China and Russia Organic Images in U.S. Media

China and Russia: Organic Destination Images in U.S. Media 55

China and Russia: Organic Destination Images in
U.S. Media
Svetlana Stepchenkova*, Yi Chen, Alastair M. Morrison
Purdue University, U.S.A.

Abstract:
The concept of destination image is central to the understanding of destination choices (Chon,
1990). However, up to now there has been a limited number of studies which investigate one of
the main components of destination image – the organic image. This study explores the organic
destination images of China and Russia by analyzing articles related to these two countries in the
U.S. newspapers for the period 2002-2004. An approach of identifying meaningful concepts in
the textual data, which combines the resources of CATPAC and WORDER software programs, is
employed. China’s increasing economic power and Russia’s influence on the world politics are
identified as the main organic image themes in the U.S. newspapers. Finally, the implications for
the countries’ DMOs are discussed.

Key words: organic image, content analysis, CATPAC, WORDER, China, Russia

1. Introduction
According to the World Tourism Organization (UNWTO) and Chinese and Russian official
tourism reports, tourism in China and Russia has been on the rise for the last 10 years, despite
some occasional annual fluctuations. In 2003, China held the 5th position in the world among the
top destinations with 36.8 million international arrivals (UNWTO, 2004a), and Russia was in the
21st position with 8.02 million international arrivals (UNWTO, 2004b). For further comparison,
the China National Tourism Administration (CNTA) reported a total of 91.66 million inbound
visitors for the year 2003 (CNTA, 2004) with foreign arrivals in the first 10 months (i.e., not
counting visitors from Hong Kong, Macao, and Taiwan) just fewer than 14 million (“China Tourism,
” 2004). The Russia’s Federal State Statistics Service (Rosstat) reported a total of 22.514 million

* Corresponding author
Svetlana Stepchenkova, Ph.D. Student, Department of Hospitality and Tourism Management, College of
Consumer and Family Sciences, Purdue University, Email: svetlana@purdue.edu.
Yi Chen, Department of Hospitality and Tourism Management, College of Consumer and Family Sciences,
Purdue University.
Alastair M. Morrison, Distinguished Professor of Hospitality and Tourism Management, College of Consumer
and Family Sciences, Purdue University, Email: alastair@purdue.edu.

56 Svetlana Stepchenkova Yi Chen Alastair M. Morrison

visitors in 2003, or 8.145 million people, without counting the arrivals from the Commonwealth
of Independent States (CIS) countries (Rosstat, 2004).
In the current structure of the international arrivals to China and Russia, the U.S. holds an
important place. In the year 2002, one year before the SARS outbreak, China received 1.12 million
visitors from the U.S., which was the 4th largest market to China after Japan, Korea, and Russia
(“China Tourism,” 2005). In 2003, the U.S. had the 8th largest share of visitors to Russia, not
counting the CIS countries, and the 5th largest share excluding the former Soviet republics of
Lithuania, Latvia, and Estonia (Rosstat, 2004). The absolute figure of total arrivals to Russia from
the U.S. in 2003 was 281,000 (Rosstat). U.S. tourists are the world’s leading travel spenders
(UNWTO, 2004a) and therefore are very attractive visitors from an economic impact standpoint.
It can be argued that, from the perspective of the U.S. pleasure travelers, China and Russia
have much in common as travel destinations. These long-haul destinations are both large countries
that have great heritage resources, scenery and art, and have shared a communist history and
planned economies. The tourist product that they promote also has much in common. For example,
cultural tourism in famous cities and architectural sites, luxury cruises along the Yangtze (China)
and the Volga (Russia) Rivers, transcontinental train travel across Moscow – Lake Baikal – Mongolia
– Beijing, “China Splendors” and “Imperial Russia” tours exploring the countries’ imperial pasts,
etc. Due to these similarities, China and Russia share, at least to some degree, the same potential
target audiences and therefore can be regarded as competitors in the U.S. pleasure travel market to
the Asian region.
The concept of destination image is central to the understanding of destination choices (Chon,
1990) and from this perspective it is of crucial importance to a host country’s DMO. To be
successfully promoted in the U.S. pleasure travel 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 CNTA
and the Federal Tourism Agency of the Russian Federation (FTA) by creating and managing the
perceptions, or images, that potential travelers hold about China and Russia as travel destinations.
Such differentiation is problematic without a thorough understanding of what visitors’ perceptions
or images are in the first place (Hunt, 1975). This study comparatively examines one aspect of
destination image, namely, the organic images of China and Russia as they are projected in the
U.S. newspapers.

2. The Organic Component of Destination Image
The general idea of the research is based on theoretical works of Gunn (1988) and Gartner
(1993) who recognized organic image as part of the overall destination image. Organic, as opposed
to induced, component of destination image, is the knowledge about a destination that is not
influenced by marketing efforts of the destination and suppliers but rather acquired in the natural


course of life. Pearce (1988) argued that individuals appear to hold an image of a destination even
if they have never visited it or been exposed to commercial forms of information. Organic image
was identified to be important as the first phase of the “accumulation of mental images about
vacation experiences” in Gunn’s model of the seven phases of the travel experience.
Destination is a major and unique product in a tourism system. When a destination is a
country in a geographical sense, the image of the destination and the country interact. Country
image was defined in literature as the total of all descriptive, inferential and informational beliefs
one has about a particular country (Martin & Eroglu, 1993). It is also the picture, the reputation,
the stereotype that consumers attach to products of a specific country. Such country features as
representative products, national characteristics, economic and political background, history, and
traditions (as cited in Quester, Dzever, & Chetty, 2000) contribute to formation of country image
which, in turn, is incorporated in the organic image (Gunn, 1988). Country image has been
found important in affecting product perceptions (Quester et al.), product preferences (De Wet,
Pothas, & De Wet, 2001), quality assessments (Janda & Rao, 1997), and brand evaluations (Agrawal
& Kamakura, 1999).
Because tourism products are experiential in nature and cannot be tried before the moment
of actual consumption, consumers are always involved in information search (Govers & Go, 2003;
Vogt & Fesenmaier, 1998). For a destination at the country level, the information search involves
various perspectives. For example, with safety while traveling being one of the primary concerns
(as cited in Crompton, 1977), the information about political stability, epidemiological situation,
environmental issues, crime rates, etc. at potential destinations, is continuously collected and
analyzed by consumers. The media heavily influence the public awareness, perceptions, and behavior,
including buying decisions (Macnamara, 2003), the most recent examples being news about SARS
(China) and terrorism (Russia), which significantly hurt the tourism industry in these countries in
2003 and 2004, respectively. A typology of organic and induced image formation agents from the
perspectives of their influence and credibility can be found in Gartner (1993) and Nolan (1976).
As Gartner argues, more credible agents are those that do not have a vested interest in promoting
a destination, i.e., books, school curricula, travel guides, television programs, documentaries,
newspaper articles, word of mouth, etc., which are collectively referred to as organic information
sources. Although a country’s DMO has very little or even no influence over organic sources, it is
important for the DMO to know what is being communicated about the country by organic
agents to the potential travel audience. Based on the knowledge of the current media coverage, the
DMO can amplify the positive aspects of the image as well as counter, if needed, negative or
inaccurate information in their induced materials.


3. Methodology
The objective of this study was to comparatively examine organic destination images of China
and Russia as they are projected by the US newspapers. Computer-assisted approach of combining
the two software programs CATPAC (Woelfel, 1998) and WORDER (Kirilenko, 2004) for
quantitative content analysis of digitized textual materials was proposed. Organic component of
destination image, being holistic in nature, has received limited attention as indicated by meta-
analyses of destination image research (Pike, 2002). Although a large amount of materials that can
be used for organic image studies is available from digital databases and the Internet, it is often
difficult to analyze due to the sheer volume of texts. Up to date, only a limited number of studies
has dealt with organic image component. Among them are the studies of travel and guide books
by Bhattacharyya (1997), Chang and Holt (1991), and Jacobsen and Dann (2003); analysis of
travelogs by Dann (1992); and content analysis of newspaper articles on tourism and gambling
(Nickerson,1995) and a cultural festival (Xie & Groves, 2003). None of the above mentioned
studies reported use of any content analysis software.

Content Analysis
There are two general classes of content analysis methods employed in social sciences -
qualitative and quantitative. The former refers to non-statistical and exploratory methods, which
involve inductive reasoning (Berg, 1995), and the latter refers to methods that are capable of
providing statistical inferences from text populations. Content analysis examines textual data for
patterns and structures within text, develops categories, and aggregates them into perceptible
constructs (Gray & Densten, 1998; Insch & Moore, 1997). Content analysis is capable of capturing
a richer sense of concepts within the data due to its qualitative basis, and, at the same time, can be
subjected to quantitative data analysis techniques (Insch & Moore). A central idea of quantitative
content analysis is that “many words of text can be classified into much fewer content categories”
(Weber, 1985, p. 7). The methodology of extracting content categories from the text and counting
the occurrences of the themes in the sampled text blocks was developed by the mid 20th century,
and is often referred to as contingency analysis (Roberts, 2000). Despite critique that the method
disregards the semantic structure of the language, which occasionally inflates the theme count,
and is not sensitive enough to reveal what meaning the speaker had intended for the identified
themes (George, 1959), contingency analysis, whether computer assisted or done manually, has
long been employed in social studies due to its clear methodological reasoning based on the
assumption that the most frequent theme in the text is most important. A quantitative content
analysis always produces a 2-dimentional data matrix suitable for further statistical analysis.
The large volumes of digital textual data available at present and the repetitiveness of the task
made the computer a natural and powerful choice for content analysis (Insch & Moore, 1997),
despite the fact that not all nuances of the language can be recognized by any given software
program. The review of selected content analysis software (16 programs) made by Alexa and Zuell


(2000) concludes that all reviewed computer programs for textual data analysis have their strengths
and weaknesses, and might not support certain operations associated with content analysis in an
efficient and user-friendly manner. Alexa and Zuell argue that this lack of support is not a problem
“if it were possible to use two or more different software packages for a single text analysis project
in a seamless and user-friendly way” (p. 318).

Proposed Approach
The approach used in the study to determine the prevalent organic image themes in the U.S.
newspapers with regard to China and Russia combines the commonly used CATPAC II software
(Woelfel, 1998) and the newly developed WORDER program (Kirilenko, 2004). As stated in the
CATPAC manual, “CATPAC is a self-organizing artificial neural network that has been optimized
for reading text. CATPAC is able to identify the most important words in a text and determine
patterns of similarity based on the way they are used in text” (Woelfel, p. 11). CATPAC can count
the most frequently used words in a textual file and has been employed in content analysis of
political speeches, focus groups interviews, marketing, and tourism-related research (Doerfel &
Marsh, 2003; Schmidt, 1998; Stepchenkova & Morrison, 2006; Zuell & Landmann, 2004).
However, CATPAC analyzes only one textual file at a time.
It is quite typical in content analysis projects that use web-based information, survey responses,
newspaper articles, etc. to encounter a large number of separate files which have to be processed.
WORDER software was specifically developed for such a purpose. During one run, WORDER is
capable of parsing through up to 1,000 textual files, looking for up to 1,000 key words, and
counting their occurrences in every file. The numerical matrix of key word frequencies is convenient
to transfer to any statistical package. Both software programs complement each other and if used
together, can broad the possibilities for researchers working with textual data. Ultimately, the
approach allows: 1) identification of destination image variables in the digital textual data using
CATPAC, and 2) counting the occurrences of these variables in every textual file with WORDER.
In the first stage of the proposed approach, CATPAC software is run on the pooled textual
data. The standard version of CATPAC can identify up to 160 most frequent meaningful words in
a file. Some auxiliary words that do not add to the meaning, i.e., prepositions, articles, conjunctions,
etc., are specified in CATPAC's Exclude file and ignored. In the CATPAC output, the image
variables are ranked in order of their frequencies; however, some words in the output might not
belong to theoretically justified pool of variables. These words can be excluded from further analysis
by adding them to the Exclude file before CATPAC is run again. Several iterations are usually
enough to obtain the desired number of the most frequent image variables, the number of iterations
being dependent on the variable frequency level at which a cut-off line has been placed. In the
second stage, image variables identified by CATPAC are used as the input for WORDER software.
They are specified for counting by the means of a WORDER input table. The other input for
WORDER prior to counting is the list of all textual file names. When activated, WORDER


counts every occurrence of every image variable in every textual file using the input table and the
list of file names. The result is an output table where the rows are analyzed files and the columns
are image variables. The table cells contain image variable frequencies counted in every textual file.
The output table produced by WORDER can be easily transferred into SPSS for further
statistical analysis and clustering purposes. While CATPAC has a clustering function, the program
does not handle files of substantial size very well and the pooled textual data is usually large in size:
dendograms, which are supposed to show how the most frequent unique words cluster into
meaningful concepts, look “like a mitten instead of a glove” (Woelfel 1998, p. 25). The proposed
approach of combining CATPAC and WORDER allows clustering of the image variables into
themes of a more holistic nature by means of factor analysis procedure built-in in the SPSS package.

Data Collection and Preparation
Sampling for media content analysis consists of three steps: selection of media forms, selection
of the period (issues or dates), and sampling of the relevant content (Newbold, Boyd-Barrett, &
Van den Bulk, 2002; see also Macnamara, 2003). For this study, we selected general U.S. newspapers
as a data source for reasons of (a) strong influence on public opinion; (b) higher circulation than
travel magazines and, therefore, higher accessibility to the general public; and (c) availability of
large amount of textual data in already digitized format. The source of the data was the LexisNexis
database, with access provided by a large Midwestern university. LexisNexis Academic News supplies
a wide variety of authoritative sources, including full-text of more than 350 newspapers from the
U.S. and around the world with such U.S. titles as Financial Times, The New York Times, and The
Washington Post, as well as prominent regional newspapers such as The Boston Globe, Chicago
Tribune, Los Angeles Times, and The Baltimore Sun (LexisNexis, 2006). The study was proposed
in early 2005, and a three-year period (2002-2004) was chosen in order to scope the whole range
of topics regarding China and Russia covered in the U.S. news, rather than a limited selection of
more immediate topics specific for a short period of time. For both China and Russia 15 articles
per month were selected alternatively from the four groups of U.S. newspapers (Northeast, Southeast,
Western, and Midwest sources) for a total of 540 articles for each country. The procedure included
finding all the articles for any given month that had the words “China/Chinese” or “Russia/Russian”
in the headlines; and then selecting 15 items using a systematic random sampling in order to
distribute the selection of 15 articles evenly through time (LexisNexis uses a chronological listing).
The systematic random sampling algorithm described by Lohr (1998) was used, when the first, or
base, number is randomized.
The whole body of the selected “Chinese” or “Russian” texts was first analyzed with CATPAC
to identify up to 100 of the most frequent meaningful words used to describe each country. These
words were regarded as the country’s organic image variables in the mass media. Second, the
WORDER program counted the identified variables in every textual file in each of the two samples.
Since major news issues change with time, textual files with pooled data for the years 2002, 2003,


and 2004 were created for China and Russia, and frequency analysis by CATPAC was performed
on every one of the six files. Prepositions, pronouns, and other auxiliary words that do not add to
the meaning were excluded from counting. It was decided prior to the analysis that the final set of
Chinese and Russian organic image variables should not exceed 100 words for the desirable case/
variable and variable/factor ratios (Kline, 1994). The final sets of China and Russia organic image
variables consisted of 96 and 86 words respectively and included the most frequent words common
for all three years, every two years and for every particular year. It was decided not to “merge”
words like “country” and “countries” into one variable at this point, since further analysis might
indicate that they were predominantly used in different language clusters; if needed, this procedure
could be done in a later stage by means of the SPSS package. However, three variables representing
important concepts were specifically created for the Chinese set: “humanrights”, “communistparty”,
and “hongkong,” and necessary substitutions were made in the original files using WORDER.
The resulting frequencies for the top 32 Chinese and Russian organic image variables are shown in
Tables 1 and 2 respectively.

Table 1 China’s Organic Image Variables: General Media, 2002-04

Variable Freq Variable Freq Variable Freq Variable Freq

company 658 city 316 industry 216 steel 190
government 586 Taiwan 302 U.S. 213 growth 190
military 499 foreign 275 power 212 leaders 184
country 474 state 268 Bush 209 job 179
market 462 president 265 technology 207 Shanghai 178
official 433 economic 246 economy 201 American 176
trade 382 price 238 high 198 Asia 174
officials 355 Hong Kong 228 school 196 cost 173


Table 2 Russia’s Organic Image Variables: General Media, 2002-04

Variable Freq Variable Freq Variable Freq Variable Freq

Moscow 729 people 428 Iraq 312 weapons 274
Putin 657 government 421 international 310 U.S. 269
year 530 states 418 country 308 former 263
united 472 time 409 against 302 security 258
world 461 nuclear 389 percent 296 military 258
president 459 war 366 company 291 foreign 254
Soviet 454 American 324 officials 286 Bush 252
oil 448 state 317 million 278 Kremlin 246

4. Results
Factor analysis by the means of SPSS package was further employed to identify the most
important image themes, or topics, for China and Russia, i.e., to cluster the image variables into
meaningful concepts of a more holistic nature. Both matrices were found to be factorable with
Bartlett’s Test of Sphericity at the p < 0.0001 significance level, and the KMO statistics of sampling
adequacy were 0.766 and 0.752 respectively. Principal Components Analysis with Direct Oblimin
rotation was used to identify 16 and 15 main factors for China and Russia respectively. The Direct
Oblimin rotation was preferred over Varimax, since it allows the factors to co-vary (Kline, 1994),
which is important in text analysis where the same word can be used in multiple language contexts.
With a number of words loaded highly on several factors, the Oblimin rotation produces the most
simple and interpretable factor structure. The numbers 16 and 15 were chosen on the basis of the
desirable variable/factor ratio (Kline) and in order to make the factors more general, or global.
Weak items that did not load higher than 0.35 on any factor were eliminated. The 83 (China) and
70 (Russia) retained variables produced an interpretable solution that explained 57.2 and 58.1
percent of the variances; however, since the factors co-varied, the sum of variances explained by
each factor did not equal the total variance.
Since the number of variables selected as a result of the CATPAC analysis was arbitrary and
based on a desirable case/variable ratio, some factors were stronger than others because the “cut-
off ” line had left below the words important for the interpretability of certain factors. For example,
it was suspected that the word “school”, which made a separate factor for the Russian sample, was
connected with the word “Beslan”, which was not initially included into the Russian list of organic
image variables. Together these two words referred to a terrorist attack in a secondary school in the
city of Beslan on 1 September 2004. It should be mentioned here that in a search for the best
interpretable solution, several numbers of factors were tried for both samples with and without


variables that had “boundary” loadings. Although some of the variables moved from factor to
factor, this did not affect the solution much, since the variables with the highest loadings stayed
firmly put in their respective factors. The identified main organic image themes are shown in Table
3 and 4 along with the variances explained and Cronbach’s reliability alphas.

Table 3 Main Organic Image Themes about China: 2002-04

Variance
No. Factor Items explained Alpha

Economic economy, bank, money,growth, central, investment,
1 5.911 0.587
Growth economic, China

2 Industry demand, price, cost, steel, industry, company, workers 4.717 0.726

World Trade
3 Organization UNWTO, trade, foreign. import, country 4.464 0.712
(UNWTO)

4 Global Market state, major, global, market, university, Chinese 4.048 0.623

5 Taiwan military, official, government, Taiwan, Asia, Washington 3.967 0.726

Technology administration, technology, Bush, sales, export, officials 3.943 0.745
6
Transfer Li

7 Government Jiang, Hu, Wen, leaders, president 3.537 0.784

8 Labor Market job, high, work, construction, center 3.176 0.672

Cultural public, school, Shanghai, U.S., old (year-old), help 3.161 0.636
9
Communication

10 SARS health, disease, SARS, news, province, city, Hong Kong 3.140 0.699

11 Human Rights human rights, world, year 3.121 0.940

12 Communist Wang, local, communist party 2.940 0.583
China

13 Asian Politics power, nuclear, American, Korea 2.909 0.423

14 Security security, council, against 2.484 0.663
Concerns

Educational students, team 2.152 0.779
15
Exchange


The identified 15 factors present China’s organic image mainly from an economic, political,
and social standpoints. The first three factors, as well as factor 8, are quite self-explanatory and
relate to China’s current economic growth. Factor 4 describes China as a global market not only
economically, but also from an educational aspect. Closer economic contacts with the U.S. bring
concerns about the price of labor and supply-demand of raw materials, such as steel (factor 2).
Since entering UNWTO in 2001, more trade activities have been going on between China, U.S.
and other foreign countries (factor 3). Factor 6 reflects transfer of U.S. modern technology for the
needs of China’s growing economy.

Table 4 Main Organic Image Themes about Russia: 2002-04

Variance
No. Factor Items explained Alpha

Yukos, company, Khodorkovsky, state, billion,
1 Yukos 4.936 0.810
business, companies

2 Iraq United, States, Iraq, nations, American 4.732 0.776

Presidential election, Vladimir, Putin, political, party, Kremlin, 4.110 0.747
3
Elections president

4 Law law, foreign, Russian, international, against, country 3.849 0.595

Natural oil, percent, gas, Russia’s Russia, million, government
5 3.691 0.690
Monopolies

6 Chechnya Chechen, Chechnya, people, war, Moscow 3.554 0.667

7 Soviet Past Soviet, Union, former, years 3.519 0.720

8 Iran nuclear, weapons, Iran, security 3.291 0.730

Russian Children
9 family, home, children, year-old, want 3.198 0.673
in the U.S.

10 NATO NATO, defense, Bush, countries 2.841 0.512

Russia-China
11 Chinese, China, Russians 2.823 0.593
Relations

Power Sector
12 power, place (take place), investment 2.778 0.635
Reform

13 Sports team, national, world, time 2.444 0.489

U.S.-Russia Space
14 space, station, program 2.318 0.725
Cooperation

15 Beslan Beslan, school 1.871 0.678


A significant number of articles in the Chinese media sample covered China’s internal affairs.
In factor 7, three names belong to the China’s former president Jiang, Zemin, current president
Hu, Jintao, and the present premier Wen, Jiabao. The Taiwan issue (factor 5) is a long-standing
one and affects China’s image and the U.S.-China relationship (Yan, 1998). Factors 13 and 14
highlight China’s influence on the political stability in the Asia, as well as show the U.S. concern
for security in Asia. The “human rights”" image theme is reflected by factors 11 and 12. The latter
tells about a “Strike Hard” campaign held by the China’s police forces annually. The case of Wang,
54, who was jailed twice in China for speaking out against the communist government, was
highlighted in the U.S. news. Factors 9 and 15 together reflect such issues as sports, adoption of
Chinese children, education and student exchange programs, as well as cultural and communication
activities in cities like Shanghai. Finally, the SARS issue made factor 10.
Out of 15 Russian organic image factors, four relate to the role that Russia plays in foreign
politics with regard to Iraq, Iran, NATO, and Russia-China relations. Factor 7, Russia’s Soviet
Past, also refers, in part, to the influence that Russia has on the world’s political arena as the
successor of the Soviet Union. Economic issues are represented by factors 5 (Natural Monopolies),
factor 12 (Power Sector Reform), and factor 1 (Yukos). The case of Yukos, Russia’s biggest oil
company, and its president Khodorkovsky, highlighted the insecurity of foreign investment in
Russia and has also been covered in the U.S. media as politically motivated. The other two themes
that deal with legal and human rights issues are Chechnya (factor 6) and Law (factor 4). The latter
generally refers to inadequacy of Russian legal system and selective use of Russian law. Russian
internal affairs are represented by factor 3 (Presidential Elections). The remaining organic image
themes refer to U.S.-Russia space cooperation (factor 14), sports (factor 13), adoption of Russian
children by American citizens (factor 9), and the Beslan tragedy (factor 15).
To conduct further comparisons, it was decided to assign one of three “favorability” values to
each factor based on whether the coverage of a topic in the U.S. media was mostly negative (-1),
neutral (0), or positive (+1). The interpretation of favorability was done by the researchers and
involved two main considerations: 1) the issue itself; and 2) the attitude to it in the U.S. media.
Some issues, e.g., Beslan tragedy, was interpreted as negative despite the mostly compassionate
coverage of the topic in the U.S. media and the fact that the U.S. and Russia have joined forces in
the “war on terrorism” since 9/11, just because the tourism industry is, generally, hurt by terrorist
activity. To illustrate the second consideration, the Iraq factor (Russia), was interpreted as negative
because Russia’s political stand on the issue was different from that of the U.S. before and after the
Iraq war, and the coverage of Russia’s position was mostly critical.


Figure 1 Favorability Comparison of the Organic Image Themes of China and
Russia

6

Positive
4
Variance explained

2

0

-2

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

Factors Russia

China

With these considerations in mind, China received positive ratings for such factors as Economic
Growth; Industry; UNWTO; Global Market; Technology Transfer; Cultural Communications;
and Educational Exchange. It was decided that the media coverage was mostly neutral for the
topics related to China’s Government; Labor Market; Asian Politics, and Security Concerns. Negative
ratings were assigned to the factors Taiwan; SARS; Human Rights; and Communist China. With
regard to Russia, the positive image issues were Russian Children in the U.S.; Power Sector Reform;
Sports; and U.S.-Russia Space Cooperation. It was decided to assign “0” favorability ratings to
such factors as Presidential Elections; Natural Monopolies; Soviet Past; NATO; and Russia-China
relationships. Negative ratings were given to the image themes of Yukos; Iraq; Chechnya; Iran;
Law; and Beslan. The factors were ranked by their importance, which was understood as the
amount of variance that they explained in the total set of organic image variables (see Tables 3 and
4), and the results of favorability comparison are displayed in Figure 1. It can be seen that the most
important organic image factors for China were generally positive, while for Russia they were
mostly negative.
The 15 factors for China and Russia were further aggregated into broader categories as shown
in Table 5. Although it was preferred to put each factor only in one category, some themes, e.g.,
Yukos (Russia), clearly belonged to two categories, in this case, Economy and Human Rights.
Figure 2 shows how the coverage of China and Russia in the U.S. media was distributed among
the broader organic image theme categories.


Table. 5 Aggregated Organic Image Themes Categories for China and Russia

Category China Russia

Human rights 2 factors: Human Rights; Communist China 3 factors: Yukos; Law; Chechnya

6 factors: Economic Growth; Industry; 3 factors: Yukos; Natural Monopolies;
Economy UNWTO; Global Market; Technology Power Sector Reform
Transfer; Labor Market

Exchange
(education, 3 factors: Global Market; Cultural 3 factors: Russian Children in the U.S.;
technology, sport, Communications; Educational Exchange Sports; U.S.-Russia Space Cooperation
human relations)

5 factors: Iraq; Iran; NATO;
Foreign policy 2 factors: Asian Politics; Security Concerns
Russia-China Relations; Soviet Past

Internal affairs 2 factors: Govrenment; Taiwan 1 factor: Presidential Elections

Safety 1 factor: SARS 2 factors: Chechnya; Beslan

Figure 2 China-Russia Organic Image Theme Comparisons

Human rights
6
5
4
Safety 3 Economy
2
1
0

Internal affairs Exchange

Foreign policy Russia
China


5. Discussion
The results of this study provide insights into the organic images of China and Russia as
portrayed by the prominent newspapers of one of the largest tourist markets in the world. It can
be seen from Figure 2 that the main aspect of China’s organic image in the U.S. is China’s rising
economic power, while for Russia the dominant organic image is the country’s influence on world
politics. This difference is also reflected in the numbers of business arrivals from the U.S. to China
and Russia: in 2004 China received 286,329 business travelers from the U.S. (“China Tourism,”
2005), while Russia hosted only 88,000 U.S. business guests in 2003 (Rosstat, 2004).
No explicit tourism-related issues emerged from the analysis; it can be stated that the revealed
organic destination image did not go much beyond the country image. Prior to the research, it
was expected that the tourism issue would make a separate factor for China’s organic image, given
the fast growth of the Chinese tourism industry and its importance for the country, as well as the
recognition of that growth by the World Tourism Organization, which has projected China’s
position as the world’s leading tourist destination by 2020 (UNWTO, 2004a). Nonetheless, it
was not the case. Similarly, for Russia, information on architectural and cultural pleasures was
overshadowed by political news and did not make a separate factor. Out of 12,847 articles in the
LexisNexis database with the word “China” in the headlines only 40 had the words “tourism” or
“tourist” in the headlines, and a good portion of these articles was about outbound Chinese tourism.
Comparatively, for Russia the numbers were 5,132 and 5.
However, organic image themes identified - economical, political, terrorism, SARS, people-
related issues, etc. - are directly related to perception of important destination attributes (Echtner
& Ritchie, 1991, 1993). Harris (as cited in Crompton, 1977) also recognized sanitary conditions,
safety from physical harm, costs of trip, and friendly people at a destination as ones of the most
influential attributes in determining destination choices. The swift way with which China suppressed
the SARS epidemic, helped the Chinese tourism industry recover after a significant dip in
international tourism arrivals in 2003 and 2004. Russia, on the contrary, has not been able to
curb the outbreaks of terrorist activity on its territory for several years, which might partly explain
why its international arrivals are growing slowly (Shpilko, 2004). Visitors should be assured that
at least the areas of primary tourist interest are safe for international travelers. As can be seen from
Figure 1, there is also more negativity in U.S. newspaper materials towards Russia than China,
and the larger numbers of U.S. arrivals to China seem to reflect that.
Yan (1998) showed that the image of China in the U.S. has been changing with the U.S.
foreign policy towards that country. The current movement in increasing the number of travelers
from the U.S. to China seems to be very favorable, since the bilateral relationships between the
U.S. and China are on the rise. Close economic ties between China and the U.S. have been
extended to tourism cooperation, with the signing of the Memorandum of Understanding by
tourism authorities of China and the U.S. (“Commerce Announcement,” 2005). Comparatively,
U.S.-Russia relationships have been cooling for a number of years now, and the once prevailing


image of Russia as an aspiring democracy has given way to tiredness with the country because of
Russia’s deviation from democratic values (Graham, 2000). However, despite the cool political
relationships between the U.S. and Russia at present, there are promising areas to improve tourism
to Russia from the U.S., as shown in Figure 1. The positively covered U.S.-Russia joint space
exploration, educational and people exchange, as well as sports are the areas around which new
tourism initiatives of the FTA can be centered.

CATPAC-WORDER Approach
The computer-assisted CATPAC-WORDER approach used in this study was intended to
broaden the scope of destination image analysis to the theoretically developed but practically not
adequately researched constructs, such as organic destination images. The approach is relatively
speedy, non-intrusive, and extremely transparent. It is not a subject to a so-called “black box”
problem, when researchers using certain software do not exactly know how the results were achieved
(Zuell & Landmann, 2004). Structurally, the approach is thematic, since it counts the occurrences
of specified image variables in the sampled text blocks, and, hence, is very close to the classical
contingency data analysis. The approach is particularly useful when a large number of textual files
representing a certain text population need to be analyzed. Additionally, the data can be “embellished
with secondary variables that measure the source’s positive or negative sentiment regarding each
theme” (Roberts, 2000, p. 263) as was demonstrated by assigning a favorability index to each of
the identified organic image factors. While the approach was used for destination image studies,
its application scope can be easily extended to other areas of content analysis research.

Limitations and Further Research
One limitation of the study was using newspapers as the only source for investigation of organic
component of destination image. While this choice was justified at the stage of study design, finding
a way to incorporate other organic image sources might have proved beneficial for identification of
more tourism-related organic image themes. As the results indicated, the recognized organic image
themes did not go beyond the characteristics of the country images of China and Russia.
As stated by Gartner (1993), a destination image changes slowly, and the bigger a country,
the slower the change is. This research supports this proposition, since it was concluded that the
U.S. newspapers predominantly endorsed the long-standing economic and political issues of
U.S.-China and U.S.-Russia relationships. Figures 1 and 2 are a reflection of the current U.S.
foreign policy concerns regarding these two countries, when China is considered as a threat to the
U.S. economy and Russia is still viewed as a danger to the U.S. international interests. It seems
interesting in future studies to apply the proposed CATPAC-WORDER methodology to smaller
competing travel destinations, such as Turkey and Egypt or Bahamas and Jamaica using Russian
and U.S. media respectively in order to investigate organic destination images in the absence of
strong political undertones.


Acknowledgement
The authors would like to express thanks to the anonymous reviewers who helped to strengthen
this article.

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