Benefits Segmentation of Visitors to Latin America

10.1177/0047287504272032 FEBRUARY JOURNAL OF 2005 TRAVEL RESEARCH Benefits Segmentation of Visitors to Latin America EMINE SARIGÖLLÜ and RONG HUANG This research presents an effective segmentation of Latin American tourists and thereby provides invaluable input and guidance for destination marketers in regard to strategic planning for the region s tourist provision. Four distinct segments are identified based on the benefits sought, and these are profiled with respect to demographics, travel behavior, expectations about the infrastructure, local environment, services, and costs, as well as the visitors personalities and interests. Globally, the most important decision drivers are found to be safety, flight and accommodation availability, and affordability. This research also presents an overview of benefits segmentation literature on tourism. Keywords: benefits segmentation; destination marketing; Latin America With abundant natural endowments of sand, sea, and sun; diverse cultures; and a rich historical heritage, Latin American countries have a great potential in the inbound travel market (Strizzi and Meis 2001). The Latin American region attracted 18.9 million international travelers (3.6% of the world total) in 2001 (World Tourism Organization 2002). Moreover, the growth rate of international tourist arrivals in Latin America was 6.8% in 2000. Nonetheless, the growth rate for visitors from North America dropped from 8.9% in 2000 to 1.8% in 2001, which is a cause for anxiety because almost three-quarters of all international tourist arrivals are either from North America or intraregional in origin (Strizzi and Meis 2001). According to Augusto Huéscar, the World Tourism Organization s (WTO) chief of market intelligence and promotion, Although North American travellers composed a great part of the international travellers for Latin America, increased competition from other regions in the world and the safety of tourists are the two greatest challenges for the Americans to overcome (Bambrad 2001, p. 1). Furthermore, Strizzi and Meis (2001) noted that the Latin American tourism industry is being adversely affected by various factors including economic and financial instability, ongoing urbanization, safety and security risks, health threats, and aviation infrastructure limitations. In fact, Latin American tourism is expected to face further challenges in the new millennium, specifically due to problems in regional economic growth and political, social, and environmental factors. In short, macrolevel evidence points toward difficult times for Latin American tourism. At the microlevel, however, there is a lack of systematic research. Microlevel analysis entails investigation of demand from the perspective of the visitor and thus presents an opportunity not only for obtaining an in-depth understanding of the visitor but also for developing the key strategic and managerial implications, particularly in regard to product offerings, targeting, and promotion. Hence, research with a microfocus is needed to complement the extant macroresearch for a complete assessment of the tourist demand for Latin America. This research represents a step in that direction. This research provides a thorough assessment of North American visitors to Latin America. It specifically (1) identifies benefits that are important for the tourists destination choice; (2) segments tourists based on the benefits sought; (3) delineates different segments with respect to demographics, travel behavior, services, infrastructure and cost expectations, personality, and interests; and (4) proposes the managerial implications for destination marketers for designing, targeting, and promoting the Latin American tourist product for the segments identified. Hence, this research has important implications for the stakeholders in the Latin American tourism industry because it provides valuable input and guidance toward strategic planning for the region s tourist offering. The contribution of this research is twofold. First, it provides an overview of the benefits segmentation literature in tourism. Specifically, prior studies are categorized in two dimensions, depending on whether (1) benefits were obtained via direct questioning as opposed to some form of indirect/inferential analysis, and (2) they are destinationspecific or general. Second, through focusing on tourists to Latin America (a region characterized by a lack of systematic study to date), this research incorporates a more comprehensive set of attributes than those used in typical tourism segmentation studies. Specifically, in addition to the standard attributes used in tourism segmentation studies (e.g., age, gender, and place of residency), we also consider various aspects of the infrastructure, local environment, services, Emine Sarigöllü (Ph.D., Wharton School, University of Pennsylvania; M.A., University of Pennsylvania; MBA, Bogazici University) is associate professor of marketing and director of the McGill Institute of Marketing (MIM) at the Faculty of Management, McGill University in Montreal, Canada. Her primary research domain is consumer choice. Rong Huang is a doctoral student at the Faculty of Management, McGill University in Montreal, Canada. Her research interests focus on branding. This research was supported by funds from the Organization of American States (OAS) and MIM. The authors thank Daniel Perna, George Vincent, and the reviewers for their helpful comments, and Dr. Anthony Miyazaki for his assistance in data collection. Journal of Travel Research, Vol. 43, February 2005, 277-293 DOI: 10.1177/0047287504272032 2005 Sage Publications

278 FEBRUARY 2005 and costs, as well as the visitors personalities, interests, and travel behavior. Hence, this study provides an in-depth understanding of Latin American visitor segments through an unusually rich profiling. Consequently, this research produces practical operational information on each segment that is translatable into strategy, specifically in terms of the design, promotion, and targeting of the tourist product. LITERATURE REVIEW Market segmentation is the process of classifying customers into groups based on different needs, characteristics, or behavior, and it has strategic implications for customer targeting and product positioning. Market segmentation is widely implemented in the tourism industry, using visitor demographics (Morrison et al. 1996; Mudambi and Baum 1997), psychographics (Bieger and Laesser 2002; Cha, McCleary, and Uysal 1995; Dodd and Bigotte 1997; Kau and Lee 1999; May et al. 2001; Mo, Havitz, and Howard 1994; Sirakaya, Uysal, and Yoshioka 2003), behavior (Court and Lupton 1997; Fodness and Murray 1998; Formica and Uysal 1998; Goldsmith and Litvin 1999; Meric and Hunt 1998; Pritchard and Howard 1997), and benefits (Kanstenholz, Davis, and Paul 1999; Tian, Crompton, and Witt 1996). Benefit segmentation is a powerful method for grouping consumers (Kotler and Turner 1993). It has been proposed that because different segments of consumers may desire different benefits from using a product, and the travel related benefits sought also vary (Woodside and Jacobs 1985). Furthermore, Haley (1968) claimed that the benefits people seek are the basic reason for the existence of true market segments and thus provide better determinants of behavior than other approaches. Indeed, benefit segmentation is shown to predict behavior better than demographics and geographic segmentation (Haley 1968; Kastenholz, Davis, and Paul 1999; Young, Ott, and Feigin 1978). Moreover, based on predictive factors and combined with key descriptive variables, benefit segmentation provides a clear insight into marketing and communication strategy formulation (Loker and Perdue 1992). Accordingly, benefit segmentation is becoming increasingly popular in tourism markets, particularly as an effective foundation for marketing strategy. In the early tourism literature, benefits were defined as visitor ratings of desired amenities and activities (Tian, Crompton, and Witt 1996). This approach was used in image studies to describe and evaluate potential visitors perception of destinations (Crompton 1979; Fakeye and Crompton 1991; Gartner 1986; Hunt 1975; Keown, Jacobs, and Worthley 1984; Mayo 1973; Um and Crompton 1992). Other researchers, however, focused on tourists motivation to travel and conceptualized various attributes as conduits to facilitate the desired psychological benefit outcomes. Early research on tourists motivation includes Lundberg (1971) and Crompton (1979), in which, respectively, 18 and 9 motivations were identified as influencers of the travelers decisions. Other researchers followed, including Bieger and Laesser (2002); Cha, McCleary, and Uysal (1995); Formica and Uysal (1998); Iso-Ahola (1982); Loker and Perdue (1992); Pearce and Caltabiano (1983); Sirakaya, Uysal, and Yoshioka (2003); Tian, Crompton, and Witt (1996); and Woodside and Jacobs (1985). Adopting a broad perspective on benefit segmentation, the present study considers prior research that took either of the following approaches: desired amenities/activities or motivation for travel. Prior studies in this domain can be categorized in terms of whether benefits were obtained via direct questioning or through some form of indirect/inferential analysis. 1 Furthermore, they can also be categorized depending on whether they were destinationspecific or general. Table 1 provides an overview of extant research on benefit segmentation in tourism. Typically, direct questioning was used to obtain the benefits sought from travel. For example, Kastenholz, Davis, and Paul (1999), in their segmentation study of north and central Portugal, identified 27 dimensions of rural experiences, such as entertainment/nightlife, opportunities for socializing, opportunities for families with children, and so on, by direct questioning. The direct approach is based on the premise that respondents can reliably sum up their vacation experiences. As Dann (1981) has pointed out, however, tourists may be unable or unwilling to reflect on and/or express real travel motives to themselves or to professional interviewers. In addition, managerial implications may be hampered when some questions are not directly related to vacation activities; managers may not be able to determine what specific activities would bring the desired benefits to tourists and, hence, would not be able to design and improve the activities accordingly. Various forms of indirect/inferential analysis were also used to derive the benefits sought (Bryant and Morrison 1980; Davis and Sternquist 1987; Dybka 1987; Johns and Gyimothy 2002; Moscardo et al. 2000; Pearce and Caltabiano 1983; Shoemaker 1994). Typically, researchers derive benefits from what tourists did or plan to do during their vacations. For example, Bryant and Morrison (1980) derived certain benefits from reports of participation in recreational and sightseeing activities during the vacation. Alternatively, Pearce and Caltabiano (1983) inferred travel motivations indirectly from travel experiences using Maslow s hierarchy of needs. The indirect approach typically involves a factor analysis and needs to be implemented very carefully to ensure that the set of activities identified is complete and comprehensive; otherwise, the range of benefits might be unduly constrained. Furthermore, it is likely that the derived benefits are rather obvious and simple in nature. It was demonstrated that the push-pull relationship (i.e., the relationship between the functional benefits and the more abstract psychological and social benefits) might influence and drive travel behavior (Klenosky 2002). In addition, different activities might offer the same or similar psychological or social benefits to tourists. Finally, because most such work uses factor analysis to infer the benefits sought and a given activity can only load on a single factor, the possibility that the activity in question might be associated with several different psychological or social benefits cannot be explored. 2 As stated earlier, benefit segmentation research can also be classified according to whether the study is destinationspecific. In destination-specific studies, tourists to the same destination are classified into segments based on the benefits sought. For example, Formica and Uysal (1998) studied visitors of the Spoleto Festival in Italy and identified six benefit factors (from the original 23 variables): socialization and entertainment, event attraction and excitement, group togetherness, cultural/historical significance, family

TABLE 1 BENEFITS SEGMENTATION IN TOURISM: PREVIOUS LITERATURE JOURNAL OF TRAVEL RESEARCH 279 Destination Specific Not Destination Specific Benefits obtained by Kastenholz, Davis, and Paul (1999) Bieger and Laesser (2002) direct questioning 27 benefits including culture and traditions and rural Travel motives including nightlife, environment, for example peaceful/quiet comfort, partner, family, nature, atmosphere, unpolluted environment, and healthy culture, liberty, sports, and sun lifestyle Cha, McCleary, and Uysal (1995) Formica and Uysal (1998) 30 motives including relaxation, 23 motives including socialization and entertainment, knowledge, adventure, travel event attraction, excitement, group togetherness, bragging, family, and sports cultural, and historical Tian, Crompton, and Witt (1996) 18 benefits including relaxation, entertainment, socializing, bonding, social recognition, selfesteem, and education May et al. (2001) 26 benefits including enjoying nature, achievement, stimulation, escape, personal/social pressure, and being with family and friends Anderson, Prentice, and Watanabe (2000) 21 motives including novelty, independence, prestige, relaxation, understanding, and utility Loker and Perdue (1992) 12 benefits including escape, relaxation, natural surroundings, excitement variety, family, and friends Benefits derived by Bryant and Morrison (1980) Shoemaker (1994) some form of indirect/ Participation in vacation outdoor and sightseeing 39 benefits including educational inferential analysis activities including hiking, fishing, tennis, historic sites, possibilities, environmental professional sports, and cultural activities aspects, resort set, sun sports, popularity of destination, value, Davis and Sternquist (1987) scenery, friend, relatives, 10 benefits including sports, sightseeing, rest, shopping, entertainment, and convenience food, entertainment, and culture Dybka (1987) Johns and Gyimothy (2002) Content of the questionnaire 12 benefits including social, cultural aspects, nature and is not provided scenery, relaxation and slower pace of life, and enthusiast activities Moscardo et al. (2000) 20 benefits including relaxation, resort, warm sunny weather, beach activities, and environmental activities Woodside and Jacobs (1985) 26 benefits including value of money, relaxation, educational, family, and friends togetherness, and site novelty. Using these factors, two further segments were identified: moderates and enthusiasts. In nondestination research, researchers study what tourists seek in general for their vacation. Such studies are typically conducted on tourists from a specific country or region. For example, Cha, McCleary, and Uysal (1995) studied the travel motivation of Japanese overseas travelers and identified six motivational factors: relaxation, knowledge, adventure, travel bragging, family, and sports, based on which three segments were found: sports seekers, novelty seekers, and family and relaxation seekers. The current study is destination-specific. Visitors to Latin America were segmented using benefits sought as the basis for study. Specific benefits were derived through an inferential approach from a comprehensive set of vacation activities including sightseeing, entertainment, and sports. Benefit segmentation was then implemented using the standard statistical methodology. What differentiates this research from the literature is what follows the segmentation process, specifically, the offering of rich practical implications to destination marketers. This was achieved through the use of a rich set of variables that provided the opportunity to

280 FEBRUARY 2005 validate and delineate the segments in relation to aspects other than the benefits sought. Specifically, this study considered visitors expectations regarding a comprehensive set of infrastructure, local environment, service, and cost factors, as well as travel behavior and typical demographics, then profiled the segments accordingly. In addition, this research also delineated the segments in terms of the visitors personalities and interests. The profiling of segments by different value and personality types complements the benefits sought and offers a deeper understanding of each segment, thereby helping travel suppliers to create packages that are more compatible with the motivations, attitudes, and opinions of the travelers, and also provides better prediction of travel behavior (Abbey 1979; Keng and Cheng 1999; Woodside and Pitts 1976). In short, this research incorporates a wider variety of vacation activity, infrastructure, environment, service, and cost aspects, as well as taking into account the personalities, interests, and demographics of the visitors, than were considered by the prior research. Consequently, this research offers rich managerial implications in relation to destination marketers in terms of the design, targeting, and promotion of the tourism product more so than the previous research, which lacks the breadth and scope of variables needed to draw such strategy implications. METHODOLOGY This research performs a market segmentation of North American tourists to Latin America based on the benefits sought. Requisite information was obtained by a questionnaire. Questionnaire Development The questionnaire contains two parts. The first part inquired about visitors general vacation preferences, such as the frequency of travel, the traveling party, information sources, and package types. In addition, respondents were asked to rate the importance of vacation activities that were presented in three categories: sightseeing, sporting activities, and entertainment. Under each category, specific activities were listed and respondents were asked to rate their relative importance from 1 (not at all important) to 5(very important). Finally, respondents rated the importance of various other decision drivers, including 15 items relating to infrastructure, local environment, services, and costs. The second part contained questions about the respondents personalities, interests, and demographics. Three categories of vacation activity sightseeing, sporting activities, and entertainment and specific personality and interests items were selected jointly with the sponsors of the research, namely, the Inter-Sectoral Unit for Tourism of the Organization of American States (OAS), following a consultation process with the representatives of member states and a review of the extant literature in academic journals. Although the activities (25 in total) are quite comprehensive, they are not necessarily exhaustive. The questionnaire was pretested first on a sample of 15 MBA students and then 20 respondents recruited by travel agencies in Montreal, then revised accordingly. Although the questionnaire was shortened following comments from the respondents, the final version was still quite demanding (10 pages) due to the extensive information needs of the OAS members. To encourage an enthusiastic response, a prize draw was introduced with a first prize of $300 and second and third prizes of $100 each. Data Collection The survey was developed by McGill Institute of Marketing (MIM) of McGill University through a research project sponsored by the Inter-Sectoral Unit for Tourism of the OAS. The survey was initially administered with the help of travel agencies in the United States and Canada. Concerns, however, about the quality of the data due to sampling error (e.g., responses from cruise visitors who did not stay overnight, or visitors who have not yet visited Latin America), and substantial missing information led to the rejection of many responses. Consequently, 94.3% of the data (250 responses) were collected at Miami airport from respondents who were awaiting their connections. Data collection was planned around the scheduled arrival times of airlines from various Latin American countries, and respondents were personally approached. Effective control of the data-collection process at the airport resulted in a good response rate and verifiable data. In all cases, attempts were made to exclude business travelers. Included were visitors who have vacationed in Latin America during the past year and stayed a minimum of 3 nights. The minimum stay criterion was implemented to ensure that the respondents had sufficient opportunity to experience various aspects of the destination, as assessed by the questionnaire. In total, 265 North Americans (Americans or Canadians) responded, with 99% originating from the United States. Analysis Data were analyzed in three stages. First, the various dimensions underlying the benefits sought were uncovered by a factor analysis using the principal component method with varimax rotation. These methods were supported in the literature and yielded the most interpretable results (Loker and Perdue 1992, p. 31). In fact, factor analysis has been widely used in visitor segmentation research (Cha, McCleary, and Uysal 1995; Formica and Uysal 1998; Johns and Gyimothy 2002; Kastenholz, Davis, and Paul 1999; Loker and Perdue 1992; Madrigal and Kahle 1994; Shoemaker 1994). Typically, factor analysis is implemented because it allows data reduction and substantive interpretation (Churchill and Iacobucci 2002). In this research, data reduction was specifically useful in the next stage of analysis, involving cluster analysis, because it eliminated correlation among the variables (which would have been problematic in cluster analysis). Furthermore, factor analysis helped identify the constructs that underlie the variables, providing a global view of the most substantive benefits sought using such constructs. The factor analysis produced orthogonal factors that summarized the 25 vacation activities. Then, the factor scores for each respondent were saved and consequently used in stage 2 for clustering them into market segments. Individuals were clustered such that those within each cluster

JOURNAL OF TRAVEL RESEARCH 281 were more similar to each other than to those in other clusters, thereby creating a situation of homogeneity within clusters and heterogeneity between clusters (Hair et al. 1998). Specifically, the K-means cluster method, which is quite common in visitor segmentation research, was implemented (e.g., Cha, McCleary, and Uysal 1995; Formica and Uysal 1998; Kau and Lee 1999; Madrigal and Kahle 1994). The K- means clustering method produces results that are less susceptible to outliers in the data, the distance measure used, and the inclusion of irrelevant or inappropriate variables (Hair et al. 1998). During the third stage, segment characteristics were delineated by various univariate and multivariate statistical procedures. Specifically, the differences among clusters in demographics, travel behavior, the ratings of various decision drivers, and the various personality and interest dimensions were assessed by suitable analyses, including ANOVA, discriminant, and chi-square. These analyses typically entail cluster analysis for the purpose of validation and segment profiling (Cha, McCleary, and Uysal 1995; May et al. 2001; Formica and Uysal 1998; Madrigal and Kahle 1994). Different statistical tests are conducted according to the characteristics (metric, categorical) of the variables. ANOVA is used to identify if there are any differences among the clusters, as measured by a comparison of mean ratings (for metric variables). Discriminant analysis is specifically used to understand how the members in one segment differ from those in another and/or to correctly classify individuals into segments. In the current study, decision drivers, personality, and interests items (all metric) are used to find the best discriminators among the identified clusters (i.e., segments). Finally, chi-square analysis is used to explore the differences between clusters in terms of categorical variables, such as demographics and travel behavior. FINDINGS The findings will be presented in three sections. First, the dimensions underlying the benefits sought will be revealed. This will be followed by results from the benefits segmentation. Finally, the segments will be profiled in terms of demographics, travel behavior, infrastructure, service and cost expectations, and personality and interests. Dimensions Underlying Benefits Sought The assessment of the benefits sought was obtained by asking respondents how important it was whether a vacation destination should provide specific sightseeing, sporting, and entertainment activities. In total, 25 activities were included in the survey for the sake of completeness. It is unlikely, however, that visitors consider each of these dimensions independently when selecting a destination; rather, they are likely to evaluate some activities similarly. Thus, it is conceivable that a relatively fewer number of dimensions represent the information found in the original 25 items. To explore the dimensions underlying visitors benefits sought, a factor analysis (using the principal components method with varimax rotation) was performed on the importance ratings of the 25 vacation activities. Because the major objective of the factor analysis was to reduce a large number of variables to a smaller set of uncorrelated variables for subsequent use in cluster analysis, Hair et al. (1998) indicated that the orthogonal rotation methods, such as varimax, are appropriate. Specifically, varimax rotation ensures a clearer separation of the factors, and it has proved very successful as an analytic approach to obtain an orthogonal rotation of factors (Hair et al. 1998). In keeping with the conventions for factor analysis, we used the following criteria: (1) factor loadings equal to or above 0.50, (2) eigenvalues equal to or above 1.0, and (3) results of the factor analysis explaining at least 50% of the total variance (Hair et al. 1998). Table 2 displays factor loadings, eigenvalues, and the explained variance. In addition, alpha coefficients for items in each factor are provided. The factor analysis grouped together items that received similar ratings and thus revealed five factors accounting for 65% of the total variance. The factor solution is acceptable since it is acceptable to consider a solution that accounts for 60% of the total variance as satisfactory (and in some instances even less), in the social science research (Hair et al. 1998, p. 104). The resultant five factors represent specific dimensions of the benefits that respondents seek when they go on vacation. The results confirm that visitors are indeed seeking a variety of vacation activities. The first factor includes a variety of typical tourist activities and is named fun & sun benefits. Specifically, 8 variables were loaded on this factor, explaining about 27% of the total variance. The second factor summarizes ecotourism and culture-related benefits and is labeled ecotourism in keeping with the highest loaded variables. Labeled as performing arts and events, the third factor includes 4 variables, including concerts and theaters. The fourth factor consists of 3 variables, representing outdoor adventure benefits, including hiking, camping, and extreme sports. Finally, the fifth factor includes 3 variables that relate to general sightseeing, such as the exploration of nature and scenery, and small towns and villages. Only one variable, golf/tennis, did not load on any factor because its loading was below the threshold of 0.5. This indicates that the level of importance attributed to golf/tennis is not related in any way to the importance attributed to the other variables. Moreover, the respondents ratings for this variable do not account for a significant portion of the total variance in the importance ratings data. Hence, golf/tennis will be excluded from further analyses. Benefit Segmentation Having recovered the major dimensions underlying the benefits sought in selecting a vacation destination, the focus next turns to visitors preferences in relation to these benefits. By analyzing the diversity in benefits sought, visitors who valued similar benefits were grouped together. A nonhierarchical cluster analysis (SPSS 10.0 Classify K- means Cluster) was implemented on the five benefits sought, and the dimensions identified above and the solutions with two, three, four, and five clusters were explored. The typical criteria for effective segmentation were considered in the analysis as follows. Effective segments 1. consist of consumers with homogeneous needs, attitudes, and responses to marketing variables (McCarthy 1982); 2. are distinctive from one another (Weinstein 1987);

282 FEBRUARY 2005 TABLE 2 BENEFITS SOUGHT DIMENSIONS: FACTOR LOADINGS Factor Performing Factor 4 Factor 5 Factor 1 Factor 2 Arts and Outdoor General Item Fun and Sun Ecotourism Events Adventure Sightseeing Dance/bar 0.781 Beach, sunbathing 0.754 Casino/gambling 0.742 Sailing/boating 0.698 General entertainment 0.687 General sport 0.671 Snorkeling/scuba diving 0.622 Amusement park 0.555 Botanic/zoologic garden 0.739 Environmental/ecology excursion 0.721 Local boat tours 0.704 Guided city tour 0.691 Art gallery/museum 0.686 History/archaeology 0.662 Musical concert 0.819 Theater play 0.796 Local event 0.634 Dining/restaurant 0.513 Hiking/camping 0.820 Extreme sports 0.796 Fishing 0.586 Nature/scenery 0.769 General sightseeing 0.705 Small towns/villages 0.682 Eigenvalue 6.716 4.219 2.717 1.548 1.008 % of variance 26.864 16.877 10.867 6.193 4.033 Alpha coefficient 0.869 0.834 0.779 0.756 0.738 TABLE 3 VISITOR SEGMENTS: MEAN BENEFIT SCORES Segment 1: Segment 2: Segment 3: Segment 4: Adventurer Multifarious Fun and Relaxation Urbane Benefit Dimension (n = 20) (n = 72) Seeker (n = 50) (n = 62) F a Post Hoc Fun & sun 1.40843 0.18714 0.45112 0.12680 23.79 All but 2-3 Ecotourism 0.67843 0.35345 0.01243 0.20163 7.58 All Performing arts & events 1.02172 0.67945 0.81398 0.19698 52.73 All but 1-3 Outdoor adventure 0.96037 0.37067 0.79832 0.09645 28.48 All General sightseeing 0.58638 0.52088 0.40250 1.11864 82.21 All but 1-2 and 1-3 a. All reported F values are significant at.000. 3. are substantial, that is, large enough to be profitable (McCarthy 1982); and 4. provide operational data that are practical, usable, and readily translatable into strategy (Weinstein 1987). A four-cluster solution (i.e., four visitor segments) was the most readily interpreted, most favorably met the above criteria, and provided the best statistical results (when variables, other than the ones used to form clusters, were used to test for cluster differences). In addition, the validity of the cluster solution was confirmed by splitting the sample into two groups and then comparing the results. The four segments are named: adventurer, multifarious, fun & relaxation seeker, and urbane. The mean benefit factor scores and statistics regarding tests of the differences between the four segments scores are displayed in Table 3. Univariate ANOVA tests confirm that the segments statistically differ in their mean benefit scores. Follow-up ad hoc tests indicate that almost all the segments, with a few exceptions, differ from each other in pairwise tests. Specifically, in regard to two benefit dimensions, namely ecotourism and outdoor adventure, all the segments differ pairwise without exception. In segment 1, the adventurer mainly seeks outdoor adventure and general sightseeing activities. This segment s lack of interest in fun and sun activities differentiates it from the other segments. This is the smallest segment; nonetheless, it constitutes 10% of the sample. The multifarious

TABLE 4 SAMPLE DELINEATION: DEMOGRAPHIC CHARACTERISTICS JOURNAL OF TRAVEL RESEARCH 283 Sample Adventurer Multifarious Fun & Relaxation Urbane (100%) (10%) (35%) (25%) (30%) Sex Female 57 40 56 51 63 Male 43 60 44 49 37 Age (F =10.44, p =.000) 18-34 46 45 41 52 51 35-54 40 45 41 34 39 55+ 14 10 18 14 10 Education Elementary 1 0 0 2 0 High school 18 25 14 17 22 Technical/vocational 9 0 7 8 10 University 53 50 62 63 52 Postgraduate 19 25 17 10 16 Occupation Employed full-time 64 79 57 71 61 Employed part-time 10 5 13 8 10 Self employed 7 5 3 4 13 Full-time student 7 5 13 6 6 Unemployed/retired 12 6 14 11 10 Household income per year (in dollars; F = 3.321, p =.02) Less than 20,000 9 10 10 6 10 20,000 to 49,999 32 53 31 40 23 50,000 to 69,999 25 11 18 27 41 70,000+ 34 26 41 27 26 makes up the largest segment, with 35% of the sample. They like to explore the destination fully and, hence, seek diverse benefits, including performing arts and events, general sightseeing, outdoor adventure, ecotourism, and fun and sun activities. In short, this segment represents tourists that seek a bit of everything. Segment 3 predominantly seeks fun and relaxation and is thus labeled accordingly. They also seek general sightseeing activities and constitute 24.5% of the sample. Finally, the urbane section comprises the visitors who exclusively seek performing arts and local events. They do not seem much interested in other benefits, including general sightseeing, fun and sun, ecotourism, and outdoor adventure. Interestingly, this is a large segment, consisting of 30% of the sample. So far, it has been established that the four segments differ in terms of the benefits sought. The segmentation analysis, however, would be meaningful only if the segments can also be differentiated in terms of characteristics other than the benefits sought. As indicated previously, in addition to having homogeneous product needs, consumers in a good market segment should also possess homogeneous attitudes and responses to marketing variables (McCarthy 1982). Furthermore, the various segments must be distinct from one another with respect to other consumer characteristics (Weinstein 1987). Finally, operational data are needed to provide practical, usable, and readily translatable information for each segment. Hence, to further examine the differences among segments and provide practical information to formulate marketing strategy, we next turn to exploring whether the four segments in fact differ in terms of demographics, travel behavior, expectations about various infrastructures, service and cost factors, and personality attributes and interests. Delineating Segments: Demographics Segment demographics are presented in Table 4. Female respondents were concentrated in the urbane and multifarious segments, whereas men were prominent in the adventurer segment. Fun and relaxation seekers were split evenly between males and females. Significant differences were found in the mean age of the respondents across each of the four segments. The post hoc analysis indicated that the multifarious segment is the oldest among the four. The segments also differed significantly in terms of their members mean household income. Post hoc analysis indicated significant differences between three segments, namely, the urbane, the adventurer, and the multifarious. The multifarious has the highest household income level, whereas the adventurer has the lowest. Most respondents are employed full-time. Those who work part-time, students, or the unemployed/retired are more likely to belong to the multifarious rather than the other segments. The segments did not differ significantly in terms of educational level. Delineating Segments: Travel Behavior Significant differences were found in travel behavior among the four segments as displayed in Table 5. Whereas more than half of the adventurer section travel more than once a year, only 13% of the urbane do so. Most members of the multifarious section travel once a year. A substantial majority of visitors have not visited Latin America previously, yet most of the adventurers were repeat visitors. The

284 FEBRUARY 2005 TABLE 5 SAMPLE DELINEATION: TRAVEL BEHAVIOR Sample Adventurer Multifarious Fun and Relaxation Urbane Frequency of travel (%; chi-square = 19, sig. =.004) Less than 1 per year 38 25 32 42 48 1 per year 39 20 48 31 39 More than 1 per year 23 55 20 27 13 Previous visit to Latin America (%; chi-square = 12, sig. =.06) No 71 40 65 80 82 Yes 29 60 35 20 18 Interest in self-organized vacations (%; chi-square = 16, sig. =.02) None 6 5 1 12 7 Somewhat 22 15 13 26 32 Very 72 80 86 62 61 Interest in all-inclusive vacations (%; chi-square = 73, sig. =.000) None 13 75 9 6 7 Somewhat 23 25 16 18 33 Very 64 0 75 76 60 Interest in cruises (%; chi-square = 51, sig. =.000) None 11 55 4 12 3 Somewhat 30 30 29 25 37 Very 59 15 67 63 60 Travel party Spouse/partner 42 59 31 48 48 Family with child 15 12 19 13 13 Family without child 13 0 19 12 11 Friends 17 11 20 20 12 Tour 4 6 6 0 3 Alone 9 12 5 7 13 Information sources (%) Travel agent (chi-square = 14, sig. =.003) 59 55 42 66 73 Internet (chi-square = 10, sig. =.02) 46 70 34 48 52 Travel book (chi-square = 8, sig. =.05) 34 50 24 44 32 Friends and family 63 65 65 68 56 Newspaper travel 29 30 30 28 29 Travel brochure 37 30 31 44 41 TV travel show 12 10 17 10 10 different segments differed considerably in regard to their interest in various types of vacations. Although most visitors indicated an interest in self-organized vacations, the multifarious were the most interested and the fun and relaxation seekers were the least interested. Major contrasts were observed in the segments interest in all-inclusive vacations; whereas the adventurers indicated almost no interest in this concept, the multifarious and the fun and relaxation seekers were very interested. Similar, yet less extreme, results were observed in relation to the interest shown in cruise vacations. Most respondents travel with their spouses. Those who travel with family and friends are, however, more likely to be multifarious, and those who travel alone are more likely to be in the adventurer or urbane segments. Interestingly, those who indicated a relatively higher interest in self-organized tours, namely, the multifarious and adventurers, were less likely to consult travel agents than the other segments. The Internet seems to be a major source of travel information for adventurers, whereas the multifarious seem to rely mostly on friends and family, along with a variety of other sources. In contrast, the urbane and the fun and relaxation seekers tend to consult travel agents and brochures. Delineating Segments: Decision Drivers Decision drivers refer to various local services, infrastructure, and cost items that visitors may consider in choosing a vacation destination abroad. These aspects complement the typical vacation benefits sought such as fun and sun, sightseeing, and outdoor adventure. In total, 14 items were included. The respondents were asked to rate the importance of each item by assigning a number from 1 = not at all important to 5 = very important. The four segments statistically differ in their importance ratings of decision driver items, as indicated by univariate ANOVA tests in Table 6. In general, the adventurers mean ratings for decision drivers were the lowest among the four segments, indicating that they are less concerned than the others about

TABLE 6 SAMPLE DELINEATION: DECISION DRIVERS, MEAN SCORES JOURNAL OF TRAVEL RESEARCH 285 3 Fun & 1 2 Relaxation 4 Decision Drivers F Value Significance Sample Adventurer Multifarious Seeker Urbane Post Hoc Health service 17.720.000 3.554 2.200 4.042 3.500 3.377 1 < 2, 3, 4 2 > 3, 4 Child day care 6.567.000 2.190 1.300 2.597 1.750 2.145 1 < 2, 4 2 > 3 Telecom service 14.518.000 3.627 2.050 3.819 3.592 3.661 1 < 2, 3, 4 Local tourist office 15.312.000 3.267 1.840 3.694 3.200 3.000 1 < 2, 3, 4 2 > 3, 4 Banking service 13.328.000 3.660 2.500 4.000 3.673 3.629 1< 2, 3, 4 2 > 4 Local transportation 7.960.000 3.582 2.421 3.875 3.408 3.758 1 < 2, 3, 4 2 > 3 Low local crime rate 5.940.001 4.090 3.300 4.239 4.306 4.098 1 < 2, 3, 4 Clean food and water 14.537.000 4.423 3.350 4.514 4.720 4.371 1 < 2, 3, 4 3 > 4 Friendly locals 13.229.000 4.122 3.200 4.289 4.500 3.935 1 < 2, 3, 4 2, 3 > 4 Accommodation affordability 4.886.003 4.315 4.000 4.486 4.500 4.129 1, 4 < 2, 3 Flight affordability 7.338.000 4.322 3.632 4.556 4.420 4.177 1 < 2, 3, 4 2 > 4 Local price level 6.055.001 3.900 4.260 4.375 4.571 4.081 1, 4 < 2, 3 Accommodation availability 15.042.000 3.350 4.277 4.583 4.320 4.210 1 < 2, 3 2 > 3, 4 Flight availability 20.765.000 3.200 4.305 4.620 4.420 4.210 1 < 2, 3 2 > 4 TABLE 7 DECISION DRIVERS DIMENSIONS: FACTOR LOADINGS Factor 1: Service & Factor 2: Factor 3: Item Infrastructure Safety Cost Health service 0.773 Child day care 0.722 Telecom service 0.738 Local tourist office 0.695 Banking service 0.675 Local transportation 0.650 Low local crime rate 0.797 Clean food and water 0.783 Friendly locals 0.779 Accommodation affordability 0.875 Flight affordability 0.810 Local price level 0.807 Accommodation availability 0.568 Eigenvalue 6.347 1.664 1.259 % of variance 45.335 11.877 8.99 Alpha coefficient 0.8536 0.8232 0.8540 infrastructure, service, and safety aspects when choosing a destination. Nonetheless, they attribute considerable importance to cost items, namely, accommodation affordability, and local price levels. The multifarious, on the other hand, assign generally higher importance ratings to decision driver items than do the other segments. They are more concerned with flight and accommodation provision, followed by safety issues. Because the multifarious are more likely than others to travel with their children and to pursue a wide variety of vacation activities, they are likely to attach more importance to infrastructure, service, safety, and cost considerations, and therefore they are likely to attribute a higher significance to all aspects of travel. Fun and relaxation seekers regard safety as the most important aspect when they are making traveling decisions, followed by price factors. The urbane, too, consider safety issues to be most important, followed by flight and accommodation considerations. Because the urbane do not seek a variety of features involving general sightseeing, ecotourism, and fun and sun activities, the various infrastructure and service aspects would be less relevant for them. Having established that the four segments differ in terms of the mean importance attributed to decision drivers for choosing destinations, we next investigate how the members in one segment specifically differ from those in another via discriminant analysis. Before running discriminant, however, a factor analysis was performed on the full set of decision drivers to determine whether the total number of drivers could be reduced to a few conceptually meaningful independent dimensions. The principal components of varimax rotation produced a three-factor solution accounting for 66% of the total variance. The emergent factors, presented in Table 7, are clear-cut. The first one, named services & infrastructure, includes health services, telecom services, and child day care. These variables reflect how well the tourism industry is prepared to cater to the needs of the visitors. The second factor, labeled safety, includes variables related to the safety aspects of the vacation, such as the crime rate and clean food and water. Factor 3, cost, includes three variables related to price levels and 1 concerning accommodation availability. The discriminant coefficients for the first function show that services and infrastructure function best differentiates the segments (see Table 8). Hence, it is the importance attributed to services and infrastructure that varies the most across each of the segments. The primary discriminator was safety on the second function and cost in the third one. The discriminant function correctly groups 43.9% of the original cases and 41.2% of cross-validated cases using the leaveone-out principle by Hair et al. (1998) and hence predicts group membership better than chance.

TABLE 8 DECISION DRIVER FACTOR SCORES: DISCRIMINANT COEFFICIENTS, UNIVARIATE FS, AND STATISTICS Standardized Discriminant Coefficient Decision Drivers Factor Score Means Fun & Relaxation Factor Function 1 Function 2 Function 3 F Value Adventurer 1 Multifarious 2 Seeker 3 Urbane 4 Post Hoc Service & infrastructure 0.853-0.534-0.106 15.964 (p =.000) -1.12 0.43-0.33-0.02 1 < 2, 3, 4 2 > 3, 4 Safety 0.570 0.742-0.385 7.954 (p =.000) -0.94 0.07 0.38-0.01 1 < 2, 3, 4 4 < 2, 3 Cost 0.397 0.281 0.883 3.603 (p =.014) -0.31 0.25 0.19-0.23 1 < 2 4 < 2, 3 Function Eigenvalue % of Variance Cumulative % Canonical Correlation 1.388 79.2 79.2.529 2.088 15.8 94.9.268 3.025 5.1 100.0.156 Test of Function(s) Wilks Lambda Chi-Square df Significance 1 through 3.653 80.902 9.000 2 through 3.906 18.747 4.001 3.976 4.652 1.031 286

JOURNAL OF TRAVEL RESEARCH 287 TABLE 9 SEGMENT DELINEATION: PERSONALITY AND INTERESTS DIMENSIONS, MEAN SCORES 3 1 2 Fun & Relax 4 F Significance Sample Adventurer Multifarious Seeker Urbane Post Hoc I like my life to be pretty much the same from week to week..933.426 2.104 1.737 2.092 2.140 2.100 I must admit that my interests are somewhat limited. 3.766.012 2.154 1.737 2.014 2.460 2.274 1 < 3, 4 3 > 2 I protect my quiet time and prefer not to socialize. 1.350.259 2.203 1.947 2.361 2.100 2.300 I prefer to travel to countries where tourism is well developed. 11.675.000 2.691 1.667 3.042 2.878 2.623 1 < 2, 3, 4 2 > 4 I try to mingle with the locals whenever possible. 2.136.097 2.916 3.300 2.917 2.735 2.779 1 > 3, 4 I sample the local cuisine as much as possible. 2.727.045 3.127 3.500 2.972 3.204 2.934 1 > 2, 4 I stay away from tourist areas and seek typical local hangouts. 3.595.015 2.565 3.158 2.507 2.347 2.567 1 > 2, 3, 4 I like to learn about things, even if I cannot really use them. 3.332.021 3.062 3.000 2.915 3.327 2.823 3 > 2, 4 I like doing things that are new and different. 1.225.302 3.237 3.454 3.254 3.286 3.089 I consider myself an intellectual. 3.909.010 2.896 2.632 3.125 2.833 2.677 1, 4 < 2 I like to learn about art, culture, and history. 9.963.000 3.380 3.750 3.611 3.280 3.065 3, 4 < 1, 2 I dress more fashionably than most people. 2.848.039 2.747 2.263 2.875 2.650 2.787 1 < 2, 4 ANOVA is used to determine which segments significantly differ from others in regard to the three decision factors. The findings provided a clear global view of segment differences in decision drivers and confirmed earlier ANOVA results concerning the original driver items: the segments indeed differ in terms of the level of importance they ascribe to service and infrastructure, safety, and cost items. A post hoc analysis indicated that the adventurers considered services and infrastructure and safety to be relatively less important than the other 3 segments. The urbane regarded safety and costs as being less important than the multifarious and the fun and relaxation seekers. Finally, the multifarious segment rated services and infrastructure as being more important than any other segment. Delineating Segments: Personality and Interests Personality and interests information was obtained because it permits a deeper understanding of the visitor segments, augmenting the typical demographics data. Furthermore, as stated at the outset, this information provides valuable input into destination marketers product design, communication, and promotion efforts by allowing the creation of products, messages, and themes that are more compatible with the personality, interests, and attitudes of tourists. Visitors were asked to describe themselves with respect to 12 items, such as I like to learn about art, culture, and history, by assigning ratings from 1 (most disagree) to 4(most agree), which were selected from previous literature (e.g., Keng and Cheng 1999; Lee and Crompton 1992; Mo, Havitz, and Howard 1994). Univariate ANOVAs and discriminant analysis were used to verify whether the four segments differed in terms of their personality and interest characteristics. The mean personality and interests ratings and statistics regarding tests of differences among the four segments ratings are displayed in Table 9. Univariate ANOVA tests confirm that the segments statistically differ in their mean importance ratings for most personality and interest items. Adventurers are most interested in exploring the local culture, engaging in activities such as trying out the local cuisine, mingling with the locals, and seeking typical local hangouts. They also like to learn about art, culture, and

288 FEBRUARY 2005 TABLE 10 PERSONALITY AND INTERESTS DIMENSIONS Factor 1: Factor 2: Factor 3: Factor 4: Item Traditional Experiential Novelty Seeking Sophisticated I like my life to be pretty much the same from week to week. 0.792 I must admit that my interests are somewhat limited. 0.749 I protect my quiet time and prefer not to socialize. 0.684 I prefer to travel to countries where tourism is well developed. 0.522 I try to mingle with the locals whenever possible. 0.799 I sample the local cuisine as much as possible. 0.641 I stay away from tourist areas and seek typical local hangouts. 0.573 I like to learn about things, even if I cannot really use them. 0.752 I like doing things that are new and different. 0.648 I consider myself an intellectual. 0.780 I like to learn about art, culture, and history. 0.649 I dress more fashionably than most people. 0.586 Eigenvalue 3.698 1.856 1.441 1.053 % of variance 26.412 13.259 10.292 7.523 Alpha coefficient 0.681 0.638 0.569 0.573 history more than others. They do not have a preference for countries where tourism is well developed. On the contrary, the multifarious prefer countries with well-developed tourism, and this preference is more marked than in the other segments. They consider themselves as intellectuals and believe they dress more fashionably than most people. Fun and relaxation seekers admit they have limited interests, yet they like to learn things, even if they cannot use them. The urbane seem to be the least interested in exploring other cultures. They are not much interested in mingling with locals, trying out local cuisine, and learning about art, culture, and history. The urbane believe they dress more fashionably than most people. Prior to discriminant analysis, a factor analysis was performed on the 12 different personality and interest items to obtain the global dimensions that summarize them. The principal components of varimax rotation resulted in a four-factor solution, as displayed in Table 10. Factor 1, traditional, includes 4 variables related to having limited interests, tending to do the same thing week after week, having a preference for quietness, and not socializing. Also loaded on this factor is the desire to travel to countries with well-developed tourism. This factor explains about 26% of the total variance. Factor 2, experiential, represents statements about the visitors interests in getting to know the locals and experiencing the local cuisine and hangouts, and explains about 14% of the variance. Factor three is assigned two variables reflecting the visitors intention to learn and to do new and different things, and is hence named novelty seeking. Finally, factor 4 summarizes three variables that relate to intellectual self-image; an interest in learning art, culture, and history; and dressing fashionably, and it is labeled sophisticated. Consistent with earlier findings concerning the original personality and interest items, it was found that the segments indeed differed in their mean importance ratings for all four factors that summarize these items (see Table 11). Furthermore, post hoc analyses provided clear-cut characterizations of each segment. Specifically, the adventurer segment is more experiential but less traditional than the other three segments. Multifarious is more sophisticated than any of the others. Finally, fun and relaxation seekers can be characterized as being more novelty seeking than the others. The discriminant coefficients for the first function indicate that the experiential dimension discriminated the four segments best. In the second function, the sophisticated dimension, as well as the third one, novelty seeking was found to be most effective in differentiating among the four segments. By conducting the leave-one-out tests (Hair et al. 1998), we found that 44.7% of the original cases and 41.5% of the cross-validated cases were correctly classified by the discriminant function. DISCUSSION AND IMPLICATIONS This study demonstrates that visitors to Latin American countries can be effectively segmented according to the benefits sought. Specifically, four distinct segments of substantial size, each consisting of visitors with similar expectations and characteristics, were identified. Segments were profiled with respect to demographics, travel behavior, services, infrastructure and cost expectations, personality, and interests, as summarized in Table 12. Such unusually rich profiling of segments allows destination managers to (1) obtain an in-depth understanding of the visitor population, (2) identify specific target segment(s), and (3) plan effective marketing strategies to reach and promote the target segment(s). An illustration of how this rich, practical, operational information on segments is translatable into strategy is provided below. Segment 1, the adventurers, is specifically interested in outdoor adventure: hiking, camping, and extreme sports (but not casinos, nightlife, and beaches). The adventurers are typically young males with a modest income. This is a small yet significant segment, because they travel often and many are repeat visitors to Latin America. They are cost conscious but do not have high expectations concerning services and infrastructure (e.g., tourist offices, day care, and telecommunications). They are experientialist tourists rather than traditional