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INTERNATIONAL JOURNAL OF TOURISM RESEARCH Int. J. Tourism Res. 14, 469–484 (2012) Published online 20 October 2011 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/jtr.876

A Social Network Analysis of Overseas Tourist Movement Patterns in Beijing: the Impact of the Olympic Games Xi Yu Leung1, Fang Wang2,*, Bihu Wu2, Billy Bai1, Kurt A. Stahura1 and Zhihua Xie2 1William F. Harrah College of Hotel Administration, University of Nevada, Las Vegas, USA 2College of Urban and Environmental Sciences, Peking University, Beijing, China

ABSTRACT

The 2008 Beijing Olympic Games had a great impact on the tourism industry in Beijing, especially on tourism flows and movements. This study used content analysis and social network analysis methods to examine 500 online trip diaries and analyze overseas tourist movement patterns in Beijing during the Olympics. The result revealed that overseas tourists were most interested in famous traditional attractions, and their movements were focused in the central city area of Beijing. The study identified the diversity of tourist attractions and the expansion of main visiting areas as the two main changes during the Olympics. Copyright © 2011 John Wiley & Sons, Ltd.

Received 14 February 2011; Revised 21 September 2011; Accepted 22 September 2011

Keywords: content analysis; overseas tourist; social network analysis; 2008 Beijing Olympic Games; tourist attractions; tourist movement patterns.

INTRODUCTION

Tourism involves the movement of peoplethrough time and space. Tourists experi-ence destinations differently, and these dif- ferences in consumption styles will be reflected

*Correspondence to: Fang Wang, No. 5 Yiheyuan Road, Haidian District, Beijing, 100871, P. R. China. E-mail: [email protected]

by differences in movement patterns (Mckercher et al., 2006). Tourist movement patterns are the spatial changes of activity locations of tourists (Lau and Mckercher, 2007). Tourist movement pat- terns contain various items of information that can be used for designing better tourist packages, pro- viding more attractive combinations of attractions, as well as developing travel guidance policies and marketing services (Asakura and Iryo, 2007; Xia et al., 2009). Understanding how tourists move through time and space has important implications for infrastructure and transportation development, product development, destination planning and the planning of new attractions, as well as manage- ment of the social, environmental and cultural impacts of tourism (Lew and Mckercher, 2006). The Olympic Games are now one of the

world’s largest events, with substantial eco- nomic, social, political and other benefits for the host nation, region and city (Toohey and Veal, 2000). The potential benefits brought by the Olympics include newly constructed event facilities and infrastructure, urban revival, an enhanced international reputation, increased tourism, as well as improved public welfare, additional employment, local business oppor- tunities and corporate relocation exposure (Ritchie and Aitken, 1984; Hall, 1987; Kasimati, 2003). In the tourism industry, one frequently cited benefit from the Olympic Games is the po- tential improvement in awareness and image of the host city or country as an international tour- ism destination (Hall, 1987; Ritchie and Smith, 1991; Li and Blake, 2009). The 2008 Beijing Olympic Games, which

took place from 8 August to 24 August, were among the most expensive ever held and had a significant influence on Beijing’s economic development, environment, transportation,

470 X. Y. Leung et al.

infrastructure and urban renewal (Sands, 2008). The Olympic Games had a major impact on the tourism industry of Beijing and China. According to a worldwide survey, about 51 percent of 26 000 people from 26 countries and regions said they intended to travel to China after the closing ceremony of the Games (Xin, 2008). This high number associated with intention was possibly caused by the positive image and increased visibility that the Olym- pics brought to Beijing and China. Weed (2008) mentioned that the Olympic

Games had an impact on tourism flow and movements during the Games, especially for those ‘event-affected’ people. After the 2008 Beijing Olympic Games, 92 percent of foreign visitors rated the Olympic venues as ‘very good’ or ‘good’ (Xin, 2008). It seemed that the Olympic venues in Beijing were becoming new tourist attractions, and overseas tourists’ movement patterns were changing as a result. Therefore, in order to maximize the tourism impact of the Olympic Games, it is helpful for host cities to understand the changes resulting from the games. In doing so, they can develop appropriate destination marketing strategies and product packages to meet the needs of Olympic-related visitors. Previous literature showed that the study of intradestination movement of tourists is limited (Lau and Mckercher, 2007), let alone the impact of the Olympic Games on intradestination movement patterns. Therefore, the purpose of this study was twofold: to (i) identify the tourism attrac- tions visited by overseas tourists and the changes in the pre-, during and post-Beijing Olympic Games periods and (ii) to examine the overseas tourist movement patterns in Beijing and the changes during these Olympic Games. The results will yield planning implica- tions for future Olympic Games and their host cities.

LITERATURE REVIEW

Tourist movement patterns

Tourist activity and spatial patterns therein is one of the major aspects of tourism geography, which focuses on explaining spatial patterns of tourist activities at different scales, such as

global, national, regional and local (Pearce, 1995). Various spatio–temporal movements of tourists can be modeled at either the micro- or macrolevel (Xia et al., 2009). At a macro level, tourists move from the generating region to destination regions or between destination regions (Leiper, 1979). These movements refer to interdestination movements. At the micro level, tourists travel within a single destination from attraction to attraction or shift from activity to activity. These are intradestination movements (Lau and Mckercher, 2007). The spatio-temporal movements of tourists are a complex process (Xia et al., 2009) and can be influenced by many different factors. These can be divided into three major categories: human ‘push’ factors (tourist role, travel party, personal motivations, prior visits, etc.), physical ‘pull’ factors (destination geomorphology and configuration) and time fac- tors (length of stay at a destination and total trip duration) (Lew and Mckercher, 2006; Lau and Mckercher, 2007).

A variety of studies have attempted to map the movements of tourists in a variety of ways. Gunn (1972) was one of the first to propose two basic types of trips, the destination trip and the touring trip. Mings and McHugh (1992) identified four movement patterns of do- mestic tourists in Yellowstone National Park in the United States as direct route, partial orbit, full orbit and fly-drive pattern. Lue et al. (1993) conceptualized five different movement patterns for pleasure vacation trips: single destination, enroute, base camp, regional tour and trip chain- ing patterns. Opperman (1995) proposed seven patterns, including two single destination (S) patterns and five multiple destination (M) pat- terns, to compare tour itineraries of international tourists from Malaysia. Flogenfeldt (1999) identi- fied four types of patterns taken by Norwegians: day trip, resort trip, based holiday and round trip. Lew and Mckercher (2002) contributed to the literature by examining the relative location of a destination within the larger itinerary pattern through an alternative approach and classified destinations into five types: Single Des- tination, Gateway Destination, Egress Destina- tion, Touring Destination and Hub Destination. Hwang et al. (2006) described different multicity trip patterns by international tourists to the Uni- ted States that were affected by origin and fa- miliarity. Ryan and Huimin (2007) conducted a

Int. J. Tourism Res. 14, 469–484 (2012) DOI: 10.1002/jtr

471A Social Network Analysis of Tourist Movement Patterns

study of a desired itinerary among students in New Zealand and China and identified two ideal itinerary patterns: open-jaw route and overlaying triangular route. Based on the previ- ous studies, Lau and Mckercher (2007) summar- ized the movement patterns into six categories: single point, base site, stopover, chaining loop, destination region loop and complex neighbor- hood. All of the above patterns capture charac- teristics of various tourists moving from origin to destinations and among destinations. These interdestination movement patterns are used by destinations to develop regional cooperation and identify marketing areas. Compared to a large body of interdestination

movement studies, less prior research has been conducted examining tourist movements within a destination. Researchers admitted that in- terdestination movement patterns have some implications in intradestination movement pat- terns because both reflect tourist movements but at different scales (Mckercher and Lau, 2008). However, the larger number of attractions in a destination creates more potential and com- plicated intradestination movement patterns than interdestination patterns (Mckercher and Lau, 2008). Mckercher (2004)’s study on Hong Kong visitors revealed that intradestination tourist movements are unique and personalized based on visitors’ own interests. Some studies discuss the spatial implications of variations in attraction site visits (Debbage, 1991; Fennell, 1996). As a conceptual work, Lew and Mckercher

(2006) modeled the intradestination movement patterns deductively in two dimensions: four types of territorial models and three types of linear path models. Most empirical studies on intradestination movement patterns have been conducted in Hong Kong, China. Lau and Mckercher (2007) found different intradestination movement patterns between first-time and repeat visitors in Hong Kong. Repeat visitors demon- strate a more varied movement pattern, while first-time visitors show a more confined move- ment pattern. Mckercher et al. (2006) identified six different patterns of long-haul visitors to Hong Kong. Three patterns were noted among main destination visitors: the Wanderer, the Tour Taker and the Preplanner, while three different patterns were identified among stopover or secondary destination visitors: the Explorer, the

Uncommitted and the Intimidated. Mckercher and Lau (2008) examined the daily movements of the fully independent pleasure tourists in Hong Kong and identified 78 discrete movement patterns and 11 movement styles. They identified six factors that influenced tourist intradestination movement patterns: territoriality, the number of journeys made per day, the number of stops made per journey, participation in a commercial day tour, participation in extra-destination travel and observed patterns of multistop journeys.

Measurement of movement patterns

Avariety of techniques have been applied in the observation of tourist movements by different scholars. The traditional tracking techniques are based on observations and interviews that require the researcher to follow an individual tourist and record his or her movements (Dumont et al., 2004). Participants are also asked to trace or retrace their spatial movements on a cartographic map using self-administered ques- tionnaires (Fennell, 1996; Wang and Manning, 1999). Recently, new tracking techniques, such as global positioning system (GPS) (Draijer et al., 2000; Arrowsmith et al., 2005), geographic information system (GIS) (Lau and Mckercher, 2007), timing systems (O’Connor et al., 2005), camera-based systems (Haritaoglu et al., 1998), personal digital assistants (PDAs) tracking devices (Hadley et al., 2003) and mobile commu- nication tracking (Asakura and Hato, 2004) have been utilized to record movement in- formation of tourists. In addition, a variety of methodological techniques, such as data min- ing, including expectation maximization (EM) clustering (Wang et al., 2006), and statistical methods, such as logistic-regression and log- linear models (Xia et al., 2010), cluster analysis (Asakura and Iryo, 2007), network analysis (Hwang et al., 2006) and Markov chains (Tobler, 1997; Xia et al., 2009), have been employed to analyze the tourist tracking data in an attempt to identify spatio-temporal movement patterns. Although new technologies are emerging,

which help the measurement and analysis of movement patterns, the very traditional tech- nique of the trip diary (travel diary) is still the most popular instrument utilized in an attempt to collect specific data of tourists’ movements, and it is primarily used in most intradestination

Int. J. Tourism Res. 14, 469–484 (2012) DOI: 10.1002/jtr

472 X. Y. Leung et al.

movement studies (Mckercher et al., 2006; Lau and Mckercher, 2007; Mckercher and Lau, 2008). GIS software is the common data analysis tool that can be used to map diary data and iden- tify movement patterns (Lau and Mckercher, 2007; Mckercher and Lau, 2008).

Social network analysis in tourism context

Social network analysis is a method used to map and measure relationships and flows between people, groups, organizations, and other connected information/knowledge en- tities (Wasserman and Faust, 1994). It provides both a visual and a mathematical analysis of human relationships. Based on graph theory, a social network represents entities and their relations as nodes and links, which form a network (Scott, 1991). The process focuses on the relationship between the actors and the pattern of interactions rather than the at- tributes of isolated individual actors (Scott et al., 2008). Social network analysis is a useful approach used to describe and interpret knowledge network, network clustering and research subject evolutions (Scott, 1991). Over the last decade, social network ana-

lysis has been introduced into tourism, and hospitality research and numerous applica- tions have been applied to different topics (Novelli et al., 2006). Structural characteristics of public–private organizational relationships have been studied, and the collaboration and coordination of organizations were suggested for future scholarship (Pavlovich, 2003; Shih, 2006; Wang and Xiang, 2007; Romeiro and Costa, 2010). Combining stakeholder theory with social network analysis, the interconnect- edness of diverse stakeholders was examined through a network lens, and the key members and their structural position were identified (Scott and Cooper, 2007; Timur and Getz, 2008). Knowledge networks were explored and visually represented using social network analysis to expand the understanding of the structure and constitution of the tourism and hospitality field not only in collaboration among the researchers (Hu and Racherla, 2008) but also with respect to tourism themes and trends (Benckendorff, 2009). When applied in studies on tourist movement

patterns, social network analysis has the same

function as GIS software to visualize trip diary data. However, the focus of social network ana- lysis is not geographic movement as GIS would suggest but rather the attractions themselves and the relationships in and among them. Ultimately, this research may provide more helpful implications for destination marketers.

METHODOLOGY

Study design

With the increasing popularity of the Internet, more and more travelers are now using the technology to obtain travel information, share travel experiences, view photographs, or pur- chase travel-related products (Chung and Buhalis, 2008). One of the significant trends of the Internet is the growing of user-generated content sites, which are websites for people to share their experiences and ideas (Green, 2007). Nowadays, more and more people pub- lish their thoughts online or read online reviews and the user-generated content clearly affects traveler’s decisions (O’Connor, 2010). With the increasing importance of these user-generated content sites, hospitality researchers begin designing studies using user-generated data (O’Connor, 2010; Stringam and Gerdes, 2010).

This study was also designed to collect user- generated data to attain tourists’ itineraries from diaries they posted after their trip. Beijing, the capital of China and host city of the 2008 Olympic Games, has diverse natural and cul- tural attractions. In 2008, Beijing received 3.36 million overseas tourists (Beijing Tourism Ad- ministration, 2009) and was valued as the fa- vorite tourism city to world tourists (Beijing became, 2008). The purpose of the study is to map and compare movement patterns of overseas tourists in Beijing during the Olympic Games. In the study, trip diaries posted from January 2001 to April 2009 were collected and coded. The 2008 Beijing Olympic Games were held from 8 August to 24 August. The data were divided into three groups. This was done in an attempt to track changes of movement patterns. The three periods are pre-Beijing Olympic Games period (before August 2007), during Beijing Olympic Games period (from August 2007 to September 2009) and post-Beijing Olympic Games period (from October 2008 to April 2009).

Int. J. Tourism Res. 14, 469–484 (2012) DOI: 10.1002/jtr

473A Social Network Analysis of Tourist Movement Patterns

Data collection

The original data were collected from 500 trip diaries on six different websites from January 2001 to April 2009, including 350 English diar- ies posted by international tourists and 150 Chinese diaries posted by tourists from Hong Kong, Macau and Taiwan. The six websites used are listed as follows:

(1) www.travelpod.com, the first international online travel blog website;

(2) www.travelblog.org, one of the most popu- lar international travel blog website;

(3) www.yahoo.com.hk, the biggest portal web- site in Hong Kong and Macau;

(4) discuss.com.hk, the most famous Web forum in Hong Kong;

(5) www.yahoo.com.tw, one of the biggest por- tal websites in Taiwan; and

(6) www.yam.com, one of the biggest portal websites in Taiwan.

Tourists who wrote diaries on these websites were mostly Free Independent Traveler (FIT) travelers, as a result their movement patterns in Beijing were of their free will. The numbers of trip diaries collected in pre-, during and post-Beijing Olympic Games periods (before August 2007, from August 2007 to September 2009 and from October 2008 to April 2009) were 160, 177 and 163 respectively.

Data analysis

The data analysis utilized two main meth- odological approaches and consisted of three major steps. First, content analysis was used to analyze the original data and construct tourists’ itineraries. Content analysis is a sys- tematic method to identify patterns, themes, biases and meanings through examining a particular body of material (Berg, 2001). Be- cause content analysis is applicable to various

Figure 1. Example of tourist flow and attraction-by-attra

types of unobtrusive data (Berg, 2001), it was select in this study to analyze words from dairies. For each dairy, tourist attractions vis- ited were chronologically coded. However, because of the anonymity of the forum, the demographic information of tourists could not be obtained. Content analysis gave a de- scriptive overview of tourist attractions visited by overseas tourists during the study period and the main alterations associated with the tourist attractions among three periods. Second, social network analysis software

NetDraw was used to map the tourist move- ments in and among the attractions (itineraries). First, three periodical attraction-by-attraction data matrixes were built after the content anal- ysis. The value in the ijth cell of this matrix indi- cated how many times tourists flowed (moved) from the ith attraction to the jth attraction. For example, suppose there were three tourist attrac- tions named A, B and C. Three people traveled from A to B, five people traveled from B to C, two people traveled from C to A and one person traveled from B to A (see Figure 1). As a result of the movements, we can generate a data matrix (Figure 1). Then, three data matrixes were input into NetDraw, and three networks were mapped. In addition, longitudinal structural changes of the network were also examined. Finally, after simplifying the three networks,

main tourist movement patterns were identi- fied from the network, and the changes of movement patterns were investigated.

FINDINGS

Tourist attractions

The results of the content analysis show that there were total 197 attractions in Beijing visited by 500 overseas tourists within the study period. The number of total trips to all attractions in Beijing was 4972, and the average number of

ction data matrix.

Int. J. Tourism Res. 14, 469–484 (2012) DOI: 10.1002/jtr

http://www.yahoo.com.hk

http://www.yam.com

474 X. Y. Leung et al.

attractions visited per capita was ten. Fifteen attractions were visited on more than 100 of the trips, and the most frequently visited attraction was Tian’anmen Square, which logged 513 trips. Seventy one attractions only had only one trip. The main tourist attractions and the num-

bers of visitors are displayed in Table 1. The tourist attractions visited changed during the hosting of the Olympic Games. In the time leading up to the Olympic Games (before August 2007), the number of attractions vis- ited and the total trips were 125 and 1437 re- spectively. During this period, 23 attractions had more than 14 trips. During the Beijing Olympic Games (from August 2007 to Septem- ber 2009), the number of attractions visited and the total trips were 123 and 1679 respect- ively. During the games, 29 attractions had more than 14 trips. In the post-Olympic Games period (from October 2008 to April 2009), the number of attractions visited and total trips were 127 and 1856 respectively. During this period, 33 attractions had more than 14 trips. The results also revealed some important

information relative to some of the tourist attractions visited during the Beijing Olympic Games period. First, the number of main tour- ism attractions visited by overseas tourists increased. Dividing the total trips in each period by the number of visitors, we found that the number of attractions visited per capita also increased. It shows that more attractions in Bei- jing were visited because of the exposure pro- vided by the Olympic Games. Tourists are willing to visit not only traditional attractions in Beijing, such as the Tian’anmen Square, The Forbidden City, The Summer Palace and The Great Wall but also new attractions, particu- larly, the Olympic venues developed for the Olympic period. Second, famous traditional attractions re-

mained as the most popular ones to overseas tourists. As we all know, Beijing is famous for its six traditional attractions: Tian’anmen Square, The Forbidden City, The Great Wall, The Summer Palace, Tiantan Park and The Ming Tombs. These traditional attractions have worldwide appeal and are considered as part of the image or brand one might associate with Beijing tourism. The results revealed that these traditional attractions continued to attract the

most number of visitors in the study period. Tian’anmen Square, The Forbidden City and The Great Wall (including all parts) were the most visited attractions in all three periods. The Summer Palace, Tiantan Park and The Ming Tombs also ranked among the 10 most visited attractions.

Third, Olympic attractions aroused great interest by overseas tourists. Olympic attrac- tions, such as Olympic Green, Bird’s Nest (Beijing National Stadium), Water Cube (National Aquatics Centre), and other Olympic venues were developed for the 2008 Beijing Olympic Games. The earliest trip diary that mentioned some of the aforementioned Olympic attrac- tions was in March 2006, when Olympic Green was still under construction. With the opening of the Olympic Games, more and more overseas tourists were attracted to visiting sporting venues. The number of tourists visit- ing Olympic attractions rose in the three peri- ods, from 14 to 90 to 185 respectively. In the post-Olympic period, the total number of overseas tourists visiting Olympic Green, Bird’s Nest and Water Cube even exceeded that of Tian’anmen Square. The Olympic venues be- come not only venues for competition but also new attractions that appealed to tourists.

Finally, new attractions are burgeoning. Be- sides Olympic venues, there are many other new attractions which that an increasing num- ber of visitors during the study period. Some of the areas and nonsporting venues that drew additional support included Shichahai Lake (Three Rear Lakes), Donghuamen Night Mar- ket, Qianmen Dazhalan Street, Bell and Drum Towers, South Luogu Lane, The National Cen- ter for the Performing Arts, 798 Art Zone and Yandai Street. These attractions become more popular because they have been supported and developed by the Beijing Municipal Gov- ernment in order to leverage the tourism im- pact of the Olympic Games.

Tourist movement network

The software NetDraw was used to map itiner- aries of overseas tourists. Three periodical net- works were constructed (see Figures 2–4) to show tourist movement networks in three dif- ferent periods. The nodes in the network stood for attractions with more than four trips in the

Int. J. Tourism Res. 14, 469–484 (2012) DOI: 10.1002/jtr

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475A Social Network Analysis of Tourist Movement Patterns

Figure 2. Overseas tourists’ movement network in Beijing (before August 2007).

Figure 3. Overseas tourists’ movement network in Beijing (August 2007–September 2008).

476 X. Y. Leung et al.

Figure 4. Overseas tourists’ movement network in Beijing (October 2008–April 2009).

477A Social Network Analysis of Tourist Movement Patterns

study period. The links referred to tourist flow (movements) between the attractions. The arrows of the links showed the direction of tourist flow. The width of the links reflected the numbers of tourists in each tourist flow. The results show that The Forbidden City and

Tian’anmen Square always sat in the center of the three networks and attracted the largest number of tourists resulting in the greatest flow. Olympic Green and The Bird’s Nest appeared in the second stage, during the Olympic period, and their importance was strengthened in the third stage, the post-Olympic period. In a longi- tudinal view, the tourist movement network developed from a one-center network in the pre-Olympic period to a several-center network in the post-Olympic period. Three subcenters of tourist flow came into being in the post-Olympic period excluding the main center of The Forbid- den City and the Tian’anmen Square area. The areas that drew the most attention were the Olympic Green area, around Yandai Street (in- cluding Hutong, Shichahai Lake, Bell and Drum Towers and South Luogu Lane) and around

Wangfujin Street and the Donghuamen Night Market area (see Figure 4). Two trends were revealed from the information as well, and this included the diversity of tourist attractions visited and the quantity visiting most popular areas pre- viously. A more detailed examination on tourism movement changes will be discussed below. Table 2 shows a series of longitudinal net-

work measures for the above three networks. Number of nodes and number of ties are basic demographic measures for networks. Among the three periods, the number of nodes did not change much, but the number of ties went up significantly in the post-Olympic period. This suggests that more tourists came to Beijing and visited almost the same attractions. This also demonstrates the positive impact of the Olympic Games on the Beijing tourism indus- try in terms of tourist flow. Network density is defined as the proportion

of all ties that are present in the network graph (Scott, 1991). Therefore, the higher network den- sity, the more the attractions are connected to each other. The densities of three periodical networks

Int. J. Tourism Res. 14, 469–484 (2012) DOI: 10.1002/jtr

Table 2. Longitudinal movement network measures

Time Period Before Aug. 2007 Aug. 2007–Sep. 2008 Oct. 2008–Apr. 2009

No. of Nodes 43 41 47 No. of Ties 261 295 385 Two-way Ties 50 71 80 One-way Ties 211 224 305 Network Density 0.145 0.180 0.178 Betwn. Central Index 61.4% 70.9% 66.5%

478 X. Y. Leung et al.

were pretty low (under 0.2), which suggests great potential to develop future variation in tourism it- ineraries. The densities of the during-Olympic and post-Olympic networks were higher than that of the pre-Olympic network, which implies that tourists visited more attractions in one trip. The connected itineraries could be an indication of destination development under the impact of the Olympic Games. Centralization refers to the extent to which a

network revolves around a single or a number of nodes. Freeman (1979) proposed the three most widely used centrality measures as degree, closeness and betweenness. In social network analysis, node betweenness centrality is an im- portant measure for centralization. Betweenness centrality considers a node as being in a favored position to the extent that the node falls on the geodesic paths between other pairs of nodes in the network, that is, the greater the nodes that depend on the node i to make connections with other nodes, the greater betweenness centrality i has. Betweenness centralization measures the betweenness of the entire network by calculating the ratio of actual sum of betweenness centrality for each nodes to the maximum possible sum (Freeman, 1979). In a network, if a single or a number of nodes are more central than the rest, the network is more hierarchical, and the between- ness centralization score is high (Freeman, 1979). The betweenness centralization scores of three periodical networks were all high (>60%), show- ing that there was a substantial degree of concen- tration among tourist attractions. In other words, most overseas tourists were more likely to visit cer- tain attractions, which were represented as the central nodes of the network. The centralization score went down from the during-Olympic to the post-Olympic period, indicating a growing diversification of tourist attractions in the post- Olympic period.

Main tourist movement patterns

In order to simplify the three periodical networks and to show clear tourist movement patterns, this study identified tourism itineraries that included five or more tourists as main tourist movement patterns. The reduced periodical networks that only showed main tourist movement patterns were mapped as Figures 5–7. The results indi- cated that there was an increasing trend in the number of main tourism movement patterns in Beijing. The tourism attractions affecting tourism movement patterns and total trips were also in- creasing. In the pre-Beijing Olympic Games period (before August 2007), the numbers of main tourism movement patterns was 22, involv- ing 19 tourism attractions and 227 trips. During the Beijing Olympic Games period (from August 2007 to September 2009), the numbers of main tourism movement patterns was 28, involving 22 tourism attractions and 365 trips. In the post- Beijing Olympic Games period (from October 2008 to April 2009), the numbers of main tourism movement patterns was 43, involving 26 tourism attractions and 460 trips.

The results also showed that tourism move- ment patterns in Beijing changed because of the impact of the Olympic Games. In the pre- Beijing Olympic Games period (see Figure 4), main tourism movement patterns revealed trad- itional itineraries, involving Tian’anmen Square, The Forbidden City, Tiantan Park, Wangfujin Street, The Summer Palace, Tsinghua and Peking University, the Ming Tombs and The Great Wall. During the Beijing Olympic Games period (see Figure 5), some new tourism itineraries emerged, which included the Olympic Green area, from Shichahai Lake to Bell and Drum Towers and from Tiantan Park to Hongqiao Market. The connections between The Summer Palace and Tsinghua and Peking University decreased, while

Int. J. Tourism Res. 14, 469–484 (2012) DOI: 10.1002/jtr

Figure 5. Overseas tourists’ movement patterns in Beijing (before August 2007).

479A Social Network Analysis of Tourist Movement Patterns

the connections between The Summer Palace and Olympic Green strengthened. In the post- Beijing Olympic Games period (see Figure 6),

Figure 6. Overseas tourists’ movement patterns in Beijin

the connectedness around the Olympic Green area was strengthened, and the connectedness from Shichahai Lake to Bell and Drum Towers

g (August 2007–September 2008).

Int. J. Tourism Res. 14, 469–484 (2012) DOI: 10.1002/jtr

Figure 7. Overseas tourists’ movement patterns in Beijing (October 2008–April 2009).

480 X. Y. Leung et al.

extended to South Luogu Lane and Yandai Street. In addition, the connectedness around southern Tian’anmen to The Forbidden City area strengthened, while that around northern area weakened. Clearly, the main visiting areas are the central

city areas, which consist of the Dongcheng, Xicheng, Xuanwu and Chongwen Districts. Some suburban areas that were frequently vis- ited included the Haidian and Chaoyang Dis- tricts. The most visited rural areas were those where the Great Wall is located. During the study period, the central city area attracted the majority of overseas tourists (more than 70%). The Chaoyang District, the area where most of the Olympic attractions are located, received an increasing number of tourists. In rural areas, the regions where the Ming Tombs and The Great Wall are located became more popular than the other areas.

IMPLICATIONS AND CONCLUSION

Using content analysis and social network ana- lysis, the study analyzed the most visited tourism attractions and the main tourism movement pat- terns in Beijing during three distinct periods,

based on data collected from 500 trip diaries of overseas visitors. The study has several import- ant findings. First, the study found that the num- bers of main tourist attractions, attractions visited per capita, and main tourism movement patterns have all increased over the course of the study period. Second, the study examined the patterns of change in the tourist attractions themselves. Famous traditional attractions are still the most sought-after attractions, but Olympic-related venues have also become must-go places for overseas tourists. Third, the study revealed that substantial tourist movement patterns extended from traditional attractions in the pre-Olympic period to the Olympic area to other new sub- centers in the post-Olympic period. Fourth, the study identified the central city area in Beijing as the core visiting area. The main visiting area expands along the Central Axis of Beijing under the impact of the Olympic attractions and other newly developed attractions.

This study provided some theoretical contri- butions. As one of the first attempts to use so- cial network analysis to examine tourist flow and tourist movement, this study offers an- other applicable method to understand these patterns and changes besides commonly used

Int. J. Tourism Res. 14, 469–484 (2012) DOI: 10.1002/jtr

481A Social Network Analysis of Tourist Movement Patterns

statistical methods. In this context, tourist movement networks and social network ana- lysis are visualized and easy to understand. In addition, this study is based on user-generated content instead of regular survey methods. As Hwang et al. (2006) mentioned, the emergence of social network-based Internet systems has and will continue to reshape the overall struc- ture of travel and make travel decisions more personalized. This study takes advantage of this new media to touch upon personalized travel itineraries. From this point of view, this study contributes to and extends the literature by adding a new method and a new viewpoint to deliberately understand tourist movement patterns. From a practical perspective, Moreira (2009)

insists that the Olympic Games also have high negative impact potential. The Olympic Games not only bring tourism opportunities but also tremendous challenges associated with tourism marketing and management to host cities. In order to leverage on the impact of Olympic tourism and to maximize tourism economi- cally, it is important for destination marketers and managers to understand tourist movement patterns and the accompanying changes dur- ing the games. The above findings provide important insights regarding host cities’ desti- nation product development and marketing under the impact of a mega event such as the Olympic Games. First, the results revealed the impact of the

Olympic Games to the tourism industry of the host city. More attractions in Beijing were visited with more tourism flows in the post- Olympic period than in the pre-Olympic period. This finding is consistent with Ritchie and Smith (1991)’s study, which found that the hosting of the Olympic Games increases the host city’s international awareness dramat- ically and attracts large numbers of visitors. The increasing number of overseas tourists and attractions visited during the Olympic period in this study indicates that the Olympics are a great boost to the tourism industry of host city. Similarly, Koldowski and Yoo (2006) pre- dicted the 2008 Olympic Games as one of the seven primary forces to positively affect the Asia Pacific tourism industry over the next decade. More overseas tourists are attracted by the Olym- pic Games and the Olympic venues themselves

become new and hot tourist attractions. The Olympic Games clearly make host cities more attractive. Second, although Beijing has plenty of tour-

ism attractions, overseas tourists are only in- terested in a handful of sites. As Shen (2003) stated, foreign visitors in China tend to go to the most popular tourist destinations. These attractions are central to the three tourist move- ment networks and are frequently visited by most of the overseas tourists. These attractions are divided into two categories: those trad- itional ones that have high reputation and brand awareness and those new ones that are developed for Olympic purposes. Host cities should emphasize both famous traditional attractions while developing new Olympic attractions in order to take full advantage of its Olympic opportunity. Third, the variety of tourist attractions an ob-

vious trend indicated by the longitudinal net- work analysis. Not only Olympic attractions but other new attractions have been developed in Beijing in order to garner attention from tour- ists. All these new attractions generate tourist flows and promote the overall Beijing tourism industry. It is suggested that host cities develop appropriate new attractions aside from Olympic venues based on the needs of overseas tourists. The variety of attractions will prolong tourists’ length-of-stay and provide more attractions to visit. A multitude of options will also change tourist movement patterns. Fourth, tourism movement patterns changed

a lot under the impact of the Olympic Games during the study period. The empirical findings of the study provided convincing evidence to support Weed (2008)’s study on Olympic ‘event-affected’ tourist flows. The study not only testifies that during Olympic periods, overseas tourists’ movement patterns do change and tourist attractions they visit also change but also specifies what these changes are and how these changes impact the whole tourism industry. Tourists tend to move from traditional attrac- tions to Olympic attractions and to other new developing attractions. Host cities should under- stand these changes within tourist movement patterns and take advantage of it. The location of new developing attractions might be chosen near the Olympic attractions or traditional attractions in order to help tourists connect them

Int. J. Tourism Res. 14, 469–484 (2012) DOI: 10.1002/jtr

482 X. Y. Leung et al.

conveniently as part of their itinerary. Appropri- ate transportation support should be provided to facilitate these changes. New tourist brochures should be provided with the introduction of new attractions and new itineraries. Finally, the expansion of main visiting area is

the other obvious trend indicated by the longi- tudinal network analysis. The Olympic Games give tourists more opportunities to explore what host cities have to offer. Because new attractions are scattered throughout the host city, more areas will be visited by tourists. More old attractions, which become overlooked by tourists, get new chances to be included as part of their itineraries if host cities develop new and compelling attractions around these old ones. The expansion of the main visiting area will re- sult in tourists’ longer stays and more spending that improves the whole tourism industry.

LIMITATIONS AND FUTURE RESEARCH

When interpreting the study results, the audi- ence is advised to note the following observa- tions. First, this study is based on the data of 500 trip diaries collected from six websites. People who wrote diaries on those websites may not be representative of the whole overseas tourist population, so the tourist movement pat- terns and tourist attractions found in the study may not accurately reflect the true patterns and attractions. Also, this study was conducted in Beijing. The unique city characteristics may have an impact on the overseas tourist population. Clearly, the results of the study are not generalizable and applicable to other cities. Sec- ond, the impact of the tour agency to the tourist movement patterns is not addressed. In this study, we could not distinguish FIT travelers from package tourists when selecting trip diar- ies. If some or many of the diaries we selected were written by package tourists, then the movement patterns found in the study may be influenced by the tour operators’ arrangements. Third, data completeness of trip diaries is also a limitation of this study. The authors have no con- trol on whether the trip diaries were complete, whether the diaries included all the stops the tourists made in Beijing. The results based on this kind of data might be somewhat mislead- ing. Last, due to the data constraints, the study ignores the demographic characteristics of the

overseas tourists, which may influence their tourist movement patterns. For example, the na- tional origin of tourists may to relate to their length of stay and their attraction preferences. In addition, length of stay may affect tourists’ travel activities. Future studies are necessary to address these issues.

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