Cultural Differences in Pictorial Destination Images

Abstract

Destination photography communicates images that shape and reshape destination perceptions of past and potential tourists and, consequently, influence their decision-making process. Using theoretical underpinnings provided in works on culture, this exploratory study examines whether destination photographs taken by tourists and posted on social-network websites are reflective of culture to which those tourists belong. Content analysis of American and Korean photographs of Russia posted on Flickr and in Korean travel blogs, respectively, was followed by chi-square and co-occurrence analyses of destination attributes, as well as geospatial analysis of image locations using ARC GIS software. Although the core of Russia’s destination image—urban, contemporary, architecturally interesting, and spacious—is similar for both cultural groups, the study found differences in the way that American and Korean tourists represent Russia as a destination. These differences are discussed from the perspective of tourists’ respective cultures.

Keywords

ARC GIS content analysis culture destination image photography

Introduction

With the arrival in recent years of new media and the development of Web 2.0 applications, user-generated content on the Internet has increasingly been considered as a credible form of word of mouth. Social network sites such as Facebook, Flickr, Instagram, and Panoramio have provided tourists with previously unimaginable opportunities to make their travel accounts truly public by uploading their stories and photographs online, an activity that has gained popularity among Internet users. From a tourist’s perspective, “photographs are a medium through which people relate to visual images and make them their own” (Albers and James 1988, p. 136) and “slideshows and photographs are a common way to communicate personal trip experiences and perceived destination images” (Schmallegger, Carson, and Jacobson 2010, p. 245). Thus, destination photography communicates images that shape and reshape potential travelers’ destination perceptions and, consequently, influence their decision-making processes. Research on the relationship between destination perceptions and visitors’ cultural backgrounds has been identified as an important direction for destination image studies that could help destination marketing organizations (DMOs) to more effectively position and promote their tourism offers in culturally different target markets (Kastenholz 2010; Reisinger and Turner 2002a, 2002b; Stepchenkova and Zhan 2013).

According to Hall (1959, p. 52), culture permeates all aspects of life: it is “a mold in which we are all cast, and it controls our daily lives in many unsuspected ways.” Through exposure to other cultures, people make sense of their own value systems and communication rules, and the ultimate reason to study a foreign culture is to learn more about how one’s own system works and to pay attention to those details of life that distinguish other cultures from one’s own (Hall 1959). Thus, arguably, people from different cultural backgrounds perceive destinations differently. In addition, certain aspects of a destination, or pull factors (Crompton 1979), are of greater interest to people of different cultures because culture often manifests itself through contrasts. However, cross-cultural research has not yet received enough attention in the tourism literature, and research on visual representations of culture is particularly scarce. Thus, using theoretical underpinnings provided by works on culture (Hall 1959; Hofstede 1980; Kluckhohn and Strodtbeck 1961), this study aims to answer the following question: Are the differences in destination images as projected in tourist photography reflective of the national cultures of the tourists who take these images? The study used Russia as a destination and American and South Korean tourists to represent two distinctly different cultural perspectives. Russia and Korea were selected to allow for insights arising out of the researchers’ own cultures and experiences, whereas the United States was selected to accommodate a supposedly culturally different point of view at the destination and to allow for comparisons with previous research on destination images of Russia by U.S. tourists, media, and travel intermediaries.

Study Background

Culture

Culture has been defined as “the collective programming of the mind distinguishing the members of one human group from another” (Hofstede 1980, p. 21). Culture permeates various levels of society; thus, “human groups” can refer to civilizations (e.g., Ancient Egypt, the West, the Orient), nations (e.g., England, France), regions within or across nations (e.g., American Indian, Pennsylvania Amish), ethnicities (e.g., Latino, African American), religions (e.g., Catholic, Baptist), occupations (e.g., lawyer, programmer), organizations (e.g., Google, McDonald’s), or gender. Hall (1959, p. 43) has defined culture as “the way of life of a people . . . the sum of their learned behavior patterns, attitudes, and material things.” In a similar vein, Kluckhohn and Strodtbeck (1961, p. 25) have stated, “culture is learned by people as a result of belonging to some particular groups, and it is that part of learned behavior which is shared with others.” Culture includes both observable elements, such as behaviors, arts, symbols, and social patterns, and nonobservable characteristics, such as values, beliefs, attitudes, and shared meanings. Thus, differences between groups in behavior patterns and perceptions of the world can be referred to as culture, and culture can serve as a guide to interpreting human behavior (Kim and Gudykunst 1988).

Much of the research in anthropology and sociology has been devoted to identifying those aspects of collective behavior along which cultures can be summarized and compared. One of the earliest theories, the theory of value orientation (Kluckhohn and Strodtbeck 1961), identifies five fundamental problems of human societies and proposes that collectively preferred solutions to these problems in any given society determine that society’s culture. The first problem deals with the primary orientation of human nature: is it evil, good-and-evil, or good? Second, what is the nature of the relationship between people and the world around them: subjugation-to-nature, harmony-with-nature, or mastery-over-nature? Third, is society oriented toward past, present, or future (the time aspect)? Fourth, what is the purpose of human activity: to express one’s self, to improve one’s self, or to achieve? Lastly, how do people establish relationships in the society: hierarchically, as equals, or based on individual merit? (cf. Reisinger and Turner 2002a, p. 298; Watkins and Gnoth 2011, p. 1276). For example, in the United States, human nature is perceived as a mixture of good and evil, mastery over nature is emphasized, society is oriented toward the future, the nature of human activity is tilted toward doing, and relationships between individuals are based on merit (Kluckhohn and Strodtbeck 1961).

Hall (1959) has viewed culture as a form of communication and differentiated cultures with regard to the context that underlines all communication messages. Low-context cultures, such as the United States and Germany, require more explicit communication than high-context cultures, such as France or Russia, where much meaning is implied and nonverbal messages play a greater role. In low-context cultures, business contracts, for example, tend to be longer because nothing is taken for granted and all details need to be spelled out, as opposed to high-context cultures in which “unwritten rules” of the culture regulate relationships. Communication through handling personal space, or territoriality, is another aspect of culture. Through managing space by keeping a “proper” distance, shifting one’s tone of voice, or assigning particular areas for various types of activities, people ascertain status, social roles, ownership, and comfortable levels of security. The time aspect is yet another point of differentiation, which describes cultures as monochronic or polychronic. In monochromic cultures (e.g., German culture), time is planned in advance and highly organized, and multiple deadlines exist to “get things done.” In polychronic cultures, time is perceived as open and flexible, an emphasis is put on process rather than product, and goals are achieved by building relationships (e.g., Italian culture). Finally, communication messages, or information flow, can be characterized as fast or slow: for example, prose versus poetry, newspaper headlines versus books, a communiqué versus an ambassador, propaganda versus art, etc. (Hall and Hall 1990, p. 5). The cross-cultural implication of the information flow aspect is that fast messages directed to people from cultures predisposed to slow message formats will miss the target.

“Complex, intangible and subtle, culture has been notoriously difficult to conceptualize and scale” (Shenkar 2001, p. 519). Following Hall (1959), Porter and Samovar (1991) have come up with a progressive scale of sociocultural differences that affect intercultural communications. According to their scale, Western and Asian cultures exhibit the most differences, that is, “the greatest number of cultural aspects are subject to variation: physical appearance, religion, philosophy, economic systems, social attitudes, language, heritage, basic conceptualizations of self and universe, and degree of technological development” (p. 12), whereas religion, sexual, and gender groups are at the lower end of the scale. However, quantitative comparisons of various cultures became truly possible with work by Hofstede (1980), who has compared values, behaviors, institutions, and organizations across nations using a large data set of IBM employees from 68 countries and classified cultural dimensions as power distance, individualism versus collectivism, masculinity versus femininity, and uncertainty avoidance. The long-term versus short-term orientation and indulgence versus restraint aspects were added to the system later; as yet, not every culture has received a score on these two dimensions. Hofstede’s work has been criticized on various grounds; for example, one criticism is that Hofstede presupposes that cultures match national territories and, thus, are homogeneous, whereas there are many nations that are composed of various cultures, for instance, French- and English-speaking Canadians (for a more comprehensive list of critical points, see Reisinger and Turner 2002a; Shenkar 2001). Nevertheless, Hofstede has developed what is arguably the most widely used system to distinguish among cultures, as indicated by the SSCI (Social Sciences Citation Index) citation counts (Litvin, Crotts, and Hefner 2004).

Hofstede’s scores for the American, Korean, and Russian cultures are presented in Table 1. Power distance reflects the degree of acceptance by people at the lower level of society that power is distributed unequally. On this dimension, Russia is the highest of the three countries, followed by Korea. The individualism–collectivism dimension reflects the interdependency of the members of a society, with Korea and the United States being the most collectivist and the most individualistic of the three cultures, respectively. A high score on the masculine–feminine dimension indicates that a society is driven by competition, achievement, and success, whereas a low score on that dimension means that the dominant values are caring for others and quality of life. The fundamental issue here is what motivates people: wanting to be the best (masculine) or liking what they do (feminine). Thus, the United States is a masculine society, whereas both Russia and Korea are feminine cultures. The dimension of uncertainty avoidance compares cultures with respect to how their members address anxiety arising out of events that are unknown and uncontrollable, that is, to what extent members of a culture feel threatened by ambiguous situations. Russia and South Korea are two of the most uncertainty-avoidant countries in the world. The long-term orientation dimension is closely related to the teachings of Confucius and can be interpreted as the extent to which a society shows a pragmatic, future-oriented perspective rather than a conventional, historical, short-term point of view. Thus, South Korea is as a long-term-oriented society, the United States is a short-term-oriented society, and no score is available for Russia.

Table 1.

Hofstede’s Dimensions Scores: American, Korean, and Russian Cultures.

Culture	Power Distance	Individualism/ Collectivism	Masculinity/ Femininity	Uncertainty Avoidance	Long-Term Orientation
American	40	91	62	46	29
Korean	60	18	39	85	75
Russian	93	39	36	95	No score

Schwartz’s (2006) quantitative classification of 76 countries on seven cultural orientations (harmony, egalitarianism, intellectual autonomy, affective autonomy, mastery, hierarchy, and embeddedness) has resulted in the United States being placed into the same group as New Zealand, the United Kingdom, Canada, Ireland, and Australia and South Korea being placed with other Confucian countries, such as Taiwan, Hong Kong, Thailand, Japan, and China. Schwartz’s orientations partially overlap those of Kluckhohn and Strodtbeck (1961) and Hofstede (1980) in such aspects as orientation toward nature, hierarchy, mastery, and embeddedness of an individual into a society (collectivity). Schwartz has concluded that the United States is low in collectivity, high in hierarchy and mastery over nature, and low in harmony with nature. South Korean culture has been classified as higher in collectivity than the United States, higher in hierarchy than the United States, and high in mastery, although less so than the United States. Russia has been placed in a group of Eastern European countries; on the whole, this group has received mixed reviews because of the profound changes these countries experienced when communist rule collapsed in the 1990s.

Cross-Cultural Research in Tourism

Research on how national stereotypes influence product perceptions has a long tradition in the marketing literature (Pizam and Sussmann 1995); however, cross-cultural differences in tourism behavior are a somewhat underresearched topic (Chen 2000; Money and Crotts 2003; Pizam and Jeong 1996). Studies have found that depending on their national cultural characteristics, tourists differ in the perceived benefits of their trips (Woodside and Jacobs 1985; Sheldon and Fox 1988), expectations of service quality (Armstrong 1997), patterns of vacation travel (Richardson and Crompton 1988), and various behavioral aspects associated with travel, such as socializing, buying souvenirs, and taking photos while abroad (Pizam and Jeong 1996; Pizam and Sussmann 1995). Of particular interest is the study by Pizam and Jeong (1996), who examined Korean tour guides’ perceptions of American, Japanese, and Korean tourists. The American–Korean pair was found to be the least similar. The tour guides considered American tourists as the most, and Korean tourists as the least, knowledgeable about foreign destinations; the researchers attributed Koreans’ lack of knowledge to their inexperience with foreign travel, which started only in 1989 in Korea. Americans were viewed as the most interested in people as opposed to artifacts and were also viewed as the most social and gregarious of the three groups. Americans preferred novelty to familiarity, took longer trips, and were more adventurous and active travelers. The finding corroborates the view by Holzner (1985), who summarized the cultural traits that influence American tourists’ travel behavior as a “love for newness, desire to be near nature, freedom to move, individualism, and social acceptance” (as cited by Pizam and Sussmann 1995, p. 903). Koreans were perceived as “having implacable loyalty to their socio-cultural identity and being unwilling to accept anything that has little in common with the Korean way of living” (Pizam and Jeong 1996, p. 278). They were described as being very particular about food, insisting on going to Korean restaurants while traveling, trusting travel-trade workers, and, more than the other two groups, loving to shop and buy gifts and souvenirs. Korean tourists also exhibit preference for traveling in groups (Prideaux and Kim 1999; Yarmy 1992).

Several studies on cross-cultural comparisons of tourist behavior have utilized Hofstede’s (1980) conceptualization of culture. Specifically, the dimensions of uncertainty avoidance, individualism–collectivism, and masculinity–femininity have been empirically tested. Thus, Money and Crotts (2003) have analyzed the effect of uncertainty avoidance on information searches, planning, and purchases related to international travel vacations for low-uncertainty-avoidant German tourists and high-uncertainty-avoidant Japanese tourists. Furthermore, Litvin, Crotts, and Hefner (2004) have replicated and extended that study using a sample of first-time visitors to the United States from 58 nations. Both studies were able to differentiate travel behaviors based on tourists’ national culture as represented by each culture’s uncertainty avoidance dimension, which was interpreted as the perception of risk associated with international travel. Litvin and Kar (2003) tested the cross-cultural applicability of the self-image congruity concept (Sirgy 1982) on tourists from individualistic and collectivist societies; the correlation between trip satisfaction and self-image congruity was stronger for tourists belonging to individualistic cultures. Chen (2000) invoked the individualism–collectivism factor in explaining information search behavior by Japanese, South Korean, and Australian visitors to the United States. Changes in behavior were most apparent in the area of business travel: Japanese and Koreans relied on travel agencies and corporate travel offices, whereas more individualistic Australians requested information directly from airlines and U.S. tourism offices. Research by Reisinger and Turner (1999) drew on the individualism–collectivism distinction in explaining the differences between Japanese tourists and Australian hosts. Subsequent studies on cultural differences between Asian tourist markets and Australian hosts (Reisinger and Turner 2002a, 2002b) have found that Korean tourists’ satisfaction related to differences in emotional displays, such as criticizing and swearing, and perceptions of providers as being punctual and respectful. Finally, Crotts and Erdmann (2000) tested whether visitors from masculine cultures evaluated tourist services more negatively than visitors from feminine cultures; those researchers found limited support for that proposition.

The surveyed literatures on culture and cross-cultural research in tourism provide a contextual background for this study. The literature review identifies various dimensions on which cultures can be compared and lays down groundwork for category development and, consequently, interpretation of results. In addition, the reviewed literature supports the premise of the study that American and Korean tourists have distinctively different cultural characteristics, as well as point out to differences in their travel behavior. The overarching research question, which has been stated as “Are the differences in destination images as projected in tourist photography reflective of the national cultures of the tourists who take these images?” has been further specified as follows:

Research Question 1: Are the images of Russia captured through the American and Korean camera lenses different in content (image attributes) and composition (co-occurrences of attributes)? Can the differences be explained from the perspective of tourists’ respective cultures?

Research Question 2: What locations in Russia are most often captured through the American and Korean camera lenses? Is there a difference in spatial distribution of the images? Can tourists’ respective cultures suggest explanations for found differences, if any?

Thus, this study is set to investigate whether manifestations of such an elusive construct as culture can be observed in tourists’ photographs and, if yes, along which dimensions these manifestations can be quantitatively captured. The study builds on research by Stepchenkova and Zhan (2013) and offers a procedure to quantitatively identify cultural differences in tourists’ photographs with enough precision for subsequent comparisons.

Method

Analysis of Images

Content and composition are two main features of image (Albers and James 1988). In their totality, appearances, or “signs,” captured in a photo comprise the image content, whereas composition is defined as the way in which appearances are linked to each other. Sets of tourism photos, along with image sets from other domains, are often characterized by redundancy in content and composition, which is indicative of a convention and is used to construct a meaning (Albers and James 1988). The analytical treatment of photographs may be quantitative (content analysis) and qualitative (semiotic analysis). Content analysis is attribute based and addresses the manifest content of image. It “breaks” an image into separate attributes and records their frequencies, co-occurrences, clustering, and other related features. Once an image set has been quantitatively described, it can be further analyzed from temporal (when the pictures were taken), geographic (where the images were taken), or production (who photographed and distributed the images) perspectives. Several tourism studies have applied content analysis to photographs (e.g., Garrod 2009; MacKay and Couldwell 2004; Stepchenkova and Zhan 2013). Garrod (2009) has compared the image of the Welsh seaside resort of Aberystwyth as depicted in visitors’ photos and destination postcards to photos taken by visitors to and residents of Aberystwyth. MacKay and Couldwell (2004) have contrasted visitors’ photos of a national historic site in the Canadian province of Saskatchewan with images that were contemporaneously used in DMO promotional materials. Stepchenkova and Zhan (2013) have comparatively analyzed destination images of Peru as projected by the country’s tourism office and as perceived by tourists; the study also used elements of semiotic analysis for a more in-depth “reading” of the images.

Because visual images imply certain meanings, these meanings are not fixed and are subject to a reader’s interpretation. Semiotic analysis addresses how the content and composition of a picture communicate intended messages through signs and symbols related to a place. The reductive transmission model of content analysis is challenged by semiotic analysis, which “treats each picture as a totality—marking the patterned relationships in its content, connecting these to parallel and contrasting structures in other pictures, and relating both to the written narratives that accompany them” (Albers and James 1988, p. 147). Thus, the technique is highly interpretive and deals primarily with the latent content of an image. To provide examples of the use of semiotics in studies involving imagery, Caton and Santos (2008) have studied travel photos made by students on a study-abroad cruise trip to a Third World country from the perspective of the Urry’s (1990) hermeneutic circle of representation theory. Markwick (2001) has analyzed visual images from Maltese postcards, and the interwoven patterns in their production and consumption, by applying the theories of tourist desire and motivation. Jenkins (2003) has investigated whether Canadian backpackers in Australia are different from mass tourists using a mixed-design study with quantitative and qualitative components.

This study examines whether destination photographs made by tourists and posted on media-sharing and social network sites are reflective of cultures to which these tourist-photographers belong. Content analysis was selected as the primary method because of its ability to manage qualitative material in a systematic, verifiable, and replicable way (Krippendorff 2004; Neuendorf 2002) and to quantitatively support conclusions about differences, if they were found. With respect to research question 1 about content and composition of the images, the approach included the following steps: (1) development of the system of categories along which the cultural differences may be found and classification of Russia’s destination image attributes found in the photographs into these categories; (2) chi-square tests to compare frequencies of image attributes in the American and Korean samples; (3) co-occurrence analysis of image attributes to obtain quantitative structure of image composition in each set of photographs. With respect to research question 2 about geospatial distribution of the images, the geographical location was extracted from image information. Then, the maps of geolocations where the photos were taken were constructed using the ARC GIS software. Finally, theories of culture, together with elements of semiotic analysis, provided the theoretical lenses to interpret the findings obtained for research questions 1 and 2.

Sample Selection

Several requirements for the photo samples were formulated. First, the photos had to have been taken by representatives of the American and Korean cultures. Second, each sample was determined to contain approximately 600 images (Stepchenkova and Zhan 2013) with, preferably, a large number of users in both samples to avoid a situation when a particular cultural perspective was represented by just a few users. Third, the photos had to be pictures of Russia taken in Russia by actual visitors, not reprints from advertisements or travel guides. Fourth, the samples had to represent the actual travel geography of tourists in Russia; thus, searches using words “Moscow” or “Saint Petersburg” were deemed not appropriate. The final criteria were accessibility and ease of image retrieval. These requirements turned out to be quite restrictive, mainly because only a fraction of users either included their place of residence or nationality in their social-media profiles or geotagged their photos. Media-sharing websites such as Facebook, Flickr, Instagram, Panoramio, Picasa, and Pinterest were investigated as potential sources for image retrieval.

Flickr’s built-in, ready-to-go programming tools were conducive to data collection. To obtain a pool of Americans’ images and to satisfy the specified criteria, researchers performed an identical search using the word “Russia” for each of 16 six-month periods, going back to January 2005. The search required each photo to be geotagged with a location within the Russian Federation, and the user of the selected photo had to list a U.S. location in his or her Flickr profile. Next, the data from all 16 periods were combined, resulting in 4,000 photographs. To ensure that the sample was composed of unique users and to meet the target sample size, researchers took all images from those users who posted up to three photographs; if a person had more than three, then the three photos were selected randomly. Thus, the final American sample (658 images, 295 unique users, M = 2.23, SD = 0.89) was diversified and randomized with respect to the number of photos and users.

The Flickr procedure could not have been replicated for the Korean sample. Researchers found that, while Americans post approximately 25% of Flickr photos, the photos by Korean users comprise only 1%. Thus, the search algorithm developed for Flickr was unable to return a large enough, representative sample of Korean photographs. It was decided to search Korean travel blogs for entries in the Korean language about travel in Russia using the most popular Korean search engine and web portal Naver, which has 70% of all searchers in South Korea (searchenginewatch.com/topic/naver). Globally, Naver is ranked as the 13^th most popular search engine in the world (statcounter.com). A search using the terms “travel” and “Russia” resulted in 6,000 blog entries. Naver’s convenient interface indicates how many photos a blog entry has; thus, blog entries with photos were accessed one by one, starting from the top, with three images taken randomly from each entry. This process of reviewing Naver results continued for as long as Naver allowed (777 photos). Because some users had posted multiple blog entries, the number of photos per user varied in the collected pool. To keep all unique users in the sample and to meet the target sample size, all photos were taken from users who posted up to ten photos; if a user had more, ten photos were selected randomly. The final Korean sample had 597 images and 139 unique users (M = 4.29, SD = 2.52). The difference in the number of unique users was somewhat expected and reflected differences in the numbers of international arrivals to Russia from the United States (262,000) and South Korea (5,000) (Goscomstat 2009).

Category Development

To develop categories in this exploratory study, approximately 20% of the selected images were examined by the authors to identify the main Russian destination attributes pictured by the photos (Glaser and Strauss 1967; Neuendorf 2002). Works on culture (Hall 1959; Kluckhohn and Strodtbeck 1961; Schwartz 2006), theory-driven destination image studies (Echtner and Ritchie 1991), and empirical research on Russia’s destination image (Stepchenkova and Morrison 2008) provided guidance for category formation. Each photo was regarded as a single unit of content and coded along the following dimensions: PEOPLE (Single, Group, Few Random, or Crowd), NATURE LANDSCAPE, PLACE (Urban Area or Rural Area), SPACE (Tourist, Residential, or Private), TRANSPORT & INFRASTRUCTURE, ACTIVITIES (Leisure, Outdoor and Sports, Way of Life, or On the Streets), SEASON (Cold Climate or Greenery), ARCHITECTURE, HERITAGE (Arts & Culture, History, Food & Things), and STATE POWER. The dimensions were in fact first-tier categories with several levels (second-tier categories). For each dimension, the second-tier categories were exhaustive (with addition of “not present” or “not applicable”) and mutually exclusive.

Formal guidelines for coding were developed and supplemented with pictorial examples. The second author coded all images, and the first author coded a subsample of 274 images to assess reliability. Cohen kappa was selected as a conservative reliability coefficient, as it provides agreement beyond chance only and is appropriate for the situation of two coders and nominal data (Neuendorf 2002). The values for Cohen kappa were as follows (with confidence intervals): PEOPLE (0.90; 0.86-0.95), NATURE LANDSCAPE (0.95; 0.89-1.01), PLACE (0.75; 0.68-0.82), SPACE (0.62; 0.55-0.69), TRANSPORT & INFRASTRUCTURE (0.86; 0.78-0.94), ACTIVITIES (0.70; 0.62-0.78), SEASON (0.91; 0.85-0.96), ARCHITECTURE (0.84; 0.77-0.90), and HERITAGE (0.76; 0.68-0.85). The agreement ranged from “substantial (.61-.80)” to “almost perfect (.81-1.00)” (Landis and Koch 1977, p. 165). The coders reviewed the issues of disagreement in the dimensions with the lowest kappa, clarified ambiguities, and strengthened the consistency of coding. As a result of this process, the categories under the HERITAGE dimension were reorganized, with the STATE POWER becoming a separate dimension, and recoded. Descriptions of the final set of categories, albeit shortened and without pictorial examples to save space, are given in Table 2.

Table 2.

Description of Image Categories.

Dimension	Category	Description^a
PEOPLE	Single	One person, reasonably in the center or the photo or one of the photo’s foci.
	Group	Two or more people, clearly together. Can be a tourist group. If there is a group in the “front” and random people or a crowd on the background, the “central” part of the photo takes preference.
	Random Few	Few random, unrelated people. People clearly visible but not necessarily foci of the picture. The overall impression of the picture should be “not busy.”
	Crowd	Crowd at a tourist site, many people on a busy street, on the metro, etc. The overall impression is “busy.”
NATURE LANDSCAPE	Nature Landscape (NL)	Nature and nature landscape with minor human-related elements. Forests, meadows, rivers and lakes, steppes, mountains, coastlines, beaches, etc. Pictures are not necessarily “panoramic” and may include trees, foliage, and natural attractions like grottos, caves, etc. at medium range. May include buildings/churches; however, nature should be a dominant theme.
PLACE	Urban Area (UA)	Clear indication of urbanized/developed environment. City skylines, city streets, plazas with monuments, buildings, shops, etc. Pictures are not necessarily “panoramic” but they should not focus on a single piece of architecture.
	Urban Area (UA)	A city skyline at night or streets with glimmering street lights, neon advertisements, as well as bar, club, and restaurant signs are coded as UA.
	Rural Area (RA)	Refers to rural areas, landscapes with elements of agriculture, farming, crop fields, gardens, orchards, growing fruits and vegetable. Picture may include people’s dachas, villages or small rustic settlements, fences in the fields, water wells, agricultural equipment (or remnants of it), and other elements of people’s presence.
SPACE	Tourist Space (TS)	Tourist sites (e.g., Winter Palace, Red Square, and Petergof). Hotels, resorts and spas, beaches with chairs and umbrellas. Hotel rooms or other hotel spaces like golf courses or tennis courts. Museums, stadiums, banks, etc. McDonald’s, Starbucks, and other restaurants.
	Residential Space (RES)	Places that are neither tourist sites nor private spaces: streets, transportation stops, residential areas around metro stations, etc.
	Private Space (PS)	Living conditions of Russian people. Photos of apartment houses in suburbs (spal’nyi raiyon), private cottages, country homes, apartments, or other private structures like barns or garages in both urban and rural areas. Photos can be made from both inside and outside.
TRANSPORT & INFRASTRUCTURE	Transport & Infrastructure (T&I)	Roads, highway networks, train tracks, navigable waterways with transport, bridges, seaports and lighthouses, water towers and power plants, and power lines. Airports, train stations, bus stops, and marina terminals. Planes, trains, passenger boats, ferries, taxis, cars, and mass transit systems such as commuter rail systems, subways, tramways, trolleys and bus transportation. Images clearly focused on bicycle paths, street signage, pedestrian walkways, parking spaces and garages are T&I.
ACTIVITIES	Leisure Activities (LEI)	People shopping, dining, sitting at sidewalk cafes, relaxing in parks or on city boulevards, as well as sightseeing in a city or at tourist sites. On excursion, touring a city on a river boat, cruising, or watching sport events. Seeking nighttime diversion at nightclubs, theaters, concerts, art performances, etc.
	Outdoors & Sports (O&S)	People engaging in outdoor, adventure, and sports activities: skiing, skating, snowboarding, sailing, kayaking, hiking, trekking, horse riding, fishing, swimming, playing soccer, tennis, etc.
	Way of Life (WOL)	WOL reflects Russian people in everyday situations in both private and public spaces: at their homes, in schools and kindergartens, at a market, in a store, in a church or at religious rituals, or at civil protests/meetings. Everyday activities may include farming, cooking, making preserves for winter, fixing a car, playing with children, talking with a buyers/seller on the market, etc.
	On the Streets (STR)	People on the move at train stations, cruise terminals, with backpacks and suitcases, crowds in the metro, people who are clearly moving
SEASON	Cold Climate (CC)	Snow and/or areas covered with snow and ice. Images focused on people on the streets in fur coats and warm clothes.
SEASON	Greenery (GR)	Focus on green elements of cities, parks, and residential areas indicating spring, summer, or fall seasons. Flower beds on the streets are GR.
ARCHITECTURE	Architecture (ARC)	Exterior and interior of buildings and historic sites: imperial palaces, cathedrals, churches, ancient fortresses, modern public buildings, in both urban and rural areas. The focus should be on the architectural structure of a building or ensemble as a whole or its architecturally significant elements (e.g., onion-shaped domes, iron-cast balconies, or laced woodwork). Architectural element should be one of the foci of the photo. Close-ups of architecturally interesting elements (e.g., doors, windows, or steeples) are ARC.
HERITAGE	Arts & Culture (A&C)	Symbols of Russian culture: e.g., traditional clothes, ornaments and designs, crafts (e.g., nested Russian dolls), etc. Symbols of Russian Orthodox Church or other religions: icons, head dresses, candles, etc. Paintings and museum art and objects.
	History (HIS)	Monuments of historic significance, e.g., Tsar-Cannon in the Moscow Kremlin. Monuments and sculptures of Russian historic figures and writers. Old photos of life in Russia or photos of the last Romanovs are HIS.
	Food & Things (F&T)	Food, toys, games, transportation tickets, etc.
STATE POWER	State Power (SP)	Symbols of modern Russian state and its Soviet past (e.g., red stars, hammer and sickle). Military parades and equipment, people in military uniform are SP. Monuments symbolizing the state, memorials of the Soviet period are SP. A few panoramic “iconic” images of Moscow Kremlin and Red Square were coded as SP. Monuments of Lenin and other state leaders are SP.

Edited to save space.

Results

Image Attributes: Chi-Square Analysis

Each photo was assessed on every dimension. If a dimension was not present in the image, that photo was coded as “not applicable/not present” on that dimension (e.g., photos of buildings were coded as “0” on the NATURE LANDSCAPE dimension). Twenty one and 13 photos in the American and Korean samples, respectively, were classified as “Other.” For example, several photos in the American sample were photos of Hillary Clinton when she visited Russia in 2011, or a few photos contained objects which the researchers could not identify. The relatively small percentage of images attributable to the “Other” category in the American (3.3%) and Korean (2.5%) pools was regarded as adequacy of the data collection process and the exhaustiveness of the categories. The “Other” category was excluded from the analysis, which reduced the sample sizes to 637 American and 584 Korean images. The frequencies of all categories were recorded and Chi-square tests were run to determine differences among the samples (Table 3). Korean images had more pictures of crowds, urban areas, tourist attractions, transportation (particularly trains), people engaged in leisure activities, photos of green spaces such as parks and gardens, and photos of Russian things, mostly food. The American sample contained more images of rural areas, private spaces, everyday activities of the Russian people, architecturally interesting buildings and churches, and representations of state power, especially police and people in military uniforms.

Table 3.

Frequencies of Image Categories: Chi-square Analysis.

	Korean (584)		American (637)		χ² Test
Image Categories	Frequency	%	Frequency	%	χ^2,a	p value^b
Single	61	10.4	65	10.2
Group	50	8.6	52	8.2
Random Few	71	12.2	47	7.4	7.971	0.005
Crowd	93	15.9	73	11.5	5.170	0.023
PEOPLE	275	47.1	237	37.3
Nature Landscape	72	12.3	80	12.6
NATURE LANDSCAPE	72	12.3	80	12.6
Urban Area	320	54.8	306	48.0	5.568	0.018
Rural Area	56	9.6	85	13.3	4.205	0.040
PLACE	376	64.4	391	61.3
Tourist	262	44.9	228	35.8	10.433	0.001
Residential	191	32.7	231	36.3
Private	17	2.9	41	6.4	8.363	0.004
SPACE	470	80.5	500	78.5
Transport & Infrastructure	164	28.1	145	22.8	4.561	0.033
TRANSPORT & INFRASTRUCTURE	164	28.1	145	22.8
Leisure	193	33.0	107	16.8	43.413	0.000
Outdoors & Sports	7	1.2	15	2.4
Way of Life	34	5.8	84	13.2	18.930	0.000
On the Streets	41	7.0	31	4.9
ACTIVITIES	275	47.0	237	37.2
Cold Climate	68	11.6	61	9.6
Greenery	142	24.3	107	16.8	10.606	0.001
SEASON	210	35.9	168	26.4
Architecture	174	29.8	235	36.9	6.889	0.009
ARCHITECTURE	174	29.8	235	36.9
Arts & Culture	46	7.9	40	6.3
History	34	5.8	53	8.3
Food & Things	30	5.1	12	2.0	8.596	0.003
HERITAGE	110	18.8	105	16.6
State Power	31	5.3	65	10.2	10.081	0.001
STATE POWER	31	5.3	65	10.2

df = 1 in all tests.

Results significant at the 0.05 level are shown.

Co-occurrence Analysis of Image Attributes

The preliminary data analysis showed that certain destination attributes (categories) co-occurred more often in one data set than in the other: for example, the Korean sample had more images of people at tourist attractions than did the American sample. To obtain a summary of the coded data and to better understand which destination attributes tended to appear together in each sample, the researchers conducted a co-occurrence analysis of the Russian destinations’ attributes. This approach followed the procedure specified by Stepchenkova and Zhan (2013) and was based on comparing actual co-occurrences of any two attributes found in the sample with the expected number of co-occurrences calculated under conditions of attribute independence.

The frequency of each category can be interpreted in terms of the probability that it will appear in an image. For example, given that in the Korean sample there were 72 photos of nature landscapes, the probability of the Nature Landscape category appearing in a Korean photo was estimated as 72 divided by 584 (total number of images in the sample), that is, 0.123. For the Cold Climate category, the probability was estimated as 68 (the number of occurrences) divided by 584, that is, 0.116. If these two attributes were independent, then the number of co-occurrences of these two categories in the sample was a random variable that was binomially distributed; the expected value and variance of that variable could be calculated using frequency data (Hogg, McKean, and Craig 2005; Stepchenkova and Zhan 2013). For the Nature Landscape and the Cold Climate categories, the expected number of co-occurrences was 8.5. However, the actual number of co-occurrences of these two attributes in the Korean sample was 16; thus, the difference between these two numbers was tested for statistical significance. It was found that the difference was indeed significant at the 0.01 level, which meant that there was a tendency for these categories to appear together in the Korean sample. The statistical analysis was conducted for each pair of attributes for both Korean and American samples.

For comparison purposes, the results of the co-occurrence analysis are summarized in Table 4 (for the ease of reading, all categories under the PEOPLE dimension, i.e., Single, Group, Random Few, and Crowd, were aggregated. The upper-right half of the table presents results for the American sample, whereas the lower-left half shows results for the Korean sample. The cells of the table contain the number of actual co-occurrences of two respective attributes if that number significantly exceeds the expected value (which is not given). The p value of the statistical test is coded as “a” (less than 0.05), “b” (less than 0.01), or “c” (less than 0.001). The cell is shaded if statistical significance of the two attributes is found in one sample but not the other. For example, the co-occurrence (albeit very small, only four instances) of the Food & Things (HERITAGE dimension) and Private (SPACE dimension) categories is significant in the American sample, but not in the Korean sample, where Food & Things is a stand-alone category.

Table 4.

Co-occurrence Analysis of Image Attributes.

	PP	NL	UA	RA	TS	RES	PS	T&I	LEI	O&S	WOL	SRT	CC	GR	ARC	F&T	SP
PP			158c		106a				106c	15c	83c	31c					34a	AMERICAN
NL				28c						9c			22c	83c
UA	187c				156c	44c		100c	85c			30c			143b		44a
RA		21c				67c								41c
TS	156c		175b						78c						151c
RES			138b	38c				89c			48b	24c
PS											20c					4c
T&I			127c			91c						18c
LEI	192c		127a		145c
O&S	7a	5c											8a
WOL	34c					24c	5c										24c
STR	41c		38c			31c		35c
CC		16b
GR		34c		30c		70c				5a
ARC			117a		125c
A&C
HIS			27a
F&T
SP
KOREAN

Note: Cells contain the number of co-occurrences of two respective categories in the images if it significantly exceeds the expected value. Letter a indicates p value <0.05; b, p value <0.01; c, p value <0.001.

For the most part, the table is symmetrical. For example, in both samples, the PEOPLE dimension is connected to such categories as Urban Area, Tourist Space, Leisure Activities, Outdoor & Sports, Way of Life, and On the Streets. However, it was observed that the tendency to picture people at tourist attractions was stronger in the Korean sample, as evidenced by a larger number of co-occurrences and a smaller p-value. At the same time, in the American sample the PEOPLE dimension was also significantly associated with STATE POWER (people in military uniforms, police, etc.), a feature that was not present in the Korean sample. In the Korean sample, Arts & Culture, Food & Things, and STATE POWER are stand-alone categories and a stand-alone dimension, whereas in the American sample, the stand-alone categories are Arts & Culture and History. A more in-depth interpretation of the results is presented in the Discussion section.

Geodistribution of Images

Geographical locations were extracted from image information. By design of the data collection procedure, each American image was geotagged and, thus, associated with a certain location in Russia. The Korean photos were not geotagged, and the locational information, namely, a city or a tourist attraction, was extracted from the image title, a short description, or a respective blog posting. Since American and Korean locational information exhibited different granularity, the individual locations were combined based on their geographical proximity: photographs taken within a short distance of each other were issued their centroid location. For example, all image locations within and around Saint Petersburg were assigned to one location, that of Saint Petersburg. This geographic aggregation allowed for a more straightforward comparison of the most popular destinations from which tourists take short-distance side trips. Further, each location was classified as belonging to one of the 13 Russian recreational regions (Stepchenkova and Morrison 2006) (Figure 1). Finally, maps of geographical distribution of the American and Korean photo samples were constructed using ARC GIS software (Figure 2).

Figure 1.

Russian Recreational Regions.

Figure 2.

Distribution of Images: Korean and American Samples.

With respect to missing data, the American sample had only two unidentified images: by the moment when image geocoordinates were downloaded, nine images had been removed from Flickr; however, the titles of the downloaded photos allowed location identification for seven out of nine photos. Geographical locations for Korean photos were manually assigned using the following procedure: (1) auxiliary information provided by tourists (image title, description, or blog discussion) was used as the primary source of geospatial data (512 instances); (2) content of the image was used to define location when the auxiliary information was ambiguous or absent (24 instances); and (3) when (1) or (2) failed, the image was rejected (23 instances). Among the rejected images, 13 photos had location specified as “Siberia” or “Siberian train,” referring to places somewhere along the Trans-Siberian Railroad, and locations of 10 photos remained unknown. Thus, 559 photos (96%) from the original Korean sample were included in the geoanalysis. Table 5 provides the actual number and percentage of photos taken in each of the 13 Russian recreational regions. Figure 2 shows the main railroads and the photos’ locations adjusted for geographical proximity.

Table 5.

Geolocational Distribution of Images.

		Korean		American
	Region	n	(%)	n	(%)
1	Western	0	0.0	2	0.3
2	North-Western^a	166	29.7	211	33.2
3	Central^b	161	28.8	260	40.9
4	Southern	0	0.0	6	0.9
5	Povolzhie	14	2.5	30	4.7
6	Urals	8	1.4	10	1.6
7	Azov-Black Sea	0	0.0	9	1.4
8	Caucasus	1	0.2	17	2.7
9	Ob-Altai	6	1.1	24	3.8
10	Yeniseisky	3	0.5	7	1.1
11	Baikal	96	17.2	29	4.6
12	Far Eastern	104	18.6	3	0.5
13	Northern	0	0.0	27	4.3
	Total	559	100.0	635	100.0

Saint Petersburg included.

Moscow included.

Discussion

This exploratory study investigated whether destination images as projected in tourism photography are reflective of tourists’ national cultures. Two sets of images of Russia captured through American and Korean camera lenses were analyzed using content analysis as the main technique, followed by statistical comparisons of the obtained frequency data: chi-square tests of destination attributes that appeared on the photos, co-occurrence analysis of these attributes, and spatial distribution of locations where the photos were taken. It was found that in terms of image content (frequency of attributes) the two sets exhibit differences on a number of dimensions: PEOPLE, PLACE, SPACE, ACTIVITIES, TRANSPORT & INFRASTRUCTURE, ARCHITECTURE, and STATE POWER. With respect to image composition (co-occurrence of attributes in the photos), however, the two sets are very similar, with only minor differences. Overall, Russia appeared as a predominantly urban, contemporary destination with developed infrastructure, featuring Moscow and Saint Petersburg as its most popular cities. Photographs pictured tourist sites in green urban and recreational areas, with people sightseeing, doing leisurely activities, and, occasionally, sports. Architecture, that is, historic buildings, palaces, and “orthodox churches with onion-shaped domes” (Stepchenkova and Morrison 2008, p. 553), was a strong element of the image. Relatively few images were set in rural areas, in proximity to residences of Russian people, or featured local people in traditional Russian or ethnic clothing.

A “vast country with lots of open spaces” and a “beautiful countryside” are the dominant images of Russia among American pleasure travelers (Stepchenkova and Morrison 2008, p. 553). In this study, however, only approximately 12% of the images in each set were classified into the NATURE LANDSCAPE dimension, further highlighting the fact that Russia appears to be a destination for predominantly urban tourism. Images of nature landscapes were mostly taken in regions such as Siberia, the Russian North, and the Far East. Prior to the study, the researchers had somewhat expected that the Korean sample, as representative of a culture with a strong interest in nature (Chen and Hsu 2000), would have more images of the spacious Russian landscapes and countryside. Although this expectation did not materialize, the Korean set of photographs nevertheless included more images featuring green parks, urban squares with flower beds, and other recreational areas (coded into the Greenery category), both inside and outside the cities. “Cold weather, snow,” another image for which Russia is famous (Stepchenkova and Morrison 2008, p. 553), was equally represented in both samples, which may reflect tourists’ preference to come to Russia during the more agreeable seasons.

The PLACE dimension represented the urban-rural distinction (Echtner and Ritchie 1993), whereas SPACE reflected how “close” tourists and hosts were; both dimensions referred to the “territoriality” and, to some extent, the uncertainty avoidance aspects of culture (Hall 1959; Hofstede 1980). The Korean sample contained significantly more images of urban areas, tourist attractions, and consequently, more images with random people and crowds on the streets. The Korean photos showed prevalence for picturing leisure-type activities, including people sightseeing, people at concerts/theaters/clubs, people at hotels and restaurants, and quite a few images of people shopping (ACTIVITIES). While the study did not distinguish between tourists and locals on the photos, as in many cases it was impossible to do with certainty (Stepchenkova and Zhan 2013), findings seem to indicate that Korean tourists tend to be leisurely and spend freely when abroad (Pizam and Jeong 1996; Pizam and Sussmann 1995). In contrast, the American sample had a higher proportion of photos in rural areas, picturing places where people actually live—suburban neighborhoods, dachas (small summer houses), or wooden cabins (Private Space)—and capturing everyday activities of the Russian people (Way of Life). Thus, the findings in the PLACE, SPACE, and ACTIVITIES dimensions were consistent with the cultural profile of American tourists as being less risk averse, more adventurous, and spatially distributed travelers who are people- rather than artifact-oriented (Pizam and Jeong 1996). Interestingly, despite a documented preference of Korean tourists to travel in groups (Prideaux and Kim 1996; Yarmy 1992), the Group category did not come significant in this study.

With mega-sport events such as the Winter Olympics and the FIFA World Cup, which Russia hosts in 2014 and 2018, respectively, the Russian government is investing in developing its tourism infrastructure to improve the country’s economic situation and boost the country’s image. The TRANSPORT & INFRASTRUCTURE dimension was included to represent the “developed-underdeveloped” destination attribute (Echtner and Ritchie 1993), as well as to reflect the “mastery over nature” value orientation (Kluckhohn and Strodtbeck 1961). While both countries scored high on “mastery over nature” in Schwartz’s (2006) classification, the Americans were given a higher score. The Korean sample, however, contained a significantly larger number of TRANSPORT & INFRASTRUCTURE representations. A more detailed analysis showed that the Korean sample had more images of trains, railway stations, and train tracks along the Trans-Siberian railroad (Fig. 2), as well as cruise boats in the Far East region. Interestingly, the desire of the Korean tourists to see how Russian people live manifested itself through taking pictures of Russian people in sleeping cars in long-distance trains. Trains are a popular means of transportation in Russia, and given the distances involved, a train journey may take a few days. On long-distance routes, passengers convert areas around their bunk beds into private spaces where they sleep, eat, change clothes, and socialize. In the Korean sample, there were quite a few images of Russian people “living” in trains.

Kluckhohn and Strodtbeck (1961) have posited that American culture is oriented toward the future, and Hall (1959, p. 95) has maintained that “compared to many other societies, ours does not invest tradition with an enormous weight.” The Confucian cultures, however, emphasize tradition. In this study, the interest in Russian history, culture, and tradition was represented by the HERITAGE and, to a certain extent, STATE POWER dimensions. Under HERITAGE, the Food & Things category was found to be significant: the Korean sample had more pictures of typical Russian food and “old fashioned” things that are still in use in Russia. Quite unexpectedly, images of traditional Russian crafts, such as matryoshka nested dolls, Gzhel pottery, Khohloma wood painting, etc. were practically absent in both samples. The authors would speculate that tourists capture on camera what they cannot take home; therefore, Koreans may have taken home the real objects as souvenirs, while Americans are just not interested in tourist artifacts in general (Pizam and Jeong 1996). The American tourists showed a stronger interest in buildings and architecture (ARCHITECTURE), which may have signaled the interest in both Russian cultural tradition and ways of organizing urban space. The American cultural pattern places more weight on “standardization of segments which are used for measuring space or into which space is divided, be it a ruler or suburban subdivision” (Hall 1959, p. 203). The Russian pattern of urban space is distinctly different (with a possible exception of Saint Petersburg, which was built according to a plan); thus, the stark contrast between the two modes of organizing urban space, American and Russian, may account, at least partially, for a higher frequency of the Architecture category in American sample.

In the History category (marginally significant with p value of 0.09), American tourists had a sizable portion of “photos of photos” featuring how Russian people lived before the October Revolution of 1917, as well as photos of the Soviet period. As indicated by geolocational information, these photos were taken in Russia, presumably in museums or historic places. The Korean outlook on history included historic artifacts (e.g., Tsar-Cannon in the Kremlin) or monuments to historic Russian figures, such as composers and writers. Finally, one of the dominant images of Russia among the Western tourists is that of “Cold War” (Stepchenkova and Eales 2011; Stepchenkova and Morrison 2008), and in this study the “Cold War” image was thought to be captured by the STATE POWER dimension. The American sample had twice as many STATE POWER images as the Korean sample, reminiscent of the complicated political relationship between the United States and Russia. The STATE POWER images predominantly featured the Russian military, people and equipment, police force, state symbols, etc.

The research question 2 about what locations in Russia are most often captured through the American and Korean camera lenses was answered by assigning places where the images were taken to 13 Russian recreational regions (Stepchenkova and Morrison 2006). The Central (including Moscow) and North-Western (including Saint Petersburg) regions were the most popular in both samples; however, Moscow was noticeably more popular in the American sample (Table 5). For Moscow, the most widely reproduced image, with similarities in both content and composition for the two samples, was Saint Basil Cathedral (American set: 36 instances, Korean set: 15 instances). Red Square, the monument to Russian national heroes Kuzma Minin and Prince Dmitry Pozharsky, and the view of the Kremlin from the Bolshoy Kamenny Bridge were also popular images. In Saint Petersburg, the Church of the Savior on Spilled Blood, the structure that has architectural similarities with the Saint Basil Cathedral, was widely reproduced from the perspective of the Griboyedov Canal in both samples. The leading position of the Church on Spilled Blood over the Winter Palace in the American sample came as a surprise, and should be considered by the Russian Federal Agency for Tourism in their promotional activities.

The Baikal and Far Eastern regions were quite popular in the Korean sample (17.2% and 18.6%, respectively): the analysis shows that a substantial share of these pictures were taken on trans-Siberian trains and cruises (Fig. 2). The other nine recreational regions had a very small number of photos, with the Western and Northern regions remaining entirely unrepresented in the Korean set. In the American sample, with the exception of Central (Moscow) and North-Western (Saint Petersburg) regions, the photos were distributed more evenly, and each recreational region was represented. Povolzhie (the Volga River), Baikal, and the Northern regions were the most popular after Central and North-Western regions, with 4.7%, 4.6%, and 4.3%, respectively. The wider, more dispersed geodistribution of photos in the American sample is consistent with such cultural features of Americans as being more adventurous, active, novelty-seeking travelers (Holzner 1985), as well as being representatives of a culture that is low on uncertainty avoidance (Hofstede 1980). It should be mentioned here, however, that the study was unable to distinguish between photographs taken by leisure and business tourists. Readers are reminded that several photos were excluded from the American sample because they were taken in a business setting. This indicates that at least some of the American tourists were business travelers; however, the share of business tourists in each sample was impossible to estimate. If there was a substantial share of business tourists in the American sample, then the wider geodistribution of American photos, as compared to the Korean set, may be attributed, at least in part, to business travelers being sent to various regions, rather than to American culture per se. The business-travel perspective did not manifest itself in the Korean sample.

Methodological Issues and Further Research

The practice of using nationality or country of residence as indicators of culture, and subsequently as a variable for market segmentation, has been criticized by Dann (1993). Globalization, multiculturalism, immigration policies, and the new political order in general have led to a situation in which nationality divisions are blurred, as more people possess multiple passports, various cultures exist within a country, and a person’s countries of birth and residence are often different. In short, “problems are encountered whenever one begins to speak of nationality, national identity, national consciousness, or even country of residence” (Dann 1993, p. 10). Nevertheless, Dann (1993) has included culture in his market differentiation system, together with such factors as personality, lifestyle, tourist roles, and social class. Crotts and Litvin (2003) have empirically tested which variable—country of birth, residence, or citizenship—represents culture best. Their findings suggest that, out of the three, country of residence is the strongest indicator of culture. Thus, for the American sample, the cultural marker was the U.S. residence indicated in Flickr profile. For the Korean sample, the cultural marker was the Korean language used by a tourist who blogged about travel to Russia on the Korean Internet platform Naver.

In determining the sample size, the study followed Stepchenkova and Zhan (2013) who had the samples of 500 and 530 and 20 categories and were able to detect the differences between samples. The sample sizes in this study, 637 (American) and 584 (Korean), ensured that each of the 22 categories (with the possible exceptions of Private Space, Outdoor & Sports, and Food & Things) was sufficiently represented to conduct parametric analyses. The sample selection criteria and randomization of the data collection algorithm are considered the strong aspects of the study. The underrepresentation of Korean users on Flickr and their preference for Naver was attributed to the disparities between the number of American and Korean tourists who traveled to Russia and the cultural character of Korean tourists as upholding their sociocultural identity (Pizam and Jeong 1996) and, hence, preferring a Korean blog platform. The ultimate goal of a content analysis study is to select data “in view of what they mean, the interpretations they enable, and the information they contain . . . to give . . . research questions a fair chance of being answered correctly” (Krippendorff 2004, p. 113); thus, the authors are reasonably sure that the two sets of photographs are representative of the two cultural perspectives. However, it is not known how the results in the Korean sample were affected by a smaller number of users and a larger number of photos per user, as compared to the American sample; therefore, the differences in the data collection procedures are considered a limitation of this research.

The quality of content analysis is highly dependent on its categories (Berelson 1952) which have to be “analytically interesting” (Pullman and Robson 2007). Developing the category system for this exploratory study was in a way “testing the waters” whether such an elusive construct as culture can be captured in the tourists’ photographs. To the authors’ opinion, the proposed two-tier system reconciles the theoretical aspects of culture as indicated by the literature and those image features through which these aspects may manifest themselves and be reliably observed. The categories were treated in a “functional” rather than “psychological” way, that is, they reflected “reality” manifested in the images, not feelings and attitudes about that reality. People, nature, urban areas, tourist sites, architecture, infrastructure, and climate are all functional destination attributes (Echtner and Ritchie 1993). Analysis of attributes of a more psychological nature, such as perceptions of cleanliness, safety, or commercialization, is currently the subject of another study, as those attributes require a different approach to photo classification, namely, averaging of scores on each attribute across a fairly large number of raters. It is quite possible that a different category system would have also allowed comparisons from the perspective of tourists’ respective cultures. Therefore, the question of what category system is more conducive to “capturing” culture-related differences, as well as the issue of data reliability as a function of the selected category system, require further investigation.

In conclusion, using user-generated content from social network sites is a research mode that is quickly gaining prominence as an alternative to surveying individuals (Lu and Stepchenkova, forthcoming). Because of the increasing relevance of visual communications and the growing number of visualizations in online media, quantitative generalizations about such visuals is a prospective line of research, as the ability to detect cultural differences in visual representations of destinations has implications for destination marketing, both in terms of destination positioning and marketing communications. This exploratory study was set to determine whether culture is manifested through tourists’ photographs and along what dimensions cultural differences may be reliably observed. Methodologically, it proposed and tested an approach to quantitatively analyze and statistically compare visual images in order to gain insights on how a popular destination is presented by tourists from distinctively difference cultures. A comparative content analysis found that there were differences in representations of Russia through American and Korean camera lenses and in geospatial distribution of photos, which were, to some degree, reflective of the cultural identities of American and Korean tourists. The study also has implications for marketing practice. While the core of the Russian destination image as transpires through the travel photographs—urban, contemporary, architecturally interesting, culture-rich, and spacious—is similar for both markets, the Korean tourists exhibited a stronger interest in leisure activities such as sightseeing, visiting museums and art collections, going to performances, and shopping. Korean tourists represent an important market for the Russian Far East and Baikal recreational regions; thus, it is imperative for the Russian Federal Agency for Tourism to bring recreational opportunities and the tourist infrastructure in those regions up to par with those available in the Central and North-Western regions. With respect to the U.S. market, the Russian Federal Agency for Tourism may consider expanding attractions that explore the history of the Russian–American relationship, including the Soviet period and the history of the Cold War between the countries, an initiative that would potentially respond to American tourists’ specific interests.

Footnotes

Authors’ Note

The earlier version of the paper was presented at the 2nd World Research Summit for Tourism and Hospitality: Crossing the Bridge, December 15–17, 2013, Orlando, Florida.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research was partially supported by the U.S. Department of Education, Funds for the Improvement of Post-Secondary Education (FIPSE). Award number is p116S100001.

Author Biographies

Svetlana Stepchenkova, PhD, is an assistant professor at the University of Florida, Dept. of Tourism, Recreation & Sport Management. The area of her research interests is destination marketing and branding, with the focus on content analysis of marketing and user-generated communications in tourism.

Hany Kim is a doctoral student at the University of Florida, Dept. of Tourism, Recreation & Sport Management. She studies cultural aspects of destination image and branding. Prior to entering the program, she worked for more than three years as a researcher at the Korean Culture & Tourism Institute in Seoul.

Andrei Kirilenko is an Associate Professor at the University of North Dakota, Dept. of Earth System Science and Policy. He received his MS degree in Applied Mathematics and a PhD in Computer Science. His research is focused on climate change and sustainability issues, specifically in relation with data mining, social networks, and spatial statistics.

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