doi:10.1016/j.tourman.2007.06

ARTICLE IN PRESS

Tourism Management 29 (2008) 548–560 www.elsevier.com/locate/tourman

Russia’s destination image among American pleasure travelers: Revisiting Echtner and Ritchie Svetlana Stepchenkovaa, Alastair M. Morrisonb, a

Department of Hospitality and Tourism Management, Purdue University, 154 Stone Hall, 700 W. State Street, West Lafayette, IN 47907-2059, USA Department of Hospitality and Tourism Management, Purdue University, 111A Stone Hall, 700 W. State Street, West Lafayette, IN 47907-2059, USA

b

Received 23 May 2006; received in revised form 8 June 2007; accepted 13 June 2007

Abstract This study measured Russia’s destination image among US pleasure travelers by the means of a Web-based survey. The methodology proposed by Echtner and Ritchie [(1993). The measurement of destination image: An empirical assessment. Journal of Travel Research, 31(Spring), 3–13] was enriched by using a combination of two software programs, CATPAC and WORDER, to analyze responses to open-ended questions about stereotypical holistic, affective, and uniqueness images and facilitate statistical comparisons of images between visitors and non-visitors to Russia. A favorability variable was operationalized on the textual data, and affective images of visitors and non-visitors to Russia were statistically compared. The study found that American travelers’ perceptions of Russia were often negative and there is a lack of awareness about Russia’s destination features. Marketing implications for Russia’s Federal Travel Agency based on the study results are discussed. r 2007 Elsevier Ltd. All rights reserved.

Keywords: Affective image; CATPAC; Content analysis; Destination image; Russia; Stereotypical holistic image; Uniqueness image; WORDER

1. Introduction Russia is a vast country with rich tourist resources of all kinds. They include unique natural features, beautiful landscapes, historical and cultural attractions, places of ethnographic interest, and good recreational opportunities.

However, while Russian outbound and internal tourism have been growing rapidly, inbound tourism is growing slowly and for the several years has been suffering from political instability associated with terrorist activity in Russia; therefore, income from international tourism is a small share of Russia’s overall economy (Russia’s State Statistics Service (Rosstat), 2006). Since the 1990s, Russia has been successfully developing its tourist offer; nevertheless, some problems still remain. Among the factors that prevent faster growth of Russia’s inbound tourism are a lack of infrastructure, especially in the country’s eastern Corresponding author. Tel.: +1 765 494 7905; fax: +1 765 496 1168.

E-mail addresses: svetlana@purdue.edu (S. Stepchenkova), alastair@purdue.edu (A.M. Morrison).

0261-5177/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.tourman.2007.06.003

areas, complicated visa procedures, rising prices for tour packages, and lack of advertising. To realize its tourism potential, the country needs not only to solve the abovementioned problems but also to attractively present itself to international travelers. To become a competitive global destination, the Federal Tourism Agency of Russian Federation (FTA) needs to develop Brand Russia which would firmly position the country among the competitive destinations of Eastern Europe and Asia. Given the size of the US tourist market and the fact that US pleasure travelers are the world’s leading travel spenders (WTO, 2006a), this segment is very attractive for the Russian tourism industry from an economic standpoint.

To be successfully promoted in a particular market, ‘‘a destination must be favorably differentiated from its competition, or positively positioned, in the minds of the consumers’’ (Echtner & Ritchie, 2003, p. 37). A desirable differentiation and positioning can be achieved by a destination’s marketing organization by creating and managing the perceptions, or images, that potential travelers hold about the destination. Therefore, the purpose

ARTICLE IN PRESS S. Stepchenkova, A.M. Morrison / Tourism Management 29 (2008) 548–560

of this study was to examine Russia’s destination image among US pleasure travelers by investigating the following questions: 1. What stereotypical holistic images do US pleasure travelers associate with Russia?

2. What affective images does Russia as a travel destination evoke?

3. What unique places and features do US pleasure travelers associate with Russia?

4. What are US pleasure travelers’ perceptions of Russia’s destination attributes?

5. Does the degree of familiarity with Russia (visitors/ non-visitors) affect the destination image of Russia?

The lack of information is evident: a destination image literature review conducted by Pike (2002) for the period of 1973–2000 found that only one out of 142 articles had dealt with Russia’s image, and this study by Pizam, Jafari, and Milman (1991) reflected the old, ‘‘Soviet’’ image of the country. The analysis of Russia’s destination image as held

by US pleasure travelers should be useful to both the FTA and Russian travel providers, to see how Russia is perceived by one of the largest tourist markets in the world, and to counter negative or inaccurate perceptions of potential visitors.

2. Study background 2.1. Destination image construct The concept of ‘‘image’’ that has been studied for several decades in such disciplines as social and environmental psychology, marketing, and consumer behavior, was introduced into tourism studies in the early 1970s by Hunt (1971), Mayo (1973), and Gunn (1972) and has since become one of the most researched topics in the field.

However, as meta-analyses of destination image studies indicated (Chon, 1990; Gallarza, Saura, & Garcia, 2002; White, 2004), due to its complexity, subjectivity, and elusive nature, the concept of destination image has been interpreted differently by various researchers. The view on destination image as an overall impression is rooted in psychological tradition and consumer behavior theory (Assael, 1984; Herzog, 1963) and was supported by Hunt (1971) and Reilly (1990). However, operationalization of the destination image construct without breaking it into separate, more evaluative elements is problematic. Tourism scholars generally agree that destination image holds at least two distinctive components—cognitive and affective (Baloglu & Brinberg, 1997; Baloglu & McCleary, 1999).

The cognitive, or perceptual, element refers to knowledge and beliefs about a destination, while the affective element refers to feelings about a destination.

Despite the composite nature of the destination image construct, in most destination image studies researchers have emphasized the cognitive dimension (Pike & Ryan,

549

2004). Strong support for cognitive interpretation of image as a set of relevant attributes is given by Gensch (1978): ‘‘Products seldom are measured or evaluated as single lump sum entities; rather, it is the attributes of the alternatives that are measured, compared, and form the basis for choice’’ (cited in Gartner, 1986, p. 636). This view was further supported by Engel, Blackwell, and Miniard (1986), who stated that image is the consumer’s subjective perceptions, which refer to how an alternative performs on important evaluative criteria.

Social and environmental psychological tradition regards cognition and affect as interrelated elements, where affect is largely dependent on cognition (references to this view can be found in Baloglu & McCleary, 1999).

However, Russell and Snodgrass (1987, p. 246) argued that ‘‘behavior may be influenced by the (estimated, perceived, or remembered) affective quality of an environment rather than by its objective properties directly’’. The affective component of destination image expresses feelings toward a destination, which can be favorable, unfavorable, or neutral. Gartner (1993) suggested that the affective component comes into play at the stage when different travel alternatives are evaluated. Furthermore, there are recent indications that emotions might be better predictors of behavior than perceptual evaluations (Yu & Dean, 2001). Despite its obvious importance, affect has generally been overlooked by destination image researchers: only six out of 142 studies surveyed by Pike (2002) studied affective images.

Gartner (1993), Pike and Ryan (2004), and White (2004) among other scholars, also recognized a third—conative or behavioral—element in the destination image construct, which is related to how travelers act toward a destination on the basis of the cognition and affect they have about it.

Conation reflects a likelihood of destination selection, or brand purchase, and can be interpreted as a propensity to visit a destination within a certain time frame (Pike & Ryan, 2004). The conative element of destination image is influenced by both the cognitive and affective components.

Familiarity plays an important role in destination image formation. It influences destination perceptions and attractiveness and represents a key marketing variable in segmenting and targeting potential visitors (Baloglu, 2001).

Familiarity can be understood as previous experience with a destination (experience dimension) and knowledge about it (informational dimension). One stream of research on familiarity and destination image compares pre- and postvisitation destination images. Phelps (1986) recognized secondary destination images, as formed by travelers’ exposure to different information sources, and primary images, which are created after actual visitation. Her research, as well as the studies done by Pearce (1982), Chon (1991), and Dann (1996), suggested that visitation affects images and changes some of the perceptions about a destination. Post-visitor perceptions were found to be more positive than those of pre-visitors. However, there are indications that a relationship between visitation and

ARTICLE IN PRESS 550

S. Stepchenkova, A.M. Morrison / Tourism Management 29 (2008) 548–560

destination images is more complicated. Pizam et al. (1991) studied pre- and post-images of a group of US students who visited the Soviet Union, and found that, basically, the images stayed the same. The other stream of research determined how destination images differed between visitors and non-visitors (Ahmed, 1991; Chon, 1991; Milman & Pizam, 1995) or non-visitors, first-timers and repeat visitors (Fakeye & Crompton, 1991). Images of visitors tend to be more favorable; however, no significant differences were found between perceptions of first-time and repeat visitors.

This suggested that most changes in destination image occur during the first visitation. Therefore Hypothesis 1 was formulated to answer research question 5: Hypothesis 1. ‘‘US residents who have visited Russia have more favorable images of the destination than those who have not’’.

2.2. Conceptualization by Echtner and Ritchie (1991) In the whole body of destination image studies, Echtner and Ritchie (1991, p. 11) proposed a somewhat unique conceptualization of the destination image construct based on an extensive review of the literature on destination image research for the period of 1975–1990:

‘‘Destination image should be envisioned as consisting of two main components; those that are attribute-based and those that are holistic.

Each of these components of destination image contains functional, or more tangible, and psychological, or more abstract, characteristics.

Images of destinations can also range from those based on ‘‘common’’ functional and psychological traits to those based on more distinctive or even unique features, events, feelings or auras’’.

The attribute-based component is captured by a series of scale items that range from tangible, or functional (beaches, shops, sports facilities, etc.), to more intangible, or psychological (receptiveness of local people, quality of service, etc.). These attributes also represent a common dimension of a destination, since every destination can be evaluated on the basis of these general criteria. The holistic component is captured by two open-ended items (Echtner & Ritchie, 1991, p. 11):

‘‘What images or characteristics come to mind when you think of _______ as a travel destination?

How would you describe the atmosphere or mood that you would expect to experience while visiting _______?’’

The first question is functional, while the second one is more psychologically oriented. Responses to the second item include affective evaluations, such as exciting, relaxing, boring, etc., and, therefore, resemble the Baloglu and Brinberg (1997) affective component (White, 2004).

Altogether, the holistic component is positioned as a mental picture, or overall representation, of the destination, and, as such, resembles the overall component of the destination image. The holistic component is important for understanding how a particular destination is categorized in the minds of consumers, and what prevailing images and stereotypes are associated with a given destination. In the following sections of this article, images derived from the answers to these two questions are referred to as ‘‘stereotypical’’ and ‘‘affective’’, respectively. The uniqueness dimension is assessed by the item:

‘‘Please list any distinctive or unique tourist attractions that you can think of in _______’’.

This component is very important for differentiating a destination from a competitive set of destinations, and will be further referred to as the ‘‘uniqueness image’’.

Thus, Echtner’s and Ritchie’s approach lies within the cognitive-affective-overall image tradition and is consistent with MacKay’s and Fesenmaier’s (1997, p. 538) view that ‘‘a destination image is a composite of various products (attractions) and attributes woven into a total impression’’.

Echtner and Ritchie (1993) suggested a conceptual framework for operationalization of all specified components of destination image, as well as proposed a convenient format for visual representation of image components. In designing the scale for measuring the attribute-based items, Echtner and Ritchie followed the framework proposed by Churchill (1979) for marketing studies. Steps such as specifying the domain of the image construct, generating a sample of items, purifying the measures using Cronbach’s alpha as an indicator, and iterative factor analysis were conducted. Thus, the issues of content validity, dimensionality, and internal consistency reliability (Peter, 1979) of the proposed scale were addressed by the researchers.

3. Methodology 3.1. Destination image measurement The composite nature of the destination image construct presents great challenges for its measurement. Strong preference has been given to structured methods when data were obtained as answers to close-ended survey questions (Pike, 2002). While structured methodologies have a number of advantages over qualitative methods, they focus on particular destination attributes and generally neglect the holistic aspect of destination image.

Qualitative studies, on the contrary, are helpful in measuring the holistic aspect, but do not facilitate statistical and comparative analyses of destination images (Jenkins, 1999). Echtner and Ritchie’s (1993) methodology framework provided a much needed balance between quantitative and qualitative aspects of image measurement.

ARTICLE IN PRESS S. Stepchenkova, A.M. Morrison / Tourism Management 29 (2008) 548–560

This research closely followed the methodology suggested

by Echtner and Ritchie (1993) with regard to the quantitative analysis of the destination image, and took their approach a step further with respect to the qualitative image assessment. It is not the purpose of this study to provide an extended literature review of the qualitative methods that have been employed in the analysis of destination images—an extensive overview can be found in Ryan and Cave (2005). However, it should be noted that content analysis of textual and/or pictorial materials by Reilly (1990), Echtner and Ritchie (1993), Dann (1996), MacKay and Fesenmaier (1997), Andsager and Drzewiecka (2002), Echtner (2002), and Ryan and Cave (2005), among others, employed sorting and categorization techniques to identify the frequencies of certain words, concepts, objects, or people, and treated the most frequent ones as image variables. The final set of image variables can contain nouns, verbs, and descriptors (i.e., adjectives and adverbs), since nouns are used to focus attention on attractions (e.g., museums, Lake Baikal), verbs describe actions or tourism types (e.g., rafting, sightseeing), and descriptors (e.g., ancient, exciting) create atmosphere (Echtner, 2002). The analysis can be computer-assisted (e.g., Ryan & Cave, 2005) or done by hand, and identified image variables are then often placed on a plane or a line along specified dimensions to provide image visualization (Echtner & Ritchie, 1993).

The large volume of textual data in qualitative studies and the repetitiveness of the task made the computer a natural and powerful choice for content analysis despite the fact that not all nuances of the language can be recognized by any given software program (Alexa & Zuell, 2000). For content analysis of open-ended questions, this study used a combination of two software programs, CATPAC (Woelfel, 1998) and WORDER (Kirilenko, 2004) in order to answer research questions 1, 2, 3 and 5 and test Hypothesis 1 not only on attribute-based items but on textual responses as well. CATPAC has been employed for more than a decade in content analysis of political speeches, focus group interviews, marketing studies, and destination images to ‘‘identify the most important words in a text and determine patterns of similarity based on the way they are used in text’’ (Woelfel, 1998, p. 11) and also because of its strong visualization capabilities. However, CATPAC analyzes only one textual file at a time.

WORDER software was developed to process in one run up to 1000 files of similar type (e.g., survey responses, newspaper articles, etc.) and count the number of specified key words/image variables in every one of them. Ultimately, the approach used in this study allows: (1) identification of destination image variables in digital textual data using CATPAC, and (2) counting the occurrences of these variables in every textual survey response with WORDER. The result is a two-dimensional data matrix, which can be easily transferred into any statistical package for further statistical analysis and clustering purposes.

Normally, a laborious ‘‘smoothing out’’ procedure should be performed on the textual data prior to analysis:

551

misspellings, synonyms, and multi-word concepts have to be taken into account (Woelfel, 1998); however, the necessary changes should concern only the meaningful words, or image variables in our case. WORDER has a built-in function that allows making changes in the data by means of the input table, simultaneously with the counting process. The details of the CATPAC-WORDER approach can be found in Stepchenkova, Kirilenko, and Morrison (2006).

The computer-assisted approach employed in this study for content analysis of textual responses to three image questions (stereotypical, affective, and uniqueness) provides a more detailed assessment of destination image and facilitates statistical comparisons of images among different groups of respondents, thus enriching the destination image measurement methodology proposed by Echtner and Ritchie (1993). The application of CATPAC-WORDER software combination discussed above and a way to compare favorability of affective images in order to test Hypothesis 1 discussed in Section 4.2 is considered a contribution of this study from the methodology standpoint.

3.2. Research instrument The original questionnaire (Echtner, 1991), with two items for each of 35 attributes, was obtained. It was decided to use only one item per attribute for this study. Two attributes, namely, degree of urbanization and extent of commercialization were thought to be better applicable to small destinations and were excluded. An accommodation/restaurants attribute was split into two separate items, since accommodation shortage is a known problem for the Russian tourist sector, but the situation is much better with restaurants.

Prior to this research, the authors conducted two exploratory studies to gain insights into induced and organic aspects of Russia’s destination image (Stepchenkova, Chen, & Morrison, 2007; Stepchenkova & Morrison, 2006). In addition, five travel professionals and seven ‘ordinary’ people were asked to provide answers to the three Echtner’s and Ritchie’s open-ended questions on Russia’s image. As a result of these prior efforts, seven Russia-specific attributes (cruises, combined trips, noncapital Russia, fishing and hunting, unique natural resources, Trans-Siberian railroad, and arts) were added to the questionnaire. Three general attributes—namely, good quality food, chance to see how people really live, and knowing something of a country’s history—were also included in the survey with the phrasing taking from Crompton (1977) for a research purpose which is not explained in this article due to a space constraint. To ensure clarity of the survey instrument, the phrasing of attribute items was borrowed, when possible, from Echtner (1991) and tested in July 2005 by a group of graduate students from a large Midwestern university.

3.3. Population and data collection The survey population came from one of the America’s oldest and largest private travel clubs (further referenced as

ARTICLE IN PRESS S. Stepchenkova, A.M. Morrison / Tourism Management 29 (2008) 548–560

552

ATC), serving tens of thousands of families in many states ( 75 000 members, 30 000 households, predominantly in the Midwest) at the time when the study was conducted.

ATC members with Internet access (about 20 000) were the sample frame for this research, and a random sample of 5000 e-mail addresses was selected from the ATC database.

These people were sent an e-mail from the ATC management team with the request to take part in the study.

The data were collected during three weeks in July–August 2005. One hundred and eighty-nine responses were obtained in the first round. A follow-up letter was sent a week later, and 148 responses were collected in the second round. There were no differences between the 1st and 2nd round respondents for all the demographic variables, except income. The aggregated profile of the respondents is given in Table 1. The total number of Russia’s Destination Image Survey Website hits was 503, the total number of submitted responses was 341, the number of usable responses was 337. These IP addresses were checked to ensure that there were no double entries.

Overall, the open-ended questions produced fewer responses than the attribute statements: question Q1 about stereotypical image (What images or characteristics come to mind when you think of Russia as a travel destination?)—316; question Q2 about affective image (How would you describe the atmosphere or mood that you would expect to experience while visiting Russia?)—313; and question Q3 about uniqueness image (Please list any distinctive or unique tourist attractions that you can think of in Russia.)—273. Eleven respondents chose to give the same answers to questions Q1 and Q2 or Q1 and Q3, putting in the answer field ‘‘See above’’, ‘‘Same as #1’’, or ‘‘See #1’’, and substitutions were made as indicated. A certain percentage of respondents chose not to submit some of the demographic data; predictably, the highest number of refusals was for the income question (14.6%). There were a number of responses that contained missing values for one or a few attributes; however, the number of missing entries was small relative to the sample size, and the responses with missing entries were kept in the data.

Table 1 Respondents’ profile Variable

Visitation

Friends and/ or relatives in Russia Gender

Education

Job

a

Levels

Whole sample

Variable

Frequency

%

Visitors Non-visitors Total Yes No Total Male Female Total

54 283 337 31 306 337 147 187 334

16.0 84.0 100.0 9.2 90.8 100.0 44.0 56.0 100.0

Age

High school Some college Associate Bachelor Master Ph.D. PNTAa Total

19 53 24 105 93 40 3 337

5.6 15.7 7.1 31.2 27.6 11.9 0.9 100.0

Marital status

Administrative Educator Executive Managerial Professional Sales/marketing Self-employed Student Retired Other PNTA Total

20 21 21 20 87 14 24 1 111 15 3 337

5.9 6.2 6.2 5.9 25.8 4.2 7.1 0.3 32.9 4.5 0.9 100.0

Income

PNTA—prefer not to answer.

Levels

Whole sample Frequency

%

18–24 25–34 35–44 45–54 55–64 65 and older PNTA

1 8 29 74 130 86 9

0.3 2.4 8.6 22.0 38.6 25.5 2.6

Total

336

99.7

Single Married With a partner Widowed PNTA

47 252 4 27 7

13.9 74.8 1.2 8.0 2.1

Total

337

100.0

6 24 48 58 81 31 41 48

1.8 7.1 14.2 17.2 24.0 9.2 12.2 14.2

337

100.0

Less than $30 000 $30 000–$49 999 $50 000–$74 999 $75 000–$99 999 $100 000–$149 999 $150 000–$199 999 $200 000 and above PNTA

Total

ARTICLE IN PRESS S. Stepchenkova, A.M. Morrison / Tourism Management 29 (2008) 548–560

4. Results 4.1. Research question 1: stereotypical holistic images By following the CATPAC-WORDER procedure described in the previous section, a list of 72 most frequent meaningful words was obtained using CATPAC. Some words, e.g., ‘‘history’’, ‘‘historic’’, ‘‘historical’’ or ‘‘large’’, ‘‘big’’, were grouped together under the most frequent name, in this case ‘‘history’’ and ‘‘large’’, to reinforce concepts, and substitutions in the data were made by WORDER. Second, the frequencies of every specified stereotypical image variable were counted in every response using WORDER. Table 2 contains overall frequencies of Russia’s stereotypical image variables.

The next step was to reduce the number of stereotypical image variables to a smaller number of image concepts by means of factor analysis. The dataset, which was obtained

by WORDER, had 45 variables and 317 cases, which gave a solid case to variable ratio of 7.04 (Kline, 1994). Principal Components Analysis with Varimax rotation was used.

Since textual responses were generally very short, e.g., ‘‘Cold. Beautiful churches’’, it was decided to look for stable word combinations, which might include as few as two words, rather than for full 3–5 word factors. Therefore, the number of factors was not specified and the option ‘‘Eigenvalues larger than 1’’ was chosen. Weak items (‘‘dark’’, ‘‘interesting’’, and ‘‘exotic’’) with low coefficients in the diagonal of the anti-image matrix (o0.40), low communalities (o0.50) and those that did not load higher than 0.35 on any factor were eliminated (Kline, 1994). The remaining variables produced 17 factors that explained 67% of the total variance.

The factor solution produced was an intermediate step to identify the final stereotypical holistic images. Guided by this solution, the factors were checked against the original data in order to ensure that word combinations containing descriptive items such as cold, beautiful, poor, old, large, great, vast, friendly, different, were not used in a negative context, which would entirely change interpretability of the

553

factors. The factor ‘‘great food’’ was eliminated as the result of this check and due to a low reliability alpha.

Another concern was that the stable word combinations produced by factor analysis did not account for large differences in frequencies between words combined in some of the image factors, e.g., in Factor 9, the word ‘‘old’’ had a frequency of 25, while the ‘‘buildings’’ word’s frequency was 39. It meant that at least 14 occurrences of the word ‘‘buildings’’ were used in other word combinations.

Therefore, factors, which contained words with large differences in frequencies, were checked against the original data as well. As a result, some high frequency words, e.g., ‘‘poor’’, were associated with such words as ‘‘lodgings/ accommodations’’, which were not originally included into the stereotypical image variables set. Finally, some image factors were combined together, since they belonged to the same image concepts, e.g., Factors 4 and 8 made one holistic image of ‘‘orthodox churches with onion-shaped domes’’, which was used in many responses. The final results of Russia’s stereotypical holistic images are given in Table 3.

Table 3 Stereotypical holistic images #

Stereotypical holistic images

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Cold weather, snow Beautiful architecture and old buildings Poor people, country, lodgings, and food choices Historic sites and places Moscow, Red Square, and Kremlin St. Petersburg, Hermitage, palaces, and museums Vast country with lots of open spaces Beautiful countryside Orthodox churches with onion-shaped domes Big cities, interesting old cities Great culture, different culture Beautiful music, ballet, art Friendly/unfriendly people Volga River Vodka

Table 2 Stereotypical image variables Variable

Frequency

Variable

Frequency

Variable

Frequency

Variable

Frequency

Cold Beautiful People History Buildings Poor Architecture Red Square St. Petersburg Moscow Country Old

69 55 54 45 39 38 37 36 34 30 28 25

Kremlin Palaces Weather Museums Churches Cities Large Interesting Onion Art Great Vast

24 23 19 19 19 18 15 13 13 13 12 12

Food Culture Friendly Domes Countryside Snow Hermitage Music Winter Dark Different Places

12 12 12 10 10 9 9 9 9 8 8 7

Orthodox Open Vodka Exotic Sites Volga River Spaces Ballet

7 7 6 6 6 5 5 5 5

ARTICLE IN PRESS S. Stepchenkova, A.M. Morrison / Tourism Management 29 (2008) 548–560

554

4.2. Research questions 2 and 5: affective images and favorability analysis

averaged across the 36 remaining responses (Cronbach’s alpha 0.786). The frequencies of each image variable were counted by WORDER in every one of 337 textual files, and the ‘‘favorability’’ values were computed for every response by simply adding together all occurrences of positive and negative image variables multiplied by their score. The frequencies of all affective image variables along with their favorability scores are given in Table 4.

To calculate the favorability value for the response: ‘‘Fascinating country. Overall, people are friendly but reserved. Boring nightlife, dull food, though’’, the following procedure was implemented. The averaged favorability scores for all affective image variables in the response (1.97 for ‘‘fascinating’’, 1.92 for ‘‘friendly’’, 0.08 for ‘‘reserved’’, and 1.19 for ‘‘boring’’ and ‘‘dull’’, since they are synonyms) were multiplied by the number of their occurrences and summed up. Response overall favorability value ¼ 1:97 þ 1:92 þ 0:08 þ ð 1:19Þ 2 ¼ 1:59, i.e., favorable. Responses that did not provide an answer to the question received a ‘‘zero’’ favorability value. The operationalized ‘‘favorability’’ variable was of continuous data type, its descriptive statistics are given in Table 5.

Since the sample sizes to test the Hypothesis 1 were so different (54 versus 283), the normality assumption for the ‘‘favorability’’ variable was checked on the smaller sample, and the distribution was found to be normal (Kolmogorov–Smirnov p ¼ 0.200, Shapiro–Wilk p ¼ 0.467). Test results for the Hypothesis 1 are given in Table 6 and they were significant at the 0.1 level.

To get insights into Russia’ affective images, the 337 textual responses to question Q2 were evaluated for favorability in order to test Hypothesis 1 not only for attribute-based items but on the textual responses as well. Using CATPAC, the study identified all evaluative descriptors (around 240) in the textual data provided by respondents, and combined them into 42 groups by synonymous meanings, as suggested by thesauri, context, and expert opinions. One word for each group, usually the most frequent one, was selected as an affective image variable. The final set of image variables contained mostly descriptive words (e.g., ‘‘fascinating’’, ‘‘cautious’’); however, two nouns, ‘‘contrasts’’ and ‘‘alcoholism’’, were also included.

In the textual data, words belonging to the same synonymic group were replaced by the representative image variable using WORDER. All negative concepts expressed in a multi-word format, such as ‘‘I would not feel safe’’ or ‘‘Russia is not well developed’’, were changed into one-word format, that of ‘‘unsafe’’ and ‘‘undeveloped’’.

The evaluative descriptors obtained in the first part of the study were assessed on a ‘‘minus 2 to plus 2’’ positive– negative scale by a group of US-born native English speakers, age 30 and above, not associated with the respondents to Russia’s Destination Image online survey.

Forty-three evaluations were received. An a priori screening criterion for valid responses was ‘‘there should be no positive response on the first ‘alcoholism’ variable’’; since a positive response would indicate that a subject did not understand the task. Three responses were eliminated on this criterion. Two more were excluded because of four or more missing entries, which might indicate a careless attitude to the evaluation process. Scores were examined for internal consistency, and two outlier results were taken out. The values of every affective image variable were

Table 5 Favorability variable: descriptive statistics Variable

N

Minimum

Maximum

Mean

SD

Favorability Valid N (listwise)

337 337

6.0832

8.7222

0.3267

2.2402

Table 4 Affective image variables: frequencies and favorability scores Variable

Frequency

Score

Variable

Frequency

Score

Variable

Frequency

Score

Friendly Somber Depressing Unfriendly Cold Poor Reserved Exciting Tense Unsafe Good Upbeat Awesome Undeveloped

85 47 45 28 18 18 17 15 15 15 15 14 14 13

1.92 0.39 1.67 1.64 0.31 1.00 0.08 1.81 1.11 1.78 1.72 1.43 1.72 0.58

Free Open Interesting Austere Hostility Unhappy Pleasant Difficult Sad Cosmopolitan Cordial Cautious Boring Fascinating

11 11 11 11 10 10 10 9 8 8 8 7 7 7

1.36 1.36 1.61 0.41 1.44 1.56 1.58 1.19 1.42 1.44 1.56 0.33 1.19 1.97

Alcoholism Hardworking Festive Contrasts Happy Uncomfortable Serene Safe Hopeful Ruthless Seedy Historical Unpleasant Relaxing

6 6 5 5 5 5 4 4 4 4 4 4 3 2

1.75 1.69 1.78 1.06 1.83 1.36 1.53 1.64 1.53 1.53 1.28 1.67 1.68 1.47

ARTICLE IN PRESS S. Stepchenkova, A.M. Morrison / Tourism Management 29 (2008) 548–560

555

4.4. Research questions 4 and 5: common destination attributes

4.3. Research question 3: uniqueness images To find what unique places and features US pleasure travelers associated with Russia, responses to question Q3 were analyzed. The CATPAC procedure on the pooled data was run, and 40 most frequent words indicating the unique Russian features were identified. Some words were grouped together (e.g., ‘‘architecture’’ and ‘‘buildings’’) to reinforce concepts. As a result of the grouping process, the final set of Russia’s uniqueness variables was produced. A table of synonyms was constructed and used as input for the WORDER program. Occurrences of every uniqueness variable were counted and entered into the SPSS database.

Responses like ‘‘do not know’’ were included into the frequency analyses as having ‘‘0’’ frequencies. As can be seen from Table 7, the list of unique Russian features is nearly exhaustive. The group of past visitors displayed a better knowledge of unique Russian features.

With respect to attribute-based items, this study closely followed Echtner and Ritchie’s (1993) framework.

Prior to analyzing the attribute-based items, eight negatively formulated statements were re-coded in positive for the consistency of measurement and ease of results interpretation. In Table 8 the attributes are arranged from most to least favorably assessed, based on the whole sample of responses. Attributes are considered positively or negatively assessed if their mean is below or above the neutral ‘‘3.00’’ value, respectively. Hypothesis 1 test results are given in the last column of Table 8. As can be seen from the table, the past visitor group gave a more favorable assessment of Russia’s destinath Varimax rotation was employed to reduce the 44 destination attributes into nine factors. Ten attributes (nature preserves; nightlife/entertainment; costs/price levels;

Table 6 Favorability variable: visitors vs. non-visitors N

Visitation

Visitors Non-visitors

54 283

Mean

0.808 0.235

Levene’s test for equality of variances

t-Test for equality of means

F

Significant

t

df

p-Value

3.572

0.060

1.726

335

0.085

Table 7 Uniqueness images #

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Unique features

St. Petersburg Red Square Kremlin Moscow Hermitage/winter palace Churches/cathedrals Museums Art Architecture Czars (imperial Russia) Palaces Cruises Summer palace Siberia Small towns St. Basil’s cathedral Lenin’s tomb Onion-shaped domes Black Sea Trans-Sib Volga River Leningrad Chernobyl Baikal

Non-visitors n2 ¼ 283

Visitors n1 ¼ 54

All respondents 336 Frequency

Mean

Frequency

Mean

Frequency

Mean

113 92 75 73 44 38 37 35 26 25 22 15 12 11 9 8 8 8 8 8 8 4 3 3

0.34 0.27 0.22 0.22 0.13 0.11 0.11 0.10 0.08 0.07 0.07 0.04 0.04 0.03 0.03 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.01 0.01

25 19 11 23 19 10 11 11 4 8 9 8 10 3 7 4 3 1 1 1 1 1 0 1

0.46 0.35 0.20 0.43 0.35 0.19 0.20 0.20 0.07 0.15 0.17 0.15 0.19 0.06 0.13 0.07 0.06 0.02 0.02 0.02 0.02 0.02 0.00 0.02

88 73 64 50 25 28 26 24 22 17 13 7 2 8 2 4 5 7 7 7 7 3 3 2

0.31 0.26 0.23 0.18 0.09 0.10 0.09 0.08 0.08 0.06 0.05 0.02 0.01 0.03 0.01 0.01 0.02 0.02 0.02 0.02 0.02 0.01 0.01 0.01

ARTICLE IN PRESS 556

S. Stepchenkova, A.M. Morrison / Tourism Management 29 (2008) 548–560

Table 8 Common destination attributes Destination attributes

Sites/museums Architecture Customs/culture Opportunity to learn Arts Scenery Family or adult oriented Non-capital Russia Cities Tourist attractions Cruises Combined trips Unique natural resources Trans-Sib Different cuisine Hospitality/friendliness Nightlife Atmosphere Tours/excursions Fairs/festivals Knowledge of Russian History Costs/price levels Fishing/hunting Life of people Nature preserves Fame/reputation Quality food Safety Ease of communication Quality of service Opportunity for adventure Sports activities Restaurants Rest and relaxation Climate Transportation Beaches Accomodations Cleanness Shopping facilities Accessibility Political stability Crowdedness Economic development

N ¼ 336

Visitors n1 ¼ 54

Non-visitors n2 ¼ 283

t-Test

Mean

SD

Mean

SD

Mean

SD

p-Value

1.64 1.65 1.77 1.87 1.87 2.01 2.17 2.18 2.22 2.28 2.32 2.33 2.33 2.38 2.43 2.45 2.47 2.52 2.57 2.61 2.64 2.65 2.66 2.69 2.77 2.90 2.93 2.98 3.04 3.05 3.05 3.06 3.09 3.15 3.20 3.21 3.22 3.23 3.27 3.27 3.35 3.44 3.60 3.84

0.76 0.71 0.63 0.72 0.71 0.83 0.65 0.76 0.89 0.94 0.75 0.79 0.82 0.68 0.89 0.85 0.79 0.77 0.84 0.92 0.97 0.81 0.73 0.79 0.86 1.02 0.85 0.87 0.84 0.68 0.79 0.68 0.72 0.73 0.89 0.73 0.86 0.82 0.74 0.76 0.78 0.89 0.70 0.71

1.20 1.43 1.67 1.58 1.48 1.83 2.19 1.91 2.00 1.69 1.98 2.06 2.31 2.41 2.54 2.06 2.26 2.30 2.15 2.56 2.09 2.19 2.76 2.76 2.57 2.28 2.70 2.78 2.93 3.11 3.13 2.98 3.00 3.09 2.69 3.02 3.15 3.17 3.41 3.00 3.28 3.21 3.83 3.87

0.49 0.79 0.67 0.60 0.64 0.84 0.74 0.73 0.97 0.77 0.76 0.86 0.95 0.71 1.18 0.92 0.83 0.94 0.81 0.98 0.52 0.93 0.78 0.93 0.87 0.91 1.11 0.86 1.04 0.84 0.70 0.76 0.89 0.93 0.82 0.92 0.86 0.84 0.90 0.97 1.15 0.93 0.75 0.73

1.72 1.70 1.79 1.92 1.95 2.05 2.16 2.23 2.27 2.40 2.38 2.38 2.33 2.37 2.41 2.52 2.52 2.56 2.65 2.63 2.75 2.74 2.65 2.68 2.81 3.02 2.98 3.02 3.06 3.04 3.04 3.08 3.10 3.16 3.30 3.25 3.24 3.25 3.24 3.32 3.36 3.48 3.55 3.83

0.78 0.69 0.62 0.73 0.70 0.82 0.63 0.75 0.86 0.93 0.73 0.77 0.80 0.68 0.82 0.81 0.78 0.72 0.82 0.91 1.00 0.76 0.72 0.76 0.86 1.00 0.78 0.87 0.80 0.64 0.80 0.67 0.69 0.69 0.88 0.69 0.85 0.81 0.70 0.70 0.69 0.88 0.68 0.71

0.000** 0.010* 0.002** 0.000**

0.004** 0.042* 0.000** 0.000** 0.006**

0.000** 0.028* 0.000** 0.000

0.000

0.000 0.034

0.023 0.041 0.007

Significant at 0.05 level.

Significant at 0.01 level.

accessibility; climate; crowdedness; rest/relaxation; chance to see how people really live; atmosphere; and arts) had either low communalities or factor loadings and were taken out to improve the characteristics of the solution. The final KMO measure of sampling adequacy was 0.902; communalities ranged from 0.500 to 0.779; all factor loadings were greater than 0.40. The total variance explained was 61.05%. The results are given in Table 9. The factors were self-explanatory and were named as Traditional Tourism (Factor 1); Infrastructure (Factor 2); Niche Tourism

(Factor 3); Safety (Factor 4); History (Factor 5); Food & Culture (Factor 6); Service (Factor 7); Adventure (Factor 8), and Family/adult (Factor 9). Factor 9 consisted of a single attribute; however, taking it out reduced the characteristics and interpretability of solution. The percentage of variance explained as well as the high factor loading justified retaining it. Cronbach’s alpha was adequate for all factors but Factor 8. All cross loadings made sense from the solution interpretability point of view. For example, Scenery from Factor 1, Traditional Tourism, also loaded

ARTICLE IN PRESS S. Stepchenkova, A.M. Morrison / Tourism Management 29 (2008) 548–560

557

Table 9 Destination image factors Factors

F1 traditional tourism

F2 infrastructure

F3 niche tourism

F4 safety

F5 history

F6 food and culture

F7 service

Variance explained Eigenvalue Cronbach’s alpha

12.84 9.13 0.86

8.36 2.59 0.74

8.01 1.77 0.77

7.46 1.45 0.73

5.66 1.37 0.62

5.49 1.23 0.62

5.42 1.19 0.58

Variables and communalities Sites/museums Architecture Tourist attractions Cities Non-capital Russia Opportunity to learn Scenery Hospitality Transportation Restaurants Shopping facilities Sports activities Unique natural resources Fishing/hunting Cruises Beaches Trans-Sib Political stability Safety Cleanness Economics Knowledge of Russian history Fame/reputation Combined trips Tours/excursions Different cuisine Customs/culture Quality food Quality of service Accommodations Fairs/festivals Ease of communication Opportunity for adventure Family or adult oriented

0.64 0.58 0.65 0.69 0.60 0.61 0.56 0.55 0.57 0.63 0.58 0.57 0.66 0.61 0.59 0.50 0.64 0.64 0.63 0.53 0.57 0.62 0.58 0.63 0.54 0.63 0.64 0.69 0.64 0.67 0.55 0.64 0.56 0.78

0.740 0.700 0.693 0.692 0.634 0.610 0.540 0.433

F8 adventure

4.26 1.02 0.48

F9 family adult 3.56 1.01

0.437 0.681 0.616 0.576 0.575 0.689 0.686 0.526 0.483 0.454

0.412 0.763 0.692 0.601 0.457

0.434

0.747 0.491 0.445 0.442

0.409 0.421

0.452 0.425

on Factor 3, Niche Tourism, along with such items as Natural Resources, Fishing/Hunting, Cruises, Beaches, and Trans-Sib. 5. Discussion 5.1. Implications for the FTA Although Russia is one of the major world tourist destinations (WTO, 2006b), it has not received enough academic attention to date. Thus, this study partly fills the gap by assessing the country’s destination image among US pleasure travelers, one of the most affluent travel markets in the world. The implications of the study have relevance to the current FTA initiative to build a successful Brand Russia.

0.426 0.673 0.616 0.515 0.701 0.642 0.638 0.754 0.450 0.855

Advertising and promotion of Russia to the international traveler has been very minimal in terms of financial resources in comparison to the efforts of other major destinations. In 2003, prior to this research, the Russian promotional budget on the federal level was USD 3.0 million (Izvestia, issue 01.21.05), which was two times less than what was spent by Paris or Singapore alone.

The result of insufficient advertising has been a lack of awareness about Russia’s tourist features as was indicated by the current study. The share of respondents who put ‘‘don’t know’’ as the answer to the question Q3 about unique Russian features was 19%. The truly unique Russian natural resources that are included in the UNESCO World Heritage List, such as the Golden Mountains of Altai, Volcanoes of Kamchatka, Virgin Komi

ARTICLE IN PRESS 558

S. Stepchenkova, A.M. Morrison / Tourism Management 29 (2008) 548–560

Forests and others, were not mentioned at all. Lake Baikal was mentioned by three people only. While a number of respondents mentioned Russia’s countryside, small towns and villages, no specific names emerged.

With regard to the other two open-ended questions, particularly about affective images, the problem was not that American pleasure travelers knew little about Russia.

The survey respondents knew various things about the country, but their perceptions were often unfavorable. Out of 42 affective image variables, 20 had negative favorability scores, and out of 337 responses, 129 and 59 had negative and zero favorability values, respectively. The ‘‘Soviet era’’ image still lingered. Poor people, country, lodgings, and food choices were often present in the responses to Q1 about the stereotypical images. ‘‘Poor’’, ‘‘undeveloped’’, ‘‘hostile towards Americans’’, ‘‘ruthless’’, ‘‘depressing’’, and ‘‘unsafe’’ country emerged from about half of responses to Q2. Such attributes of the Soviet era, as the Cold War, Lenin’s tomb, Stalin, and Leningrad, were also mentioned.

The survey respondents did not agree whether Russian people were friendly or not, which was registered in their answers to both Q1 and Q2. Respondents who thought Russian people to be friendly often added such descriptors as ‘‘somber’’ and ‘‘reserved’’. These attitudes can be partially explained by the age of the respondents, more than 70% of whom were more than 55 years old. Another possible explanation is the complicated procedure of obtaining a Russian visa. The lack of positive materials about Russia in the US general media also plays a role in American pleasure travelers’ negative perceptions of the country. The attribute-based ‘‘hospitality-friendliness’’ item indicated that visitors thought Russian people were friendlier (mean 2.06) than non-visitors (mean 2.52).

However, the ‘‘hospitality-friendliness’’ perceptions of non-visitors are very important for the FTA, since they might interfere with the desire to go Russia. No country that wants to develop a strong tourism sector can afford to be perceived as unfriendly to visitors. The branding approach might be the answer to this problem, since the visitor’s satisfaction is in large part a matter of expectations (Chon, 1990). Careful branding of the Russian nation as the reserved people who are cordial to guests and open and warm to friends might be successful. To reinforce the politeness/cordial perception, extensive human resources training programs in the hospitality and tourism sector are also of primary importance and should be initiated by the FTA. With regard to functional attributes, significant differences were registered for 19 items with visitors giving more favorable assessments. This is a very interesting finding for the FTA because it suggests that quality of Russia’s tourist offer is, in fact, better than the non-visitors think it is.

Given that no significant differences were registered between visitors and non-visitors in terms of demographic characteristics, the differences in evaluations can be attributed to the differences in the actual and perceived

Russia’s tourism attributes. The results indicated that adequate promotional information is needed to correct the negative perceptions of non-visitors.

Several of Russia’s functional common attributes such as economic development, accessibility, shopping facilities, cleanliness, accommodations, beaches, transportation, restaurants, and sports activities were ranked negatively (mean score higher than 3.0) by the respondents (see Table 8). Four of these items—transportation, restaurants, shopping facilities, and sport activities—made up a separate Infrastructure factor, and two more items— economic development and accommodations—had loadings greater than 0.40 on this factor (see Table 9). This indicated that the level of infrastructure is a consideration for US pleasure travelers in the process of destination selection. Therefore, promotion of tourism types that are less sensitive to levels of infrastructure development is advisable, since they potentially have a higher probability of success (Ilyina & Mieczkowski, 1992). These tourism types are also less sensitive to the service levels, with service being another important consideration for potential travelers.

As this study indicated, ‘‘traditional tourism’’ has the strongest position image-wise in the minds of US pleasure travelers (see Table 9). Historical sites and museums, capital and provincial cities rich in architecture and cultural heritage, beautiful scenery, and opportunities to interact with Russian people should be combined in an attractive package. Up-to-date information on the safety and hygiene conditions, as well as infrastructure levels, should be effectively communicated. Another possibility is the Trans-Siberian journey with stopovers in unique nature preserves and cultural and historical locations. The levels of comfort, service, and infrastructure of such a trip are high for the first- and second-class ticket holders. Given the average age of the ATC members, they might not be the audience for adventure or eco-tourism travel offers.

Hypothesis 1 addresses the relationship between image and visitation. As indicated in Section 2.1, the nature of this relationship is complex and multi-faceted. The act of visiting a destination can certainly change the image one has of that destination. This can best be examined in terms of pre- and post-visitation images, which were not available in this study. In turn, the favorability of a destination’s image can influence whether one chooses to visit the destination in the first place. Therefore, association between image and visitation is a two-way cause–effect relationship.

However, the authors feel that, from a marketing standpoint, the direction of the relationship is not as important as the existing ‘‘image-visitation’’ association itself. ‘‘The more favorable the image is, the more likely visitation will occur’’ direction stresses the need for adequate advertising of Russia in the US travel market. The ‘‘destination image changes as a result of actual visitation’’ direction implies that with regard to this study, the actual Russian offer (assessed by the past visitors) is better than the perceived one (assessed by non-visitors), which again highlights the

ARTICLE IN PRESS S. Stepchenkova, A.M. Morrison / Tourism Management 29 (2008) 548–560

necessity of adequate marketing communications to various groups of potential first- and repeat visitors.

5.2. Generalizability of results Generalizability of the results was a concern in this study. Does the obtained sample of ATC members truly represent American leisure travelers? To examine this question, three comparisons were made: (1) between ATC members and US pleasure travelers to Europe; (2) between population under study (ATC members with Internet access) and the whole ATC membership; and (3) between the obtained sample and the population under study. First, to answer how well ATC members represent the entire population of US long-haul tourists, the entire ATC membership profile (Morrison, So, Beldona, Feng, & Stepchenkova, 2004) was qualitatively compared to the profile of a typical US traveler to Europe (European Travel Commission (ETC), 2001). US outbound pleasure travelers tend to be more highly educated than the US adult population as a whole and wait until they are older to do the bulk of their international long-haul travel. Additionally, travelers to Europe are more affluent than the average US outbound traveler, and three-quarters of them travel as couples. The proportion of younger members in the ATC is twice as small as that of American travelers to Europe; therefore, it was concluded that ATC members were representative of the older US pleasure travelers group. Second, the sample of ATC members obtained in this study was compared to the overall ATC membership profile. Significant differences were found for the ‘‘age’’, ‘‘education’’, and ‘‘job’’ variables. Respondents of this study were older and more educated, and had a larger share of professionals and retirees. This finding was somewhat expected, since the population of this study was limited to ATC members with Internet access. While the comparison suggested that the study sample was not representative of the entire ATC membership, the profile of the respondents did correspond to that of the older, affluent, and well-educated US pleasure travelers to Europe. Finally, the low overall response rate ( 7%) did not allow conclusion that the opinions of people who participated in the survey were representative of the entire population under study (ATC members with Internet access). To check for non-response bias, two groups of the survey respondents, 1st and 2nd stage, were compared. The groups were found to be the same for the ‘‘visitation’’ and all the demographic variables but income, a result that does not disconfirm that the obtained sample and the population under study are the same. Therefore, while the question of how representative the sample was of the entire ATC membership with Internet access still remains, studying the sample group is very valuable from a marketing standpoint because of their demographic characteristics which indicate economic power and predisposition for long-haul travel.

559

5.3. Limitations and further research The study confirmed that the parsimonious set of 35 scale items on common destination attributes proposed by Echtner and Ritchie (1993) can be successfully used for a very broad range of destinations, including such large and diverse countries as Russia. Factor analysis conducted on a 35-item set resulted in seven factors, the interpretation of which had much in common with the factors obtained by Echtner and Ritchie (1993). Adding Russia-specific attributes made the factor solution less stable, and 10 attributes had to be taken out. The resulting factors were essentially the same with one notable exception: Russia-related attributes mostly fell into the Niche Tourism factor. This suggests that including new, destination-specific attributes, into a set of well established attributes should undergo a rigorous selection procedure, similar to that which was employed by Echtner and Ritchie (1993).

Russia as a tourist destination does not equal Russia as a country. Kotler and Gertner (2002, p. 251) pointed out that ‘‘a country’s image results from its geography, history, proclamations, art and music, famous citizens and other features’’. Destination and country images are overlapping constructs (Mossberg & Kleppe, 2005), and Russia’s destination image is undoubtedly influenced by the country’s image, however, it is not clear to what degree.

Therefore, it is important to assess how Russia’s destination image is affected by the often negative coverage of Russia as a political entity in the US general media. The question as to whether these two images can be separated in the minds of potential travelers to Russia has direct relevance to successful building of Brand Russia.

This study dealt with the image of Russia as a travel destination among US pleasure travelers. However, the US is only one potential market for Russia’s inbound tourism.

The large distance between the two countries might have a negative effect on how Russia is perceived by US travelers as suggested by Reilly (1990). Other, geographically closer markets might be better suited for focused promotional efforts of the FTA, because they might already possess a more favorable and accurate image of Russia that would require less effort and finance to enhance and positively induce.

References Ahmed, Z. U. (1991). The influence of the components of a state’s tourist image on product positioning strategy. Tourism Management, 12, 331–340.

Alexa, M., & Zuell, C. (2000). Text analysis software: Commonalities, differences and limitations: The results of a review. Quality and Quantity, 34, 299–321.

Andsager, J. L., & Drzewiecka, J. A. (2002). Desirability of differences in destinations. Annals of Tourism Research, 29(2), 401–421.

Assael, H. (1984). Consumer behavior and marketing action. Boston: Kent.

Baloglu, S. (2001). Image variations of Turkey by familiarity index: Information and experiential dimensions. Tourism Management, 22(2), 127–133.

ARTICLE IN PRESS 560

S. Stepchenkova, A.M. Morrison / Tourism Management 29 (2008) 548–560

Baloglu, S., & Brinberg, D. (1997). Affective images of tourism destinations. Journal of Travel Research, 35(Spring), 11–15.

Baloglu, S., & McCleary, K. W. (1999). A model of destination image formation. Annals of Tourism Research, 26(4), 868–897.

Chon, K. (1991). Tourism destination image: Marketing implications.

Tourism Management, 12, 68–72.

Chon, K.-S. (1990). The role of destination image in tourism: A review and discussion. Revue de Tourisme, 45(2), 2–9.

Churchill, G. A., Jr. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(February), 64–73.

Crompton, J. L. (1977). A system model of the tourist’s destination selection process with particular reference to the role of image and perceived constraints. Ph.D. dissertation, Texas A&M University, College Station.

Dann, G. (1996). Tourists’ images of a destination: An alternative analysis. Journal of Travel and Tourism Marketing, 5(1–2), 41–55.

Echtner, C. M. (1991). The measurement of tourism destination image.

MBA thesis. The University of Calgary. Calgary, Alberta.

Echtner, C. M. (2002). The content of Third World tourism marketing: A 4A approach. The International Journal of Tourism Research, 4, 413–434.

Echtner, C. M., & Ritchie, J. R. B. (1991). The meaning and measurement of destination image. The Journal of Tourism Studies, 2(2), 2–12.

Echtner, C. M., & Ritchie, J. R. B. (1993). The measurement of destination image: An empirical assessment. Journal of Travel Research, 31(Spring), 3–13.

Echtner, C. M., & Ritchie, J. R. B. (2003). The meaning and measurement of destination image. The Journal of Tourism Studies, 14(1), 37–48.

Engel, J. F., Blackwell, R. D., & Miniard, R. W. (1986). Consumer behavior. Chicago: The Dryden Press.

ETC (European Travel Commission). (2001). Growing the American market for travel to Europe: Market trends and opportunities./http:// www.etc-corporate.org//DWL/MENLO_ETC%20Member%20Report. pdfS. Last accessed on January, 23, 2007.

Fakeye, P. C., & Crompton, J. L. (1991). Image differences between prospective, first-time, and repeat visitors to the Lower Rio Grande Valley. Journal of Travel Research, 29(Fall), 10–16.

Gallarza, M. G., Saura, I. G., & Garcia, H. C. (2002). Destination image: Towards a conceptual framework. Annals of Tourism Research, 29(1), 56–78.

Gartner, W. C. (1986). Temporal influence on image change. Annals of Tourism Research, 13, 635–644.

Gartner, W. C. (1993). Image formation process. Journal of Travel and Tourism Marketing, 2, 191–216.

Gensch, D. H. (1978). Image-measurement segmentation. Journal of Marketing Research, 15, 384–394.

Gunn, C. A. (1972). Vacationscape: Designing tourist regions. Austin: Bureau of Business Research, University of Texas.

Herzog, H. (1963). Behavioral science concepts for analyzing the consumer. In P. Bliss (Ed.), Marketing and the behavioral sciences (pp. 76–86). Boston, MA: Allyn & Bacon.

Hunt, J. D. (1971). Image: A factor in tourism. Unpublished Ph.D. dissertation, Colorado State University, Fort Collins.

Ilyina, L., & Mieczkowski, Z. (1992). Developing scientific tourism in Russia. Tourism Management, September, 327–331. ‘‘Izvestia’’ newspaper (January 21, 2005). Not everyone can sell the native land successfully. Last accessed on August 6, 2005 from /http:// www.izvestia.ru/capital2/1042830_printS, in Russian.

Jenkins, O. H. (1999). Understanding and measuring tourist destination images. The International Journal of Tourism Research, 1(1), 1–15.

Kirilenko, A. P. (2004). WORDER (Version 2.0) [Computer software]. /http://www.und.nodak.edu/instruct/kirilenko/worder/index.htmlS. Last accessed on January 22, 2007.

Kline, P. (1994). An easy guide to factor analysis. New York: Routledge.

Kotler, P., & Gertner, D. (2002). Country as a brand, product, and beyond: A place marketing and brand management perspective.

Journal of Brand Management, 9(4–5), 249–261.

MacKay, K. J., & Fesenmaier, D. R. (1997). Pictorial element of destination in image formation. Annals of Tourism Research, 24, 537–565.

Mayo, E. J. (1973). Regional images and regional travel behavior.

Research for changing travel patterns: Interpretation and utilization.

In Proceedings of the travel research association, fourth annual conference (pp. 211–218).

Milman, A., & Pizam, A. (1995). The role of awareness and familiarity with a destination: The Central Florida case. Journal of Travel Research, 33(2), 21–27.

Morrison, A. M., So, S.-I., Beldona, S., Feng, R., & Stepchenkova, S. (2004). The Ambassadair membership value study (comparison report).

West Lafayette, IN, USA: Purdue University, Tourism and Hospitality Research Center (PTHRC).

Mossberg, L., & Kleppe, I. A. (2005). Country and destination image— different or similar image concepts? The Service Industries Journal, 25(4), 493–503.

Pearce, P. L. (1982). Perceived changes in holiday destinations. Annals of Tourism Research, 9(2), 145–164.

Peter, J. P. (1979). Reliability: A review of psychometric basics and recent marketing practices. Journal of Marketing Research, 16(February), 6–17.

Phelps, A. (1986). Holiday destination image: The problem of assessment.

An example developed in Menorca. Tourism Management, 7, 168–180.

Pike, S. (2002). Destination image analysis—a review of 142 papers from 1973 to 2000. Tourism Management, 23, 541–549.

Pike, S., & Ryan, C. (2004). Destination positioning analysis through a comparison of cognitive, affective, and conative perceptions. Journal of Travel Research, 42(May), 333–342.

Pizam, A., Jafari, J., & Milman, A. (1991). Influence of tourism on attitudes: US students visiting USSR. Tourism Management, March, 47–54.

Reilly, M. D. (1990). Free elicitation of descriptive adjectives for tourism image assessment. Journal of Travel Research, 28(Spring), 21–26.

Russell, J. A., & Snodgrass, J. (1987). Emotion and environment. In D. Stockols, & I. Altman (Eds.), Handbook of environmental psychology (pp. 245–280, p. 246).

Russia’s Federal State Statistics Service (Rosstat). (2006). Tourism and tourist resources in Russia. Official publication. Publishing center: ‘‘Statistics of Russia,’’ Moscow, Russia [in Russian].

Ryan, C., & Cave, J. (2005). Structuring destination image: A qualitative approach. Journal of Travel Research, 44(2), 143–150.

Stepchenkova, S., Chen, Y., & Morrison, A. M. (2007). China and Russia: Organic destination images in US media. China Tourism Research, 3(1), 55–72.

Stepchenkova, S., Kirilenko, A. P., & Morrison, A. M. (2006). Facilitating statistical analysis of digital textual data: A two-step approach.

In The 11th annual hospitality and tourism graduate student education and research conference proceedings: Vol. XI. Advances in hospitality and tourism research. Seattle, WA, January 5–7, 2006.

Stepchenkova, S., & Morrison, A. M. (2006). The destination image of Russia: From the online induced perspective. Tourism Management, 27(5), 943–956.

White, C. J. (2004). Destination image: To see or not to see? International Journal of Contemporary Hospitality Management, 16(5), 309–314.

Woelfel, J. K. (1998). CATPAC: Users guide. New York, NY: RAH Press, The Galileo Company.

World Tourism Organization (WTO). (2006a). Tourism highlights (Edition 2004). Last retrieved on January 22, 2007 from /http://www.unwto. org/facts/eng/highlights.htmS. World Tourism Organization (WTO). (2006b). Inbound tourism—World’s top tourism destinations. Last retrieved on January 22, 2007 from /http://www.world-tourismS. Yu, Y., & Dean, A. (2001). The contribution of emotional satisfaction to consumer loyalty. International Journal of Service Industry Management, 12(3), 234–250.