1. ARTICLE IN PRESS
Tourism Management 27 (2006) 943–956
www.elsevier.com/locate/tourman
The destination image of Russia: From the online induced perspective
Svetlana Stepchenkovaa, Alastair M. Morrisonb,Ã
a
Department of Hospitality and Tourism Management, Purdue University, 154 Stone Hall, 700 W. State Street, West Lafayette, IN 47907-2059, USA
b
Department of Hospitality and Tourism Management, College of Consumer and Family Sciences, Purdue University, 111A Stone Hall, 700 W. State Street
West, Lafayette, IN 47907-2059 USA
Received 1 May 2005; accepted 3 October 2005
Abstract
With the Internet becoming a prominent means of destination marketing and promotion, this study compared US and Russian website
materials related to travel to Russia to determine whether the two sides differed in: (1) most frequently mentioned places within the
country; and (2) descriptions of Russia as a travel destination. These different perspectives can contribute to a better understanding of
the induced component of Russia’s destination image in the online environment and their analysis resulted in important marketing
implications. The research approach for this study was to view every website as a case in the selected sample of the whole population of
US and Russian websites. Two software programs, CATPAC II and WORDER, were applied to analyze the content of website materials
and solve the technical issues of destination counting. This study clearly suggests that US tour operators are narrowly positioning Russia
as mainly being a historic and cultural destination, with a relatively tight geographic emphasis on the western portion of the country.
Technically and content-wise, the Russian websites require the greatest improvements. They tend to be loaded with information and not
particularly well targeted to specific countries of visitor origin or market segments by travel interests.
r 2005 Published by Elsevier Ltd.
Keywords: CATPAC; Content analysis; Destination image; Induced image; Russia; WORDER
1. Introduction Goskomstat), a total of 22.51 million people visited Russia
in 2003, or 8.15 million people without counting the
1.1. Russian inbound tourism and the US component arrivals from the CIS countries (Rosstat, 2004). The US is
one of the most important countries for inbound tourism
Starting in the early 1990s and to date, Russia has to Russia: in 2003, 281,000 US residents visited Russia, the
undergone changes in its political, economic, and social fifth largest share of visitors (not including arrivals from
spheres that have had a huge impact on inbound tourism. the former Soviet republics) (Russia’s Federal State
While in the former Soviet Union international travelers Statistics Service (Rosstat), 2004). Given the size of the
had limited tourist options to choose from, today US market and the fact that American pleasure travelers
numerous tourist companies offer exciting and diverse are the world’s leading travel spenders (World Tourism
Russian tourism products. The openness of Russia as a Organization (WTO), 2004), this segment is very attractive
travel destination and rising quality of its tourism offer has for the Russian tourism industry from an economic
been reflected in the growing numbers of tourist arrivals for standpoint.
the last 10 years. WTO estimates Russian tourism potential However, the positioning of the country for the US
as 47 million international travelers by 2020 (World consumer segment is problematic without a thorough
Tourism Organization (WTO), 2003). According to Rus- understanding of how Russia as a vacation destination is
sia’s Federal State Statistics Service (Rosstat, former perceived by potential US pleasure travelers (Ahmed, 1991;
Hunt, 1975). The lack of information is evident: the
ÃCorresponding author. Tel.: +1 765 494 7905; fax: +1 765 496 1168. destination image literature reviewed by Pike (2002) for the
E-mail addresses: svetlana@purdue.edu (S. Stepchenkova), period of 1973–2000, found only one out of 142 articles
alastair@purdue.edu (A.M. Morrison). that dealt with Russia’s image. Moreover, that study by
0261-5177/$ - see front matter r 2005 Published by Elsevier Ltd.
doi:10.1016/j.tourman.2005.10.021

2. ARTICLE IN PRESS
944 S. Stepchenkova, A.M. Morrison / Tourism Management 27 (2006) 943–956
Pizam, Jafari, and Milman (1991) reflected the old, Baud-Bovy, 1977) and, as such, encompass stereotypes
‘‘Soviet’’ image of the country. While a comprehensive about a destination (Jenkins, 1999). According to Echtner
study of Russia’s destination image remains to be done, and Ritchie (1993), ‘‘holistic and unique images are
this research attempted to gain initial insights into what particularly important in determining how a particular
image of Russia is transmitted by American and Russian destination is categorized (stereotype holistic impressions)
tour operators in the online environment. and differentiated (unique attractions, auras) in the minds
The comparative analysis of destination images is of of the targeted markets.’’
interest from at least two perspectives. First, it would be Destination image is formed by processing information
reasonable to propose that US tour operators know more from various sources over time (Assael, 1984), which are
about US tourists and what they are seeking. Therefore, to categorized into organic and induced (Gartner, 1993;
see how Russia is presented by the tour operators of one of Gunn, 1972). The organic sources (books, school curricu-
the largest tourist markets in the world should be useful to lum, news, movies, actual destination visits, etc.) do not
both the Federal Tourism Agency of the Russian Federa- have a vested interest in promoting a destination, while the
tion (FTA) and Russian travel providers. It should be induced sources (travel brochures, advertisements, posters,
helpful to better understand the demand for the Russian videos, and, most recently, the Internet) are a means of
tourism product and, if needed, in countering negative or communicating marketing messages of the destination and
inaccurate information. Second, Russian tour operators suppliers to a chosen travel audience. As Morgan and
should know more about the destinations within Russia. ´
Pritchard (2001, p. 275) pointed out, a country’s ‘‘cliched
Thus, the Russian perspective should make US tour identity can y be reshaped and given greater complexity
operators more aware of the travel opportunities within through effective and consistent marketing.’’ Messages
Russia that they are missing. Therefore, the primary transmitted by the induced sources contribute to the
objectives of this study were to: formation of the induced component of destination images,
since, as Gartner (1989, p. 16) argued, ‘‘because of
1. Identify destinations within Russia mentioned most economic and time cost, vacation travel to a distant
frequently by US and Russian tour operator websites. destination will usually be undertaken only after an
The comparative analysis of frequencies will shed light extensive information search. In the absence of actual
on the awareness levels of attractions and places of visitation, destination images are formed through induced
interest within Russia among US tour operators. agents.’’
2. Pinpoint the most frequent meaningful words (these
might include ‘‘Moscow’’, ‘‘Kremlin’’, ‘‘church’’) when 1.3. Internet as tourism marketing medium
tour operators offer Russia as a destination and to
uncover the common themes in their descriptions of The Internet is becoming a prominent medium in
Russia. The most frequent meaningful words were tourism marketing (O’Connor & Murphy, 2004; Oh,
interpreted as the induced image variables and were Kim, & Shin, 2004). It has been actively used by hotels
used to compare the US and Russian perspectives on (Baloglu & Pekcan, 2006; Fam, Foscht, & Collins, 2004;
Russia as a travel destination. Scharl, Wober, & Bauer, 2004), airlines (Chu, 2001), travel
¨
+
agencies (Ozturan & Roney, 2004), convention and visitors
bureaus (Yuan, Gretzel, & Fesenmaier, 2003) and other
1.2. Destination image and its induced component destination marketing organizations (Doolin, Burgess, &
Cooper, 2002; Stamboulis & Skayannis, 2003). Travel and
Although an exact meaning of the term ‘‘destination tourism services appear to be especially well suited for
image’’ is difficult to define (Echtner & Ritchie, 1991; Internet marketing because of their intangibility as well as
Pearce, 1988), there is a general agreement among scholars high price, risk, and involvement levels. On the demand
that destination image is a multi-faceted, composite side, an increasing number of people are using the Internet
construct, which consists of interrelated cognitive and for information search because the World Wide Web
affective evaluations woven into overall impressions provides more in-depth materials and richer content
(Assael, 1984; Baloglu & McCleary, 1999; Gartner, 1993; compared with conventional promotional agents (Govers
MacKay & Fesenmaier, 1997). Some researchers empha- & Go, 2003; Heung, 2004).
sized the influence of destination image on destination The websites of American and Russian tour operators
choice and argued that the image construct follows the providing travel to Russia were chosen as induced image
three-element attitude model from psychology, which agents since: (a) the popularity of the Internet as a means
includes cognitive, affective, and behavioral elements of advertising, promoting, and selling destinations is
(Breckler, 1984; Pike & Ryan, 2004; White, 2004). Echtner growing in both countries and; (b) coordinated efforts by
and Ritchie (1991, 1993) suggested that the destination the Russian government and tour operators to promote
image construct consists of three dimensions: attribute- Russia have begun relatively recently, and Russian-made
holistic, functional-psychological, and common-unique. printed materials are scarce (‘‘Izvestia’’, March 11, 2003).
Images can be shared by groups of people (Lawson & Currently, the Internet is the least expensive and fastest

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S. Stepchenkova, A.M. Morrison / Tourism Management 27 (2006) 943–956 945
way for Russian officials and tour operators to reach the 2.2. Proposed approach for destination image measurement
international traveler. of textual data
The composite nature of the destination image construct
2. Methodology
presents great challenges for its measurement. In addition,
destination image measurement techniques are dependent
2.1. Sample selection
on the original data format. Strong preference has been
given to structured methods of image measurement when
Russia-related texts from 212 websites of US and
data were obtained as answers to closed-ended survey
Russian tour operators, official sources, and travel guides
questions (Echtner & Ritchie, 1991; Pike, 2002). While
were collected and stored for further content analysis as
structured methodologies have a number of advantages
212 plain text documents. These files were regarded as a
over qualitative methods, they focus on particular destina-
sample from a population of all US and Russian websites
tion attributes and generally neglect the holistic, or overall,
promoting trips to Russia. Due to the global nature of the
aspect of destination image. Qualitative studies, on the
Internet, there were cases that were difficult to classify as
contrary, are advantageous to measuring the holistic
either US or Russian, for which a new category of
aspect, but do not facilitate statistical and comparative
‘‘Partnership’’ was employed. The classification into US,
analyses of destination images (Jenkins, 1999). Among the
Russian, or Partnership websites is described below:
studies dealing with content analysis of textual and/or
pictorial materials are those by Andsager and Drzewiecka
A company operating from the US with a website in the (2002), Dann (1996), Echtner (2002), Echtner and Ritchie
.com domain was considered a US company. The US (1993), MacKay and Fesenmaier (1997), Reilly (1990), and
sample included the websites of the US Tour Operator Tapachai and Waryszak (2000). These researchers em-
Association (23), American Society of Travel Agents ployed sorting and categorization techniques to identify the
(13), cruise lines (18), top 50 travel agencies (6), as well frequencies of certain words, concepts, objects, or people,
as 19 ‘‘independent’’ websites, and 24 Internet travel which for ease of further reference are referred to as
guides. All these websites with the exception of the ‘‘meaningful words.’’ The most frequent meaningful words
‘‘independent’’ category were included into the sample in these and other qualitative studies were treated as image
by going through the comprehensive lists obtained variables, or dimensions, of the destination image con-
through the associations’ websites or through the Yahoo struct. This study proposes a new approach for content
search engine. analysis of textual data using the combination of long-on-
Websites on travel to Russia with English language the-market CATPAC II software (Woelfel, 1998) and the
content in the .ru domain were considered Russian. newly developed WORDER program (Kirilenko, 2004),
Unfortunately, a complete list of the Russian companies which allows effective identification of destination image
catering to international travelers could not be obtained. variables and clustering them into image themes of more
Websites were found through the search engines, as well holistic nature.
as from the websites of such organizations as The The CATPAC II software ‘‘is a self-organizing artificial
Moscow Times, Moscow International Travel Tour- neural network that has been optimized for reading text.
ism Exhibition, official Moscow city website, Yellow CATPAC identifies the most important words in a text and
pages, etc. Websites on travel to Russia in the domains determines patterns of similarity based on the way they are
.com, .net, and .org were considered Russian if a used in text’’ (Woelfel, 1998, p. 11). CATPAC has been
Russian company owned them, had a Russian postal used to conduct content analysis of political speeches,
address, and a predominantly Russian staff. The focus groups interviews, and marketing studies (Doerfel
Russian sample included 84 tour operator websites, 6 Marsh, 2003; Schmidt, 1998), as well as in tourism-related
websites of Russian official sources, and 13 travel research (Jeon Morrison, 2003; Kim, Xiang,
guides. Fesenmaier, 2005). However, CATPAC works with only
Websites that belonged to companies with offices one file at a time, which makes it unsuitable for analyzing
both in the US and Russia (and elsewhere in the world) multiple individual responses or large samples of textual
with Russian staff were considered Partnerships (e.g., data, like the websites in this study. WORDER, in
White Nights: www.wnights.com). Companies with contrast, is able to count the frequencies of up to 1000
offices in the US, Russian people on staff, and Russian specified words in up to 1000 specified files in one run with
agents in Russia specializing in travel to Russia were table-formatted output transferable to an SPSS file.
also considered Partnerships (e.g., Sokol Tours: www. To attain the first objective, a master list of 344
sokoltours.com). destinations within Russia was compiled based on the
standard division of Russia into 13 recreational regions
In the SPSS file, every website was coded as US, Russian, (Goskomstat, 2000). These destinations were counted in
or Partnership; tour operator, official source, or travel every file of the sample using the WORDER software
guide; and by file length. program and their frequencies were entered into SPSS.

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946 S. Stepchenkova, A.M. Morrison / Tourism Management 27 (2006) 943–956
New integrated regional variables were computed by 3. Changing plural nouns into the singular form (e.g.,
aggregating the original destinations by region, e.g., the ‘‘palaces’’ into ‘‘palace’’) and counting synonyms as one
Far East or Central integrated variables. Then t-tests were word (e.g., ‘‘monastery’’, ‘‘cloister’’, ‘‘convent’’, and
conducted to compare frequencies of the regional inte- ‘‘abbey’’ were counted as ‘‘monastery’’) to reinforce the
grated variables for the American and Russian samples. concept.
With regard to the second objective, the US and Russian
files were combined into two respective groups and content To indicate how to ‘‘smooth out’’ the original data, a list
analyzed by CATPAC, which resulted in the list of the of key words was constructed. It included destinations,
most frequent words used in the textual data. The task of which were very likely to have different spellings, and
the researchers was to choose the most meaningful words, image variables as indicated by the initial CATPAC
or image variables, setting aside such auxiliaries as articles, analysis of the original data. Table 1 gives an example
prepositions, pronouns, and/or words related to organizing from the list of the key words. Every row corresponds to a
tours (the auxiliary words were easily excluded from certain key word with all its various forms. When either of
counting by placing them into an Exclude file). The final these forms was encountered in the analyzed text, it was
set of image variables contained nouns, verbs, and substituted by the spelling specified in the first column and
descriptors (i.e., adjectives and adverbs), since nouns are counted as such. In the counting process, WORDER
used to focus attention on attractions (e.g., museum, creates a new file where the specified substitutions are made
Baikal), verbs describe actions or tourism types (e.g., and does not change the original data.
rafting, sightseeing), and descriptors (e.g., ancient, exciting) A number of tests were run to ensure that the results of
create atmosphere (Echtner, 2002). counting by WORDER were consistent with that from
Then WORDER obtained a response from every website CATPAC II. The small inconsistencies encountered might
in the sample on the selected image variables. Since have been due to the various approaches to counting of
CATPAC does not cope well with files of substantial special cases arising when converting different Web text
size—dendograms, which are supposed to show how the encodings to a MS Word file, then to a .txt file. Otherwise,
image variables cluster into meaningful concepts, look it was found that the results were generally consistent. The
‘‘like a mitten instead of a glove’’ (Woelfel, 1998, p. 25)— overall research design is depicted in Fig. 1.
clustering the image variables into image themes was done
using factor analysis. It was run on the combined sample to
2.4. List of destinations
determine the main aspects of the induced Russian image.
To compile a master list of the destinations within
2.3. Solving special issues of content analysis Russia to be counted, the accepted division of Russia into
13 recreational regions as illustrated in Fig. 2 was followed.
A laborious ‘smoothing out’ process regarding the The main tourist destinations in every region, as indicated
meaningful words should be performed on the textual data by: (a) the Goskomstat reports (Goskomstat, 2000); (b) the
prior to using the CATPAC program (Schmidt, n.d.). The official website of the Federal Tourism Agency of the
combination of CATPAC and WORDER software can Russian Federation; (c) UNESCO; (d) websites of admin-
efficiently solve the following issues of content analysis: istrative entities of the Russian Federation; and (e) the
researchers, were included.
1. Making spelling of the key words (i.e., destinations and The master list of the destinations contained: (a) capitals
image variables) consistent in all text files. Even within a of federal entities; (b) large industrial centers; (c) old
single website the spelling may be inconsistent (e.g., Russian cities, architectural and historical places;
Saint-Petersburg vs. Sankt-Petersburg); across all the (d) centers of Russian folk art; (e) cities along the main
websites the issue is of enormous proportion. waterway systems; (f) major ports; (g) places connected
2. Changing multi-word concepts, e.g., ‘‘Peter the Great’’, with famous Russian people; (h) resorts and spas; (i) places
into a one-word format for further counting. If not, it of famous battles; (j) famous railways and major cities
would be difficult to distinguish how many times the along them; (k) biggest rivers, cruise and white water;
word ‘‘great’’ referred to Peter the Great and how often (l) mountain regions and peaks; (m) nature reserves; (n)
it was counted with the meaning of ‘‘magnificent’’ or lakes; (o) UNESCO World Heritage List sites; (p) unique
‘‘splendid.’’ locations; and (q) destinations in neighboring countries,
Table 1
Sample from the list of keywords
Performance Performances Concert Concerts Show Shows
Peterhof Peterhoff Petergof Petergoff
Redsquare Red Square

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S. Stepchenkova, A.M. Morrison / Tourism Management 27 (2006) 943–956 947
List of keywords:
List of List of most frequent
Original words to describe
text files keywords
files: text Russia
from
Websites
WORDER:
Text Smoothing
Function: Exclude
CATPAC file
Consistent spelling
Synonyms
Plural/singular Smoothed out text
files Data Analysis:
Multi-word
Comparisons of
concepts
U.S./Russian destination
U.S./Russian/ frequencies
Partnership,
affiliation, file T-tests of integrated
size Destination destination variables
SPSS
Counting Function Factor Analysis of Russian
induced image variables
Fig. 1. Research design.
(e.g., ‘‘cruise’’) were also specified for counting by
WORDER in every one of 212 files in order to better
interpret the destination frequency results. These words
were given the title of ‘‘help words.’’ Since many
destinations in the master list provided different forms of
tourism, the help words gave a clearer indication of the
prevalent tourism types, e.g., high mean frequencies of
Yaroslavl, Uglich, Kostroma, and Samara destinations
along with those of such help words as ‘‘cruise’’, ‘‘ship’’
and ‘‘river’’ indicated that the cruises along the Volga river
were being offered.
3. Results
Fig. 2. Russian recreational regions. 1: Western; 2: North-Western; 3:
Central; 4: Southern; 5: Povolzhje; 6: Urals; 7: Azov-Black Sea; 8:
Caucasus; 9: Ob’-Altai; 10: Yeniseisky; 11: Baikal; 12: Far Eastern; and
3.1. Estimation of richness of the destination pool
13: Northern.
After the text smoothing process, the frequencies of all
which are frequently included by tour operators into specified destinations were counted by WORDER and
combined tours, cruises, or railway journeys. The total entered in SPSS. Out of 344 destinations, 43 had zero
number of destinations selected was 344, many belonging frequencies, 45 destinations appeared in only one website,
to several categories. It is well known that after the Russian 34 in only two websites. The destinations with zero
Revolution in 1917, many ancient Russian cities were frequencies can be roughly divided into three main types:
renamed after prominent communist figures, and starting
at the end of the 1980s, the original names were given back Industrial cities or administrative centers, which offer
to many of these locations. Nowadays, these cities are very little to the international traveler in terms of
sometimes referred to by using both names together, e.g., architecture, arts, and scenery.
‘‘Vyatka, former Kirov.’’ To avoid double counting, the Places with historical, religious, and/or cultural impor-
current name was retained for the analysis. When a tance, far from main transportation centers and/or
location had equally used names, e.g., Zagorsk/Sergiyev lacking in infrastructure.
Posad, it was decided to count both occurrences but to Local parks, which cannot compete with the well-known
assign them to one variable, in this case, Zagorsk. nature reserves of Caucasus, Altai, Baikal, etc.
2.5. List of help words It is natural to assume that not all the destinations that
are mentioned in the sampled websites were actually
Besides the 344 destinations, 30 words, which indicated included into the master list for counting. To estimate the
tourism activities (e.g., ‘‘hunting’’, ‘‘rafting’’), main nature richness of the destination pool and to see how many
features (e.g., ‘‘river’’, ‘‘mountain’’), and types of travel destinations might have ‘‘slipped through’’ undetected, the

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948 S. Stepchenkova, A.M. Morrison / Tourism Management 27 (2006) 943–956
Chao and jackknife statistical methods of extrapolated larger in Russian websites, which might indicate more
richness were used (Chao, 1987; Oksanen, 2002). Chao’s extensive destination coverage. For destinations common
extrapolation of destination richness gave 330.78 destina- to both lists, the standard deviation for the US websites
tions; first- and second-order jackknife methods gave was smaller for all destinations except Peterhof. This
345.78 and 356.78 destinations, respectively. Overall, the means that the US tour operators gave a more balanced
specified pool of 344 destinations was considered to be a description of the main Russian places of interest. There
very good scope. was more agreement among US tour operators as to what
were the best places in Russia to visit. The large standard
3.2. Destination frequencies deviations of destination variables in the Russian websites
reflected the specialization of the Russian tour operators
The top 25 destinations for 79 US and 84 Russian tour on one or another destination or set of destinations. It is
operator websites are given in Table 2; help words which especially evident with the Baikal–Irkutsk–Listvyanka–Ol-
were also counted in order to identify the dominant khon destination set around Lake Baikal, and also true for
tourism types are also shown in italics. In round numbers, Kamchatka, Novosibirsk, Petrozavodsk, Vladivostok, Al-
the average length of Russian-related texts on US websites tai and Solovki, all of them being Siberian, Far Eastern, or
was 5000 words with a standard deviation of 6700 words, Northern destinations. However, there were relatively large
while for Russian websites these parameters were 9700 and standard deviations in the US list for Kamchatka,
13,400, respectively. The destination means were generally Sakhalin, and the Amur River (Russian North and Far
Table 2
Top 25 destinations for US and Russian tour operator websites
US—79 sites Mean SD Russian—84 Mean SD
sites
1 St. Petersburg 25.14 30.55 1 Moscow 29.33 46.32
2 Moscow 23.48 29.66 2 St. Petersburg 26.20 42.34
Cruise 4.89 8.05 3 Baikal 15.69 44.25
3 Peterhof 3.99 6.95 Mountain 13.11 34.75
Ship 3.10 4.92 Volcano 10.76 34.62
4 Volgaa 2.54 4.70 Hunting 9.63 59.92
5 Helsinkia 2.24 7.32 4 Irkutsk 7.98 17.73
6 Yaroslavl 2.00 3.79 5 Kamchatka 6.52 19.23
7 Uglicha 1.90 3.67 Fishing 6.18 14.99
Mountain 1.80 7.59 Rafting 4.05 10.78
8 Novgorod 1.73 3.56 Skiing 3.67 10.21
9 Kamchatka 1.68 10.13 Nature reserve 3.52 26.99
10 Kizhi 1.67 3.00 Ship 3.45 10.00
11 Pavlovska 1.66 4.90 6 Vladimir 3.44 6.33
12 Finland 1.61 2.49 7 Altaib 3.27 12.20
13 Vladimir 1.51 3.11 8 Vladivostok 3.18 12.96
14 Vladivostok 1.49 6.86 9 Listvyankab 3.02 9.06
15 Irkutsk 1.30 4.55 10 Novosibirsk 3.01 14.31
16 Kazan 1.20 2.99 11 Yaroslavl 2.71 7.90
17 Amura 1.19 9.68 12 Olkhonb 2.60 10.04
18 Suzdal 1.16 3.50 13 Petrozavodskb 2.58 14.31
19 Sakhalina 1.14 10.01 14 Suzdal 2.56 5.54
20 Baikal 1.13 4.02 15 Peterhof 2.54 4.24
21 Zagorsk 1.09 1.99 16 Kizhi 2.50 9.36
22 Trans-Siba 1.06 3.83 17 Novgorod 2.33 5.07
23 Kostroma 1.01 2.30 Resort 2.02 6.81
24 Golden Ring 0.95 2.06 18 Kazan 2.00 7.47
25 Far Easta 0.87 4.91 19 Kostroma 1.93 4.90
— Cruise 1.93 4.41
— 20 Zagorsk 1.89 3.62
Volcano 0.72 3.30 21 Solovkib 1.82 11.13
— Geyser 1.76 6.04
Hunting 0.68 3.96 22 Uralsb 1.76 5.86
— 23 Barnaulb 1.68 7.83
— 24 Valaamb 1.65 5.39
— 25 Golden Ring 1.62 4.51
a
Destinations that did not make Russian Top 25 list.
b
Destinations that did not make US Top 25 list.

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East) indicating that these destinations were described and Adding Russian official websites and Russian and US
promoted by some US websites more heavily. A close look travel guides increased the samples (Russian-100, US-103)
into the data revealed that several US tour operators, in but did not much change the frequency picture. In the 103-
fact, specialized in travel to Russian Far East, and their US top-25 list there were the same destinations with the
websites had very extensive coverage of the region and its mean frequencies only slightly changed. Standard devia-
natural resources. tions were smaller, which means that such comprehensive
Two capitals, Moscow and St. Petersburg, were at the websites as travel guides mentioned more destinations and
very top of both lists with St. Petersburg having the places of interest than the websites of tour operators. For
absolute largest mean in the US sample. Moscow, being the the Russian sample, the words ‘‘Solovki’’, ‘‘Barnaul’’, and
Russia’s official capital and main transportation center, ‘‘Valaam’’ gave way to ‘‘Volga’’, ‘‘Trans-Sib’’, and
was reflected by the largest mean in the Russian sample. In ‘‘Caucasus’’, which was understandable, since Barnaul
the US list, Helsinki and Finland appeared, which made the short list by being the major transportation
confirmed that the US tour operators offered many tours center for Altai region (seventh place in the Table 2, for the
to Russia as part of longer itineraries and cruises. The Russian list). Not very many Russian tour operators work
words ‘‘cruise’’, ‘‘ship’’ and ‘‘Volga’’ along with destina- with Trans-Sib packages either, while travel guides tell
tions of Kizhi, Yaroslavl, Uglich, Kostroma, and Kazan, more about it. And, again, Caucasus entering the list was
that are located along the main waterways, showed that very much in line with adventure travel, promoted by the
cruises and river tours were very popular with US tour Russian side.
operators. Peterhof, Pavlovsk and the number 26 destina-
tion of Tsarskoye Selo in the US list are all day-trip 3.3. Integrated regional variables: t-tests
destinations from St. Petersburg. They were especially
popular with US tour operators, as well as Zagorsk, a day- In order to get a broader picture of the US and Russian
trip destination from Moscow. travel offers, all destinations belonging to the same
There seems to be a consensus on both the US and recreational region were aggregated into one integrated
Russian sides as to what are the best Russian cities from variable by adding up the frequencies of the original
the historical and architectural standpoint. They are the so- destinations. Using the division given in Fig. 2, 13 regional
called Golden Ring cities of Suzdal, Vladimir, Yaroslavl, integrated variables were computed. Since it was noted
Zagorsk, and Kostroma, as well as Novgorod. However, from the data that there were joint tours of Russia–Fin-
Vladimir’s results were most definitely inflated, since it was land, Russia–Ukraine, and Russia–Mongolia–China, one
impossible to separate the city’s name from a widely used more regional variable, namely Foreign ¼ Beijing+Fin-
Russian personal first name. Vladimir and Suzdal are land+Helsinki+Kiev+Odessa+Ulaanbaatar, was com-
geographically close and often mentioned together, so puted to see how often US and Russian tour operators
Vladimir’s true frequency should be closer to that of offered travel to Russia as part of multi-country trips.
Suzdal. Normality tests for the regional integrated variables
The Trans-Siberian Railway (Trans-Sib) and the cities revealed that the data did not come from normally
along it—Yaroslavl, Novosibirsk, Irkutsk, Vladivostok distributed populations, and outliers and skewedness were
and, possibly, Kazan (strictly speaking, Kazan is not on detected. However, the t-test method is quite robust to
the Trans-Sib but there are tourist train routes which go deviations from normality if sample sizes are large enough
through Kazan because of its beauty and historical and do not differ too much. Table 3 presents the results of
importance)—also made the US top 25 list. In the US list, the t-test comparisons of the means of the regional
the number 34 destination was Beijing, which indicates that integrated variables for the two independent samples, the
some US tour operators offered the combined Trans- websites of US and Russian tour operators.
Siberian and Trans-Mongolian journey, when from Lake For the long-established destinations of Moscow, St.
Baikal a train goes to Beijing through the steppes of Petersburg, North-Western and Central regions, significant
Mongolia to make this epic journey even more exotic. differences in frequency of mention were not detected. The
While the mentioned cities were also in the Russian list, the North-Western region is famous for Kizhi cruises from St.
variable Trans-Sib was only number 45 there. Petersburg, as well as for the old Russian cities of
From this short list, it can also be concluded that the Novgorod and Pskov. The Central region includes the
Russian side actively promoted hunting and fishing as well Golden Ring cities and most centers of Russian folk art.
as adventure travel, which was indicated by such destina- Together, these two regions share cruises from Moscow to
tions as Baikal, Kamchatka, Altai, and the Urals, along St. Petersburg and back, which are popular with both sides.
with the words ‘‘mountain’’, ‘‘volcano’’, ‘‘hunting’’, ‘‘fish- The Foreign regional variable was the only one with a
ing’’ and ‘‘rafting.’’ It seems that the Russian potential higher mean for the US sample, indicating that US tour
regarding adventure travel was also being recognized by operators more often included Russia as a part of multi-
the US side, judging from the words ‘‘mountain’’, country tours.
‘‘volcano’’, ‘‘hunting’’ combined with such destinations as The Povolzhje region is famous for Volga river tours and
Kamchatka, Sakhalin, the Amur River, and Baikal. Volga-Don cruises to the Ukraine. The Southern region is

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950 S. Stepchenkova, A.M. Morrison / Tourism Management 27 (2006) 943–956
Table 3
t-test comparisons between US and Russian websites for integrated regional variables
Integrated regional Mean US sample—79 sites Mean Russian sample—84 t-statistic Sig
variables sites
St. Petersburg 25.14 26.20 0.183 0.855
Moscow 23.48 29.33 0.966 0.335
North-Western without St. 15.20 21.57 1.123 0.263
Petersburg
Central without Moscow 11.06 17.44 1.586 0.115
Foreign 5.61 2.74 À2.200 0.030*
Northern 4.42 18.18 2.222 0.028*
Povolzhje 4.03 6.36 1.130 0.260
Far Eastern 2.85 4.73 0.681 0.497
Baikal 2.82 31.98 3.189 0.002**
Urals 1.05 3.69 2.109 0.038*
Azov-Black Sea 0.70 0.83 0.270 0.787
Ob’-Altai 0.67 11.42 2.582 0.012*
Caucasus 0.61 5.18 2.106 0.038*
Yeniseisky 0.48 5.18 2.815 0.006**
Western 0.25 1.00 1.619 0.109
Southern 0.00 0.57 1.021 0.310
*0.01opp0.05, **pp0.01.
mostly agricultural, and both sides do not see many
attractions there for the foreign traveler, which was
indicated by the very low means. The Western region—
Kaliningradskaya oblast—is probably considered Eur-
opean rather than Russian, and not offered either.
Although the Azov-Black Sea region has the main Russian
resorts, which are quite popular with Russians, their
quality and service standards are lower than what is
expected by the foreign traveler, and are too close to the
Caucasus with its unstable political situation.
The Russian North, Siberia (Ob’-Altai, Yeniseisky, and
Baikal regions), Urals, and Caucasus were the regions
where significant differences were registered. It is consistent
Fig. 3. Integrated regional variables: t-test comparisons of US and
with the policy declared by the Russian tourism authorities
Russian websites.
that Siberia, especially Lake Baikal, is the main develop-
mental focus of the Federal Tourism Agency of the
Russian Federation (‘‘Izvestia’’, March 11, 2003). The quite remarkable and represents quite a high level of
Russian side promotes the Caucasus as a skiing and agreement between the US and Russian sides on the
mountain destination with the highest peak in Europe. The historical, cultural, and arts components of the Russian
Russian Far East did not come up as being significant in tourism image. The remaining 17 words showed that the
the analysis. The visual representation of the t-test results is US side explored in more depth the cultural attractions of
given in Fig. 3. Moscow and St. Petersburg, river cruises, and Russia’s
Soviet past. The Russian side placed more emphasis on the
3.4. Russian induced image variables country’s vast and unique natural resources with their
fishing and hunting opportunities.
For the comparative analysis of the most frequent
meaningful words used to describe Russia, the 103 US 3.5. Factor analysis: induced image themes
files were combined together and 100 Russian and 9
Partnerships files made the other text body. It was decided To get further insights into the overall induced images of
to join the Partnership files with the Russian sample Russia as a tourist destination, 212 files smoothed by
because the Partnership websites exhibited more simila- WORDER, were combined into one file and then analyzed
rities to the Russian websites. The CATPAC II analysis by CATPAC II. It was decided to exclude from the analysis
revealed that out of the 60 most frequent words, 43 were all words that related to the organization of tours, such as
the same for both sides, as indicated by Table 4. This is ‘‘arrival’’, ‘‘breakfast’’, ‘‘bus’’, etc. As a result, a list of the

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Table 4
Top 60 most frequent words used to describe Russia
## US—103 Russian ## US—103 Russian
partnerships—109 partnerships—109
1 Moscow City 31 Enjoya Campb
2 City Moscow 32 Capital Volcanob
3 Petersburg Museum 33 Royala Town
4 Palace River 34 Sovieta Collection
5 Museum Lake 35 Sea Monument
6 Cathedral Petersburg 36 Summer Irkutskb
7 Great Ancient 37 Fortress Huntingb
8 Famous Cathedral 38 Local Sea
9 Church Church 39 Train Train
10 River Palace 40 Cruisea Square
11 Art Architecture 41 Sightseeing Uniqueb
12 Century Century 42 Lake Theater
13 Ancient Great 43 Red Squarea Kamchatkab
14 World Famous 44 Peterhofa Tsar
15 Kremlin Baikalb 45 Besta Performance
16 Beautiful History 46 Siberia Waterb
17 Architecture Monastery 47 War Stationb
18 Tsar Art 48 Region Woodenb
19 Monastery Mountainb 49 Gardena Capital
20 History World 50 Europea Summer
21 Performance Excursion 51 Metroa Fishingb
22 Island Siberia 52 Stylea Peter the Great
23 Collection Island 53 Winter Palacea Fortress
24 Town Region 54 Monument Hermitage
25 Hermitage Beautiful 55 Nevaa Valleyb
26 Peter the Great Kremlin 56 Volgaa War
27 Large Large 57 Pushkina Natureb
28 Square Park 58 Shipa Whiteb
29 Theater Local 59 Petera Nationalb
30 Park Villageb 60 Excursion Sightseeing
a
Words that did not make Russian Top 60 list.
b
Words that did not make US Top 60 list.
70 most frequent meaningful words used to describe Russia ‘‘central’’—that did not load higher than 0.35 on any
remained. These words were regarded as the overall factor, were eliminated. Looking at the interpretability of
induced image variables of Russia. They were counted by the remaining factors, it was noted that factor 8 did not
WORDER in every file of the sample, and entered into contain any words that loaded strongly on it. However, the
SPSS as new variables. It was proposed that these words words ‘‘sea’’, ‘‘white’’, ‘‘monastery’’, ‘‘island’’ suggested
described the major image themes as they appeared in the that this factor was the Russian North/Solovki theme with
Russia-related texts from the sampled websites. The factor one of the key words, namely Solovki, left out of the
analysis method was employed to look into the underlying analysis, since it ranked lower than 70 in the CATPAC II
structure of these variables and identify the main image list (Solovki is a group of islands in the White Sea, which
themes. was famous for its monastery ensemble and served as a
As was expected, the correlation matrix displayed high prison under Stalin’s regime. Now it is a popular tourist
pair-wise coefficients between most of the variables, and attraction with cruises from St. Petersburg). To check this
communalities for all the variables were higher than 0.60. hypothesis, as well as stability of the solution, the analysis
The Bartlett’s Test of Sphericity was significant with was repeated with the ‘‘Solovki’’ variable included, and the
po0:001, and the KMO statistic of sampling adequacy final results are given in Table 5. The solution explained 85
was 0.916. Ten factors with Eigenvalues greater than 1.0 percent of the original variance, however, since the factors
were extracted, which explained 84.2 percent of the co-varied, the sum of variances explained by each factor
variance. To rotate the factors, the Direct Oblimin rotation was larger than the total variance.
was preferred over Varimax (Kline, 1994), since it allows All the factors were self-explanatory, with image themes
the factors to co-vary. With a number of variables loaded easily identified as Culture and History, Nature Parks,
highly on several factors, the Oblimin rotation produced Siberia and Baikal, Cruise Tours, Moscow, St. Petersburg,
the most simple and interpretable factor structure. Weak Country and State, Solovki, Kamchatka, and Hunting.
items—‘‘beautiful’’, ‘‘traditional’’, ‘‘world’’, ‘‘famous’’, and Consistent with the previously described results, the main

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Table 5
Factor analysis: Main Russian image themes
Factors F1 F2 F3 F4 F5 F6 F7 F8 F9 F10
Culture and Nature Siberia Cruise tours Moscow St. Country Solovki Kamchatka Hunting
history parks Baikal Petersburg state
Cronbach’s 0.877 0.880 0.948 0.748 0.947 0.947 0.855 0.873 0.871 0.603
alpha
Variance 15.600 10.320 17.035 5.279 16.316 15.735 11.526 6.045 10.304 6.050
explaineda
Monument 0.767
Architecture 0.597
Museum 0.562
History 0.535
Unique 0.503 0.434
City 0.463
Park 0.740
National 0.727
Mountain 0.506 À0.418
White 0.490 0.484
Baikal À0.897
Irkutsk À0.830
Village À0.720
Siberia À0.684
Lake À0.657
Local À0.637
Special À0.628
Best À0.623 0.365
Train À0.611
Wooden À0.579
Water À0.572
Town À0.562
Station À0.530
Ancient À0.459 0.406
Cruise 0.716
Sightseeing 0.647
Enjoy À0.396 0.631
Kremlin 0.905
Monastery 0.744
Tsar 0.700
Moscow 0.681 0.448
Century 0.624
Soviet 0.541 0.433
Square 0.479 0.470
Cathedral 0.475 0.393
Performance 0.388 0.464 0.370
Art 0.451
Collection 0.445 0.378
Church 0.440 0.360
Residence 0.394 0.415
Palace 0.894
Petergr 0.799
Royal 0.756
Hermitage 0.676
Great 0.602
Petersburg 0.447 0.585
Fortress 0.579
Summer 0.507
Style 0.483
Theater 0.396 0.461
War 0.352 0.403 0.360
Region 0.659
Country 0.454 0.612
Large 0.501
Capital 0.437
Northern 0.431 0.432
Sea 0.350 0.386 0.373

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Table 5 (continued )
Factors F1 F2 F3 F4 F5 F6 F7 F8 F9 F10
Culture and Nature Siberia Cruise tours Moscow St. Country Solovki Kamchatka Hunting
history parks Baikal Petersburg state
Solovki 0.921
Island À0.374 0.413
Volcano 1.038
Kamchatka 0.890
Fishing 0.445 0.598
Excursion 0.457 0.492
River 0.419
Hunting 1.033
Camp 0.368 0.539
Extraction method: principal components analysis.
Rotation method: Oblimin with Kaiser normalization.
Rotation converged in 35 iterations.
a
The solution explained 85% of the total variance. Since the factors co-vary, the sum of factor variances is larger than 85.
themes were related to the cultural, historical, and arts Tour operator websites had both textual and pictorial
aspects of the Russian image (themes Culture and History, material. Although visual imagery is acknowledged to play
Moscow, St. Petersburg), as well as the nature aspect an important role in destination image formation (Andsa-
(Kamchatka, Nature Parks, Baikal and Siberia). A new ger Drzewiecka, 2002; MacKay Fesenmaier, 1997), the
theme, Country and State, was also identified. proposed method is suitable for content analysis of textual
data only. Furthermore, the study dealt with images as they
were transmitted by the suppliers of Russia-related travel
4. Limitations products. Further research is needed to see how the
promoted images have been interpreted by consumers.
A question that still remains is how representative was This research was the first to use WORDER software,
the sample of the whole population of US and Russian and some content analysis issues to be aware of have
websites? While the researchers were fairly confident that emerged. WORDER substitutes the apostrophe character
they had a good scope of US websites by going through the for a space while counting. Because of that, the Don River
comprehensive lists of USTOA, ASTA, travel guides, etc., quite unexpectedly came up into the top 25 list of
they could not be so certain about the Russian sample. A destinations. Although cruises along the Volga-Don route
reliable statistical method to estimate how many sites were are quite popular, the counting was inflated by ‘‘don’t’’
left out from the analysis was not found. However, one of words, which were substituted for ‘‘don’t’’ by WORDER.
the indications of a sufficient scope might be that the The ‘‘Don’’ variable was excluded from the analysis.
researchers began encountering the same websites from
different sources. 5. Conclusions and implications
The research team firmly believes that the two sets of
tour operators were comparable, but acknowledges that The first objective of this research was to identify
there may be some differences in the nature and objectives destinations within Russia mentioned most frequently by
of the providers of these websites. For example, in the US US and Russian tour operator websites. In general, there
tour operators and wholesalers tend to specialize in tour was a substantial overlap between the top 25 destinations
development, marketing and administration. They tend not in the two groups of websites. Seventeen destinations were
to operate as travel agencies, and focus almost exclusively common: St. Petersburg, Moscow, Peterhof, Yaroslavl,
on outbound tourism from the US. It is more likely that at Novgorod, Kamchatka, Kizhi, Finland, Vladimir, Vladi-
least some of the Russian tour operators have more vostok, Irkutsk, Kazan, Suzdal, Baikal, Zagorsk, Kostro-
diversified operations, perhaps functioning both as in- ma, and Golden Ring. Among the top 25 destinations in
bound and outbound tour operators, and as travel agents. the US tour operator sites, eight were not found in the top
Another limitation of the study was the insufficient 25 Russian list and vice versa.
number of cases for comparative factor analysis of the There were several notable differences between the
image themes that would use the US and Russian samples results for the US and Russian tour operator websites.
separately. Since out of 60 most frequent meaningful words First, US tour operators gave a more balanced description
to describe Russia, 43 were the same, to capture differences of the main places of interest, indicating more agreement
in the images about 60–70 variables and consequently more among US tour operators as to the best places in Russia to
than 150 cases for each side would have been needed. visit. The Russian tour operator websites were more

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954 S. Stepchenkova, A.M. Morrison / Tourism Management 27 (2006) 943–956
specialized, with a focus on one destination or a specific set great scope to broaden or further diversify the positioning
of destinations. For example, some Russian tour operators strategies of US tour operators. For example, the US sites
heavily promoted hunting, fishing, and adventure travel in could be expanded by adding information to target special-
eastern Russia. Second, when the destinations were interest or niche markets such as adventure travel, culinary
grouped into the 13 Russian recreational regions plus a and nature-based tours, and fishing and hunting. The
foreign country category, t-tests detected seven significant website of the Federal Tourism Agency of the Russian
differences for these 14 integrated regional variables. The Federation (http://www.russiatourism.ru/eng/default.asp,
US tour operator websites only had the higher mean for 2004) provides useful guidance in this respect, by listing
the ‘‘foreign’’ regional variable; while the Russian tour ecological tourism, sport and extreme tourism, fishing and
operator websites had significantly more coverage for the hunting tourism, mountain skiing tours, and sea and river
other six variables (Northern; Baikal; Urals; Ob’-Altai; cruising as niche market segments. Other image themes can
Caucasus; Yeniseisky). This shows that the US tour also be more prominently highlighted in US tour operator
operator websites were much narrower in their geographic websites, some of which are currently being used by the
focus on Russian destinations, but were more likely to Russian counterparts. The nature parks theme and the
discuss Russia in conjunction with other neighboring broader perspective of Russia’s outstanding natural
countries. features and attractions is an example of a topic that
The second objective was to identify the most used could be expanded.
meaningful words when tour operators offer Russia as a Technically and content-wise, the Russian websites
destination and to uncover the common themes in their require the greatest improvements. They tend to be loaded
descriptions of Russia. These words and themes were with information and not particularly well targeted to
interpreted as the induced image components and used to specific countries of visitor origin or market segments by
compare the US and Russian perspectives on Russia as a travel interests. Moreover, there is no portal website of
travel destination. The 10 image themes identified were information to Russia’s tour operators, such as the US has
culture and history; nature parks; Siberia and Baikal; through the US Tour Operators Association (USTOA) and
cruise tours; Moscow; St. Petersburg; country and state; the National Tour Association (NTA). The Russian Union
Solovki; Kamchatka; and hunting. The most prevalent of Travel Industry or RUTI (formerly known as RATA,
themes were related to the cultural, historical, and arts Russian Association of Tourist Agencies) could provide
aspects of the Russian image (culture and history; Moscow; such a portal, but at the time of writing, the English version
St. Petersburg), and the natural features of Russia of the organization’s website was not fully developed.
(Kamchatka; nature parks; Baikal; Siberia). Generally, the websites of the Russian tour operators need
In considering the management implications of these to be given a more professional appearance to inspire
major conclusions, it should be realized that modern greater confidence and trust in potential travelers from the
Russian tourism is still very young when compared to other US. There is also a need for greater consistency in the
destinations, perhaps just 15 years old. It is suspected that content of the Russian websites, particularly with respect
many of the Russian sites were originally designed and to destinations names, even on the official websites. The
written for the domestic market, and have been directly Federal Tourism Agency of Russian Federation (FTA) is
translated from Russian into English without adequate represented in the US by the Russian National Group
thought given to cultural differences and varying destina- (RNG), a private representative firm. A cursory analysis of
tion preferences. The Russian websites, in particular, need RNG’s current website clearly indicated that it was also in
to be carefully redesigned and customized according to need of great improvement in appearance, user friendliness,
major country of origin target markets. In the case of the market positioning and segmentation.
US, the Russian tour operator websites should be adjusted It is true that this research study only considered one
to make them more similar to the US tour operator component of destination image and in just one format
websites, which probably better reflect the current market through a single research method: content analysis of
demands of US travelers to Russia. digital information on the Web. Undoubtedly, the natural
This study clearly suggests that US tour operators are environment images of Russia remain to be determined in a
narrowly positioning Russia as mainly being a historic and variety of media. With regard to Russia’s natural environ-
cultural destination, with a relatively tight geographic ment image, the FTA has little influence over the country’s
emphasis on the western portion of the country. The US political and economic agenda and cannot control negative
tour operators also have another specific focus on Russia media coverage, which results from complicated world
as a destination for cruises. The positioning strategies of politics. Moreover, images have a considerable amount of
the Russian tour operators are much broader and stability over time (Fakeye Crompton, 1991; Gartner
geographically dispersed. Additionally, the Russian web- Hunt, 1987) and the larger the entity (like Russia), the
sites place a much greater emphasis on Russia’s natural slower the image change (Gartner, 1993). However,
features, and on special-interest travel activities beyond the through induced agents (including these websites), Russian
country’s history, heritage, and culture. Since Russia is the tourism marketers can stress its positive features and
world’s largest country measured in land area, there is address apprehensions that potential visitors might have

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about traveling there (Kale Weir, 1986). Although Doolin, B., Burgess, L., Cooper, J. (2002). Evaluating the use of the
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destination image: An empirical assessment. Journal of Travel
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through the development and application of the WOR- Fakeye, P., Crompton, J. (1991). Image differences between prospective,
DER software. The methodology of combining CATPAC first-time, and repeat visitors to the Lower Rio Grande Valley. Journal
and WORDER software allows for wider use of statistical of Travel Research, 30(2), 10–16.
methods in the content analysis of textual materials. Fam, K. S., Foscht, T., Collins, R. D. (2004). Trust and the online
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frequencies of the variable of interest are identified in every Federal Tourism Agency of the Russian Federation (2004). Official
file. Special issues of content analysis, such as different website at: www.russiatourism.ru.
spelling of image variables, plural/singular, synonyms, and Gartner, W. C. (1989). Tourism image: Attribute measurement of state
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