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MARKET POSITIONING ANALYSIS
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Annals of Tourism Research, Vol. 29, No. 4, pp. 987–1003, 2002 2002 Elsevier Science Ltd. All rights reserved. Printed in Great Britain 0160-7383/02/$22.00
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MARKET POSITIONING ANALYSIS A Hybrid Approach Joseph S. Chen Eastern Michigan University, USA Muzaffer Uysal Virginia Polytechnic Institute and State University, USA Abstract: This research introduces a hybrid approach including correspondence analysis and logit modeling to contrast the relative market position of Virginia with eight other eastern US states and the District of Columbia. The data obtained from a series of telephone surveys are analyzed. In a competitive analysis, the study areas are described by youth/mature and culture/nature dimensions relating to activity images as well as by beach/mountain and urban/country dimensions in connection with attraction images. Pennsylvania appears to be an immediate competitor of Virginia in regard to provision of leisure activities. The study concludes with discussions on critical findings along with marketing implications. Keywords: destination images, positioning analysis. 2002 Elsevier Science Ltd. All rights reserved. Re´sume´: L’analyse du positionnement marketing: une approche hybride. Cette recherche pre´sente une approche hybride qui comprend l’analyse de correspondance et le modelage logit pour contraster la position relative du marche´ de la Virginie avec huit autres e´tats de l’est des E´tats-Unis et le district de Columbia. On analyse des donne´es qui ont e´te´ obtenues d’une se´rie de sondages par te´le´phone. Dans une analyse compe´titive, les champs d’e´tude sont repre´sente´s par les dimensions de jeunesse/aˆge muˆr et culture/nature relatives aux images d’activite´s ainsi que les dimensions plage/montagne et campagne/ville concernant les images d’attractions. L’e´tude se termine par une discussion des conclusions critiques et des implications pour le marketing. Mots-cle´s: images de destination, analyse de positionnement. 2002 Elsevier Science Ltd. All rights reserved.
INTRODUCTION Understanding competitors’ strengths and weaknesses is of paramount importance for marketers, developers, and planners involved in strategy development. Creating a competitive market position is vital to long-term success, and thus seizing market competitiveness should be of great interest to promoters. Crouch and Ritchie (1995) pointed out that development plans, organizational structures, and marketing strategies drove destinations in their quest to compete, but these efforts
Joseph Chen is Director of the Hotel and Restaurant Management Program at Eastern Michigan University (206 Roosevelt Hall, Ypsilanti MI 48197, USA. Email). His scholarly areas entail applied market research and consumer behaviors. Muzaffer Uysal is Professor of Tourism in the Department of Hospitality and Tourism Management at Virginia Polytechnic Institute and State University. His research interests focus on demand analysis, marketing, and international tourism. 987
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inevitably relied on some knowledge of what it was that made a location viable. Additionally, to enhance their competitiveness, attractions must ensure that their overall appeal, and the quality and integrity of the experiences they deliver, must equal or surpass that of many alternatives open to potential tourists (Crouch and Ritchie 1999:139). Indeed, tourism as an experience takes place in destinations where leisure activities and new and different cultures are experienced. To maintain or capture a significant population, marketing firms are often challenged to create positive product perceptions. Some service providers may need to augment supply resources to match demand and meet tourist expectations, and thus to create an enhanced perception of the existing resources (Uysal, Chen and Williams 2000). Many scholars have conducted image research to identify places’ relative strengths and weaknesses (Fakeye and Crompton 1991; Franz 1988; Gartner and Hunt 1987; Haahti 1986; Pearce 1997). Goodall (1990) suggested that building product images helps add competitive advantages. Furthermore, Gilbert (1990) articulated that tourism establishments should promote themselves using the distinct attributes to entice demand. Based on the literature, an assessment of tourist’s perceptions helps marketers position their products and services more effectively. A plethora of studies have provided conceptual frameworks on the formation of image (Calantone, di Benedetto, Hakam and Bojanic 1989; Court and Lupton 1997; Echtner and Ritchie 1993; Gartner and Hunt 1987; Goodrich 1977; Gunn 1972; Milman and Pizam 1995; Pearce 1982; Phelps 1986; Reilly 1990). Gunn (1972) first conceptualized tourists’ destination image to include two main dimensions: “organic image” which deals with tourists’ impression of a destination without physically having visited the place, and “induced image” which is forged through exposure to promotional materials and modified by actual visitation. Fakeye and Crompton (1991), applying Gunn’s theory, protracted the categorization to three conceptual facets: “organic, induced, and complex”. These formation phases are then linked to the different functions of promotional efforts and their operationalization is a function of the level of knowledge of the place, exposure to promotions, and the nature of onsite experience. The organic facet exists prior to exposure to any promotional information from destinations, whereas an induced facet occurs when tourists actively search for and are exposed to marketing messages from promoters and suppliers. A complex facet incorporates experiences at the destination. Consequently, the tourists may modify their existing perceptions of the place after having the experience. Gartner (1993), in his study of the formation process, further delineated that the roles of different agents (traditional advertising, information requested from professional travel advisors, independently produced reports, movies and news articles about places, solicited and unsolicited information and feedback from individuals who may have visited places) affected the formation. Gartner pointed out that the
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final outcome could be directed through the selection of an appropriate formation mix. Besides the theory building, scholars have taken pragmatic approaches to apply the image frameworks into marketing research. Gartner and Hunt (1987), in their analysis of the change of image of Utah over a 12-year period, revealed that a mix of organic and induced influences determined the shift for non-resident tourists. In his followup study Gartner (1989 a, b) further developed a multidimensional scaling analysis to help in targeting specific market segments effectively. In an effort to develop a more rigorous construct, Echtner and Ritchie (1991, 1993) derived various attributes in measuring perceptions of overseas attractions. Their framework is based on a two-dimensional approach involving three continuums: attribute-holistic, functional-psychological, and common-unique. The attribute-holistic continuum is derived from the notion that image should be composed of perceptions of individual attributes (such as nature of facilities, climate, and friendliness of people) as well as holistic impressions (including mental pictures) of the place. The functional-psychological continuum is based on the distinction between directly measurable (functional) characteristics and those less tangible or more difficult to observe (psychological). The third continuum presents those perceptions based on “common” characteristics (such as clean beaches) and “unique” features (like a place for lovers). By using the framework, Echtner and Ritchie (1993) developed various constructs consisting of those components and thus made significant measurement contribution. Overall, their continuum outline resembles the cognitive, affective, and conative components described by Gartner (1993) and applied by Dann (1996). Most empirical image studies have used two different sets of attributess: designative and evaluative. The former pertain to the cognitive component, while the latter deal with the affective component. Specifically, the cognitive echoes tourists’ prior product knowledge while the affective reflects related feelings. Researchers have attempted to utilize either one or both components to delineate the market position of places. For example, Gartner and Hunt (1987) applied cognitive attributes, including camping, sightseeing, skiing, hunting, fishing, visiting national parks (cities or national forests), enjoying summer temperatures, and the receptiveness of residents into a longitudinal study for the State of Utah. Walmsely and Young (1998) integrated eight evaluative attributes into a market-positioning map. Baloglu (1996) developed and tested an image formation model whereby he delineated the direct and indirect impacts of both affective and cognitive attributes on formation while controlling possible intervening and moderating variables. The above sets of attributes are similar to the push and pull concept of motivations: the first represents more of the affective component and the second more of the cognitive attributes. Their behavioral manifestation, the conative, is analogous to onsite consumption behavior because it is an action component. Furthermore, the formation of the conative attribute depends on the images developed during
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the cognitive stage and evaluated during the affective stage (Dann 1996; Gartner 1993). A closer examination of past research further reveals different orientations towards the analysis of destination images; researchers have studied one single place (Ahmed 1994; Bignon, Hammitt and Norman 1998; Crompton 1979; Fakeye and Crompton 1991; Gartner and Hunt 1987; Illiewich 1998) or a few places together (Bramwell and Rawding 1996); and some have used travel intermediaries as a proxy for tourists in order to shed light on the way potential tourists might perceive a given location (Dimanche and Moody 1998; Gartner and Bachri 1994; Roehl 1990). Moreover, a few have compared the images at different points in time (Gartner and Hunt 1987) and of different tourism resources at one point in time in a given place (Gartner and Shen 1992). Recently, studies on regional (Baloglu 1996) and subregional images (Wang 1998) have surfaced, allowing promoters to tailor their communication mix to entice given segments. Mazanec (1995), in his exploratory study of four luxury hotels in Vienna, used a neurocomputing approach to unveil the relative positions of the hotels. He implied that the neural network technique was restricted to an exploratory analysis due to the lack of statistical significance. His work has encouraged researchers to reinvent the current research paradigm in market positioning studies. Beyond these attempts, attributes were also incorporated into behavioral investigations concerning the relationships between perceptions and behavioral consequences. For example, Chen and Hsu (2000) found that destination images influence travel decisions, including trip planning timeframe, budgeted cost, and number of days spent at the destination. Although the above works have made notable contributions to the body of knowledge few have applied the theory to a competitive market positioning study (Pearce 1997). Ashworth (1990) conducted one with a regional focus by examining several selected Mediterranean countries, including Cyprus, Greece, Egypt, Tunisia, Turkey, Malta, and Spain. It emphasized common and differentiating features and analyzed how these countries project themselves as destinations using different techniques ranging from the use of travel agents to resort brochures. Ashworth (1990) pointed out that the correspondence between the projected and received place images was critical. The former based on similar features may show variations from place to place, and the extent to which it is received may also exhibit dissimilarities (Ashworth and Voogd 1988, 1990; Baloglu 1996). Therefore, it may be of great importance to promoters to assess their relative regional positioning as perceived by potential tourists. POSITIONING ANALYSIS To augment the methodologies found in the extant literature, this study attempts to render a new approach that could be applied to market positioning studies from a regional perspective. More specifically, it contrasts the relative market position of Virginia with eight
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other eastern US states and the District of Columbia (or Washington DC). By application of a hybrid analytical approach involving correspondence analysis and logit modeling, the paper offers new insights to researchers and practitioners to develop more effective and actionable strategies in an increasingly competitive environment. Accordingly, two research questions are developed here: one, what are the dominant images induced by the District of Columbia and nine eastern US states including New York, Pennsylvania, Maryland, Virginia, West Virginia, North Carolina, South Carolina, Georgia, and Florida, and, two, are there any states sharing similar market positions with Virginia in regard to the provision of leisure activities and availability of attractions? If yes, what are the differences between Virginia and the state(s) having similar market positions? The above research questions are to explore each study state’s strengths and weaknesses regarding its images perceived by potential tourists. To promote a product effectively, marketers ought to recognize the images of a country along with that of a product when deploying their strategies (Kim and Young 1997). Plainly, the image of the country where the products are manufactured is likely to affect consumers’ choices. Similarly, the image of a state may have a profound influence on tourists’ choice of the intangible and tangible services rendered by tourism establishments in the state. Thus, examining this image should enable marketers to create a better positioning plan from a regional perspective. Sampling In reference to sampling procedure, this study was performed in two stages. In 1992, the mail omnibus questionnaires were first randomly mailed to a panel of nationally representative households selected to conform to the latest available US census data by National Family Opinion which maintains an active panel of households to conduct surveys on a variety of issues, including tourism. Since this research team had a partnership with the organization, the latter handled the data collection as part of the research action plans. In this mail survey, respondents were asked to indicate their tourism experiences in Virginia and eight other competitive states and the District of Columbia. A simple random mailing to 80,000 panel households produced 54,000 (67.5%) usable responses, of which 5,802 were pleasure tourists who had visited Virginia. This refined sampling frame provided an easy way to identify those who had experienced Virginia and surrounding states and a significant amount of demographic and recent travel data about each of these households. In the second stage, telephone surveys were conducted. It was decided to build a structured sample with quotas by household distance and by visitation, so that sufficient numbers of tourists to Virginia and respondents from specific distances to Virginia would be available for analysis. Because it was known that an accurate weighted total sample could be built using weights from the first panel survey, it was decided that structured samples could give dependable, nationally-rep-
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resentative data. Weights were assigned based on the percentage of a nationally representative sample within five tourism zones. The five zones of origin were defined as within 1/2 day drive, 1/2 to 1 day drive, 1-2 day drive, 2-day plus drive, and Virginia residents. It is also important to mention that the opinions of the household respondents may not reflect a national base of tourists who could also choose to take a pleasure vacation outside the Eastern and South Atlantic States. The telephone surveys ended on August 31, 1992, and generated 1,318 useful samples for this competitive market analysis. Furthermore, since this study adopted a multi-stage random sampling procedure, the randomness of the sample was retained throughout the process, helping enhance the generalizability of the study. Attributes and Data Analysis The attributes used in the study and derived from literature review and an expert panel, are categorized into two domains, the first related to leisure activities provided by those study areas, the second tied to attractions surrounding the study area. The nature of attributes included reflects cognitive elements of places. The activity-specific domain includes 17 attributes: sightseeing, shopping, having good restaurants, playing golf and tennis, skiing, hiking, walking or backpacking, canoeing or rafting, bicycling, attending spectator sports, attending cultural events, visiting theme parks, attending festivals or special events, offering activities for children, horse racing, hunting and fishing, visiting Civil War sites, and sightseeing historical buildings and sites. The attraction-specific domain embodies nine attributes: beaches, mountains, cities, resorts, state or national parks, quaint towns and villages, natural features or wonders, beautiful countryside, and architectural or engineering wonders. Respondents were asked to select one best state from the pool of 10 study areas, in regard to different types of leisure activities and attractions. The above “implicit importance” approach manifests respondents’ inclination to visit a specific state (such as Florida) in pursing a particular activity (such as visiting theme parks). Since the method replicates respondents’ framework for decision making (by comparing several states the respondent would likely consider for a vacation), the attributes are evaluated in an exercise similar to an actual decision rather than a hypothetical one. A hybrid two-stage analytical approach was used to interpret the relative market positioning of each state. In the first stage, two-dimensional correspondence analyses were first used to provide perceptual maps revealing the relative position on each domain among 10 competitive study areas. In the second stage, if any area was identified as a close competitor to Virginia, a post-hoc, inferential analysis using logit modeling was employed to further compare the strengths and weaknesses in relation to the perceived images between Virginia and its competitor(s). Correspondence analysis is an interdependence technique that uses the singular value decomposition to analyze contingency tables from
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multinominal data (Thompson 1995). The main interest of the method lies in the characterization of the structure of the row and/or column variables. Its most direct application involves portraying the correspondence of categories of variables. The benefit of the technique is in its unique ability to represent rows and columns in a joint space (Hair, Anderson, Tatham and Black 1998). In this study, two correspondence analyses were performed. The first examined the competitive market position pertaining to 17 activityspecific attributes; the second reviewed the position of attraction-specific attributes. In these treatments, attributes were regarded as a row variable and the 10 states as a column variable. To visualize the relationship between a row and column variable, canonical normalization was used to derive maps portraying the relative market position of each state in the context of tourists’ perceptions of it. As a result, all row and column categories were plotted in the derived maps. All categories in both column and row variables constituted plotted points. The total number of points was equal to the sum of the number of categories of column and row variables. The proximity between a pair of points was used to interpret the underlying relationship between them. For example, points that were closely aligned revealed a strong relationship. Although correspondence analysis provides interpretable results illustrating the relationship between column and variable, it is a multivariate descriptive statistical method and does not contain a test of significance. To further detect the significant relationship between the row and column variable, other supplementary approaches, including weighted least squares and loglinear analysis, which are more parsimonious in nature, have been suggested by marketing researchers (Hoffman and Franke 1986). To render rich but parsimonious implications in this study, when a state having a similar market position with Virginia was identified in the joint plot, the logit modeling, a loglinear analysis, was further employed to reveal any significant difference between tourists’ image of the state and the competing one. In the logit modeling, a series of Maximum-Likelihood Analysis of Variance statistics was first performed to cross-validate whether or not the independent variable was significantly related to the dependent one. “State” was treated as the dependent variable since the respondent had to choose a preferred state for all attributes. “Image” was used as the independent variable. If the result showed a significant relationship (p<.05) between the variables, a logit model was then adopted. In logit notation, the model was written as: Logit OiC = α + βjxk where OiC = the odds of occurrence on Virginia over the competing state, α = the intercept parameter, ßj = the vector of slope parameter,
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and xk = the explanatory variable. In logit analysis, the parameters for each level of the independent variable were estimated. According to the results, a set of logit models was constructed. For example, when the activity-specific variable containing 17 levels was treated as the independent variable, the results derived 17 logit models with one set of estimated parameters in each model. The estimated parameters showed the probability of being in a specific state for each level of a categorical variable. If the derived parameter for a specific category, such as sightseeing, was significant, it indicated that respondents’ perceptions of this opportunity available in Virginia vs. the competing state differed significantly. Study Results The descriptive analysis of respondent demographic traits showed that the average age of respondent was 51 years, but the largest age group in the sample was the 35 to 50 years group (36%). Male respondents constituted about 70% of the study sample, and the same percentage were married. The average household size was 2.6 (but nearly one-fourth had more than three members); 43% had incomes greater than $50,000; and in respect to respondents’ life stage, 40% were identified as parents, whereas singles accounted for the smallest sample size (12.8%). Hair et al (1998) suggested that cumulative variance could be used as a criterion to determine the dimensionality. To better balance the explained variance and interpretability, this study arbitrarily selected a two-dimensional solution to determine the market position of each state. In Table 1, the correspondence analysis on 17 activity-specific attributes revealed that two dimensions with singular values of .46 and Table 1. Correspondence Analysis on Activity-Specific Images Dimension
Singular Value
Inertia
1 2 3 4 5 6 7 8 9 Total
.46284 .34134 .28886 .19725 .12862 .09318 .06660 .04297 .03121
.21422 .11651 .08344 .03891 .01654 .00868 .00444 .00185 .00097 .48556
Proportion Explained .441 .240 .172 .080 .034 .018 .009 .004 .002 1.000
Cumulative Proportion .441 .681 .853 .933 .967 .985 .994 .998 1.000 1.000
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.34 had 68% cumulative variance. The above were mapped in a twodimensional plot to reveal the market position for each study area. Figure 1 illustrates that the first dimension divided by the vertical axis is distinguished by two extremes: offering activities for children and visiting Civil War sites. Because visiting the latter tends to be an activity appealing to older, educated tourists, the first dimension, therefore, was labeled as youth/mature dimension. The second dimension disjoined by the horizontal axis is manifested by the variables of attending cultural events and hiking, walking or backpacking. This dimension was identified as culture/nature. In Figure 1, Virginia falls into the mature/nature domain. It implies that Virginia had a strong image on offering mature/nature-related activities. Moreover, the state seemed to have a similar market position with Pennsylvania. Visiting Civil War sites and snow skiing were the most important elements dominated by these two states. Regarding the distinctive advantages enjoyed by other competing states, Florida appeared to have strengths on visiting theme parks and offering activities for children; South Carolina led the images of bicyc-
See left-hand column of Table 3 for the labels of activity-related images. NY=New York, PA=Pennsylvania, MD=Maryland, VA=Virginia, WV=West Virginia, NC=North Carolina, SC=South Carolina, GA=Georgia, FL=Florida, DC=Washington DC.
Figure 1. A Joint Plot of Activity-Specific Images
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ling and golf and tennis; Georgia topped the horse racing category; North Carolina was the best state for hunting and canoeing; New York was likely to be a dominant state for sightseeing, visiting historical buildings, and sites, as well as having good restaurants; and Washington DC was considered the best place for attending cultural events, such as visiting museums. West Virginia and Maryland did not have a singular, dominant position. Although the former was considered one of the best for hiking, North Carolina, Virginia, and Pennsylvania also held a strong market position for hiking activities. In addition, Maryland, and New York shared the lead on attending festivals and special events. Meanwhile, Maryland and Georgia seemed to have the leading positions of attending spectator sports and shopping. In general, results revealed that Virginia had great potential to attract tourists who were interested in nature-based activities (such as hunting, canoeing, hiking, and snow skiing) and Civil War sites. With respect to the interrelationship among the categories of the column variable “state,” Figure 1 illustrates that West Virginia, Florida, and Washington DC notably deviated from the other seven. In other words, the latter constituted a competing cluster with allied competitive edges in which West Virginia, Florida, and Washington DC were unable to prevail. Moreover, as for market position of the three states outside the competing cluster, Florida and Washington DC unveiled unique superior positions on certain leisure activities over other states. West Virginia was the only one that neither belonged to the cluster nor had any distinct traits over others. Table 2 shows a two-dimensional solution containing 91% of the total explained variance, with singular values of .51 and .41 pertaining to the correspondence analysis on 9 attraction-specific perceptional items. Beaches and mountains distinguish the first dimension (Figure 2). Consequently, this dimension was named beach/mountain. Architectural or engineering wonders and beautiful countryside separate the second dimension. Because architectural wonders are more likely to be found in an urban area, dimension two was labeled urban/country. According to these two dimensions, Virginia had a strong image on Table 2. Correspondence Analysis on Attraction-Specific Images Dimension
Singular Value
Inertia
1 2 3 4 5 6 7 8 Total
.51962 .41482 .15052 .11552 .06678 .04243 .02203 .01603
.27000 .17207 .02266 .01334 .00446 .00180 .00049 .00026 .48508
Proportion Explained .557 .355 .047 .028 .009 .004 .001 .001 1.000
Cumulative Proportion .557 .911 .958 .986 .995 .998 .999 1.000 1.000
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mountain and country. In Figure 2 it seems that Virginia did not share any similar market position with other study areas. It appeared to lead in having quaint towns and villages, mountains, beautiful countryside, and natural features and wonders, while Virginia, Pennsylvania and Maryland shared a leading position as the best state for state or national parks. Florida led in the area of beaches and resorts. New York was solely regarded as the best place for touring architectural or engineering wonders while it shared with Georgia as the best state for city tours. Above all, in terms of the number of images it led, Virginia appeared to have an advantage in attraction-specific images when compared with the other nine study areas. Regarding the interrelationship among study areas, Figure 2 illustrates that West Virginia, North Carolina, South Carolina, and Washington DC could not generate unique attraction-specific images when competing with other six states investigated here. In addition, Florida could differentiate itself from the other study areas. The results connote that Florida presented unmatchable advantages. Because Pennsylvania and Virginia held a similar market position on
1=beaches, 2=mountains, 3=cities, 4=resorts, 5=state or national parks, 6=quaint towns and villages, 7=natural features or wonders, 8=beautiful countryside, 9=architectural or engineering wonders. NY=New York, PA=Pennsylvania, MD=Maryland, VA=Virginia, WV=West Virginia, NC=North Carolina, SC=South Carolina, GA=Georgia, FL=Florida, DC=Washington DC.
Figure 2. A Joint Plot of Attraction-Specific Images
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provision of activity, logit modeling was employed to detect cognitive variations between these competing states. Table 3 shows that 14 out of 17 categories differed significantly. Offering activities for children, hunting, and visiting historical buildings and sites are the only three attributes not found to be significantly different between these states. This implies that they shared the similar market on the above three attributes. With the SAS Catmod procedure on logit modeling (Stokes, Davis and Koch 1997), a positive sign on the sum of intercept and the estimate of a specific attribute indicates that the attribute is more likely to fall into the reference group one—Virginia. Conversely, a negative sign implies it is likely to belong to the reference group two—Pennsylvania. According to this rule, the level having the highest probability of falling into group one or two could be identified. According to the resultant logit model (Table 3), Virginia had stronger images of 12 activities including: sightseeing, shopping, having good restaurants, playing golf and tennis, snow skiing, hiking, walking or backpacking, canoeing or rafting, bicycling, attending cultural events, attending festivals or special events, horse racing, and visiting Civil War sites. On the other hand, Pennsylvania had only two prevailing images: attending spectator sports and visiting theme parks. These results indicate that Virginia appeared to have a stronger market position on 12 of the attributes, while Pennsylvania had a stronger position on two of them. Table 3. Logit Analysis on Activity-Specific Images Attribute
Coefficient Standard Error
Intercept 1. Sightseeing 2. Shopping 3. Having Good Restaurants 4. Playing Golf and Tennis 5. Snow Skiing 6. Hiking, Walking or Backpacking 7. Canoeing or Rafting 8. Bicycling 9. Attending Spectator Sports 10. Attending Cultural Events 11. Visiting Theme Parks 12. Attending Festivals or Special Events 13. Offering Activities for Children 14. Horse Racing 15. Hunting and Fishing 16. Visiting Civil War Sites 17. Visiting Historical Building, and Sites
0.7641 0.5854 ⫺0.3641 ⫺0.4958 0.5651 ⫺0.3645 0.4776 0.2441 0.5937 ⫺1.1094 ⫺0.6463 ⫺2.2269 0.3066
0.0283 0.1147 0.1135 0.1081 0.1600 0.0852 0.0935 0.0986 0.1211 0.1278 0.0811 0.1314 0.1029
731.45 26.07 10.29 21.04 12.47 18.29 26.07 6.13 24.03 75.34 63.43 287.27 8.88
.0000a .0000a .0013a .0000a .0004a .0000a .0000a .0133a .0000a .0000a .0000a .0000a .0029a
0.1938 1.0145 0.0266 1.1995 0.0001
0.1389 0.1450 0.0923 0.0931 0.0716
1.95 48.96 0.08 166.17 0.00
.1631 .0000a .7736 .0000a .9993
a
P value <.01.
ChiSquare
P Value
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CONCLUSION This study reveals the relative market position of 9 US states and Washington DC. As for provision of leisure activities, Virginia has a great market potential to attract those who are interested in naturebased tourism activities and Civil War related events. While Pennsylvania has a similar market position with Virginia in regard to tourism activities, the latter clearly has more dominant advantages than its counterpart. In addition, regarding attraction-specific elements, Virginia is considered the best state for quaint towns, mountains, beautiful countryside, natural wonder, and state and national parks. The dual plots exhibit distinct market positions for the states. The plots could be used to help practitioners comprehend visually which state posed more threat to it and which ones less. Marketers might further utilize this information to categorize the current competitors into three different strategic zones, including immediate, intermediate, and distant competitors in their strategies (Table 4). In this case, according to the distance of the perceptual map between Virginia and the competing areas, it could consider Pennsylvania as an immediate competitor for the activity aspect, and New York, Maryland, North Carolina, South Carolina, Georgia, and West Virginia forging a competitive cluster along with Virginia and Pennsylvania in the resulting dual plot as intermediate rivals. However, since Florida and Washington DC are far away from Virginia in the plot, marketers should regard them as distant competitors. Likewise, for the attractionspecific component, Virginia marketers should view Pennsylvania, New York, Maryland, North Carolina, South Carolina, Georgia, and West Virginia as intermediate competitors and Florida and Washington DC as distant competitors. Virginia was not able to create a leading image as the best state for cultural tourism, yet visiting Civil War sites was a popular activity perceived by respondents. Because Washington DC was considered as the best place for cultural activities including visiting museums, Virginia tourism planners may cooperate with those practitioners from Washington DC to develop actionable, mutually beneficial marketing plans Table 4. The Virginia’s Potential Competitors Activities
Attractions
Immediate Competitor Intermediate Competitor
Pennsylvania New York Maryland North Carolina South Carolina Georgia West Virginia
Distant Competitor
Florida Washington DC
N/A Pennsylvania New York Maryland North Carolina South Carolina Georgia West Virginia Florida Washington DC
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to attract cultural tourists to Virginia. Also, it is desirable that Virginia practitioners committed to this submarket could create a strategic alliance with those businesses centering on nature-based tourism in Virginia that enhances the product offerings and eventually helps augment their current visitation. It is important to consider the limitations of the study. The stability of the findings may change over time due to new promotional efforts and product enhancement in competing locations. However, the effect may take time and show variation from place to place. As Gartner and Hunt (1987) pointed out, given that the image was created through a long-term formulation process, it is unlikely that the states’ images will be altered over a few years. Furthermore, they discovered that some might be changed by various promotional activities. For future studies, researchers might compare the effect on image change due to the use of promotion channels such as the Internet, TV, and brochures. The results may help them improve their product offerings and service delivery in a timely and effectively manner. The second limitation is that perhaps not all cognitive attributes necessary to represent the natural and cultural characteristics have been investigated. However, the list of attributes used is broad and generic enough to delineate similarities and differences among the states. In addition, the findings were limited nine eastern states and Washington DC. Unlike the extant literature reporting the perceptions of attractions, this research takes a further approach to develop sets of cognitive attributes measuring tourists’ image of activities and attractions. By using these sets of image attributes as descriptors, this research makes three methodological and conceptual contributions to the application of market positing analysis. First, past research does not extensively investigate the relative market position from a regional perspective. This study illustrates the strengths and weaknesses of each state, furnishing practitioners with a holistic view of their relative market positions while comparing their position to that of adjacent states. The resulting data are especially useful for developing competitive marketing efforts. For example, the cluster of activity-specific attributes representing dominant images for a particular state could be included in promotional campaigns that differentiate its uniqueness from other competing ones. Second, unlike previous studies using continuous measurement scales, an implicit scheme in regard to activities and attractions was deployed in this study. With this type of measurement, the top-rated state for a future vacation concerning a particular activity/attraction category of interest (such as sightseeing and shopping) could be identified. This technique renders a better estimation of taking vacation trips for a particular purpose (such as shopping) or to a particular place (like beach). For example, regarding the attribute of shopping, if Virginia receives the highest mean score based on a five-point Likerttype scale, it may not be the first state that respondents may select for this purpose because they do not compare it with other states simultaneously. However, by using implicit measurement, if Virginia is chosen as the best state for shopping, it is likely that it could be the
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first selected state for the shopping trip. In conclusion, this technique provides a better alternative in analyzing the relative market position of products and places. Third, the research paradigm involving a hybrid approach incorporating correspondence analysis and logit modeling enables marketers to improve precision in understanding and increasing their current market positions. Marketers could easily use the joint plot derived from a correspondence analysis to grasp their current market position, as well as to see what firm, state, region, or country shares a similar competitive position with them. Moreover, the post-hoc analysis—logit modeling—can help the marketers foresee what attributes are superior or inferior to their competitors. Accordingly, relative positioning strategies might be rendered. In conclusion, there was no market positioning study incorporating logit and correspondence analyses to interpret the relative market position. This study provides hybrid analytical techniques to delineate the uniqueness of each place and contrast the competitiveness among areas under investigation. Scholars and practitioners having interests in marketing studies should be encouraged to adopt this new paradigm or further introduce other types of approaches that can promise tourA ism businesses deliverable strategies in a competitive environment.왎 Acknowledgements—The authors wish to express appreciation to the following individuals: John Williams, Daniel Williams, Mark Brown, and National Family Opinion staff members, Greensboro, NC office. The paper was written based on a larger state funded project carried out by D. Williams, M. Uysal, and National Family Opinion.
REFERENCES Ahmed, A. 1994 Determinants of a State’s Tourist Image and their Marketing Implications. Journal of Hospitality and Leisure Marketing 2(1):pp. 55–69. Ashworth, G. 1990 Products, Places and Promotion: Destination Images in the Analysis of the Tourism Industry. In Marketing Tourism Places, G. Ashworth and B. Goodall, eds., pp. 121–142. London: Routledge. Ashworth, G., and H. Voogd 1988 Marketing the City: Concepts, Processes and Dutch Applications. Town Planning Review 59:65–80. 1990 Can Places be Sold for Tourism? In Marketing Tourism Places, G. Ashworth and B. Goodall, eds., pp. 1–16. London: Routledge. Baloglu, S. 1996 An Empirical Examination of Determinants of Tourist Destination Image. Unpublished Doctoral Dissertation, Virginia Polytechnic Institute and State University, Blacksburg, VA. Bignon, V., W. Hammitt, and W. Norman 1998 French Perceptions and Images of the United States as a Destination. Tourism Analysis 3:159–171. Bramwell, B., and L. Rawding 1996 Tourism Marketing Images of Industrial Cities. Annals of Tourism Research 23:201–221. Calantone, R., C. di Benedetto, A. Hakam, and D. Bojanic 1989 Multiple Multinational Tourism Positioning Using Correspondence Analysis. Journal of Travel Research 28(2):25–32.
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Chen, J., and C. Hsu 2000 Measurement of Korean Tourists’ Perceived Images of Overseas Destinations. Journal of Travel Research 38:410–415. Court, B., and R. Lupton 1997 Customer Portfolio Development: Modeling Destination Adopters, Inactive, and Rejecters. Journal of Travel Research 35(1):35–43. Crompton, J. 1979 An Assessment If the Image of Mexico as a Vacation Destination and the Influence of Geographical Location upon that Image. Journal of Travel Research 17:18–23. Crouch, G., and J. Ritchie 1995 Destination Competitiveness and the Role of the Tourism Enterprise. In Innovation Technology and Information Management for Global Development and Competitiveness, E. Kaynak and T. Erem, eds., pp. 43–48. Hummelstown PA: International Management Development Association. 1999 Tourism, Competitiveness, and Societal Prosperity. Journal of Business Research 44(3):137–152. Dann, G. 1996 Tourists’ Image of Destination: An Alternative Analysis. In Recent Advances in Tourism Marketing Research, D. Fesenmair, J. O’Leary and M. Uysal, eds., pp. 41–55. New York: Haworth. Dimanche, F., and M. Moody 1998 Perceptions of Destination Image: A Study of Latin American Intermediary Travel Buyers. Tourism Analysis 3:173–180. Echtner, C., and J. Ritchie 1991 The Meaning and Measurement of Destination Image. Journal of Tourism Studies 2(2):2–12. 1993 The Measurement of Destination Image: An Empirical Assessment. Journal of Travel Research 22(4):3–13. Fakeye, P., and J. Crompton 1991 Image Differences Between Prospective, First-Time, and Repeat Visitors to the Lower Rio Grande Valley. Journal of Travel Research 29(2):pp. 10–16. Franz, J. 1988 The Seaside Resorts of Southeast Asia. Tourism Recreation Research 10(1):15–30. Gartner, W., and T. Bachri 1994 Tour Operators’ Role in the Tourism Distribution System: An Indonesian Case Study. In Global Tourist Behavior, M. Uysal, ed., pp. 161–180. New York: International Business Press. Gartner, W., and J. Hunt 1987 An Analysis of State Image Change Over a Twelve-Year Period (19711983). Journal of Travel Research 26(2):15–19. Gartner, W. 1989a Image Formation Process. Journal of Travel and Tourism Marketing 2(3):199–212. 1989b Tourism Image: Attribute Measurement of State Tourism Products Using Multidimensional Scaling Techniques. Journal of Travel Research 28(2):16–20. 1993 Image Formation Process. In Communication and Channel Systems in Tourism Marketing, M. Uysal and D. Fesenmair, eds., pp. 191–215. New York: Haworth. Gartner, W., and J. Shen 1992 The Impact of Tianamen Square on China’s Tourism Image. Journal of Travel Research 30(4):47–52. Gilbert, D. 1990 Strategic Marketing Planning for National Tourism. The Tourist Review 45(1):18–27. Goodall, B. 1990 The Dynamics of Tourism Place Marketing. In Marketing Tourism Places, G. Ashworth and B. Goodall, eds., pp. 259–279. London: Routledge.
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Goodrich, J. 1977 A New Approach to Image Analysis through Multidimensional Scaling. Journal of Travel Research 16(3):3–7. Gunn, C. 1972 Vacationscape. Austin TX: University of Texas. Haahti, A. 1986 Finland’s Competitive Position as a Destination. Annals of Tourism Research 28:16–20. Hair, J., R. Anderson, R. Tatham, and W. Black 1998 Multivariate Data Analysis (5th ed.). Upper Saddle River NJ: Prentice Hall. Hoffman, D., and G. Franke 1986 Correspondence Analysis: Graphical Representation of Categorical Data in Marketing Research. Journal of Marketing Research 23:213–227. Illiewich, S. 1998 Deconstructing Destination—Image. In Proceedings of 29th annual conference of Travel and Tourism Research Association, pp. 50–58. Boulder CO: Travel and Tourism Research Association. Kim, C., and J. Young 1997 Brand Popularity. Country Image and Market Share: An Empirical Study. Journal of International Business Studies 28:361–386. Mazanec, J. 1995 Positioning Analysis with Self-Organizing Maps: An Exploratory Study on Luxury Hotels. The Cornell Hotel and Restaurant Administration Quarterly 37(1):80–95. Milman, A., and A. Pizam 1995 The Role of Awareness and Familiarity with a Destination: The Central Florida Case. Journal of Travel Research 33(3):21–27. Pearce, D. 1997 Competitive Destination Analysis in Southeast Asia. Journal of Travel Research 35(4):16–24. Pearce, P. 1982 Perceived Changes in Holiday Destinations. Annals of Tourism Research 9:145–164. Phelps, A. 1986 Holiday Destination Image (the Problems of Assessment: An Example Developed in Menorca. Tourism Management 7:168–180. Reilly, M. 1990 Free Elicitation of Descriptive Adjectives for Tourism Image Assessment. Journal of Travel Research 28(4):21–26. Roehl, W. 1990 Travel Agent Attitudes toward China After Tiananmen Square. Journal of Travel Research 29(2):16–22. Stokes, M., C. Davis, and G. Koch 1997 Categorical Data Analysis. Cary NC: SAS Institute. Thompson, P. 1995 Correspondence Analysis in Statistical Package Programs. The American Statistician 49:310–316. Uysal, M., J. Chen, and D. Williams 2000 Increasing State Market Share through a Regional Positioning. Tourism Management 21:89–96. Walmsley, D., and M. Young 1998 Evaluation Image and Tourism: The Use of Personal Constructs to Describe the Structure of Destination Images. Journal of Travel Research 36(3):65–69. Wang, S. 1998 Image Analysis of the Pearl River Delta Sub-Regional Travel Destinations. Unpublished Master Thesis. The Hong Kong Polytechnic University, Hong Kong. Submitted 12 March 1999. Resubmitted 27 January 2001. Accepted 17 August 2001. Final version 9 October 2001. Refereed anonymously. Coordinating Editor: Robert W. McLellan
Annals of Tourism Research, Vol. 29, No. 4, pp. 987–1003, 2002 2002 Elsevier Science Ltd. All rights reserved. Printed in Great Britain 0160-7383/02/$22.00
PII: S0160-7383(02)00003-8
MARKET POSITIONING ANALYSIS A Hybrid Approach Joseph S. Chen Eastern Michigan University, USA Muzaffer Uysal Virginia Polytechnic Institute and State University, USA Abstract: This research introduces a hybrid approach including correspondence analysis and logit modeling to contrast the relative market position of Virginia with eight other eastern US states and the District of Columbia. The data obtained from a series of telephone surveys are analyzed. In a competitive analysis, the study areas are described by youth/mature and culture/nature dimensions relating to activity images as well as by beach/mountain and urban/country dimensions in connection with attraction images. Pennsylvania appears to be an immediate competitor of Virginia in regard to provision of leisure activities. The study concludes with discussions on critical findings along with marketing implications. Keywords: destination images, positioning analysis. 2002 Elsevier Science Ltd. All rights reserved. Re´sume´: L’analyse du positionnement marketing: une approche hybride. Cette recherche pre´sente une approche hybride qui comprend l’analyse de correspondance et le modelage logit pour contraster la position relative du marche´ de la Virginie avec huit autres e´tats de l’est des E´tats-Unis et le district de Columbia. On analyse des donne´es qui ont e´te´ obtenues d’une se´rie de sondages par te´le´phone. Dans une analyse compe´titive, les champs d’e´tude sont repre´sente´s par les dimensions de jeunesse/aˆge muˆr et culture/nature relatives aux images d’activite´s ainsi que les dimensions plage/montagne et campagne/ville concernant les images d’attractions. L’e´tude se termine par une discussion des conclusions critiques et des implications pour le marketing. Mots-cle´s: images de destination, analyse de positionnement. 2002 Elsevier Science Ltd. All rights reserved.
INTRODUCTION Understanding competitors’ strengths and weaknesses is of paramount importance for marketers, developers, and planners involved in strategy development. Creating a competitive market position is vital to long-term success, and thus seizing market competitiveness should be of great interest to promoters. Crouch and Ritchie (1995) pointed out that development plans, organizational structures, and marketing strategies drove destinations in their quest to compete, but these efforts
Joseph Chen is Director of the Hotel and Restaurant Management Program at Eastern Michigan University (206 Roosevelt Hall, Ypsilanti MI 48197, USA. Email
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inevitably relied on some knowledge of what it was that made a location viable. Additionally, to enhance their competitiveness, attractions must ensure that their overall appeal, and the quality and integrity of the experiences they deliver, must equal or surpass that of many alternatives open to potential tourists (Crouch and Ritchie 1999:139). Indeed, tourism as an experience takes place in destinations where leisure activities and new and different cultures are experienced. To maintain or capture a significant population, marketing firms are often challenged to create positive product perceptions. Some service providers may need to augment supply resources to match demand and meet tourist expectations, and thus to create an enhanced perception of the existing resources (Uysal, Chen and Williams 2000). Many scholars have conducted image research to identify places’ relative strengths and weaknesses (Fakeye and Crompton 1991; Franz 1988; Gartner and Hunt 1987; Haahti 1986; Pearce 1997). Goodall (1990) suggested that building product images helps add competitive advantages. Furthermore, Gilbert (1990) articulated that tourism establishments should promote themselves using the distinct attributes to entice demand. Based on the literature, an assessment of tourist’s perceptions helps marketers position their products and services more effectively. A plethora of studies have provided conceptual frameworks on the formation of image (Calantone, di Benedetto, Hakam and Bojanic 1989; Court and Lupton 1997; Echtner and Ritchie 1993; Gartner and Hunt 1987; Goodrich 1977; Gunn 1972; Milman and Pizam 1995; Pearce 1982; Phelps 1986; Reilly 1990). Gunn (1972) first conceptualized tourists’ destination image to include two main dimensions: “organic image” which deals with tourists’ impression of a destination without physically having visited the place, and “induced image” which is forged through exposure to promotional materials and modified by actual visitation. Fakeye and Crompton (1991), applying Gunn’s theory, protracted the categorization to three conceptual facets: “organic, induced, and complex”. These formation phases are then linked to the different functions of promotional efforts and their operationalization is a function of the level of knowledge of the place, exposure to promotions, and the nature of onsite experience. The organic facet exists prior to exposure to any promotional information from destinations, whereas an induced facet occurs when tourists actively search for and are exposed to marketing messages from promoters and suppliers. A complex facet incorporates experiences at the destination. Consequently, the tourists may modify their existing perceptions of the place after having the experience. Gartner (1993), in his study of the formation process, further delineated that the roles of different agents (traditional advertising, information requested from professional travel advisors, independently produced reports, movies and news articles about places, solicited and unsolicited information and feedback from individuals who may have visited places) affected the formation. Gartner pointed out that the
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final outcome could be directed through the selection of an appropriate formation mix. Besides the theory building, scholars have taken pragmatic approaches to apply the image frameworks into marketing research. Gartner and Hunt (1987), in their analysis of the change of image of Utah over a 12-year period, revealed that a mix of organic and induced influences determined the shift for non-resident tourists. In his followup study Gartner (1989 a, b) further developed a multidimensional scaling analysis to help in targeting specific market segments effectively. In an effort to develop a more rigorous construct, Echtner and Ritchie (1991, 1993) derived various attributes in measuring perceptions of overseas attractions. Their framework is based on a two-dimensional approach involving three continuums: attribute-holistic, functional-psychological, and common-unique. The attribute-holistic continuum is derived from the notion that image should be composed of perceptions of individual attributes (such as nature of facilities, climate, and friendliness of people) as well as holistic impressions (including mental pictures) of the place. The functional-psychological continuum is based on the distinction between directly measurable (functional) characteristics and those less tangible or more difficult to observe (psychological). The third continuum presents those perceptions based on “common” characteristics (such as clean beaches) and “unique” features (like a place for lovers). By using the framework, Echtner and Ritchie (1993) developed various constructs consisting of those components and thus made significant measurement contribution. Overall, their continuum outline resembles the cognitive, affective, and conative components described by Gartner (1993) and applied by Dann (1996). Most empirical image studies have used two different sets of attributess: designative and evaluative. The former pertain to the cognitive component, while the latter deal with the affective component. Specifically, the cognitive echoes tourists’ prior product knowledge while the affective reflects related feelings. Researchers have attempted to utilize either one or both components to delineate the market position of places. For example, Gartner and Hunt (1987) applied cognitive attributes, including camping, sightseeing, skiing, hunting, fishing, visiting national parks (cities or national forests), enjoying summer temperatures, and the receptiveness of residents into a longitudinal study for the State of Utah. Walmsely and Young (1998) integrated eight evaluative attributes into a market-positioning map. Baloglu (1996) developed and tested an image formation model whereby he delineated the direct and indirect impacts of both affective and cognitive attributes on formation while controlling possible intervening and moderating variables. The above sets of attributes are similar to the push and pull concept of motivations: the first represents more of the affective component and the second more of the cognitive attributes. Their behavioral manifestation, the conative, is analogous to onsite consumption behavior because it is an action component. Furthermore, the formation of the conative attribute depends on the images developed during
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the cognitive stage and evaluated during the affective stage (Dann 1996; Gartner 1993). A closer examination of past research further reveals different orientations towards the analysis of destination images; researchers have studied one single place (Ahmed 1994; Bignon, Hammitt and Norman 1998; Crompton 1979; Fakeye and Crompton 1991; Gartner and Hunt 1987; Illiewich 1998) or a few places together (Bramwell and Rawding 1996); and some have used travel intermediaries as a proxy for tourists in order to shed light on the way potential tourists might perceive a given location (Dimanche and Moody 1998; Gartner and Bachri 1994; Roehl 1990). Moreover, a few have compared the images at different points in time (Gartner and Hunt 1987) and of different tourism resources at one point in time in a given place (Gartner and Shen 1992). Recently, studies on regional (Baloglu 1996) and subregional images (Wang 1998) have surfaced, allowing promoters to tailor their communication mix to entice given segments. Mazanec (1995), in his exploratory study of four luxury hotels in Vienna, used a neurocomputing approach to unveil the relative positions of the hotels. He implied that the neural network technique was restricted to an exploratory analysis due to the lack of statistical significance. His work has encouraged researchers to reinvent the current research paradigm in market positioning studies. Beyond these attempts, attributes were also incorporated into behavioral investigations concerning the relationships between perceptions and behavioral consequences. For example, Chen and Hsu (2000) found that destination images influence travel decisions, including trip planning timeframe, budgeted cost, and number of days spent at the destination. Although the above works have made notable contributions to the body of knowledge few have applied the theory to a competitive market positioning study (Pearce 1997). Ashworth (1990) conducted one with a regional focus by examining several selected Mediterranean countries, including Cyprus, Greece, Egypt, Tunisia, Turkey, Malta, and Spain. It emphasized common and differentiating features and analyzed how these countries project themselves as destinations using different techniques ranging from the use of travel agents to resort brochures. Ashworth (1990) pointed out that the correspondence between the projected and received place images was critical. The former based on similar features may show variations from place to place, and the extent to which it is received may also exhibit dissimilarities (Ashworth and Voogd 1988, 1990; Baloglu 1996). Therefore, it may be of great importance to promoters to assess their relative regional positioning as perceived by potential tourists. POSITIONING ANALYSIS To augment the methodologies found in the extant literature, this study attempts to render a new approach that could be applied to market positioning studies from a regional perspective. More specifically, it contrasts the relative market position of Virginia with eight
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other eastern US states and the District of Columbia (or Washington DC). By application of a hybrid analytical approach involving correspondence analysis and logit modeling, the paper offers new insights to researchers and practitioners to develop more effective and actionable strategies in an increasingly competitive environment. Accordingly, two research questions are developed here: one, what are the dominant images induced by the District of Columbia and nine eastern US states including New York, Pennsylvania, Maryland, Virginia, West Virginia, North Carolina, South Carolina, Georgia, and Florida, and, two, are there any states sharing similar market positions with Virginia in regard to the provision of leisure activities and availability of attractions? If yes, what are the differences between Virginia and the state(s) having similar market positions? The above research questions are to explore each study state’s strengths and weaknesses regarding its images perceived by potential tourists. To promote a product effectively, marketers ought to recognize the images of a country along with that of a product when deploying their strategies (Kim and Young 1997). Plainly, the image of the country where the products are manufactured is likely to affect consumers’ choices. Similarly, the image of a state may have a profound influence on tourists’ choice of the intangible and tangible services rendered by tourism establishments in the state. Thus, examining this image should enable marketers to create a better positioning plan from a regional perspective. Sampling In reference to sampling procedure, this study was performed in two stages. In 1992, the mail omnibus questionnaires were first randomly mailed to a panel of nationally representative households selected to conform to the latest available US census data by National Family Opinion which maintains an active panel of households to conduct surveys on a variety of issues, including tourism. Since this research team had a partnership with the organization, the latter handled the data collection as part of the research action plans. In this mail survey, respondents were asked to indicate their tourism experiences in Virginia and eight other competitive states and the District of Columbia. A simple random mailing to 80,000 panel households produced 54,000 (67.5%) usable responses, of which 5,802 were pleasure tourists who had visited Virginia. This refined sampling frame provided an easy way to identify those who had experienced Virginia and surrounding states and a significant amount of demographic and recent travel data about each of these households. In the second stage, telephone surveys were conducted. It was decided to build a structured sample with quotas by household distance and by visitation, so that sufficient numbers of tourists to Virginia and respondents from specific distances to Virginia would be available for analysis. Because it was known that an accurate weighted total sample could be built using weights from the first panel survey, it was decided that structured samples could give dependable, nationally-rep-
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resentative data. Weights were assigned based on the percentage of a nationally representative sample within five tourism zones. The five zones of origin were defined as within 1/2 day drive, 1/2 to 1 day drive, 1-2 day drive, 2-day plus drive, and Virginia residents. It is also important to mention that the opinions of the household respondents may not reflect a national base of tourists who could also choose to take a pleasure vacation outside the Eastern and South Atlantic States. The telephone surveys ended on August 31, 1992, and generated 1,318 useful samples for this competitive market analysis. Furthermore, since this study adopted a multi-stage random sampling procedure, the randomness of the sample was retained throughout the process, helping enhance the generalizability of the study. Attributes and Data Analysis The attributes used in the study and derived from literature review and an expert panel, are categorized into two domains, the first related to leisure activities provided by those study areas, the second tied to attractions surrounding the study area. The nature of attributes included reflects cognitive elements of places. The activity-specific domain includes 17 attributes: sightseeing, shopping, having good restaurants, playing golf and tennis, skiing, hiking, walking or backpacking, canoeing or rafting, bicycling, attending spectator sports, attending cultural events, visiting theme parks, attending festivals or special events, offering activities for children, horse racing, hunting and fishing, visiting Civil War sites, and sightseeing historical buildings and sites. The attraction-specific domain embodies nine attributes: beaches, mountains, cities, resorts, state or national parks, quaint towns and villages, natural features or wonders, beautiful countryside, and architectural or engineering wonders. Respondents were asked to select one best state from the pool of 10 study areas, in regard to different types of leisure activities and attractions. The above “implicit importance” approach manifests respondents’ inclination to visit a specific state (such as Florida) in pursing a particular activity (such as visiting theme parks). Since the method replicates respondents’ framework for decision making (by comparing several states the respondent would likely consider for a vacation), the attributes are evaluated in an exercise similar to an actual decision rather than a hypothetical one. A hybrid two-stage analytical approach was used to interpret the relative market positioning of each state. In the first stage, two-dimensional correspondence analyses were first used to provide perceptual maps revealing the relative position on each domain among 10 competitive study areas. In the second stage, if any area was identified as a close competitor to Virginia, a post-hoc, inferential analysis using logit modeling was employed to further compare the strengths and weaknesses in relation to the perceived images between Virginia and its competitor(s). Correspondence analysis is an interdependence technique that uses the singular value decomposition to analyze contingency tables from
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multinominal data (Thompson 1995). The main interest of the method lies in the characterization of the structure of the row and/or column variables. Its most direct application involves portraying the correspondence of categories of variables. The benefit of the technique is in its unique ability to represent rows and columns in a joint space (Hair, Anderson, Tatham and Black 1998). In this study, two correspondence analyses were performed. The first examined the competitive market position pertaining to 17 activityspecific attributes; the second reviewed the position of attraction-specific attributes. In these treatments, attributes were regarded as a row variable and the 10 states as a column variable. To visualize the relationship between a row and column variable, canonical normalization was used to derive maps portraying the relative market position of each state in the context of tourists’ perceptions of it. As a result, all row and column categories were plotted in the derived maps. All categories in both column and row variables constituted plotted points. The total number of points was equal to the sum of the number of categories of column and row variables. The proximity between a pair of points was used to interpret the underlying relationship between them. For example, points that were closely aligned revealed a strong relationship. Although correspondence analysis provides interpretable results illustrating the relationship between column and variable, it is a multivariate descriptive statistical method and does not contain a test of significance. To further detect the significant relationship between the row and column variable, other supplementary approaches, including weighted least squares and loglinear analysis, which are more parsimonious in nature, have been suggested by marketing researchers (Hoffman and Franke 1986). To render rich but parsimonious implications in this study, when a state having a similar market position with Virginia was identified in the joint plot, the logit modeling, a loglinear analysis, was further employed to reveal any significant difference between tourists’ image of the state and the competing one. In the logit modeling, a series of Maximum-Likelihood Analysis of Variance statistics was first performed to cross-validate whether or not the independent variable was significantly related to the dependent one. “State” was treated as the dependent variable since the respondent had to choose a preferred state for all attributes. “Image” was used as the independent variable. If the result showed a significant relationship (p<.05) between the variables, a logit model was then adopted. In logit notation, the model was written as: Logit OiC = α + βjxk where OiC = the odds of occurrence on Virginia over the competing state, α = the intercept parameter, ßj = the vector of slope parameter,
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and xk = the explanatory variable. In logit analysis, the parameters for each level of the independent variable were estimated. According to the results, a set of logit models was constructed. For example, when the activity-specific variable containing 17 levels was treated as the independent variable, the results derived 17 logit models with one set of estimated parameters in each model. The estimated parameters showed the probability of being in a specific state for each level of a categorical variable. If the derived parameter for a specific category, such as sightseeing, was significant, it indicated that respondents’ perceptions of this opportunity available in Virginia vs. the competing state differed significantly. Study Results The descriptive analysis of respondent demographic traits showed that the average age of respondent was 51 years, but the largest age group in the sample was the 35 to 50 years group (36%). Male respondents constituted about 70% of the study sample, and the same percentage were married. The average household size was 2.6 (but nearly one-fourth had more than three members); 43% had incomes greater than $50,000; and in respect to respondents’ life stage, 40% were identified as parents, whereas singles accounted for the smallest sample size (12.8%). Hair et al (1998) suggested that cumulative variance could be used as a criterion to determine the dimensionality. To better balance the explained variance and interpretability, this study arbitrarily selected a two-dimensional solution to determine the market position of each state. In Table 1, the correspondence analysis on 17 activity-specific attributes revealed that two dimensions with singular values of .46 and Table 1. Correspondence Analysis on Activity-Specific Images Dimension
Singular Value
Inertia
1 2 3 4 5 6 7 8 9 Total
.46284 .34134 .28886 .19725 .12862 .09318 .06660 .04297 .03121
.21422 .11651 .08344 .03891 .01654 .00868 .00444 .00185 .00097 .48556
Proportion Explained .441 .240 .172 .080 .034 .018 .009 .004 .002 1.000
Cumulative Proportion .441 .681 .853 .933 .967 .985 .994 .998 1.000 1.000
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.34 had 68% cumulative variance. The above were mapped in a twodimensional plot to reveal the market position for each study area. Figure 1 illustrates that the first dimension divided by the vertical axis is distinguished by two extremes: offering activities for children and visiting Civil War sites. Because visiting the latter tends to be an activity appealing to older, educated tourists, the first dimension, therefore, was labeled as youth/mature dimension. The second dimension disjoined by the horizontal axis is manifested by the variables of attending cultural events and hiking, walking or backpacking. This dimension was identified as culture/nature. In Figure 1, Virginia falls into the mature/nature domain. It implies that Virginia had a strong image on offering mature/nature-related activities. Moreover, the state seemed to have a similar market position with Pennsylvania. Visiting Civil War sites and snow skiing were the most important elements dominated by these two states. Regarding the distinctive advantages enjoyed by other competing states, Florida appeared to have strengths on visiting theme parks and offering activities for children; South Carolina led the images of bicyc-
See left-hand column of Table 3 for the labels of activity-related images. NY=New York, PA=Pennsylvania, MD=Maryland, VA=Virginia, WV=West Virginia, NC=North Carolina, SC=South Carolina, GA=Georgia, FL=Florida, DC=Washington DC.
Figure 1. A Joint Plot of Activity-Specific Images
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ling and golf and tennis; Georgia topped the horse racing category; North Carolina was the best state for hunting and canoeing; New York was likely to be a dominant state for sightseeing, visiting historical buildings, and sites, as well as having good restaurants; and Washington DC was considered the best place for attending cultural events, such as visiting museums. West Virginia and Maryland did not have a singular, dominant position. Although the former was considered one of the best for hiking, North Carolina, Virginia, and Pennsylvania also held a strong market position for hiking activities. In addition, Maryland, and New York shared the lead on attending festivals and special events. Meanwhile, Maryland and Georgia seemed to have the leading positions of attending spectator sports and shopping. In general, results revealed that Virginia had great potential to attract tourists who were interested in nature-based activities (such as hunting, canoeing, hiking, and snow skiing) and Civil War sites. With respect to the interrelationship among the categories of the column variable “state,” Figure 1 illustrates that West Virginia, Florida, and Washington DC notably deviated from the other seven. In other words, the latter constituted a competing cluster with allied competitive edges in which West Virginia, Florida, and Washington DC were unable to prevail. Moreover, as for market position of the three states outside the competing cluster, Florida and Washington DC unveiled unique superior positions on certain leisure activities over other states. West Virginia was the only one that neither belonged to the cluster nor had any distinct traits over others. Table 2 shows a two-dimensional solution containing 91% of the total explained variance, with singular values of .51 and .41 pertaining to the correspondence analysis on 9 attraction-specific perceptional items. Beaches and mountains distinguish the first dimension (Figure 2). Consequently, this dimension was named beach/mountain. Architectural or engineering wonders and beautiful countryside separate the second dimension. Because architectural wonders are more likely to be found in an urban area, dimension two was labeled urban/country. According to these two dimensions, Virginia had a strong image on Table 2. Correspondence Analysis on Attraction-Specific Images Dimension
Singular Value
Inertia
1 2 3 4 5 6 7 8 Total
.51962 .41482 .15052 .11552 .06678 .04243 .02203 .01603
.27000 .17207 .02266 .01334 .00446 .00180 .00049 .00026 .48508
Proportion Explained .557 .355 .047 .028 .009 .004 .001 .001 1.000
Cumulative Proportion .557 .911 .958 .986 .995 .998 .999 1.000 1.000
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mountain and country. In Figure 2 it seems that Virginia did not share any similar market position with other study areas. It appeared to lead in having quaint towns and villages, mountains, beautiful countryside, and natural features and wonders, while Virginia, Pennsylvania and Maryland shared a leading position as the best state for state or national parks. Florida led in the area of beaches and resorts. New York was solely regarded as the best place for touring architectural or engineering wonders while it shared with Georgia as the best state for city tours. Above all, in terms of the number of images it led, Virginia appeared to have an advantage in attraction-specific images when compared with the other nine study areas. Regarding the interrelationship among study areas, Figure 2 illustrates that West Virginia, North Carolina, South Carolina, and Washington DC could not generate unique attraction-specific images when competing with other six states investigated here. In addition, Florida could differentiate itself from the other study areas. The results connote that Florida presented unmatchable advantages. Because Pennsylvania and Virginia held a similar market position on
1=beaches, 2=mountains, 3=cities, 4=resorts, 5=state or national parks, 6=quaint towns and villages, 7=natural features or wonders, 8=beautiful countryside, 9=architectural or engineering wonders. NY=New York, PA=Pennsylvania, MD=Maryland, VA=Virginia, WV=West Virginia, NC=North Carolina, SC=South Carolina, GA=Georgia, FL=Florida, DC=Washington DC.
Figure 2. A Joint Plot of Attraction-Specific Images
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provision of activity, logit modeling was employed to detect cognitive variations between these competing states. Table 3 shows that 14 out of 17 categories differed significantly. Offering activities for children, hunting, and visiting historical buildings and sites are the only three attributes not found to be significantly different between these states. This implies that they shared the similar market on the above three attributes. With the SAS Catmod procedure on logit modeling (Stokes, Davis and Koch 1997), a positive sign on the sum of intercept and the estimate of a specific attribute indicates that the attribute is more likely to fall into the reference group one—Virginia. Conversely, a negative sign implies it is likely to belong to the reference group two—Pennsylvania. According to this rule, the level having the highest probability of falling into group one or two could be identified. According to the resultant logit model (Table 3), Virginia had stronger images of 12 activities including: sightseeing, shopping, having good restaurants, playing golf and tennis, snow skiing, hiking, walking or backpacking, canoeing or rafting, bicycling, attending cultural events, attending festivals or special events, horse racing, and visiting Civil War sites. On the other hand, Pennsylvania had only two prevailing images: attending spectator sports and visiting theme parks. These results indicate that Virginia appeared to have a stronger market position on 12 of the attributes, while Pennsylvania had a stronger position on two of them. Table 3. Logit Analysis on Activity-Specific Images Attribute
Coefficient Standard Error
Intercept 1. Sightseeing 2. Shopping 3. Having Good Restaurants 4. Playing Golf and Tennis 5. Snow Skiing 6. Hiking, Walking or Backpacking 7. Canoeing or Rafting 8. Bicycling 9. Attending Spectator Sports 10. Attending Cultural Events 11. Visiting Theme Parks 12. Attending Festivals or Special Events 13. Offering Activities for Children 14. Horse Racing 15. Hunting and Fishing 16. Visiting Civil War Sites 17. Visiting Historical Building, and Sites
0.7641 0.5854 ⫺0.3641 ⫺0.4958 0.5651 ⫺0.3645 0.4776 0.2441 0.5937 ⫺1.1094 ⫺0.6463 ⫺2.2269 0.3066
0.0283 0.1147 0.1135 0.1081 0.1600 0.0852 0.0935 0.0986 0.1211 0.1278 0.0811 0.1314 0.1029
731.45 26.07 10.29 21.04 12.47 18.29 26.07 6.13 24.03 75.34 63.43 287.27 8.88
.0000a .0000a .0013a .0000a .0004a .0000a .0000a .0133a .0000a .0000a .0000a .0000a .0029a
0.1938 1.0145 0.0266 1.1995 0.0001
0.1389 0.1450 0.0923 0.0931 0.0716
1.95 48.96 0.08 166.17 0.00
.1631 .0000a .7736 .0000a .9993
a
P value <.01.
ChiSquare
P Value
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CONCLUSION This study reveals the relative market position of 9 US states and Washington DC. As for provision of leisure activities, Virginia has a great market potential to attract those who are interested in naturebased tourism activities and Civil War related events. While Pennsylvania has a similar market position with Virginia in regard to tourism activities, the latter clearly has more dominant advantages than its counterpart. In addition, regarding attraction-specific elements, Virginia is considered the best state for quaint towns, mountains, beautiful countryside, natural wonder, and state and national parks. The dual plots exhibit distinct market positions for the states. The plots could be used to help practitioners comprehend visually which state posed more threat to it and which ones less. Marketers might further utilize this information to categorize the current competitors into three different strategic zones, including immediate, intermediate, and distant competitors in their strategies (Table 4). In this case, according to the distance of the perceptual map between Virginia and the competing areas, it could consider Pennsylvania as an immediate competitor for the activity aspect, and New York, Maryland, North Carolina, South Carolina, Georgia, and West Virginia forging a competitive cluster along with Virginia and Pennsylvania in the resulting dual plot as intermediate rivals. However, since Florida and Washington DC are far away from Virginia in the plot, marketers should regard them as distant competitors. Likewise, for the attractionspecific component, Virginia marketers should view Pennsylvania, New York, Maryland, North Carolina, South Carolina, Georgia, and West Virginia as intermediate competitors and Florida and Washington DC as distant competitors. Virginia was not able to create a leading image as the best state for cultural tourism, yet visiting Civil War sites was a popular activity perceived by respondents. Because Washington DC was considered as the best place for cultural activities including visiting museums, Virginia tourism planners may cooperate with those practitioners from Washington DC to develop actionable, mutually beneficial marketing plans Table 4. The Virginia’s Potential Competitors Activities
Attractions
Immediate Competitor Intermediate Competitor
Pennsylvania New York Maryland North Carolina South Carolina Georgia West Virginia
Distant Competitor
Florida Washington DC
N/A Pennsylvania New York Maryland North Carolina South Carolina Georgia West Virginia Florida Washington DC
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to attract cultural tourists to Virginia. Also, it is desirable that Virginia practitioners committed to this submarket could create a strategic alliance with those businesses centering on nature-based tourism in Virginia that enhances the product offerings and eventually helps augment their current visitation. It is important to consider the limitations of the study. The stability of the findings may change over time due to new promotional efforts and product enhancement in competing locations. However, the effect may take time and show variation from place to place. As Gartner and Hunt (1987) pointed out, given that the image was created through a long-term formulation process, it is unlikely that the states’ images will be altered over a few years. Furthermore, they discovered that some might be changed by various promotional activities. For future studies, researchers might compare the effect on image change due to the use of promotion channels such as the Internet, TV, and brochures. The results may help them improve their product offerings and service delivery in a timely and effectively manner. The second limitation is that perhaps not all cognitive attributes necessary to represent the natural and cultural characteristics have been investigated. However, the list of attributes used is broad and generic enough to delineate similarities and differences among the states. In addition, the findings were limited nine eastern states and Washington DC. Unlike the extant literature reporting the perceptions of attractions, this research takes a further approach to develop sets of cognitive attributes measuring tourists’ image of activities and attractions. By using these sets of image attributes as descriptors, this research makes three methodological and conceptual contributions to the application of market positing analysis. First, past research does not extensively investigate the relative market position from a regional perspective. This study illustrates the strengths and weaknesses of each state, furnishing practitioners with a holistic view of their relative market positions while comparing their position to that of adjacent states. The resulting data are especially useful for developing competitive marketing efforts. For example, the cluster of activity-specific attributes representing dominant images for a particular state could be included in promotional campaigns that differentiate its uniqueness from other competing ones. Second, unlike previous studies using continuous measurement scales, an implicit scheme in regard to activities and attractions was deployed in this study. With this type of measurement, the top-rated state for a future vacation concerning a particular activity/attraction category of interest (such as sightseeing and shopping) could be identified. This technique renders a better estimation of taking vacation trips for a particular purpose (such as shopping) or to a particular place (like beach). For example, regarding the attribute of shopping, if Virginia receives the highest mean score based on a five-point Likerttype scale, it may not be the first state that respondents may select for this purpose because they do not compare it with other states simultaneously. However, by using implicit measurement, if Virginia is chosen as the best state for shopping, it is likely that it could be the
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first selected state for the shopping trip. In conclusion, this technique provides a better alternative in analyzing the relative market position of products and places. Third, the research paradigm involving a hybrid approach incorporating correspondence analysis and logit modeling enables marketers to improve precision in understanding and increasing their current market positions. Marketers could easily use the joint plot derived from a correspondence analysis to grasp their current market position, as well as to see what firm, state, region, or country shares a similar competitive position with them. Moreover, the post-hoc analysis—logit modeling—can help the marketers foresee what attributes are superior or inferior to their competitors. Accordingly, relative positioning strategies might be rendered. In conclusion, there was no market positioning study incorporating logit and correspondence analyses to interpret the relative market position. This study provides hybrid analytical techniques to delineate the uniqueness of each place and contrast the competitiveness among areas under investigation. Scholars and practitioners having interests in marketing studies should be encouraged to adopt this new paradigm or further introduce other types of approaches that can promise tourA ism businesses deliverable strategies in a competitive environment.왎 Acknowledgements—The authors wish to express appreciation to the following individuals: John Williams, Daniel Williams, Mark Brown, and National Family Opinion staff members, Greensboro, NC office. The paper was written based on a larger state funded project carried out by D. Williams, M. Uysal, and National Family Opinion.
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