Network structures and commercial portal sites: Implications for Web advertising planning

Network structures ofcommercial portal sites

Implications for web advertising planning

Dongyoung SohnUniversity of South Florida

Joonhyung JeeThe University of Texas at Austin

Existing standards for the audience measurement and vehicle valuation of a websitedepend heavily on the traffic information of separate web pages. As a result, the hyper-link structure of a website has not been properly examined. This study attempts toextend the scope of existing web advertising planning perspectives by redirecting atten-tion to the relational characteristics of websites as advertising vehicles. With two meas-ures developed from network analysis methods (network density and network centrality),the hyperlink structures of five popular portal/search engine sites are analysed and com-pared. Based on the analysis, this study develops a typology for classifying web pages anddiscusses how the typology can be used to design effective web advertisements.

IntroductionThe online advertising market has grown exponentially since the graphi-cal browser was introduced. According to the Interactive AdvertisingBureau (IAB), total online advertising expenditure in the USA had alreadyreached $4.6 billion as of the first six months of 2004 (IAB 2004). Althoughthere have been some fluctuations in online advertisement spending, ithas become apparent that the internet is not a trivial medium, but ratheran essential element of the advertising media mix.

Consistent with this trend in advertising expenditures, web trafficanalysis and audience measurement issues have received considerableattention from advertisers and media professionals (Coffey 2001). Forexample, Leckenby and Hong (1998) attempted to view the web as a

International Journal of Advertising, 24(4), pp. 425-440© 2005 Advertising Association

Research Center, www.warc.com 425

INTERNATIONAL JOURNAL OF ADVERTISING, 2005, 24(4)

medium equivalent to the traditional mass media, and applied the exist-ing media exposure distribution models, which had been developed forreach/frequency estimation for mass media, to the web. They found thatmost of the existing media exposure models produced acceptable estima-tion results with minimal errors. This fmding was striking because itrevealed empirically that the web might be more akin to mass media thanthe portrayal of the one-to-one communication medium, embraced bymany early proponents of the web, implied. Similarly, Cannon (2001)argued that applying conventional media planning procedures to webadvertising could be beneficial for developing common standards for avariety of media types including the internet, television and magazine.

Conversely, some scholars have resisted this perspective of identifyingthe web with the traditional mass media. For instance, Hoffman andNovak (2000) argued that the web should be understood as a unique com-munication medium differentiated from traditional mass media. Theymentioned that 'traditional media models offer little guidance, and mayeven hinder the process of capturing the value in consumer response inthe virtual environment'. The main issue underlying the academic con-troversies is whether or not the web is a new advertising medium thatshould be distinguished from the traditional mass media. Typically, theweb has been characterised as an 'interactive' medium that enables con-sumers to engage in one-to-one interactions with messages/content (e.g.Coyle & Thorson 2001; Fortin & Dholakia 2003; Schlosser 2003).

A problem with this conception of the web as a bidirectional medium isthat it obscures the truly distinguishable characteristic of the medium,namely that the web is the largest communication network ever created byhumans. In other words, the web is an electronically networked environ-ment where people actively move from one place to another throughmulti-directional paths based on interlocked hyperlinks. This characteris-tic, which clearly differentiates the web from traditional mass media,has not been fully recognised or addressed by advertising scholars andpractitioners.

Due partly to the ignorance of this characteristic of the web, audiencemeasurement and message-vehicle valuation of the web have been sub-stantially dependent on the traffic information of separate web pages. Asa result, the interconnections among web pages have not been consideredas useful information for web advertising planning, even though the

426

NETWORK STRUCTURES OF COMMERCIAL PORTAL SITES

hypermedia environment of the web is characterised by the connectivity ofcomponent websites and web pages (Hoffman & Novak 1996).

Contrary to the prior approach to the web, this study directs our atten-tion to tbe relational characteristic of the web and, therefore, attempts toextend the scope of the current practice of web advertising planning.Speciflcally, this study identifies a website (e.g. http://www.yahoo.com) ascomprising web pages that are interconnected. Such a characteristic of awebsite makes consumers pass through predetermined paths to reach aweb page of their interest. In a given website, however, web pages differin the pattern of their interconnections, which is likely to affect con-sumers' behaviour on each web page, such as traffic, duration time andattention to content. Analysis of the hyperlink structure of a website (i.e.connection patterns of component web pages), therefore, should helpadvertisers develop effective strategies for placing web advertisements(e.g. banner ads) on the site.

As an initial step in this regard, this study attempts to show that exist-ing websites differ in their hyperlink structure. More specifically, thisstudy analyses and compares the hyperlink structures of five popularcommercial portal/search engine websites - Yahoo.com, Google.com,AOL.com, MSN.com and Altavista.com - by employing various measure-ments developed from network analysis methods (Wasserman & Faust1994). This study does not intend to provide any generalisable guidelinesfor valuating and selecting appropriate message vehicles for web advertis-ing planning. Instead, the primary emphasis is placed on the need toanalyse the hyperlink structure of a target website before placing webadvertisements on the site, and how various network measurements canbe used for this purpose.

The web and its hyperlink structureTelevision and magazines normally consist of programmes or pages thatare sequentially aggregated, which forces audiences to watch or read con-tents one after another. Therefore, the alternative courses of action audi-ences can take with these media should be limited to passively followingthe order of contents linearly presented. In contrast, the web is a struc-tured medium. Each web page is connected to multiple others, directly or

427


indirectly, through the mediation of bridging pages. From the intercon-nections, a variety of structures of communication and patterns mayemerge. This networked characteristic of the web enables individuals tobrowse contents in any order, though their browsing pattern will still beconstrained by the predetermined structure of hyperlinks. A hyperlink isa consequence of a strategic decision made by a designer to attract audi-ences. The internal structure of a website is an emergent outcome of thepatterns of interconnections between individual documents/pages. Eventhough each individual cannot recognise the entire structural pattern ofhyperlinks within a site, his/her routes of moving may be shaped by itsconnection patterns.

The above discussion suggests that examining the hyperlink structureofthe web is crucial to understanding individuals' movements on the webat two levels: (1) across different websites, and (2) across different webpages of a single website. For instance, at the level of multiple websites,van der Merwe et al. (2004) analysed how websites were interconnectedand, subsequently, identified websites that played a critical role in devel-oping strategic alliances with other relevant, websites. In general, mar-keters link their website to other websites to enhance the informationalvalue of their website or to facilitate their business on the web. Given this,van der Merwe et al. (2004) argued that the best way for a website todevelop effective alliances with other websites would be to identify a'structural hole' (Burt 1995). Specifically, it is often impossible or ineffi-cient to link a website to all relevant websites. Therefore, a more effectivestrategy in this case would be to link a target website to another websitethat is a key player in the current linkage with other relevant websites.

Even though marketers have considered web pages an important vehi-cle in which to place web advertisements, few studies have analysed thehyperlink structure at the level of multiple web pages of a single website.Examining a website's internal link structures may provide useful infor-mation not only for evaluating the roles and relative values of each indi-vidual page based on its position in the network, but also forunderstanding the structural characteristics ofthe site. The information onthe structural property of a site can be used for developing effective andefficient advertising strategy.

428


Network analysis: concepts and measurementsAnalysing the hyperlink structure of a website requires taking a relationalperspective, which Rogers and Bhowmik (1970) defined as 'a researchapproach in which the unit of analysis is a relationship between two ormore individuals' (p. 524). Accordingly, this study applies the relationalperspective to studying the hyperlink structure of a website by replacing'individuals' with 'component web pages'. Unlike conventionalapproaches to online audience measurement, which focus only on the traf-fic information of separate web pages, the fundamental unit of analysis inthis study is a hyperlink between web pages.

A hyperlink between web pages can be examined in terms of variousaspects, such as its existence/absence, strength, stability or direction (i.e.outgoing link vs incoming link). By taking these aspects into account,researchers may examine the direct as well as indirect relationships amongweb pages to reveal the whole hyperlink structure of a website. Given this,the basic data in a network analysis usually take the form of an TV x TVmatrix of the component nodes {N), which can be either symmetric orasymmetric. Let us call this the matrix X. Each cell (x) of the matrix indi-cates the existence/absence, the strength or the stability of a link betweentwo nodes. In a non-directional matrix, the direction of a link is ignored (x.= x.j), while the direction is considered in a directional matrix (x. ^ Xj).

Between two web pages, A and B, individuals can move from A to B orfrom B to A. Though individuals move between the same two web pagesin this case, their intents of the movement can be quite different depend-ing on the direction. Therefore, this study includes the direction of a linkin the analysis. In the matrix, ' 1 ' indicates the existence of a link, whereas'0' indicates the absence of a link. Finally, this study examines two criticalaspects of a website - network density and network centrality - which are themost widely used measurements of a network developed from the socialnetwork analysis.

Network densityDensity can be broadly defined as 'the general level of linkage among thepoints in a graph' (Scott 2000, p. 69). In social network analysis, a point

429


refers to an individual or a node, while a graph refers to the graphicalrepresentation of links among points. If the strength of each link is con-tinuously measured, the density ofa network can be defined as 'the meanstrength of connections among units in a network' (Marsden 1990, p. 453).Consequently, a website's network density should reflect the general levelof connectivity - that is, the extent to which web pages of the site areinterconnected with one another.

The mathematical formula for calculating network density, which isdenoted by D, is:

max > > , % ; *

where %. = relationship value between node / and/ (iX* = maximum possible relationship value.

If only the binary-coded information of a link (existence vs absence) isconsidered, network density simply refers to 'the number of actually-occurring relations or ties as a proportion of the number of theoretically-possible relations or ties' (Garton eta/. 1997). In this case, Sx.. indicates thetotal number of links existing in a network (denoted by L). Note, however,that this study considers the direction of each link. As a result, the maxi-mum possible number of links must be n{n- 1), making the network den-sity formula take the form oi D = Lln{n - 1). Network density scores canrange from 0.00 to 1.00. 'A ratio of 1.00 would indicate a structure in whicheach node is connected bidirectionally to every other node' (Jackson1997). In our case, this implies that a completely connected website (den-sity = 1.00) allows visitors to move through web pages with no constraints,while a loosely connected website offers limited alternative paths.

Network centrality

Typically, in a given website, a web page is not linked to all the other webpages but only to particular web pages. Consequently, it is likely that webpages differ in their prominence (i.e. centrality) depending on their link-age to other web pages. Centrality is a concept referring to the importanceor prominence of a particular node relative to other nodes as well as thedegree of centralisation in the entire network. Measures of centrality have

430


been developed to 'attempt to describe and measure properties of "actorlocation' in a social network" (Wasserman & Faust 1994, p. 169).

In analysing the value of web pages as an advertising vehicle,researchers often refer to the traffic generated by each web page, withoutconsidering how the web page is linked to other web pages. The abovediscussion on the concept of centrality, however, suggests that such a prac-tice is limited. In other words, web pages - even ones of equivalent traffic- may play different roles in structuring a website. Consequently, individ-uals may have different reasons to visit different web pages depending onhow the pages are linked to other pages.

To date, a variety of centrality measurements have been developed.Among them, two measures have been used widely: degree centrality andbetweenness centrality (Bonacich 1987). First, degree centrality, which isthe simplest measure, indicates the number of direct links that connectadjacent nodes to a focal node. Given this, the centrality of a focal node iscalculated by summing the number of links directly connected to others.With asymmetric data, we can further specify degree centrality either asin-degree (incoming links) centrality or as out-degree (outgoing links) cen-trality (Wasserman & Faust 1994; Scott 2000).

Second, betweenness centrality 'measures the extent to which a partic-ular point lies "between" the various other points in a graph: a point of rel-atively low degree may play an important "intermediary" role and so bevery central to the network' (Scott 2000, p. 86). In other words, as opposedto degree centrality, betweenness centrality identifies a node that bridgesan entire network. Therefore, a node that has only a few direct connec-tions with other nodes can be still considered central when it assumes aparticular position in a network. This measure is particularly important inthat it reflects the positional advantage or disadvantage of a node in a net-work. For example, the degree centrality measure may not properly iden-tify the node in a critical position bridging oxh&x nodes, because it considersonly the direct links to adjacent nodes.

As mentioned earlier, the primary purpose of this study is to show howwebsites differ in terms of their hyperlink structures. Therefore, this studyattempts not to identify central web pages in a single website, but to illu-minate how these measurements can be applied to reveal the differencesin the link distribution across websites. Freeman (1979) suggested a gen-eral approach to measure the group-level centralisation of a network

431


as follows:

In the above formula, 'KJ<n*') is the largest centrality score, either degreeor betweenness, among the g nodes in a network, whereas \(«,) is the cen-trality score of a particular node. Thus, the numerator of the formula indi-cates the sum of the differences between the largest centrality score andthe centrality score of each node. The denominator is the theoretical max-imum possible sum of the differences that can occur in a network of gnodes. In order to measure the group betweenness centralisation index,the observed betweenness index of each node is considered, while eachnode's degree centrality is observed to calculate the group-level degreecentralisation index. Consequently, the network centrality (k^ rangesfrom 0.00 to 1.00: the value will be 0.00 if all nodes' centrality scores areequal, while it will be 1.00 if all existing connections are completely con-centrated on a single node.

Hyperlink structures of five websitesUsing the network measurements shown above, the overall degree of con-nectedness (i.e. density) and the group-level network centrality of a web-site can be examined systematically. In this study, the hyperlink structuresof five popular commercial portal/search engine sites were analysed:Yahoo.com, Google.com, AOL.com, MSN.com and Altavista.com. Thesesites were selected from the top 25 web properties listed by Nielsen/NetRatings in the fourth quarter of 2002. The reason for selecting thesesites was that their commercial portal sites were considered the mostattractive vehicles for placing web advertisements.

In each website, web pages and their links were identified by using thehyperlink-visualisation software LinkViewer (http://www.gradetools.com/).In most cases, however, the boundaries of a website cannot be delineatedclearly because each website contains a number of inter-domain/inter-server hyperlinks. This means that a website is not a clearly separableentity from others, but rather part of a larger network. For this reason, this

432


Study counted only the web pages and links within the current main serverof each website. Since it was also cumbersome to handle all web pages andlinks existing in a server, only those collected by the program within 30minutes were considered.

Through this procedure, the software visualised the hyperlink structureof each website. The number of web pages and their existing links werecounted based on the visualised diagram. The total number of web pagesand links collected across the five websites was 589 and 1261, respectively.The data were analysed using the network analysis program UCINET V(Borgatti<?/^/. 1999).

Density of the websites

Table 1 illustrates the descriptive statistics including the network densityof the five websites. Overall, the density scores of these sites seem small,and the differences in the scores are not significant. For example,MSN.com has the largest density score (0.02), which is still far less than0.10. This shows that the connectedness among web pages in the five sitesis less than 2%. Contrary to the idealised portrayal of hypermedia envi-ronment, the internal network of the websites is extremely sparse, thussubstantially constraining alternative paths visitors to the sites can take.

This finding is not totally unexpected, though, because a portal siteusually contains countless web pages, and it becomes almost impossible tolink the pages to all the other pages. In fact, the level of network densitymay be negatively correlated with the number of web pages, because themaximum possible number of links increases exponentially as the number

Table 1: Descriptive

Altavista.comAOL.comGoogle.comYahoo.comMSN.com

Statistics of five

Density

0.0130.0100.0090.0180.020

* Total number of hyperlinks observed

portal/search engine sites

Descriptive

Std dev

0.1120.0980.0940.1310.139

statistics

Sum*

426.00119.00163.00100.00140.00

Variance

0.0120.0100.0090.0170.019

433


of web pages increases (e.g. if a network consists of « nodes, the maximumpossible number of relations is n{n - 1)). Low density scores imply thatmost pages contained in the commercial portal sites lack connections withother pages. This suggests that visitors to those sites are given a limited setof choices.

On the other hand, the standard deviation scores ofthe five websites arerelatively larger than their density scores. This indicates that the websitesconsist of web pages that vary greatly with regard to the number of linksto other web pages. To put it another way, in each website examined, par-ticular web pages play a prominent role in structuring the entire network.This critical aspect of the websites can be examined and compared moreelaborately by network centralisation measures.

Centrality of the websitesAs mentioned earlier, the group-level degree centrality can be groupedinto two kinds based on directionality: out-degree centralisation and in-degree centralisation. Table 2 shows that the websites are centralised onlyin terms of out-degree, while their in-degree centrality scores are relativelylow.

According to Jackson (1997), websites can be classified into four differ-ent types depending on two critical aspects: connectedness and domi-nance (see Table 3). Note that the two aspects in fact correspond to thenetwork measurements employed in this study, density and centrality,respectively, which makes Jackson's (1997) classification scheme applica-ble to the findings of this study. Using the scheme, therefore, the above

Table 2: Network

Altavista.comAOL.comGoogle.comYahoo.comMSN.com

centralisation indices of five portal/search

Degree centralisation

Out-degree

0.63010.72410.49470.83340.6427

In-degree

0.02570.00850.02850.06330.0163

engine sites

Betweenness centralisation

Betweenness (%)

0.03870.02610.04140.13250.0161

434


Table 3: Jackson's

High connectednessLow connectedness

Source: Jackson (1997)

(1997) classification scheme

High dominance

Satellite structureIndex (star) structure

of websites (structures)

Low dominance

Hypertext/associative structureLinear, narrative structure

finding suggests that the five websites have an 'index (star) structure'. Inother words, the websites consist of web pages that are loosely intercon-nected but still include a particular web page(s) that has prominently more'out-going' links to other pages. Finally, regarding the network between-ness centrality, a bigh score indicates that in a given website, a few webpages function as crucial bridges, through which other web pages are con-nected with one another.

Figure 1 summarises the above discussion. From this, it is clear that thefive websites are highly centralised in terms of outgoing links, whichmeans that the moving paths in the portal sites are largely unidirectional.

Figure 1: Comparative illustrations of the centrality of five portal/search enginesites

1.0

0.8

0.6

0.4

0.2

0.0

Out-degree

AltaVista AOL Google Yahoo! MSN

435


Among the five websites, Yahoo.com is the most centralised in this regard,while Google.com is the least centralised. In terms of the betweenness-centrality, Yahoo.com is also more centralised than the other websites.

Classification of web pages: implications for webadvertisingAs mentioned before, in a given website, individuals' behaviour on a par-ticular web page (i.e. traffic, duration time and attention to content) can besubject to how the page is linked to other web pages. Understanding howindividuals' web browsing patterns are influenced by the hyperlink struc-tures is crucial for developing effective web advertising strategy. Based onthe network analysis, therefore, this study develops a typology for classi-fying web pages and discusses how this typology can be used to designeffective web advertisements.

First, in a given website, a web page can function as a hub where indi-viduals initiate their navigation to other web pages. Conceivably, such aweb page has substantially more 'out-going' links than 'in-coming' links.In most websites, including the ones examined in this study, such a webpage is likely to be a front page. Depending on marketers' strategy, how-ever, a website can be designed to have multiple hubs. Regarding indi-viduals' behaviour, a hub page should generate high traffic because thepage provides an array of links to different interests and, therefore, appealsto individuals who have different needs when visiting the website.

Note, however, that a hub page is inherently transitional. In otherwords, individuals visit a hub page primarily to specify their interest in aparticular link provided on the page. Consequently, individuals' durationtime on a hub site should be only as long as it takes them to spot the link(s)of their interest. Likewise, individuals are less likely to attend to contenton a hub page, other than that which matches their interest. Given this, aneffective web advertisement on a hub page would be one that is highlyeye-catching. Its message can be general since individuals visiting thepage are likely to have different interests. The message, however, shouldbe made concise, otherwise it could easily be bypassed.

Second, a web page can be designed to have substantially more 'in-coming' links than 'out-going' links. Such a web page is called a sink page.

436


Although not impressive, the in-degree centrality of the five websites (seeTable 2), particularly Yahoo.com, suggests that these websites indeedinclude sink pages. Similar to a hub page, a sink page is also likely to gen-erate high traffic because the page can be the final destination of individ-uals initiating the navigation with different needs. In a commercialwebsite, a web page in which a final transaction is made for different itemscan be an example. The high traffic expected on a sink page makes thepage as good a place for web advertisements as a hub page. However, dif-ferent strategies are required to design an effective web advertisement ona sink page because individuals' behaviour on this page may differ fromthat on a hub page. Specifically, a sink page provides content that corre-sponds to individuals' interest (e.g. final transaction). Consequently, indi-viduals are likely to stay longer on a sink page as compared to a hub page,paying more attention to the content at the same time. Different from thecase of a hub page, therefore, an effective web advertisement on a sinkpage would be one that corresponds to individuals' interest, possibly witha more detailed message.

Finally, a website can include several 'cliques' in which web pages aremore densely interconnected. In practice, web pages that refer to the sameinterest are likely to be interconnected and, therefore, form a clique. Sincedifferent cliques refer to different interests, they typically sbare few links;rather, they are linked through a particular web page(s). Given this, such abridge page has a limited number of links to other web pages, but stillplays a critical role in structuring the entire network of a website.Compared to a hub page and a sink page, a bridge page is less likely togenerate high trafflc because individuals visit the page only when theyshift initial interest (e.g. purchase) to another one (e.g. entertainment)and, therefore, move to another clique. Once visited, however, a bridgepage can generate high duration time as well as intense attention to thecontent because the page is likely to provide individuals with an overviewof their new interest. Similar to the case of a sink page, therefore, an effec-tive web advertisement on a bridge page would be one that corresponds toindividuals' new interest with a more detailed message. The above dis-cussion is summarised in Table 4.

437


Table 4

Types of

Hub

Sink

Bridge

: Web advertising strategy for different types

web page Individuals' behaviour

High trafficShort duration timeLimited attention to content

High trafficLong duration timeIntense attention to content

Low to moderate trafficLong duration timeIntense attention to content

of web page

Effective web advertising

Eye-catchingGeneral, concise message

Interest-matching, detailed message

Interest-matching, detailed message

ConclusionThere exist two opposite perspectives towards the web. On the one hand,the web has been identified as a mass medium, much like television ormagazines. Based on this perspective, current online advertising dollarshave been concentrated heavily on some popular front pages with hightraffic records, disregarding the structural relationships between pages. Asa result, potential opportunities for web advertising have not beenexplored fully, and most online pages are valuated inappropriately.

On the other hand, the web has been considered an ideal medium thatenables individuals to navigate freely through randomly dispersed web-sites and web pages. This study confirms, at least partially, that both viewsare biased. In other words, a website is designed to have a particular hyper-link structure, which provides individuals with numerous alternativepaths, but still with constraints. Such a characteristic of the web has a crit-ical implication for web advertising: on a given website, individuals'behaviour on a web page is subject to the structural properties of the site.As an exploratory study in this regard, this study develops a typology thatcan be used as a reference. Future studies are expected to provide moreempirical justification of the typology.

438


ReferencesBonacich, P. (1987) Power and centrality: a family of measures. American Journal of

Sociology, 92(5), pp. 1170-1182.Borgatti, S.P, Everette, M.G. & Freeman, L.C. (1999) UCINET5.0 Version LOO.

Natick: Analytic Technologies.Burt, R.S. (1995) Structural Holes: The Social Struaure of Competition. Cambridge, MA:

Harvard University Press.Cannon, H.M. (2001) Addressing New media with conventional media planning.

Journal of Interactive Advertising, 1(2).Coffey, S. (2001) Internet audience measurement: a practitioner's view. Journal of

Interactive Advertising, 1(2).Coyle, J.R. & Thorson, E. (2001) The effects of progressive levels of interactivity and

vividness in web marketing sites. Journal of Advertising, 30(3), pp. 65-77.Eortin, D.R. & Dholakia, R.R. (2005) Interactivity and vividness effects on social

presence and involvement with a web-based advertisement. Journal of BusinessResearch, 8(3), pp. 387-396.

Freeman, L.C. (1979) Centrality in social networks: I. conceptual clarification. SocialNetworks, 1(215), p. 239.

Garton, L., Haythornthwaite, C. & Wellman, B. (1997) Studying online socialnQVNOxks. Journal of Computer-Mediated Communication, 3(1), pp. 1^5.

Hoffman, D.L. & Novak, TP. (1996) Marketing in hypermedia computer-mediatedenvironments: conceptual foundations. Journal of Marketing, 60, July, pp. 50-68.

Hoffman, D.L. & Novak, T P (2000) When exposure-based web advertising stopsmaking sense (and what CDNOW did about it),http://elab.vanderbilt.edu/research/papers/pdf/manuscripts/CDNOW-pdf.pdf

IAB (2004) IAB/Pwc reports third record setting quarter for internet advertising.Interactive Advertising Bureau.

Jackson, M.H. (1997) Assessing the structure of communication on the World WideWeb. Journal of Computer-Mediated Communication, 3( 1).

Leckenby, J.D. & Hong, J. (1998) Using reach/frequency for web media planning.Journal of Advertising Research, 38(1), pp. 7-20.

Marsden, P.V. (1990) Network data and measurement. Annual Review of Sociology,no. 16, pp. 435^63.

Rogers, E.M. & Bhowmik, D.K. (1970) Homophily-heterophily: relational conceptsfor communication research. Public Opinion Quarterly, 34(4), pp. 523-538.

Schlosser, A.E. (2003) Experiencing products in the virtual world: the role of goal andimagery in influencing attitudes versus purchase intentions. Journal of ConsumerResearch, 30, September, pp. 184-198.

Scott, J. (2000) Social Network Analysis: A Handbook. London: Sage,van der Merwe, R., Pitt, L. & Berthon, P (2004) Elucidating elusive ensembles: the

strategic value of informal internet networks. European Management Journal, 22(1),pp. 12-26.

Wasserman, S. & Faust, K. (1994) Social Network Analysis: Methods and Applications.Cambridge: Cambridge University Press.

439


About the authorsDongyoung Sohn (PhD, The University of Texas at Austin) is an assistantprofessor in the School of Mass Communications at the University ofSouth Florida. His primary research interests include the impact of digitalmedia on consumer's information processing and communication, andsocial network analysis.

Joonhyung Jee is a doctoral candidate at the University of Texas atAustin. His primary research interest is in branding and the analysis ofconsumers' knowledge structure.

Address correspondence to Dongyoung Sohn,Assistant Professor,School of Mass Communications, University of South Florida, 4202 EastFowler Avenue, CIS 1040, Tampa, Florida 33620-7800, USA.

Email: dsohn(§)cas.usf.edu

440

Network structures and commercial portal sites: Implications for Web advertising planning

Documents

Transcript of Network structures and commercial portal sites: Implications for Web advertising planning