Stepchenkova chenmorrisonctr

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

China and Russia: Organic Destination Images inU.S. MediaSvetlana Stepchenkova*, Yi Chen, Alastair M. MorrisonPurdue 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 authorSvetlana Stepchenkova, Ph.D. Student, Department of Hospitality and Tourism Management, College ofConsumer and Family Sciences, Purdue University, Email: [email protected] 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 Consumerand Family Sciences, Purdue University, Email: [email protected].

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


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

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

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

No. Factor ItemsVariance

Alphaexplained

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

5.911 0.587

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

World Trade3 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

6Technology administration, technology, Bush, sales, export, officialsTransfer Li

3.943 0.745

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

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

9CulturalCommunication

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

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 CommunistChina

Wang, local, communist party 2.940 0.583

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

14 SecurityConcerns

security, council, against 2.484 0.663

15EducationalExchange

students, team 2.152 0.779


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

No. Factor ItemsVariance

Alphaexplained

1 YukosYukos, company, Khodorkovsky, state, billion,business, companies

4.936 0.810

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

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

4.110 0.747

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

5Natural oil, percent, gas, Russia’s Russia, million, governmentMonopolies

3.691 0.690

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

9Russian Childrenin the U.S.

family, home, children, year-old, want 3.198 0.673

10 NATO NATO, defense, Bush, countries 2.841 0.512

11Russia-ChinaRelations

Chinese, China, Russians 2.823 0.593

12Power SectorReform

power, place (take place), investment 2.778 0.635

13 Sports team, national, world, time 2.444 0.489

14U.S.-Russia SpaceCooperation

space, station, program 2.318 0.725

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.


6

4

2

0

-2

-4

-61 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Factors

Negative

Positive

Var

ianc

e ex

plai

ned

Russia

China

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

Russia

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 Cooperationhuman relations)

Foreign policy 2 factors: Asian Politics; Security Concerns5 factors: Iraq; Iran; NATO;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

6

5

4

3

2

1

0

Russia

China

Human rights

Economy

Exchange

Safety

Internal affairs

Foreign policy


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.

ReferencesAgrawal, J., & Kamakura, W. A. (1999). Country of origin: A competitive advantage? International Journal

of Research in Marketing, 16(4), 255-267.

Alexa, M., & Zuell, C. (2000). Text analysis software: Commonalities, differences and limitations: Theresults of a review. Quality and Quantity, 34(3), 299-321.

Berg, B. L. (1995). Qualitative research methods for the social sciences. Boston: Bacon and Allyn.

Bhattacharyya, D. P. (1997). Mediating India: An analysis of a guidebook. Annals of Tourism Research, 24(2),371-389.

Chang, H.-C., & Holt, G. R. (1991). Tourism as consciousness of struggle: Cultural representations ofTaiwan. Critical Studies in Mass Communication, 8(March), 102-118.

China National Tourism Administration (CNTA). (2004). Foreign visitors arrivals by region and nationality.Retrieved May 19, 2005, from http://www.cnta.com/lyen/2fact/foreign%20visitor-2.htm

China tourism statistics. (2004, December 9). Chinatour.com. Retrieved May 19, 2005, from http://www.chinatour.com/data/data.htm

China tourism statistics. (2005). China National Tourist Office. RetrievedDecember 29, 2005, from http://www.cnto.org/chinastats.asp

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

Commerce announcement of the US/China tourism MOU (2005). ITA Office of Travel & Tourism Industries.Retrieved December 29, 2005, fromhttp://tinet.ita.doc.gov/outreach/mou_memo.html

Crompton, J. L. (1977). A system model of the tourist's destination selection process with particular reference to therole of image and perceived constraints. Unpublished doctoral dissertation, Texas A&M University, CollegeStation.

Dann, G. M. S. (1992). Travelogs and the management of unfamiliarity. Journal of Travel Research, 30(4),59-63.

De Wet, A. G., Pothas, A.-M., & De Wet, J. M. (2001). Country of origin: Does it matter? Total QualityManagement, 12(2), 191-200.

Doerfel, M. L., & Marsh, P. S. (2003). Candidate-issue positioning in the context of presidential debates.Journal of Applied Communication Research, 31(3), 212-237.

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

Echtner, C. M., & Ritchie, J. R. B. (1993). The measurement of destination image: An empirical assessment.Journal of Travel Research, 31(4), 3-13.

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

Gartner, W. C. (1993). Image formation process. Journal of Travel and Tourism Marketing, 2(2/3), 191-216.

George, A. (1959). Quantitative and qualitative approaches to content analysis. In A. Raftery (Ed.), Sociologicalmethodology (pp. 135-144). Oxford: Basil Blackwell.


Govers, R., & Go, F. M. (2003). Deconstructing destination image in the information age. InformationTechnology and Tourism, 6(1), 13-29.

Graham, T., Jr. (2000, April 12). Russia's presidential elections. Retrieved December 29, 2005, from http://www.carnegieendowment.org/publications/index.cfm?fa=viewandid=528

Gray, J. H., & Densten, I. L. (1998). Integrating quantitative and qualitative analysis using latent andmanifest variables. Quality and Quantity, 32(4), 419-431.

Gunn, C. A. (1988). Vacationscapes: Designing tourist regions. New York: Van Nostrand Reinhold.

Hunt, J. D. (1975). Image as a factor in tourism development. Journal of Travel Research, 13(3), 1-7.

Insch, G. S., & Moore, J. E. (1997). Content analysis in leadership research: Examples, procedures, andsuggestions for future use. Leadership Quarterly, 8(1), 1-25.

Jacobsen, J. K. S., & Dann, M. S. G. (2003). Images of the Lofoten Islands. Scandinavian Journal ofHospitality and Tourism, 3(1), 24-47.

Janda, S., & Rao, C. P. (1997). The effect of country of origin related stereotypes and personal beliefs onproduct evaluation. Psychology & Marketing, 14(7), 689-702.

Kirilenko, A. P. (2004). WORDER (Version 2.0) [Computer software]. Retrieved December 29, 2005, fromhttp://kirilenko.org/worder

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

Lohr, S. L. (1998). Sampling: Design and analysis (1st ed.). Pacific Groves, CA: Duxbury Press.

Macnamara, J. R. (2003). Media content analysis - Its uses, benefits and best practice methodology. RetrievedDecember 27, 2005, from www.masscom.com.au/book/papers/media_content.html

Martin, I. M., & Eroglu, S. (1993). Measuring a multi-dimensional construct: Country image. Journal ofBusiness Research, 28(3), 191-210.

Newbold, C., Boyd-Barrett, O., & Van den Bulk, H. (Eds.). (2002). The media book. London: Arnold.

Nickerson, N. P. (1995). Tourism and gambling content analysis. Annals of Tourism Research, 22(1), 53-66.

Nolan, S. D. (1976). Tourists' use and evaluation of travel information sources: Summary and conclusions.Journal of Travel Research, 14(3), 6-8.

Pearce, P. L. (1988). The Ulysses factor: Evaluating visitors in tourist settings. New York: Spring-Verlag.

Pike, S. (2002). Destination image analysis - A review of 142 papers from 1973 to 2000. Tourism Management,23(5), 541-549.

Quester, P. G., Dzever, S., & Chetty, S. (2000). Country-of-origin effects on purchasing agents' productperceptions: An international perspective. The Journal of Business and Industrial Marketing, 15(7), 479-489.

Roberts, C. W. (2000). A conceptual framework for quantitative text analysis. Quality and Quantity, 34(3),259-274.

Rosstat. (2004). Tourism and tourist resources in Russia. Moscow, Russia: Author.

Schmidt, M. (1998). Quantitative analysis of qualitative interviews: Theoretical Considerations and EmpiricalAnalysis. Retrieved December 29, 2005, from http://www.mic.cbs.dk/marcus/GBPapers/AMA98/MarcBos.htm

Shpilko (2004, April 11). What interferes with transformation of Russia into strong tourist power? RetrievedOctober 25, 2005, from www.2r.ru/turizm_in_russia.asp

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


Vogt, C. A., & Fesenmaier, D. R. (1998). Expanding the functional information search model. Annals ofTourism Research, 25(3), 551-578.

Weber, R. P. (1985). Basic content analysis. Beverly Hills, CA: Sage.

Woelfel, J. K. (1998). CATPAC: User's guide. New York: RAH Press.

World Tourism Organization (UNWTO) (2004a). Tourism Highlights, Edition 2004. Retrieved May 18,2005, from http://www.world-tourism.org/facts/highlights/HIGHLIGHTS%20INGLES%2020041.pdf

World Tourism Organization (UNWTO) (2004b). Inbound Tourism - World's Top Tourism Destinations.Retrieved May 18, 2005, from http://www.world-tourism.org/ facts/tmt.html

Xie, P. F., & Groves, D. (2003). Identifying cultural iconography: A case study of Canadian-Americanfestival. Event Management, 8(2), 91-97.

Yan, W. (1998). A structural analysis of the changing image of China in the New York Times from 1949through 1988. Quality and Quantity, 32(1), 47-62.

Zuell, C., & Landmann, J. (2004, August 17-20). Computer-assisted content analysis without dictionary? Paperpresented at the Sixth International Conference on Logic and Methodology. Retrieved December 27,2005, from http://www.gesis.org/Methodenberatung/Textanalyse/Comp-Inhaltsanalyse/rc33_proceeding.pdf

LexisNexis. (2006). Retrieved February 14, 2007, from http://www.lexisnexis.com/academic/universe/academic/features.asp

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