Chung, Jin Young, and Buhalis, D., 2008, Information needs in online social networks, Information Technology and Tourism, Vol.10(4), pp.267-282. more

Information Technology & Tourism, Vol. 10 pp. 267–281 Printed in the USA. All rights reserved. 1098-3058/08 $60.00 + .00 Copyright  2008 Cognizant Comm. Corp. www.cognizantcommunication.com INFORMATION NEEDS IN ONLINE SOCIAL NETWORKS JIN YOUNG CHUNG* and DIMITRIOS BUHALIS† *Department of Recreation, Park, and Tourism Sciences, Texas A&M University, College Station, TX, USA †School of Service Management, Bournemouth University, Dorset, UK This article examines the relationship between perceived benefits and participation in an online travel community, of representative social networking sites on the Internet, to understand what actually makes actors participate in social networks. Findings reveal that three factors (information acquisition, social-psychological, and hedonic) are main benefits influencing participation and attitude towards an online travel community. In addition, the multiple regression analysis indicates that information acquisition benefits are perceived as the most important influential elements. Some of the results are found not to be consistent with the findings of previous research. This study provides tourism-related organizations with useful information on how to utilize online communities for their marketing strategy. Key words: Social networks; Online community; Information needs Introduction A comprehensive understanding of multidimensional social structures and complicated relations within modern society has gradually become a demanding task for researchers. Furthermore, the innovative development of information technologies has sparked some sociologists interested in issues about social networks in virtual space (Garton, Haythornthwaite, & Wellman, 1999; Wellman, 1997). Kozinets (1999) estimated that over 40 million Internet users would participate in some type of online virtual communities by 2000, and the popularity of online communities has seen increased growth. This phenomenon has spread to all industries and locations. Specifically, potential travelers use the Internet to obtain travel information, share their experiences, develop a relationship with people from various destinations, view wonderful photos, or purchase travel-related products. As much as Internet websites provide virtual communities and blog functions related to travel, many travel-oriented websites are largely based on online communities. Additionally, from the information flow perspective, as information communication technologies have increasingly influenced consumer behavior in tourism (Buhalis, 1998; Poon, 1993; Sheldon, 1997), the role of online communities has gained attention both as a reliable information source for travelers and as an emerging marketing channel for marketers (Armstrong & Hagel, 1997; Kozinets, Address correspondence to Jin Young Chung, Department of Recreation, Park, and Tourism Sciences, Texas A&M University, Francis Hall, 2261 TAMU, College Station, TX 77843-2261, USA. Tel: (979) 845-6583; Fax: (979) 845-0446; E-mail: jy0914@ tamu.edu 267 268 CHUNG AND BUHALIS (Heider, 1958; Shaver, 1985). Wellman (1983) classifies the traditions of social network analysis into three approaches: the anthropological concept; the sociological conception of social structure; and structural explanations of political processes. While norm-based cultural systems within bounded groups have been traditionally paid attention to in anthropology, several anthropologists have changed their interests into systems of concrete ties (Barnes, 1971; Nadel, 1957). Accordingly, the new concepts of system define a network as a set of ties connecting individuals in social systems across bounded groups (Wellman, 1983). Social networks were also approached in two types of sociological foundations such as formalists and structuralism. The formalists in sociology particularly emphasize the importance of triads, treating them as the basic elements of social structure. In structuralism, researchers examine social networks from a whole or personal viewpoint. While whole networks deal with all of the ties connected, personal networks approach the links, questioning how the patterns of ties affect the information and resource flow (Wellman, 1983). For instance, Granovetter’s (1974) study on job information search and Lee’s (1969) research on abortionists demonstrate how personal networks influence access to information regarding very intimate data. The latter case requires specific close-knit collective actions with high levels of shared interests and norms, comparing with much more fleeting and, thus, more open-ended network with a large universe of options encountered in the travel context. The third tradition of social network analysis argues that the position of actors in social structures yields the political processes. Specifically, they focus on relationships between nation-states and interest groups in terms of exchange and dependency. Although researchers in this tradition would not use even network-analytic terms, they argue that different patterns of their relations in social structure cause uneven allocations of resources (Wellman, 1983). Additionally, some of researchers in this tradition use networks to demonstrate that asymmetric international relationships between the core and periphery of the world 1999; Wang, Yu, & Fesenmaier, 2002). The purpose of this article is, therefore, to examine the relationship between perceived benefits and participation in an online travel community, of representative social networking sites on the Internet, in order to understand what actually makes actors participate in social networks. In the following section, the main theoretical foundations related to online social networks, including social networks theory and information, needs will be explored. Then empirical study results in this research will be analyzed and discussed in comparison to findings of previous research. Social Networks and Online Community Social Networks Theory Social network refers to a structure composed of actors (nodes) and their relationships (links) within a society (Scott, 2000; Stokowski, 1992; Wasserman & Faust, 1994). Social structure has been widely perceived as one of the integral concepts in sociological analysis, and has been studied via two main traditions: exchange theory and network analysis (Cook & Whitmeyer, 1992). Cook and Whitmeyer argue that both approaches have similar conceptions of action and structure, but the latter is based on more empirical observations and analytical techniques. Accordingly, in several approaches, social network analysis (SNA) has been developed by sociologists to effectively articulate the complexity of social structures (Cook & Whitmeyer, 1992), and the attempts to adapt this method have been recently incorporated into a variety of disciplines. Social networks theory is also defined as a body of knowledge used to identify entities embedded in a social context (Granovetter, 1985), and measure their relations, roles, and ties within networks (Cook & Whitmeyer, 1992; Monge & Contractor, 2003). Scott (2000) argues that social network analysis is very meaningful because it attempts to analyze relational data instead of attribute data for understanding social structures. Nonetheless, it still can be argued that individuals tend to engage in cognitive attribution process when they want to identify stable characteristics of the other and features of the social environment INFORMATION NEEDS IN ONLINE SOCIAL NETWORKS have an influence on the socioeconomic development of dependent countries (see Baron’s, 1957, and Frank’s, 1967, “dependency theory”). On the other hand, Scott (2000) chronologically analyzes the development of social network analysis from a methodological perspective. He claims that since the 1930s, social networks theory has been developed according to three main strands: sociometric analysis based on graph theory; methods used by the Harvard researchers of the 1930s; and the style of analysis developed by the Manchester anthropologists. Sociometric analysis was influenced by the “gestalt” theory of German scholars, and focused on sociometry and group dynamics. Moreno initially devised the “sociogram” consisting of points and lines, which has been well elaborated upon, to date (Scott, 2000). The points and paths represent individuals and their relationships respective to a given group. This idea has been adapted by other scholars, which has resulted in a graph theory composed of points and connecting lines indicating positive and negative relations, in particular, within small groups. Secondly, anthropologists and sociologists at Harvard University were interested in informal interactions within social systems, and subsequently investigated the formation of “cliques.” Influenced by Durkheim and a British anthropologist, Radcliffe-Brown, scholars at Harvard closely examined factory (e.g., Hawthorne electrical factory) and community (New England community) life in America, trying to uncover the existence of cohesive subgroups within each community. The Hawthorne study was the first major research project to use sociograms, and revealed that people are integrated into societies or communities not only through formal political and economic organizations, but also through informal and personal relations such as those shared by family and clique members (Scott, 2000). In contrast to the Harvard school, the Manchester anthropologists emphasized conflict and change. Gluckman, one of the representative researchers at the Manchester school, argued that conflict and power are essential factors in social structures, requiring “negotiation, bargaining, and coercion” to address those elements for social integration (Scott, 2000, p. 27). Additionally, the Manchester school 269 struggled to apply the social networks concepts to a study of central African town societies. As Wellman (1983) points out, the anthropologists focused on Third World migrants from rural areas to cities, and described how the migrants maintain ties to both their original places and new city communities. These three traditions of thought developed and influenced one another, and eventually consolidated into the current social network analysis used at Harvard in the 1960s and 1970s (Scott, 2000). The “Harvard breakthrough” has two innovative characteristics distinguishing it from previous strands: the use of algebraic models for groups and the application of multidimensional scaling. Harrison White focused on this integrated standpoint, and had a significant influence on related scholars and theories after he developed his mathematically oriented model of social structure (Scott, 2000). Beyond Physically Based Relationships Emerging technologies such as email, online bulletin boards, instant messenger, and online communities allow people to extend their ties or relations with others in virtual spaces (Preece, 2000; Wellman, 1997). From an information exchange perspective, the transition in terms of types of relationships particularly enables individuals to receive and generate information beyond conventional channels (Armstrong & Hagel, 1997; Poon, 1993; Sheldon, 1997). Today people tend to go online when they fail to get information via physically based relationships, or they go directly to the Internet to the exclusion of contact with physical networks. The diffusion of technology have made that situation totally different from what Granovetter (1974) explained in his study about seeking job information and what Lee (1969) described in his early work about searching for doctors who undertake illegal termination. Individuals do not feel overwhelming social pressure to remain with their family members or friends anymore (Preece, 2000). Instead, they tend to utilize extended social networks even across remote distances and among actors with barely an acquaintanceship. The adoption of technologies, including the Internet and wireless and mobile devices, has facili- 270 CHUNG AND BUHALIS plays the critical role in expanding the networks of individuals in a virtual space (Wellman, 2001). However, while the concept of an online community has been a central component of the Internet since its emergence, it has proven to be difficult to define the online community as it has developed (Wang et al., 2002). Although many researchers have attempted to define the online virtual community, and have also attempted to grasp its features (Armstrong & Hagel, 1997; Fernback & Thompson, 1995; Preece, 2000; Shelton & McNeeley, 1997), the following definition of online community is the most frequently quoted. Virtual communities are cultural aggregations that emerge when enough people bump into each other often enough in cyberspace. A virtual community is a group of people who may or may not meet one another face to face, and who exchange words and ideas through the mediation of computer bulletin boards and networks. (Rheingold, 1991, pp. 57–58) tated the growth of social networking in virtual space (Wellman, 2001), and formed the shape of new online virtual communities. For instance, Myspace.com, one of the biggest social networking websites in the US, has over 47.3 million members, with new users numbering 160,000 a day, and has increasingly expanded since its inception. One of the main functions of social networking sites is to provide user-generated recommendations and reviews drawn from the experiences of other members. Information Needs As tourism represents one of the informationintensive industries, the information plays a critical role in tourism to make a right choice (Pan & Fesenmaier, 2006; Poon, 1993; Sheldon, 1997). People undoubtedly search for information in order to solve problems or, specifically, to make a destination choice. However, it also has been found that individuals who collect travel information do not always have actual intention to travel (Messmer & Johnson, 1993; Urry, 1990; Woodside, 1990). In addition to the functional needs, people attempt to use information for sharing with others, viewing pictures, or simply enjoying. Vogt and Fesenmaier (1998) argue that information search needs are expanded beyond functional needs towards four additional dimensions: hedonic, innovation, aesthetic, and sign needs. While functional needs refer to needs as motivated efforts that are directed at or contribute to a purpose, hedonic needs involve the pursuit of enjoyment. Aesthetic needs mean the search for visual stimulation for the imagination. Innovation needs involve a tendency towards new products and information, and sign needs are symbolic expressions and social interaction. Based on the conceptual model regarding the relationship between consumer behavior and the multiple information needs, hedonic, innovative, aesthetic, and visual needs are also found to play a significant role in information search process (Nishimura, Waryszak, & King, 2006; Vogt & Fesenmaier, 1998). Online Community As mentioned earlier, an online virtual community presents the traits of social networking, and It is also argued that people are able to do everything they do in real life on the computer: chatting, discussing, exchanging knowledge, sharing emotional support, finding friends, falling in love, and playing games (Rheingold, 1991). Preece (2000), in one of the most cited books on the concept of the online community, claims that an online community has basic operational elements such as people, a purpose, policies, and a computer system for supporting its community. In other words, an online community is defined as a virtual place in which people with a shared purpose interact on the basis of the policies established by using the computer system. Based on components developed by Preece (2000), Wang et al. (2002) proposed a conceptual model for defining an online community. The model is composed of three perspectives: virtual community as place, virtual community as symbol, and virtual community as virtual. They argue that an online community is perceived as a virtual place in which individuals can form social relationships and discover new opportunities. This is similar to a physical community; however, an online community is different from a physical social structure in terms of the nature of its being virtual. Preece (2000) argues that the online commu- INFORMATION NEEDS IN ONLINE SOCIAL NETWORKS nity captures the interests of diverse disciplines, and attempts to analyze the perspectives taken on by various groups such as: multidisciplinary, sociology, technology, virtual world, and e-commerce. First, sociologists have also attempted for years to define the basic concept and characteristics of a community. Although the physical factors such as size, location, and boundaries were initially deemed to be the important criteria to use when defining communities, the degree and type of relationships among participants were also found to be critical indicators (Haythornthwaite & Wellman, 1998; Wellman, 1997). In sociology, the strength of these relationships can be measured on the basis of network analysis; however, the research on online virtual communities is immature with regards to comparing them with physically and geographically defined communities (Preece, 2000). Conversely, the technology perspective stands opposite to the sociology viewpoint. Some technical terms imply the figures and functions of an online community. Technology-based individuals may focus on these terms and technical specifications of the online community rather than understand its social implications. Preece (2000) proposes the virtual world perspective of the online community, because she believes that community members may indulge in utilizing the virtual space to enable to represent their faces as 3D objects. The multidisciplinary viewpoint was identified by a report made at the Computer-Human Interaction Conference (Preece, 2000), and the following core attributes were included: (1) members have a shared goal, interest, need, or activity that provides the primary reason for belonging to the community; (2) members engage in repeated, active participation, and often, intense interactions, strong emotional ties, and shared activities occur among participants; (3) members have access to shared resources, and policies determine the access to those resources; (4) reciprocity of information, support, and services among members is important; (5) there is a shared context of social conventions, language, and protocols. (Whittaker, Issacs, & O’Day, 1997, p. 27) 271 strong & Hagel, 1997). It seems relatively easy to gain advertising effects in areas where many online users gather in dense groups to actively share their experiences and information. Online business entrepreneurs, therefore, strive to make users stay on their websites as long as possible. From a marketing perspective, an online virtual community is one of the most crucial information-gathering and distribution places in which diverse marketing strategies are utilized. Organizations in various industries may observe market trends and customer needs without additional market research, and also promptly receive and analyze consumers’ feedback about their own services and products. Perceived Benefits and Needs of Online Community Although an online community brings advantages and opportunities to both suppliers and customers, the successful operation of a community largely depends on understanding members and community (Kim, Lee, & Hiemstra, 2004; Kozinets, 1999). Accordingly, the characteristics of community and members’ fundamental needs should be investigated for developing and maintaining proper online virtual community (Wang et al., 2002). Table 1 shows that benefits and needs of online community toward Internet users have been studied by many researchers. It was consequently found that Internet users perceive different benefits, depending on individual profiles or their needs to visit online communities. Armstrong and Hagel (1997) claim that an online community provides four different values to members: transaction, interest, fantasy, and relationship. Wang and Fesenmaier (2004b) argue that travel online community provides functional, social, psychological, and hedonic benefits to members, and find that social and hedonic benefits have a major impact on members’ level of participation in community activities. Functional benefits are associated with information gathering and the convenience and efficiency beyond the time and geographical limits (Wang & Fesenmaier, 2004a). Social benefits refer to communication with other members, building relationships, exchanging ideas and opinions, and getting involved (Angehrn, 1997; Preece, 2000; Wang & Fesenmaier, 2004a). Finally, an online community not only has social and technical implications, but also provides a commercial impact on business players (Arm- 272 CHUNG AND BUHALIS Table 1 Benefits and Needs of Online Community Researchers Armstrong and Hagel (1997) Angehrn (1997) Wang et al. (2002) Bagozzi and Dholakia (2002) Rodgers and Sheldon (2002) Wang and Fesenmaier (2004b) Kim et al. (2004) Benefits or Needs Transaction, interest, fantasy, relationship Information, communication, distribution, transaction (ICDT model) Functional, social, psychological Functional, hedonic Research, communication, surfing, shopping; WMI (Web motivation inventory) Functional, social, psychological, hedonic Membership, influence & relatedness; integration and fulfillment of need; Shared emotional connection (adapted from McMillan, 1996 Preece (2000) argues that these social benefits are encouraged when trust on community and members is largely built. For instance, people are more likely to share their private opinions and provide valuable information if they recognize who the members are and what the community is. The similarity is the critical factor influencing establishing trust toward virtual communities, even though there is risk of “wishful thinking” fallacy. On the other hand, if individuals trust a certain community, their belonging and affiliation will arise. These are deemed to be psychological benefits. Some researchers argue that psychological benefits are initially required when people join the community (Bressler & Grantham, 2000). However, the psychological benefits may be gained as a result of continual communication and activities—extended social benefits. Finally, Wang and Fesenmaier (2004a) argue that community members desire not only functional, social, and psychological benefits, but also fun, enjoyment, and amusement—hedonic benefits. Online Communities and Tourism The emerging social networks on the Internet are undoubtedly worth adapting to tourism because tourism is an information-intensive industry with an integral part of extended information search and flow (Poon, 1993; Sheldon, 1997). Recently, an online travel community has become popular as a credible information network because it provides potential tourists with trustworthy reviews and recommendations. In an online travel community established by one of the social networking sites, users are able to find plenty of in- formation from like-minded users beyond their actual friendship circle or family members, and are also likely to build relationships with other users or to become involved in the community regardless of geographical restriction. In other situations, they may simply desire fun, enjoyment, or amusement in and of itself. These functional, socialpsychological, and hedonistic benefits are mainly identified as the critical factors causing online users to participate in the online community (Armstrong & Hagel, 1997; Wang & Fesenmaier, 2004b). For instance, some travel-based social networking sites such as VirtualTourist.com, CouchSurfing. com, Lonelyplanet.com, IgoUgo.com, and Tripad visor.com play a major role in providing up-todate information on destinations by connecting members located around the world (Beith, 2004). VirtualTourist.com, one of the most popular and largest online travel communities, has over 800,000 registered members from more than 220 countries, and 1.4 million travel tips on over 25,000 destinations. There are 2.6 million photos posted on members’ personal pages (VirtualTourist, 2006). In particular, the feature of “Meet other members” provides a variety of functions in order to extend community networks and enhance users’ involvement. Individuals can build their own networks by using “friendship” function. The members’ profile such as travel experience, interests, and the contribution rating helps them to create the favorite friends list. Some travelers may successfully find “friends” who live in a destination where they want to visit and are expected to give them real travel tips. Interestingly, the members of this community site frequently have offline meetings as INFORMATION NEEDS IN ONLINE SOCIAL NETWORKS well. Anyone can post a noncompulsory meeting at any destination. The offline meeting reportedly helps users to make stronger relationships with other participants, and ramify their networks. IgoUgo.com is another well-known online travel community with over 350,000 members. It has about 300,000 travel reviews and 200,000 photos on destinations, hotels and travel products since it was started in June 2000 (Beith, 2004). Consequently, around 5 million online users make use of such sites to build their virtual relations, and see 30 million pages every month, leading to a high level of participation and an exchange of copious amounts of information with members, whether they are real travelers or merely interested locals (Niininen, March, & Buhalis, 2006; Virtual Tourist, 2006). Methods In order to develop social networks based on community needs and to facilitate community members’ activities, it is very important to understand what community users want and how respective benefit or need contributes to increase level of participation and create positive attitude. Accordingly, needs or benefits perceived by members are initially identified. The relationship between the members’ benefits and the level of participation in an online community is secondly examined, and whether the benefits have an influence on members’ attitudes toward an online community is also investigated. This research therefore has objectives as follows: identify members’ fundamental benefits in an online travel community; examine how much respective benefit has an influence on level of participation in an online travel community; investigate whether the benefits affect members’ attitudes toward an online travel community. On the basis of the literature review in the previous part, four independent variables and two dependent variables are identified in the current study. Wang and Fesenmaier (2004b) found that social and hedonic benefits are mainly related to level of participation in online travel communities. Hence, social benefits, including communication with other members, and hedonic benefits, such as fun, enjoyment, and entertainment, are taken as 273 independent variables in this study. In addition to social and hedonic benefits, members in travelrelated communities undoubtedly have very strong motivation to acquire travel information (Armstrong & Hagel, 1997). The relationship between functional benefits and level of involvement therefore should be examined. Consequently, functional, social, psychological, and hedonic benefits of community users are selected as the independent variables, while the level of members’ participation and attitude toward an online community are the dependent variables. In line with the conceptual framework, four hypotheses were formulated with regard to the relationships between members’ benefits and level of participation in an online travel community. H1. Functional benefits have a significant impact on level of participation in online community. H2. Social benefits have a significant impact on level of participation in online community. H3. Psychological benefits have a significant impact on level of participation in online community. H4. Hedonic benefits have a significant impact on level of participation in online community. Four more hypotheses were formulated with regard to the relationships between perceived benefits and members’ attitudes toward an online travel community. H5. Functional benefits have a significant influence on attitude toward online community. H6. Social benefits have a significant influence on attitude toward online community. H7. Psychological benefits have a significant influence on attitude toward online community. H8. Hedonic benefits have a significant influence on attitude toward online community. The sampling of this research is South Korean Internet users. It was recently revealed that South Korea is one of the top five countries by number of Internet users, and also ranks third by Internet penetration (International Telecommunication Union, 2003). In addition, Internet users in Korea are almost 70% of total population, and over 90% of the young generation uses the Internet in daily life. Korea is also the third country in the world in terms of average hours spent online (comScore, 274 CHUNG AND BUHALIS Korean was designed in the web form and posted on the Korean online survey website (World Survey Inc.; www.wsurvey.net). This website is widely known as a professional online survey site so that it is believed that both online survey panels registered to the website and nonregistered Internet users visit the website to participate in a survey. By answering an online survey, a respondent is expected to gain an incentive, such as virtual points for online shopping. This online questionnaire was posted on a bulletin board of the first page of the website during 30 days from June 26 to July 26. Consequently, 419 Internet users participated in the online survey, and 217 of them were found to be online travel community users. The methodology chosen in this study has some limitations. First of all, the research instrument in this study relied mainly on Wang and Fesenmaier’s (2004b) questionnaire for American Internet users, in which the statements and terms were estimated to be taken from the American perspective. Accordingly, errors in translation into Korean might have occurred despite the revision through a pilot survey. Secondly, because research into the online travel community is still at the exploratory stage and there is a lack of earlier research findings in tourism (Kim et al., 2004; Wang & Fesenmaier, 2004b), a qualitative methodology could have been used as an alternative (Sekaran, 2003). For instance, an in-depth interview or a focus group interview may be substituted for questionnaires or, alternatively, both interviewing and a questionnaire could be used as an integrated methodology in order to collect data. Finally, the level of participation was arguably defined as the period of membership and the frequency of visiting online community, and the means of two values were regarded as the degree of participation in current research. Compared to Wang and Fesenmaier’s (2004b) study in which the level of participation was defined as the amount of time spent using online community per week, different results may be estimated. Results Among 419 valid questionnaires, half of the respondents (51.8%) were found to have experience using an online travel community (n = 217). Of 2006). Furthermore, Koreans indicate cultural characteristics such as “collectivist” (Hofstede, 1991). Thus, it is expected that research on online travel communities in South Korea will result in significant findings. The questionnaire was composed of 26 closed questions and was divided into five sections: benefits from online community activities, level of participation in an online travel community, attitude toward an online travel community, frequently used information sources, and demographic information. The structure of the questionnaire was fundamentally based on Wang and Fesenmaier’s (2004b) research instrument, and the part for attitude and information source were added to the questionnaire. Items for measuring the level of participation in an online travel community were designed from two perspectives. First, the period of using online communities was asked: “How long have you been using the online travel community?” Second, the frequency of usage was asked: “When deciding travel destinations or planning trips, how often do you go online to participate in the online travel community per week?” In this study, attitude was constructed in three dimensions, such as trust/reliable, positive/negative, and loyalty, based upon a literature review in tourism, marketing, and management information (Bagozzi & Dholakia, 2002; Heijden, 2003; Hwang & Cho, 2005; Kim et al., 2004; Lin & Lu, 2000; Moon & Kim, 2001; Reichheld & Schefter, 2000). The data were electronically collected through a questionnaire designed by HTML. For electronic data collection methods, the web form with online panel has been increasingly known, and has recently become very popular with private companies and research institutes in order to collect consumer data and conduct online surveys (e.g., Microsoft.com’s research panel and ACNielsen’s Your voice panels). Individuals participate in the online survey of the web form throughout e-mail including the link of the web form or visitation to bulletin board of the web form. It has been found that if some incentives to respondents are considered, the web form has three major advantages: fast response, low cost, and easy to design in comparison with mail, fax, and e-mail methods (Weible & Wallace, 1998). In this research, an electronic questionnaire in INFORMATION NEEDS IN ONLINE SOCIAL NETWORKS the community users, the number of females (n = 127) was larger than the number of male respondents (n = 90). As seen in the results of age groups, which were divided into five categories, the vast majority of online travel community users are in their 20s (50.7%) and 30s (41.5%). Almost 80% of the respondents are also full-time employed (52.1%) and students (26.3%). The majority of community users were therefore described as students or full-time employed groups in their 20s to 30s. In order to identify benefits perceived by members in an online travel community, factor analysis was conducted. The value (0.835) of Kaiser-MeyerOlkin (KMO) measure of sampling adequacy (MSA) shows the appropriateness of applying factor analysis in this research (Hair, Ortinau, & Bush, 2000). Bartlett’s test of Sphericity value (χ2) was 1334.047 at p = 0.000 significance level, which indicated that a significant correlation existed among at least some of the variables (Kim et al., 2004). As a result of Principal Component Analysis of factor analysis with VARIMAX rotation, three main factors were subsequently found according to the categorized variables with factor loadings (Table 2). Among factor loadings, only a factor loading of 0.50 or higher was consider to be valid in this Table 2 Factor Analysis (n = 217) Variables Obtaining up to date information Efficient to search information Convenient to find information Sharing experiences Having trust in the community Seeking identity Keeping relationship with members Seeking a sense of belonging Getting involved with members Having fun with contents Entertainment To be amused by members Cronbach’s alpha Eigenvalue % of variance Cumulative % of variance Information Acquisition 0.858 0.853 0.802 0.700 0.537 0.060 0.099 0.135 0.206 0.014 0.299 0.248 0.842 5.096 42.470 42.470 SocialPsychological 0.017 0.038 0.176 0.191 0.405 0.817 0.792 0.790 0.756 0.156 0.273 0.463 0.843 2.037 16.979 59.449 Hedonic 0.118 0.068 0.074 0.163 0.190 0.160 0.137 0.276 0.172 0.901 0.798 0.643 0.816 1.110 9.247 68.696 275 research. Factor 1 was named “Information Acquisition” because its five variables are related to information gathering: obtaining up-to-date information, efficient information search, convenience in finding information, sharing experiences, and having trust information in the community. Factor 2 was named “Social-Psychological” with four variables because some researchers called them social benefits (seeking identity and getting involved with members) and psychological benefits (keeping relationship with members and seeking a sense of belonging) (Kim et al., 2004; Wang & Fesenmaier, 2004b; Wang et al., 2002). Factor 3 (Hedonic Benefits) is composed of three variables, all pertaining to hedonic benefits: having fun with contents, entertainment, and to be amused by members. The three factors account for 68.69% of the total variation in the original 12 variables. Cronbach’s alpha coefficients for the three factors are 0.842, 0.843, and 0.816, which shows the high level of reliability of these items. Additionally, all factors have Eigenvalues greater than 1.0—Factor 1 (5.096), Factor 2 (2.037), and Factor 3 (1.110)— which demonstrates that they are useful for the multiple regression analysis. The relationship between respective benefits (independent variable) and level of participation (dependent variable) was investigated using multi- KMO: 0.835. Bartlett text: χ2 = 1334.047, significant at p = 0.000. 276 CHUNG AND BUHALIS Table 3 Regression Between Benefits and Participation Variables (Constant) Information acquisition benefits Social-psychological benefits Hedonic benefits Coefficient 3.076 0.322 0.234 0.296 SE 0.068 0.068 0.068 0.068 Beta 0.290 0.211 0.267 t 45.425 4.737 3.440 4.362 Sig. 0.000 0.000 0.001 0.000 R2 = 0.200; adjusted R2 = 0.189; F = 17.768, p = 0.000. ple regression analysis. Table 3 displays the results of the multiple regression analysis. The factor scores derived from the preceding factor analysis were used as the input variables in this analysis (Hair, 1998). The F-value (17.768, p = 0.000) demonstrates that the results of the regression model are statistically significant. Consequently, the three variables were found to be significant at the p < 0.05 level, and the first four hypotheses (H1, H2, H3, and H4) were supported. Additionally, according to the standardized coefficient (beta value), it may be argued that information acquisition benefits (0.290) have much stronger impact on level of participation than social-psychological (0.211) and hedonic benefits (0.267). It is not largely consistent with the previous findings of some researchers, and the reason will be discussed in the following section. Secondly, the relationship between respective benefits (independent variable) and attitude (dependent variable) was analyzed. Table 4 shows the results of the second multiple regression analysis. The F-value (66.843, p = 0.000) demonstrates that the results of the regression model are statistically significant. Consequently, the three variables were found to be significant at the p < 0.05 level, and the four hypotheses (H5, H6, H7, and H8) were supported on the basis of the findings. In adTable 4 Regression Between Benefits and Attitude Variables (Constant) Information acquisition benefits Social-psychological benefits Hedonic benefits dition, according to the standardized coefficient (beta value), it may be argued that information acquisition benefits (0.509) have much stronger impact on attitude to online community than socialpsychological (0.307) and hedonic benefits (0.363). Discussion As a result of the data analysis, three main factors were identified as benefits perceived by community members: information acquisition, social-psychological, and hedonic benefits. The first benefit derived from the factor analysis is the “information acquisition benefit,” and is composed of five variables: obtaining up-to-date information, efficient information search, convenience in finding information, sharing experiences, and having trust in the community. Although the last two variables (sharing experiences and having trust in the community) were part of the “social benefit” according to the literature review, it was found that they belong to the “information acquisition benefit” as a result of the factor analysis. The second benefit is “social-psychological benefit.” It has two dimensions: social benefits (seeking identity and getting involved with members) and psychological benefits (keeping relationship with members and seeking a sense of belonging). Coefficient 3.658 0.317 0.191 0.227 SE 0.031 0.031 0.031 0.031 Beta 0.509 0.307 0.363 t 119.542 10.350 6.235 7.385 Sig. 0.000 0.000 0.000 0.000 R2 = 0.485; adjusted R2 = 0.478; F = 66.843, p = 0.000. INFORMATION NEEDS IN ONLINE SOCIAL NETWORKS In a current study, the Korean respondents perceived social and psychological benefits as one single benefit in online travel communities. On the other hand, Wang and Fesenmaier (2004b) argue that social and psychological benefits are distinct variables, and there is no significant relationship between psychological benefits (belonging, relationship, and affiliation) and level of participation in virtual communities. The difference between the two studies’ findings results from the cultural difference between the two respondent groups. The sampling in the present research was Korean, whereas the sampling of Wang and Fesenmaier’s (2004b) research was American. According to Hofstede’s (1991) cultural dimensions, Korea scores low on the Individualism Index (IDV), while the US scores very high, indicating that Koreans have a high degree of involvement in groups or communities with collective traits. These cultural dimensions are in line with the distinctive cultural characteristics. While Korean respondents show that their social needs and psychological needs are inseparable, American participants demonstrate that they would not search for psychological benefits in online communities. The cultural dimension causing different perception and behavior in an online community should be identified as a topic for further study (Grace-Farfaglia, Dekkers, Sundararajan, Peters, & Park, 2006). The last benefit is “hedonic benefit” regarding fun, entertainment, and amusement. It is also supported by the previous findings that surfing and fantasy needs are major factors motivating Internet users for participating in online communities (Armstrong & Hagel, 1997; Bagozzi & Dholakia, 2002; Rodgers & Sheldon, 2002). The results of the first multiple regression analysis indicate that all three benefits have a significant impact on the level of participation in an online travel community. The fact that the benefits have an influence on level of participation is consistent with the results of Wang and Fesenmaier (2004b). However, the findings in their research showed that functional needs have a negative relationship with level of participation, while the research here found that there is a positive relationship between functional needs and level of participation. This positive relationship explains that an online travel community usually has ex- 277 plicit and robust purposes of providing information with users. The gap between the two research findings seems to be because there is difference in operationally defining a concept. Wang and Fesenmaier (2004b) defined level of participation as the amount of time spent using the online travel community per week. In their study, it was measured by asking the question: “How long, on average, do you go online to participate in this online travel community per week?” In this research, on the other hand, the level of participation was measured as follows: “When deciding travel destinations or planning trips, how often do you go online to participate in online travel community?” The frequency of visiting an online community was used as an instrument to measure the degree of participation in this study. The result of the two studies therefore infers that individuals with a purpose of gathering information visit online communities more often, but usually do not go online for as long as the average population. Wang and Fesenmaier (2004b) also argue that if members only want to look for information efficiently, they are not willing to spend a great deal of time using online travel communities. The second multiple regression analysis shows that there are significant relationships between benefits and attitude toward an online travel community. Of the perceived benefits, the information acquisition benefits were found to have the greatest impact on member’s attitude, while hedonic and social-psychological benefits were also found to be significant factors helping community users to have positive attitude, trust, and loyalty toward an online travel community. Indeed, these results are consistent with the survey findings by Survey Site (Peter, Olson, & Grunert, 1999). SurveySite, one of the famous worldwide research firms, conducted an Internet user survey with regard to what makes users repeatedly visit websites. As a result of the research, it was found that good content is the most important element to attract Internet users to return to websites, and an enjoyable experience on the first visit also induces them to repeat a visit (Rice, 1997). Conversely, Hwang and Cho (2005) argue that there is no significant relationship between functional needs and attitude toward the online travel 278 CHUNG AND BUHALIS ment. The results of ANOVA between age groups therefore demonstrated that online community functionalities pertaining to social, psychological, and hedonic needs would attract Internet users in their 40s and above rather than younger groups. Conclusion The scope and nature of social networks are transformed due to the adoption of new technologies such as the Internet (Wellman, 2001). This emerging technology enables individuals to extend their relations, but it may also offer challenges regarding tracking networks in the virtual society. Instead, benefits of the online community are drawn from an information flow perspective. The fact that individuals are able to exchange information in online social networks gives justification for understanding tourist behavior in the online community. Tourism is one of the highest involvement industries, which means potential tourists want to get as much knowledge as they can to reduce risk. An online community with a high number of transactions between Internet users, therefore, will be valuable information networks for tourists. This study found that the degree of impacts on participation and attitude differs by the type of benefits. Specifically, information acquisition benefits, such as efficient and convenient information search, have much more influence on the degree of participation and attitude toward the online travel community than other benefits. It was consistent with Vogt and Fesenmaier’s (1998) argument that functional needs are perceived as the most important factor influencing information search. Consequently, it could be argued that as the information acquisition needs are satisfied, members would be retained in the online community and also would demonstrate a more positive attitude towards it. For instance, sufficient information and up-to-date content might help the online travel community to attract Internet users, including potential tourists, and allow community users to make a repeat visit. In addition, the high level of participation and positive attitude leads members to generate more valuable contents (Armstrong & Hagel, 1997). The results of this study also could be evalu- community, whereas there is a significantly positive relationship between social and psychological needs and attitudes toward the online community. In other words, even if functional benefits, including information acquisition, efficiency, or convenience, are satisfied, they would be not associated with members’ attitude toward the online community (Hwang & Cho, 2005). Hwang and Cho divide the concept of attitude into two dimensions: emotional and behavioral attitude. Emotional attitude represents members’ affiliation and belonging to the online community, whereas behavioral attitude is related to overall evaluation of behavior pertaining to community activities (Hwang & Cho, 2005; Peter et al., 1999). The comparison of means between groups showed that there was significant difference in sociodemographic characteristics such as gender, occupation, and age. However, no significant difference was found in education level. In comparison with the male group, the female group demonstrated a higher degree of participation, but there was no significant difference in attitude toward the online travel community. The level of participation also differed by occupation. The full-time homemaker group was found to use the online travel community more often than the full-time employed. It may be argued that because homemakers have relatively more leisure time than fulltime workers, they are likely to conduct on-going search activities (Mill & Morrison, 2002). Three age groups (e.g., 20s, 30s, and 40s and above) showed that there was a significant difference in social-psychological needs, hedonic needs, and level of participation. While respondents in their 40s and above indicated that they evenly hold three kinds of needs, individuals in their 20s and 30s showed that they are much interested in functional benefits including information acquisition rather than social-psychological and hedonic benefits. It may be argued that young people tend to use the online community as relatively task-oriented activities such as information gathering in a convenient and efficient way. Consequently, the 40s and above group showed a higher level of participation in the online travel community because they visit online communities for diverse purposes: information acquisition, seeking belonging, communication with members, or simply enjoy- INFORMATION NEEDS IN ONLINE SOCIAL NETWORKS ated in terms of information sources. Solomon et al. (2002) argue that consumers and marketers have their own perspectives of each stage of the consumption process. In the prepurchase stage, consumers are particularly interested in which information sources are the best and most credible, and on the other hand, marketers are concerned with a marketing channel in which effective advertising and efficient marketing strategies are available. A basic understanding of the traits of the online community is therefore required to develop an informative and substantial online community, and to provide users with real values. It subsequently results in enhancing the value of the online community as an information source and in becoming an attractive virtual market place for marketers. Accordingly, consumers are able to obtain valuable information from the online community, and suppliers can acquire clientele. The online community undoubtedly will play a much more important role as an information networks in the Internet (Armstrong & Hagel, 1997; Buhalis, 2003; Kozinets, 1999; Wang et al., 2002; Werry, 1999). The extended model with regard to how participation and attitude affect intention to travel or purchasing behavior could be proposed for further research. This attempt may be meaningful because it has been found the fact that not everyone who collects travel information actually intends to travel (Messmer and Johnson, 1993; Urry, 1990; Woodside, 1990). The efforts to operationally define a concept and design a research instrument on the basis of population’s social-demographic characteristics will be necessary to reduce error in translation and to measure a concept accurately. Cross-cultural research also will be significant when identifying members’ benefits in an online travel community. Because social or psychological benefits are deemed to be easily affected depending on different cultural background, respondents from different cultures are expected to result in noticeable results. Additionally, social network analysis (SNA) with corresponding techniques and specialized software can be applied to analyze the social relations in an online community. Implications First of all, this study provides travel marketing organizations with useful information on how to 279 utilize an online social network as a marketing channel, and inspires tourism marketers to understand an online community. Specifically, suppliers in tourism, including Internet-based travel companies, should be interested in the change in the Internet trend. Creating or developing firm online communities eventually brings more profit to businesses by increasing customer loyalty towards products and services (Wang & Fesenmaier, 2004b). Secondly, the results of this study show that the majority of community users in their 20s or 30s perceive the travel-related social network as a goal-oriented place to fulfill their functional needs. It may be therefore said that sufficient up-to-date information and reliable content contribute to give rise to its popularity and prosperity. Biographical Notes Jin Young Chung is doctoral student in the Department of Recreation, Park and Tourism Sciences at Texas A&M University. His research interest focuses on tourist behavior and e-Tourism. Dimitrios Buhalis is Professor at Bournemouth University, and his research interest includes e-Tourism and tourism marketing. 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