hyperlink–affiliation network structure of top web sites: examining affiliates with hyperlink in...

Hyperlink–Affiliation Network Structure of Top Web Sites:Examining Affiliates with Hyperlink in Korea

Han Woo Park and George A. BarnettDepartment of Communication, School of Informatics, State University of New York at Buffalo,Buffalo, NY 14260. E-mail: [email protected]; [email protected]

In-Yong NamDepartment of Advertising and Public Relations, Silla University, Pusan, Korea. E-mail: [email protected]

This article argues that individual Web sites form hyper-link–affiliations with others for the purpose of strength-ening their individual trust, expertness, and safety. Itdescribes the hyperlink–affiliation network structure ofKorea’s top 152 Web sites. The data were obtained fromtheir Web sites for October 2000. The results indicatethat financial Web sites, such as credit card and stockWeb sites, occupy the most central position in the net-work. A cluster analysis reveals that the structure of thehyperlink–affiliation network is influenced by the finan-cial Web sites with which others are affiliated. Thesefindings are discussed from the perspective of Web sitecredibility.

Statement of Problem

The Internet represents a new channel for communica-tion. As a result, we recently have witnessed surprisinggrowth in Internet studies across many disciplines.1 Al-though researchers have differently conceptualized the In-ternet, it is generally characterized as the network of net-works (Berners-Lee, 1999). Particularly, in the context ofthe network economy (Shapiro & Varian, 1999), the net-work attribute of Internet is altering business processes. Theassociation between two or more Web sites is helping thesecompanies doing business on the Internet (dot-com ore-commerce companies) enhance their efficiencies in termsof technological sophistication, brand reputation, and cus-tomer management. In fact, commercial Web sites in tran-sition toward greater competition can get into managerialdifficulties if they do not satisfy various customers’ needs orconcerns such as quality content or transaction security.

Nonetheless, it is difficult for a dot-com company in theearly stage of its development to take all the steps necessaryto meet on-line consumers’ expectations, because of theInternet’s complex infrastructure. This situation is acceler-ating the networking among commercial Web sites for thepurpose of either minimizing the weaknesses of individualWeb sites or strengthening their competitive positions.

What makes Web sites form networks with others on theInternet? What elements influence the affiliation amongWeb sites? What does the associational structure look like?What type of Web sites occupies more prominent positionrelative to other commercial Web sites? Despite the impor-tance of those questions, there has been little empiricalresearch in the field of Internet communication. Far lessattention has been paid to the structural association patternamong Web sites and what facilitates these patterns. Inresponse to the lack of research, this article examines theseissues.

This study also may offer insights to predict the rise (orfall) of specific types of Web sites in relation to diffusion ofe-commerce. These results may be useful for developing aneffective marketing (or advertising) strategy on the Internet.Overall, the possible outcome of this study is to providevarying levels of support for predicting the development ofcommercial Web sites based on the pattern of affiliationamong sites.

Theoretical Framework and Literature Review

Role of Credibility in Hyperlink–Affiliation Networks

As new network technologies such as the Internet havepermeated society (Castells, 1996), they become a drivingforce changing the organizational form of companies, froma mechanism of hierarchy or power to a variety of networkforms (Achrol & Kotler, 1999). These network structuresoccur over the Internet. Various types of Web sites such as

1 The Association of Internet Researchers may be good example. It wasestablished with the advancement of the crossdisciplinary field of Internetstudies. See, AoIR web site. http://aoir.org.

© 2002 Wiley Periodicals, Inc. ● Published online 18 March 2002 in WileyInterScience (www.interscience.wiley.com). DOI: 10.1002/asi.10072

JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, 53(7):592–601, 2002

Amazon.com and Travelocity.com are creating networksthat link their consumers and e-retailers. How do commer-cial Web sites choose their affiliates in cyberspace? Giventhat many Web sites are still in an early stage of develop-ment, a partner Web site’s credibility may be a criticalelement in forming affiliation networks.

An Internet user’s behavior of visiting or purchasingfrom a specific Web site may be understood as a form ofpersuasion. It entails a change in one’s attitude or behavior.In the field of communication, a communicator’s credibility,in this case, Web site’s credibility, has been regarded as oneof the most influential factors in the persuasion process(Berlo, Lemert, & Mertz, 1969; Hovland, Janis, & Kelly,1953; Hovland & Weiss, 1951; McCroskey & Teven,1999). Recently, communication researchers have turnedtheir attention to Web site (or Internet) credibility (Flanagin& Metzger, 2000; Johnson & Kaye, 1998; Kiousis, 1999;Reeves & Nass, 1996; Schweiger, 2000; Tseng & Fogg,1999). Various definitions can be summarized as the degreeto which people believe the Web site in terms of its trust-worthiness, expertise, and safety.

Each dimension of Web site credibility may be definedas follows: Trustworthiness refers to the truthfulness of aWeb site’s contents and the site’s reputation. Expertise isthe Web site’s competence. When applied to a commercialWeb site, this can be largely divided into expertise as aninformation medium and a shopping medium. The formermeans how complete, useful, and timely the services ofWeb site are compared to others. The latter contains howcompetent a specific Web site is in time-saving, conve-nience, price, eye-shopping ease and assortment of productsoffered. Safety is defined as how secure or reliable the Website’s technical systems are for online payment and personalinformation. Thus, once users perceive that a Web site lackscredibility, they are likely to stop visiting it or performingfinancial transactions.

Past studies (McGinnies & Ward, 1980; Ohanian, 1991;Yoon, Kim, & Kim, 1998) suggested, the three dimensionsof Web site credibility exert different influence on thepersuasion. For example, a shopping Web site namedMazon.com can be perceived as having expertise (e.g.,competent in saving time, convenience, or low price), but itmay be risky or not trustworthy. People who count onvender’s trustworthiness would not buy any commodityfrom Mazon.com. Also, another Web site, Ahoo.com may beperceived as trustworthy (e.g., high reputation) or having asecure on-line payment system but not an expert. In thiscase, Ahoo.com loses those who prefer a Web sites’s exper-tise. Given the possibility of various combinations, it sug-gests that affiliations among Web sites are more likely tooccur according to the type of credibility they lack or desireto strengthen.

Based on the credibility theory described earlier, one caninfer that Web sites would prefer those Web sites with highcredibility in selecting their affiliates. Several studies fromvarious fields corroborate credibility theory. Ching, Hol-sapple, and Whinston (1996) examined networks among

information technology firms and found that the reputationof a company had a strong relationship to the selection ofcurrent and potential partners. Further evidence can befound in more recent studies, in relation to Web site trust-worthiness. Gefen (2000) studied Amazon.com. People’strust2 influenced by familiarity had a positive impact oninquiring information about books and buying them fromthe Web site. For these reasons, Amazon.com uses a varietyof strategies to enhance people’s trust. Amazon.com fur-nishes their visitors with favorable book reviews written byprestigious writers and critics, as well as articles suppliedfrom other credible sources, such as The New York Times.This contributes to persuading prospective consumers tobuy a book from Amazon.com.

As a result, Amazon.com may be good partner for lesserknown or moderately trustworthy Web sites that want toassociate with or enhance their own perceived trust. In fact,thousands of Web sites are affiliated with Amazon.com(Shapiro & Varian, 1999). In addition to trustworthiness,having a network with credible Web sites such as Amazon.com could boost their perceived expertness. Consumerson-line tend to want a variety of services (U.S. Departmentof Commerce, 2000). They like to check the price, quality,and information about products before buying from a site.Thus, for Web sites to satisfy digital shoppers, sites need toreinforce their competency by strengthening their visitor’snavigating position (Evans & Wurster, 1999). A powerfulnavigation comes from various dimensions of extension:inter-Web site connection, cooperation between differenttypes of Internet businesses, quality, and quantity of productinformation, and management of customer information. Inthis process, networks among Web sites become organizedaround a core Web site, a core topic area, and integratedinfomediaries (Hagel & Armstrong, 1997). Eventually, arelatively incompetent Web site benefits from a larger net-work, because a core Web site offers their visitors a broaderrange of navigation opportunities. Once such a network isbuilt, a core Web site’s expertness also increases because itis more comprehensive.

The last factor that influences network structure is thesecurity of a Web site. No matter how trustworthy andexpert the services of the Web sites, potential buyers willnot come back if their technical systems are not reliable.Public opinion surveys have reported people’s concern withon-line privacy protection and transaction security (Hsu &

2 One might ask the question, are trust and credibility synonymousconcepts? In other words, how different (or similar) are the two concepts?According to Tseng and Fogg (1999), trust generally indicates a positivebelief about the perceived dependability of a person, object, or process. Forexample, it is different from credibility when involving the effectiveness oftechnological capability, like a “trust system” frequently used in computertechnology (Stefik, 1999). But, it can be used synonymously with credi-bility when referring to the psychological construct such as people’s beliefsor expectations.

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Park, 2001).3 The slow development of secure electronicpayment system is perceived as the primary barrier to therealization of the Internet’s commercial potential (Camp,2000; Crocker & Stevenson, 1999). Thus, having affilia-tions with financial Web sites such as credit card companiesmay be an effective way for common Web sites to reducetransaction risk and gain consumer’s trust. Hagel and Arm-strong (1997) argue that, in the early stage of a virtualcommunity, a specialized technical support service can playa principal role in organizing Web sites that are highlyfragmented across multiple communities. These may bethose sites with technical skills in operating on-line mone-tary transactions.

Web sites get connected with others for the purpose ofstrengthening their trustworthiness, expertness, and safety.Based on credibility theory, a series of studies conducted bythe Persuasive Technology Laboratory at Stanford Univer-sity found that having a partner’s Web site hyperlinked mayinfluence people’s perceived credibility of certain sites(Fogg et al., 2001).4 Thus, a Web site that intends toincrease its credibility adds hyperlinks to credible Websites. A Web site perceived as highly credible receives manylinks from others. Thus, the credibility of a Web site can bebetter measured by how central it is in terms of the numberof incoming links, that is, the number of Web sites that arelinked to a given site (Kleinberg, 1999; Palmer, Bailey, &Faraj, 2000).

In this sense, an examination of the affiliates with hy-perlinks is a reasonable approach to determine the relationalstructure among Web sites and the role of credibility in thenetwork. No matter what services are linked among Websites, affiliation is meaningful if services flow over theInternet by using a reciprocal hyperlink exchange such astext links, buttons, even banner advertising.

A Korean Case: Information Society and E-commerce

Since the early 1990s, the level of informatization hasbecome a barometer of national competitiveness and qualityof life.5 Thus, not only in the advanced nations, such as the

United States, the European Union, and Japan, but also indeveloping nations such as Malaysia, the “information so-ciety” is being pursued as a national development strategy.The Korean government strongly pushed an informationsociety policy to gain profit from informatization. Rapiddevelopment of its information sector has become nationalpolicy. To accomplish this end, the Korean government hasvigorously pursued a wide range of information programs:inauguration of the Ministry of Information and Communi-cation in 1994, the establishment and amendment of relatedlaws and regulations to facilitate information society andreduce government intervention, and increase the invest-ment in information and communication technologies. Ac-cording to Park (2001a), government policy, during 1990sfocused on creating favorable attitudes toward the diffusionof information technologies such as Internet.

Due to the government’s commitment to diffusing infor-matization, the number of Koreans using the Internet hasincreased rapidly: 0.14 million in 1995, 1.6 million in 1997,10 million in 1999, and 11.3 million in January 2000 (Ko-rean Network Information Center).6 Nobody in Korea pre-dicted such explosive growth in Internet use. In fact, theCenter estimated 3.8 million Internet users in December1999. Compared to other countries, the low prices of ac-cessing the Internet may have contributed to the increase ofInternet users (OECD, 2001). Recently, the “Netizen” pop-ulation in Korea is going on-line to shop as well as to surffor information. Korea ranked No. 1 in Asia in per-capitale-commerce retail revenue in 1999 (Wiseman, 2000). Thistrend indicates that the Internet business boom has begun inKorea.

Regardless of the rapid increase in Internet usage andon-line shopping, dot-coms face a constant risk such as acontraction in the Korean Internet stock market or tumblinginterest rates. Such a crisis is more likely to occur amongsmall Internet companies rather than commercial Web sitessponsored by conglomerates (Chaebol) such as Samsung orthe LG Group (Terazono, 2000). Under these conditions,dot-com companies are expanding their resources throughaffiliation with other Web sites. They believe that thisstrategy will significantly boost their competitive edge inboth persuading people to visit their sites and improvingtheir performance in terms of technological sophistication,brand equity, and customer management. In the process of

3 According to a recent survey on e-commerce in the United States, thetwo major reasons users don’t shop on-line are concern over personalprivacy and credit card security (Abramson, 2000).

4 Webcredibility is an official Web site for Web credibility researchconducted by the Persuasive Technology Laboratory at Stanford University(http://www.webcredibility.org).

5 For example, the International Data Corporation (IDC), a researchand consulting company on information technology, rates annually thesituation of informatization around world-wide countries according toInformation Society Index (ISI). Just as GDP measures economic wealth,the ISI measures computer infrastructure, Internet infrastructure, informa-tion infrastructure, and social infrastructure. In 2,000 survey, Swedenranked the first. See, IDC Web site. http://www.idc.com. Theoretically, theterm “informatization” is different from the term “information society.”According to Webster (1995), the information society is used to emphasizea decisive break with past eras. On the other hand, informatization is oftenadopted to mean that present society is in the process of digitizing infor-mation, maintaining past social relations. But, informatization frequently

used in Korea is believed to be irrelevant with theoretical distinctionbecause, in most cases, it tends to mean the former. Thus, informationsociety and informatization are interchangeably used here. The “Basic Acton Informatization Promotion,” basic guiding principles on building theKorean Information Infrastructure (KII) and creating an information soci-ety, defines informatization as “making each sector of society work oraccelerate their efficiency through the production, distribution or utilizationof information” (Subsection 2 of section 2). In addition, to explain that theusage of informatization is influenced by the term “modernization” may bemore convincing. For the last few decades, to be developed, countriesincluding Korea needed be modernized (Lerner, 1962; Rogers, 1976).Similarly, at present, they need to be informatized.

6 Korean Network Information Center provides official statistics inrelation to Internet in South Korea (http://www.nic.or.kr).

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selecting their counterparts, Web sites tend to put theirpriorities on the sites created by conglomerates because oftheir high credibility among consumers.

The research that follows examines the structure of hy-perlink–affiliations among Korea’s top Web sites and therole a Web site’s credibility play in forming the network. Itsuggests that current hyperlink–affiliation network on theWeb is likely to be among those that can leverage anindividual Web site’s trustworthiness, expertness, andsafety synergies. The article makes use of the methods ofnetwork analysis to describe emerging structure of affilia-tion networks.

Methods

Network Analysis

The structure of the network among Web sites may beexamined through network analysis. Network analysis is aset of research procedures for identifying structures in socialsystems based on the relations among the system’s compo-nents rather than the attributes of individual cases (Richards& Barnett, 1993; Rogers & Kincaid, 1981). The methodmay be generalized to describe the patterns of communica-tion among different social systems or individuals in thespecific system. This article describes the relations amongKorea’s top Web sites based on the hyperlinks that may beembedded in the Web pages’ titled affiliation program. Inother words, this study defines the affiliation among Websites based on the interdomain hypertext links.

In context of e-commerce, Hoffman and Novak (1996)suggest the usefulness of the network concept in hyperme-dia environments such as the Internet. Garton, Haythorn-thwaite, and Wellman (1997) advocate the use of networkanalysis for the examination of social relations specificallyinvolving the Internet. Recently, researchers in the field ofcomputer-mediated communication have begun to adopt themethod of bilateral hyperlink networks used in this article.Examples include the structure of international Internetflows (Barnett, Chon, Park, & Rosen, 2001); a cyber-com-munication structure between politicians (Park, Barnett, &Kim, 2000); the role of geographic borders in cyberspace(Halavais, 2000); the impact of homophily in purchasingbooks on-line (Kreb, 2000); a consumer’s trust of a Web sitein relation to e-commerce (Palmer et al., 2000). Hyperlinkson the Web are considered not simply as a technologicaltool but as a newly emerging communicational channel. TheWeb site is regarded as an actor, and the hyperlink amongsites represents a relational connection (Park, in press).

The basic network data set is an n � n matrix S, wheren equals the number of nodes in the analysis. A node is theunit of analysis. It may be an individual or higher levelcomponent, such as an organization or a Web site out ofwhich the system is composed. In this case, the nodes areWeb sites. Each cell, sij, indicates the strength of the rela-tionship among nodes i and j. In communication research,this relationship is generally the frequency of communica-

tion among the nodes. The frequency may be restricted to aparticular topic, communication channel (Internet) or lan-guage. For example, sij could be the frequency of commu-nication over the Internet between i (Yahoo.com) and j(Amazon.com). In this case, sij is a simply a zero or a 1,depending if there is hyperlink between nodes i and j. S issymmetrical (sij � sij) when one is not concerned withdirectionality, which node initiates communication. In thoseinstances when the source and receiver of the informationare differentiated, S is asymmetrical (sij � sij). In this case,we are concerned with the directionality of the hyperlinks.

Given its form, a number of different mathematical orstatistical methods may be applied to S to facilitate thedescription of the structure of the network. In this article,graph theoretical methods (Hayes, 2000; Wasserman &Faust, 1994) and cluster analysis (Aldenderfer & Blashfield,1984) are employed to describe the structure of the networkamong top Web sites in Korea. They will be discussed ingreater detail below.

The Data: Network of Affiliates with Hyperlink

Data on the network of Web sites’ affiliations via hyper-link were obtained using the following methods. First, 152Web sites were chosen based on the list of the most fre-quently visited Web sites published by the Internet Metrix7

for August, 2000. The list contained 175 Web sites that arethe most popular among Korean Web users and classifiedaccording to category. The nine categories are: search/portal, community/chat/email, life information (such astravel, leisure), communication/computer, finance, shop-ping/auction, entertainment, stock, and Internet service pro-viders. Eighteen Web sites8 such as www.yahoo.com werenot included in the study because they are not Korean, andgenerally are known as global Web sites not confined tospecific country. Also, five Web sites9 that ranked in twocategories or two times were excluded.

Second, data on the hyperlink–affiliation among Websites were obtained from a Web page titled affiliation pro-gram opened by the Web sites. Specifically, when affiliationWeb sites are mentioned on the Web page of an affiliationprogram in either of two individual Web sites, they havehyperlink. A 1 was placed in the matrix; if not, a 0 wasplaced in the matrix. This forms a binary connectivitymatrix. The data were gathered during a 7-day period, fromOctober 11 to 17, 2000.

7 Internet Metrix is a famous Internet evaluation institution in Korea(http://www.internetmetrix.com).

8 Not included were: www.yahoo.com, www.msn.com, www.lycos.com, www.altavista.com, www.gohip.com, www.go.com, www.about.com, www.golo.com, www.excite.com, www.theglobe.com, www.microsoft.com, www.real.com, www.windowsmedia.com, www.macro.media.com,www.shockwave.com, www.winamp.com, www.passport.com, www.lucky7.com.

9 www.dreamwiz.com, www.msn.com, www.cityscape.co.kr, www.keb. co.kr, www.koreamusic.net were excluded from the analysis.


Analysis Procedures

Structure of NetworkThe structural analysis of a communication network gen-

erally starts to identify nodes, in this case, Web sites, thatplay significant role in the network (Richards & Barnett,1993; Rogers & Kincaid, 1981). The important componentstreated by network analysis are group members that com-prise a group. A group is a set of at least three members thatsatisfy the following qualifications (Richards, 1995): Agroup member is one that has more than 50% of its linkswith other members in the same group. It must have at leasttwo links with other members. The group should remainconnected, even if 10% of the members of the group areremoved.

Once groups and their members are detected, the follow-ing indicators are used to examine the overall characteristicsof the network and the links (or relations) among groups’members (Richards, 1995). Connectedness is simply de-fined as a node’s number of links. Centrality is the meannumber of links required to reach all other nodes in a group,such that the lower the value the more central the node.Integrativeness is the proportion of a focal node’s links thatare connected to one another. Density is the actual numberof links divided by the number of possible links [n(n � 1)/2](for nondirectional data). To perform these analyses, NE-GOPY was used in this research. NEGOPY (Richards,1995), a computer program, was developed to analyze com-munication networks.10

The global centrality measured does not consider thedirectionality of hyperlinks. It considers only the number oflinks required to reach each of the other nodes. As a result,other indicators of centrality were examined including Free-man’s degree measures (Freeman, 1979). Freeman’s degreecentrality consists of ingoing and outgoing degree. Indegreerefers to the number of links a node receives from the othernodes, while outdegree is the number of links originatingfrom a node. Ingoing degree can be interpreted as theperceived credibility of sites in the affiliation network. Also,outgoing degree may be a good indicator of expertise cred-ibility in that it indicates certain Web site’s expansiveness.To calculate Freeman’s centrality measures, another net-

work analysis software, UCINET-X (Borgatti, Everett, &Freeman, 1999) was used.11

Cluster AnalysisCluster analysis identifies those groupings or clusters of

nodes that best represent their measured relations. The clus-tering procedure operates as follows: From the n � n matrixof similarity (S), the pair of nodes with the greatest simi-larity is treated to form a cluster, C1. Then a new similaritymatrix, S*, is generated with the pair of nodes combined asa single node. S* is an n �1 * n � 1 matrix. The process isrepeated by adding a third node to C1 or a new pair of nodesis combined to form C2. The process continues until all nnodes are included to form cluster Cm. Cluster Cm includesall nodes. This process assumes that the distances in S areunambiguous. That is, sij � sij. In this case S was madesymmetrical by taking the average of sij and sij. Johnson’shierarchical cluster analysis (Johnson, 1967) was performedusing the algorithm from UCINET-X (Borgatti et al., 1999).

Results

Structure of Network

One group comprising of 44 Web sites was identified.12

The group density is 0.091, with the maximum possible

10 For a complete description of the algorithm used for computing eachindex, see Richards (1995). In fact, NEGOPY is able to find a morecomplex communication role indicators: a direct liaison is a Web site thathas more than 50% of its linkage with members of groups in general, butwith members of more than one group. It provides direct connectionsbetween the groups it is connected to. Indirect liaison (or multistep liaison)is a node that has less than 50% of its links with members of groups ingeneral. It provides indirect or multistep connections between groups byconnecting liaisons, who have direct connections with members of groups.An attached isolate has only one link, whereas an isolate has no linkswhatsoever. If two attached isolates are linked to one another, they arecalled a dyad. Tree node is the first Web site to which one or more attachedisolates are attached. Past research has used NEGOPY to analyze thestructure of the communication (Barnett, 1999; Paccagnella, 1998; Park etal., 2000).

11 For a complete description of the algorithm used for computing eachindex, see Borgatti et al. (1999).

12 Isolates or nongroup members are www.empas.co.kr, www.cityscape.co.kr, www.fireball.co.kr, www.altavista.co.kr, www.iloveschool.co.kr,www.sayclub.com, www.send2u.co.kr, www.lettee.com, www.ttl.co.kr,www.cizmail.com, www.n-top.com, www.damoim.net, www.woorizip.com, www.any21.com, www.flyasiana.com, www.healthkorea.net, www.enclean.com, www.street.co.kr, www.barota.com, www.ticketpark.com,www.gogo.co.kr, www.woorijip.com, www.eznara.com, www.netpark.co.kr, www.alpha.co.kr, www.speed012.co.kr, www.skwillb.com, www.namo.co.kr, www.shinsegi.com, www.trafficinc.com, www.hansboom.com, www.emoney.co.kr, www.banktown.com, www.kookminbank.com,www.chbmoney.com, www.yescard.co.kr, www.richnjoy.com, www.estorm.co.kr, www.kyobobook.co.kr, www.buynjoy.com, www.getpc.co.kr, www.cdfree.co.kr, www.happy2buy.com, www.shopplaza.net,www.buy6.com, www.lgnara.com, www.unbid.net, www.koreamusic.net,www.applesoda.com, www.netpoint.co.kr, www.nkino.com, www.leadernet.net, www.pointpot.co.kr, www.xnews.co.kr, www.joabox.com,www.bugsmusic.co.kr, www.141.pe.kr, www.joyluck.co.kr, www.yuksul.com, www.intz.com, www.elibero.co.kr, www.stockuniv.com, www.threestock.com, www.etomato.co.kr, www.kosdoctor.co.kr, www.hitc.co.kr, www.goodi.co.kr, www.brandstock.co.kr, www.ekudos.co.kr, www.vipstock.com, www.korea-stock.com, www.stockone.co.kr, www.chollian.net, www.shinbiro.com, www.channeli.net, www.thrunet.com, www.kornet.net, www.iworld.net, www.elim.net, www.taegu.net, www.mochanni.com, www.weppy.com, www.hansolm.com, www.everland.com, www.tourpia.com, www.0to7.com, www.speed011.co.kr, www.chb.co.kr, www.bccard.co.kr, www.ecoin.co.kr, www.shihhan.com, www.waawaa.com, www.lgeshop.com, www.080-flowers.com, www.mnet27.com, www.yasisi.co.kr, www.bestez.com, www.dws.co.kr, www.lgsec.co.kr, www.stockcaster.com, www.netsgo.com, www.hananet.net, www.hitel.net, www.nownuri.net, www.borahome.net, www.sarang.net, www.cgiworld.net, www.daum.net.


number of within-group links 946, observed number ofwithin-group links 86. The group is sparsely connected. Onehundred five Web sites were isolates, 80 of which did nothave any hyperlinks to the group. Twenty-five were at-tached with only one hyperlink. Two found a separate dyad,and one acted as a tree node. The centralities for the 44group Web sites are presented in Table 1. Finance-relatedWeb sites-credit card, bank, insurance, stock-samsungcard.co.kr, samsungfire.com, samsungfn.com, keb.co.kr,samsunglife.com are the most central groups in the affilia-tion network. Next, lycos.co.kr (search/portal), ahnlab.com(communication/computer), sportscom.co.kr (life informa-

tion), samsungmall.co.kr (shopping) are the next most cen-tral Web sites. Most peripheral in the group are opentown.com (community/chat/email) and daishin.co.kr (stock).

The centrality of samsungcard could be explained by thefact that payment by credit card is the most common trans-action on the Internet. Also, financial companies such asbanks play important roles as trusted parties in credit cardtransactions. Their high centrality scores can be interpretedas the degree of involvement of major Web sites in com-mercial activities: the more business they perform, morelikely that the financial company plays an important role inthe network. Also, stock Web sites such as thinkpool.comand samsungfn.com are relatively central nodes. Given thefact that nearly 57% of all stock trades are conducted onlinein Korea (Wiseman, 2000), it is not surprising that stockWeb sites occupy prominent positions in the network.13 Asshown in Women.com, strategic alliance with the stock sitescan encourage many people to visit a Web site often (Rosen,2000).

One of the more interesting findings is the central posi-tions of those Web sites14 that are affiliated with the Sam-sung Group, a conglomerate (Chaebol) in Korea. This maybe partially explained by the fact that Samsung is trying totake their superiority to the Internet as well as off-line(Terazono, 2000). As a result, all of the businesses areindirectly influenced or supported by Samsung’s strongInternet policy despite being independent Web sites.

Next, the Freeman’s centralities for the 44 connectedWeb sites are presented in Table 2. The degree centralitiescomprised of ingoing and outgoing degrees showed thatsamsungmall.co.kr (shopping) and thinkpool.com (stock)occupied the most central positions in indegree measurewith samsungcard.co.kr in the outdegree. Both sites, sam-sungmall and thinkpool, received five links from other sitesin the network. On the other hand, 12 links originated fromsamsungcard. Compared to samsungcard’s outgoing degree,its ingoing degree (3) is relatively small but larger than theindegree mean (1.977).

Overall, the centralities are quite similar to the generalcentrality scores, indicating that centrality based solely onthe pattern of linkage and the direction of the links betweenthe nodes ranked the Web sites similarly.

Cluster Analysis

The cluster analysis of the network of hyperlink affilia-tions revealed that the 44 Web sites formed four subgroups,most of which centered around financial Web sites (seeTable 3). In clustering, each Web site’s link with the finan-cial Web site was stronger than to any other Web site.

13 In the case of the United States, the amount of stock trading doneon-line is estimated from a quarter to a half (Wiseman, 2000). Thus, Koreais called a world leader in computerized stock trading.

14 They are www.samsungcard.co.kr, www.samsungfire.com, www.samsungfn.com, wwww.samsunglife.com and www.samsungmall.co.kr.

Table 1. Connectedness, centrality, and integration of individual groupmember Web sites.

Web sites Links Centrality Integration

www.samsungcard.co.kr 15 2.12 0.022www.samsungfire.com 8 2.26 0.071www.samsungfn.com 5 2.30 0.200www.keb.co.kr 10 2.33 0.022www.lycos.co.kr 4 2.40 0.167www.samsunglife.com 7 2.42 0.048www.ahnlab.com 7 2.42 0.048www.sportscom.co.kr 5 2.42 0.000www.samsungmall.co.kr 5 2.44 0.400www.lgcapital.com 8 2.47 0.036www.koreanair.co.kr 3 2.47 0.000www.thinkpool.com 5 2.49 0.000www.allat.co.kr 7 2.49 0.095www.n016.com 4 2.49 0.000www.naver.com 4 2.49 0.167www.freechal.com 4 2.51 0.333www.csclub.co.kr 3 2.56 0.000www.paxnet.co.kr 3 2.58 0.333www.okcashbag.com 3 2.58 0.000www.hangame.com 6 2.72 0.000www.korexmall.co.kr 2 2.72 0.000kr.yahoo.com 4 2.72 0.000www.unitel.co.kr 2 2.77 0.000www.auction.co.kr 4 2.81 0.000www.netian.com 2 2.81 0.000www.dreamwiz.com 2 2.84 0.000www.joylink.co.kr 5 2.86 0.000www.sellpia.com 3 2.86 0.000www.gosamsung.co.kr 2 2.91 0.000www.interpark.com 2 2.91 0.000www.webtour.com 2 2.91 0.000www.yes24.com 2 2.95 0.000www.018.co.kr 2 2.95 0.000www.findall.co.kr 3 3.05 0.000www.skylove.co.kr 2 3.14 0.000www.dreamx.net 2 3.16 0.000www.icine.com 2 3.19 0.000www.intizen.com 2 3.19 0.000www.lgtel.co.kr 3 3.21 0.000www.hanmir.com 2 3.30 0.000www.simmani.com 2 3.35 0.000www.weathernews.co.kr 2 3.44 0.000www.opentown.com 2 3.47 0.000www.daishin.co.kr 2 3.65 0.000

Group number: 44, maximum possible number of within-group links:946, Observed number of within-group links: 86, group density: 0.091.


Cluster A, comprised of 13 members, includes threefinancial Web sites (samsungcard, samsunglife, allat), twocommunication/computer (ahnlab, 018), two shopping/auc-tion (gosamsung, samsungmall, korexmall), two commu-nity/chat/email (freechal, netian), and three others (sam-sungfn, lycos, unitel). They form a cluster by being affili-ated with the most central Web site, samsungcard.co.kr.This cluster also shows that five Samsung-related Web sitesare clustered together. The samsunglife.com is the officialWeb site of Samsung Life Insurance, samsungfn.com the

Web site of Samsung Finance, and samsungcard.co.kr theWeb site of Samsung Card. Two Web sites, samsungmall.co.kr and gosamsung.co.kr, are run by Samsung Corpora-tion and by Samsung Electronics, respectively. Despite be-ing independent, these five companies systematically coop-erate under the umbrella of Samsung.

In cluster B, 10 Web sites are closely linked with a stockWeb site, thinkpool.com, and a financial Web site, samsungfire.com. Next, cluster C, the largest cluster consisting of 15members, contains various types of Web sites ranging overtwo financial sites, two community/chat/e-mail sites, threeshopping/auction sites, two life/travel/leisure sites, twosearch/portal sites, two communication/computer sites, andtwo entertainment sites. Cluster D is a set of groups thatdoes not render a clear interpretation.

Table 2. Freeman’s degree centralities.

Web sites

Degree

Ingoing Outgoing

kr.yahoo.com 1.000 3.000www.lycos.co.kr 4.000 0.000www.naver.com 3.000 1.000www.hanmir.com 0.000 2.000www.dreamwiz.com 2.000 0.000www.simmani.com 1.000 1.000www.netian.com 2.000 0.000www.freechal.com 0.000 4.000www.okcashbag.com 3.000 0.000www.skylove.co.kr 1.000 1.000www.intizen.com 0.000 2.000www.opentown.com 2.000 0.000www.webtour.com 2.000 0.000www.koreanair.co.kr 3.000 0.000www.sportscom.co.kr 2.000 3.000www.findall.co.kr 0.000 3.000www.weathernews.co.kr 2.000 0.000www.n016.com 4.000 0.000www.lgtel.co.kr 3.000 0.000www.018.co.kr 2.000 0.000www.ahnlab.com 0.000 7.000www.lgcapital.com 1.000 7.000www.samsungfire.com 2.000 6.000www.samsungcard.co.kr 3.000 12.000www.keb.co.kr 1.000 9.000www.samsunglife.com 2.000 5.000www.allat.co.kr 2.000 5.000www.auction.co.kr 4.000 0.000www.yes24.com 2.000 0.000www.interpark.com 2.000 0.000www.csclub.co.kr 3.000 0.000www.gosamsung.co.kr 2.000 0.000www.samsungmall.co.kr 5.000 0.000www.sellpia.com 3.000 0.000www.korexmall.co.kr 2.000 0.000www.hangame.com 0.000 6.000www.joylink.co.kr 0.000 5.000www.icine.com 2.000 0.000www.paxnet.co.kr 3.000 0.000www.daishin.co.kr 2.000 0.000www.samsungfn.com 1.000 4.000www.thinkpool.com 5.000 0.000www.dreamx.net 1.000 1.000www.unitel.co.kr 2.000 0.000Mean 1.977 1.977Standard deviation 1.288 2.896

Network centralization (outdegree) � 24.419%.Network centralization (indegree) � 7.364%.

Table 3. Clustering of Web site.

Group Web sites Category

A www.lycos.co.kr Search/portalwww.samsunglife.com Financewww.ahnlab.com Communication/computerwww.gosamsung.co.kr Shopping/auctionwww.freechal.com Community/chat/e-mailwww.allat.co.kr Financewww.samsungmall.co.kr Shopping/auctionwww.samsungfn.com Stockwww.018.co.kr Communication/computerwww.netian.com Community/chat/e-mailwww.samsungcard.co.kr Financewww.korexmall.co.kr Shopping/auctionwww.unitel.co.kr Internet service provider

B www.intizen.com Community/chat/e-mailwww.skylove.co.kr Community/chat/e-mailwww.auction.co.kr Shopping/auctionwww.samsungfire.com Financewww.koreanair.co.kr Life/travel/leisurewww.paxnet.co.kr Stockwww.yes24.com Shopping/auctionwww.sportscom.co.kr Life/travel/leisurewww.thinkpool.com Stockwww.hanmir.com Search/portal

C www.keb.co.kr Financewww.okcashbag.com Community/chat/e-mailwww.interpark.com Shopping/auctionwww.webtour.com Life/travel/leisurewww.lgcapital.com Financewww.dreamwiz.com Search/portalwww.csclub.co.kr Shopping/auctionwww.sellpia.com Shopping/auctionwww.naver.com Search/portalwww.findall.co.kr Life/travel/leisurewww.lgtel.co.kr Communication/computerwww.opentown.com Community/chat/e-mailwww.hangame.com Entertainmentwww.joylink.co.kr Entertainmentwww.n016.com Communication/computer

D kr.yahoo.com Search/portalwww.weathernews.co.kr Life/travel/leisurewww.simmani.com Search/portalwww.daishin.co.kr Stockwww.icine.com Entertainmentwww.dreamx.net Internet service provider


The results of the network analysis of the 44 groupmembers are graphically summarized in Figure 1.15 Thereare four clear clusters with the financial service Web site atthe center of groups A, B, and C.

Discussion

Hyperlinks between two Web sites, which was initiatedby Amazon.com, is becoming the representative form ofaffiliation among Web sites (Shapiro & Varian, 1999). Thisform of networking is increasing rapidly. In this context, thepurpose of this article was to examine the structure of anemerging affiliation network among Web sites and the fac-tors affecting this pattern. To do this, this article usedcommunicator’s credibility theory and network analysis.

In this study, financial Web sites containing credit card,banking, and insurance occupied the most central positionsin the network. Also, stock Web sites such as www.thinkpool.com were fairly central. Another important finding was thatthe central positions of those Web sites that are affiliatedwith the Samsung Group, a conglomerate (Chaebol) in Korea.In addition, the cluster analysis revealed that the structure ofthe affiliation network is influenced by the finance-relatedWeb sites to which other Web sites are affiliated.

The major financial Web site appeared as the reason forforming clusters. This supports the security syndrome hy-pothesis that most of consumers are afraid of the safety ofon-line payment system when they perform transactactionson the Internet. Therefore, an affiliation with a financialcompany having a better security system is becoming animportant element for commercial Web sites. In the case ofKorea, it is important to have affiliation with a technicallyreliable Web site when considering the poor availability ofan adequate infrastructure for secure transactions. The useof encrypted transactions on the Web is one of the deter-

mining factors impacting the spread of e-commerce activi-ties. Among OECD countries, Korea’s availability of secureserver affecting encrypted transactions is relatively low(OECD, 2001).

The positions and roles of Web sites having high cen-trality scores were analyzed as if there were a causal asso-ciation between the results and Web site’s intention toincrease credibility. This interpretation should be viewedskeptically. At this point, the authors have no theoretical orempirical reason to indicate what causal factors or anteced-ents exist. Also, the Web sites in the sample may all beconsidered as credible to a start-up Web site. In addition toWeb site’s credibility, such factors as advertising (Hoffman& Novak, 2000), homophilous attributes (Park et al., 2000),or interface (Park, 2001b) may account for hyperlink net-works among Web sites. Besides, the data is systematicallybiased. Even though some Web sites have affiliation withothers, its affiliates were excluded if they were not found onthe Web site or hyperlinked. Further, the definition of affil-iation may differ among Web sites.

Nonetheless, the primary implications of this researchreside in finding an emerging network among Web sites,describing the structural pattern of the affiliation network,and predicting in what direction it will evolve. To ourknowledge, no study has clearly documented these issues.This research is also an early effort to apply network anal-ysis to the Internet. In this regard, this study should bedistinguished from seemingly similar studies in computer–communication field (e.g., Li, Kuo, & Russell, 1999) be-cause they examined the linear impact of individual credi-bility factors on electronic commerce.

The results describe the affiliation structure of KoreanWeb sites. Despite South Korea’s significance in relation tothe development of Internet, it may be a hasty generalizationto regard results based upon Korean Web sites as a globalphenomenon. Future research will investigate North Amer-ican and the European nations that play a more prominentrole on the Internet. As data become accessible, this re-search will be extended to an international comparison andthe measurement between centrality score and credibilityvalue on each Web site.

As Barnett et al. (2001) put it, new communicationnetworks are in the process of evolution incorporating otherelements from within the existing social system. The affil-iation network among Web sites may be related to under-lying social perceptions. The authors plan to conduct asurvey of Web users in Korea, to rate the 44 Web sites basedon the three dimensions of credibility (trustworthiness, ex-pertness, and safety) and compare their centralities in thenetwork with perceived credibility.

Further, qualitative research such as indepth interviewswith Web masters is also suggested to examine the reasonsthey affiliate with other sites via hyperlinks. Finally, weneed to study the affiliation network among Web sites overtime. We suspect that it will become denser and larger overtime as the other frequently visited sites affiliate with the 44sites that compose the current group.

15 The structure of the hyperlink network was graphically displayedwith KrackPlot (Krackhardt, Lundberg, & O’Rourke, 1993). KrackPlot isa computer program that draws a sociogram, producing visual representa-tions of the relationships among the nodes, in this case, the Web sites.

FIG. 1. Hyperlink–affiliation network among 44 Korean Web sites.


Acknowledgments

An earlier version of this article was presented to theInternational Sunbelt Social Network Conference, Buda-pest, Hungary, April 2001. The authors are grateful toAlexander Halavais for helpful comments and suggestionswhile writing the article.

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