annals of Tourism Research, 15(3),

430–435.

Ryan, C., & Glendon, I. (1998). Application of leisure motivation scale

to tourism. Annals of Tourism Research, 25(1), 169–184.

Schul, P., & Crompton, J. L. (1983). Search behavior in international

vacationers: Travel-specific lifestyles and sociodemographic vari-

ables. Journal of Travel Research, 22(Fall), 25–30.

Shoemaker, S. (1994). Segmenting the US travel market according to

benefits realized. Journal of Travel Research, 33(2), 8–21.

Sussmann, S., & Rashcovsky, C. (1997). A cross-cultural analysis of

English and French Canadians’ vacation patterns. International

Journal of Hospitality Management, 16(2), 191–208.

Tian, S., Crompton, J. L., & Witt, P. A. (1996). Integrating constraints

and benefits to identify responsive target markets for museum

attractions. Journal of Travel Research, 35(2), 34–44.

Woodside, A. G., & Jacobs, L. W. (1985). Step two in benefit

segmentation: learning the benefits realized by major travel

markets. Journal of Travel Research, 23(Fall), 14–24.

Woodside, A. G., & Pitts, R. E. (1976). Effects of consumer lifestyles,

demographics, and travel activities on foreign and domestic travel

behaviour. Journal of Travel Research, 14(Winter), 13–15.

World Tourism Organization. (2000). Tourism highlights 2000.

Madrid: World Tourism Organization.

Yannopoulos, P., & Rotenberg, R. (1999). Benefit segmen-

tation of the near-home tourism market: The case of upper

New York state. Journal of Travel & Tourism Marketing, 8(2),

41–55.

Further reading

Andersen, V., Prentice, R., & Wantanabe, K. (2000). Journey for

experiences: Japanese independent travelers in Scotland. Journal of

Travel & Tourism Marketing, 9(1/2), 129–151.

Cha, S., McCleary, K., & Uysal, M. (1995). Travel motivations of

Japanese overseas travellers: A factor-cluster segmentation ap-

proach. Journal of Travel Research, 34(1), 33–39.

Chen, J. S. (2003). Market segmentation by tourists’ sentiments.

Annals of Tourism Research, 30(1), 188–191.

Chon, K., & Singh, A. (1995). Marketing resorts to 2000: Review of

trends in the USA. Tourism Management, 16(6), 463–469.

Communique June. (1999). France strategic segmentation study.

Canadian Tourism Commission. (posted 1999-June 01). URL:

http://www.canadatourism.com/en/ctc/ctx/ctxnews/general/article-

deails.cfm?articleID=10022&language=english

Dickson, P. R., & Ginter, J. L. (1987). Market segmentation, product

differentiation, and marketing strategy. Journal of Marketing,

51(April), 1–10.

Galloway, G. (2002). Psychographic segmentation of park visitor

markets: evidence for the utility of sensation seeking. Tourism

Management, 23, 581–583.

Milner, L., Collins, Tachibana, R., & Hiser, R. (2000). The Japanese

vacation visitor to Alaska: A preliminary examination of peak and

off season traveler demographics, information source utilization,

trip planning, and customer satisfaction. Journal of Travel &

Tourism Marketing, 9(1/2), 43–56.

Oh, H. C., Uysal, M., & Weaver, P. A. (1995). Product bundles and

market segments based on travel motivations: a canonical

correlation approach. International Journal of Hospitality Manage-

ment, 14(2), 125–136.

Ratneshwar, S., Warlop, L., Mick, D. G., & Seeger, G. (1997).

International Journal of Research in Marketing, 14, 256.

Wendel, M., & Steenkamp, J.-B. E. M. (1989). A clusterwise regression

approach to benefit segmentation. International Journal of Research

Marketing, 6, 241–258.

Wind, Y. (1978). Issues and advances in segmentation research.

Journal of Marketing Research, 15, 317–337.