mapping diversities and tracing trends of cultural

Mapping Diversities and Tracing Trends ofCultural Homogeneity/Heterogeneityin Cyberspace

Elad Segev

School of Politics, International Relations, & Philosophy

Keele University

Niv Ahituv

The Faculty of Management

Tel Aviv University

Karine Barzilai-Nahon

The Information School

University of Washington

Many Internet global commercial services were originally initiated in the United States

and later exported to other countries, continents, and cultures. By analyzing content

and form characteristics of two leading portal sites—MSN and Yahoo!—on 33 and 23

(respectively) of their local country homepages in a comparative and longitudinal study

over a period of six months, this study addresses two research questions: What differ-

ences, if any, are found among homepages of the same parent site located in different

cultures (or targeted at audiences in different cultures)? What trends, if any, do those

differences show over time? Although MSN is a U.S. brand, the analysis of its home-

pages reveals increasing cultural heterogeneity and localization of content and form. In

contrast, Yahoo!’s homepages are found to be more similar to each other and to the

‘‘parent’’ American portal. A metric was developed to measure the distance between var-

ious websites in terms of form and content, along with a structured procedure to analyze

and cluster groups of websites. The results suggest that the diversity of local homepages

in MSN and Yahoo! follows the geographic and ethnographic scatter of their countries.

doi:10.1111/j.1083-6101.2007.00373.x

Introduction

Many Internet global commercial services (news portals, ISPs, weather portals, andthe like) were originally initiated in the United States and later exported to other

countries, continents, and cultures. An interesting question is to what extent cultural

Journal of Computer-Mediated Communication

Journal of Computer-Mediated Communication 12 (2007) 1269–1297 ª 2007 International Communication Association1269

Dow

nloaded from https://academ

ic.oup.com/jcm

c/article/12/4/1269/4582996 by guest on 14 February 2022

variance is reflected through the Internet and whether one dominant culture, such asthat of the U.S., enforces cultural homogeneity in cyberspace. The Internet is a unique

cultural space in that it combines a variety of means and forms of communication(e.g., mutual interaction, broadcasting, self-searching for information, forum

groups, human computer interaction) and different types of content (e.g., text,image, voice, video). This provides a wide range of opportunities, which in turnhave significant social and cultural implications (DiMaggio, Hargittai, Neuman, &

Robinson, 2001).

Cultural Diversity in Cyberspace

Some scholars have suggested that mass communication channels shaped by earlymedia forms (e.g., written press, radio, television) might create a global uniform

taste. They argued that commercial considerations and dependence on advertise-ments might produce homogeneous and popular programs or texts in order toattract the attention of maximally large audiences (Adorno, 1991; Bagdikian, 2004;

Mattelart, 1991; Shils, 1963). Eventually, together with the advent of the Internet andthe increasing availability and openness of information (Ahituv, 2001), some media

channels would be likely to represent a certain universal homogeneity. This claimstands in contrast to the increasing customization of production (Caves, 2000;

Shapiro & Varian, 1999; Sunstein, 2001) and the long-tail theory, which shows thatprofit follows the economics of abundance (Anderson, 2006).

Other researchers suggest that economic imperatives will keep the Internet fromrealizing its technical potential as a source of cultural abundance (Castells, 1996;

Neuman, McKnight, & Solomon, 1998). They indicate that although entrance bar-riers to the network may be low, competitors can bias the attention of users andconsequently confine it (Barzilai-Nahon, 2006). In fact, 80% of visits are to only

0.5% of websites (Waxman, 2000). Similarly, some scholars suggest that onlineinformation is not equally spread throughout the global network but rather is chan-

neled by economic, technological, cultural, and social factors (Barabasi, 2001). Fromthis perspective, online information is controlled by dominant actors and is concen-

trated in ‘‘live-zones,’’ i.e., information-saturated centers that have a global culturalimpact (Kellerman, 2002; Lash, 2002). Moreover, evidence has been reported that the

English language dominates online content out of proportion to its share of nativespeaker Internet users (Global Reach, 2004).

The question is whether the Internet promotes individual and local expressions

and therefore heterogeneous culture, or whether it serves as an instrument fordominant organizations (and nations) to increase markets and reinforce common,

homogeneous dispositions. This study proposes quantitative tools to explore cul-tural heterogeneity through popular online artifacts. The idea of exploiting quanti-

tative tools to study cultural differences is not new in the social sciences; however, theattempt to establish a metrics of distance between the content and form of different

websites is quite new.

1270Journal of Computer-Mediated Communication 12 (2007) 1269–1297 ª 2007 International Communication Association

Dow


ic.oup.com/jcm


A recent study by Norris (2004) measured the bridging and bonding functions ofvarious online groups in the context of ideological, political, and social homogeneity

and heterogeneity. Her study used an ordinary least squares (OLS) regression modelto predict these measures, while taking into account cultural attributes such as

interests, lifestyle, religious group, and more.Hofstede (1997) suggested that national culture has significant implications for

organizational behavior. He defined culture as ‘‘the collective mental programming’’

(p. 5) that occurs along various dimensions. Hofstede measured cultural differencesin the workplace using survey data from IBM employees in over 50 countries.

Ultimately, he designed a special index for each dimension and ranked the countriesby their cultural differences in the workplace.

Content and Form as Cultural Artifacts

Various recent studies have implemented Hofstede’s (1997) framework to explorethe design and content of websites in different countries (Callahan, 2005; Cyr &

Trevor-Smith, 2004; Lo & Gong, 2005; Sheridan, 2001; Zhao, Massey, Murphy, &Liu, 2003). Their findings suggest that differences in website form and content reflect

a variety of cultural attributes. Relatedly, a recent study by Wurtz (2005) comparedthe form and content of transnational corporation websites in different countries. It

revealed that the websites of high-context cultures (Hall & Hall, 1990) such as Japan,China, and Korea, in which communication is relatively indirect and rich in gestures,

displayed more extensive use of graphic elements and indirect messages. By contrast,websites of low-context cultures, such as Germany, Norway, and the U.S., in which

communication is relatively direct, were more static and displayed rather directmessages. In terms of content, it was found that websites of high-context culturestended to emphasize family and social values, while websites of low-context cultures

tended to emphasize freedom and personal lifestyle. Similar trends were observed inother studies (Leonardi, 2002; Marcus & Gould, 2000), indicating that different

national websites place different emphases on collective and individual values.While a growing number of studies have focused on website design and layout

(e.g., animations, colors, graphical density, and navigation menus) as culturalmarkers (Badre, 2000; Becker, 2002; Callahan, 2005; Cyr & Trevor-Smith, 2004;

Sun, 2001; Tsui & Paynter, 2004), fewer studies have been conducted on differencesin content and their cultural implications. Most content analyses are based onHofstede’s (1997) frameworks and thus are limited in focus.

Robbins and Stylianou (2003) looked at the information provided by the largestglobal corporation websites. They found that website content is significantly different

across national cultures. Singh and Baack (2004), Singh, Furrer, and Ostinelli (2004),and Singh, Xhao, and Hu (2003) compared the content of American, Mexican,

European, Indian, and Chinese websites, identifying significant differences inemphasis on family and social values, masculinity, and power. Additionally, the

authors argued that online users prefer localized over standardized web content.


Dow


ic.oup.com/jcm


In contrast to cultural sensitivity and localization of content and form, Kang andCorbitt (2001) revealed trends of homogeneity and standardization in web design.

They examined the differences in display colors, images, animation, and function-ality of 10 U.S.-based IT companies and their Australian and Singaporean versions.

Their study concluded that local homepages used a design similar to the parent U.S.website, taking only minor account of cultural issues.

Finally, a study by Schmied (2003) looked at websites of transnational corpo-

rations, and particularly websites of airline companies, finding certain trends ofstandardization in content and form, but at the same time identifying distinctive

local cultural markers. In short, there are very few studies that indicate trends ofhomogeneity in web design and content, and even then their findings still suggest

that there are local elements that cannot be ignored.By exploring cultural diversity through differences in the content and form of

national homepages, it is presumed that online culture may be reflected throughcontent and form. Barber and Badre (1998) defined the term ‘‘culturability’’ as therelationship between culture and usability in the design of webpages. They argued

that the interface design, interactivity and content reflect cultural sensitivity andunderstanding of the targeted audience. Their study indicated that there are design

patterns (or what they also called ‘‘cultural markers’’), which are highly prevalentwithin different cultures and different genres. Their study indicates, for example, that

Brazilian websites tend to be more colorful and Lebanese websites tend to use lightgraphics and more text (Badre, 2000; Barber & Badre, 1998). Similarly, the present

study uses the term ‘‘culture’’ somewhat loosely, as a means to distinguish among thedifferent countries and their respective websites. There is no intention to explore the

various aspects of this term but rather to enable a comparison of national websites ascultural products or ‘‘artifacts’’ (Appadurai, 1996) with different form and contentattributes. Thus, the term ‘‘online culture’’ is used here to refer to national cultures as

reflected on the Internet.While most of the aforementioned studies compared content and form elements

using Hofstede’s framework, in this study we develop an original metric to measurethe diversity of content and form online. We choose to study the Internet portals

MSN and Yahoo! mainly due to their significant rating and popularity in theobserved countries (Nielsen/Netratings, 2004; see also Appendix C). We focus on

the differences in their local homepages, assuming that the previous decisions of webdesigners and corporate strategies are incorporated in their content and form, andthat they are constantly affected by the values and norms of their local audiences.

Van Couvering (2004) suggested that MSN and Yahoo! conform to the masscommunication business model known as the ‘‘dual product’’ market. On the one

hand, they produce cultural artifacts for individuals, and on the other hand, theyproduce advertisements. But here the business models of MSN and Yahoo! differ, in

that MSN gets 29% of its revenue from advertisements, while Yahoo! gets 82% of itsrevenue from advertisements (Van Couvering, 2004). These different revenue strat-

egies also suggest that MSN benefits more from direct subscription, and therefore


Dow


ic.oup.com/jcm


compels its customers to pay more attention to local and customized cultural arti-facts, than its competitor Yahoo!. Consequently, it was expected that the content and

form of national homepages in MSN would be more diverse and localized than inYahoo!. The results of this study strongly support that hypothesis.

Research Questions

This article addressed two primary research questions:

RQ1: What are the differences in content and form among local homepages of the same parent

site?

RQ2: What trends, if any, do these differences display over a period of time?

The first question explores diversity and raises sub-questions such as: What arethe similarities of each homepage to the parent U.S. site? Is there any relation

between geographical or ethnographical diversity and online cultural diversity?The second question explores trends and raises sub-questions such as: Is it possible

to trace trends in content and form of popular Internet portals? If so, are theyprogressing toward cultural homogeneity or toward heterogeneity?

Since both MSN and Yahoo! originated in the U.S., we expected to find cultural

homogeneity in local homepages, reflecting the form and content of the parent U.S.site, and an increasing similarity in their form and content over time.

Methodology

In order to capture the content and form of different local homepages, an observa-tion item list was constructed. The item list is an electronic form that was used toreport content and form in the different local homepages of MSN and Yahoo!. One

month of observations (May 2003) served as a pilot sample to fine-tune the item list.Subsequently, samplings of content and form were taken over a period of six months

(June-November 2003) from the 33 MSN local homepages and 23 Yahoo! localhomepages (see Appendix A for the full list of countries). Since six months is

a relatively short period, we focus our analysis on the diversity of local homepages,and suggest implications concerning their trends mainly to encourage further inves-

tigation in this domain.The content and form elements of the item list that served as a basis for com-

parison across local homepages followed two main principles: consistency and

remarkableness. Consistency refers to the form and content elements that appearin all or most local homepages. Thus, it is useful to compare the number of pictures

in different local homepages only if it is a consistent element, that is, it appears in allor most local homepages. It is less useful to compare elements that are not consistent,

such as the number of banners with sound, which hardly ever appear.Remarkableness refers to the form and content elements that are most distin-

guishable and that are referred to as ‘‘cultural markers’’ by Badre (2000). Some form


Dow


ic.oup.com/jcm


elements, such as banners and active (animated) banners, were identified in previousstudies as remarkable form elements (Cyr & Trevor-Smith, 2004; Lo & Gong, 2005;

Wurtz, 2005; Zhao et al., 2003); these may further reflect high-context cultures (Hall& Hall, 1990). Similarly, number of links, number of menus, number of headlines,

search box, and page layout have been identified as remarkable form elements withdistinctive cultural attributes (Barber & Badre, 1998; Callahan, 2005; Cyr & Trevor-Smith, 2004).

Based on previous studies of ‘‘cultural markers’’ and our ongoing observations ofMSN and Yahoo!, we identified 10 form elements for examination: number of

frames, number of banners, number of active banners, number of background col-ors, number of photos, number of main links (headline links), number of links in

general, size of search box, number of menus, and number of pop up windows. Eachone of these elements was further operationalized in detail; thus, for example, the

number of pictures is the number of any graphics above 40 pixels. Although oneresearcher conducted the coding, the detailed operationalization of each elementhelped to maintain the consistency of the coding process.

Measuring content quantitatively is often a less straightforward process. Here wefocus on topical classification of seven categories: news, economics, entertainment,

sports, shopping, technology, and general information. These topics were chosen dueto their high frequency of appearance in all local homepages. Classifying content by

topics was a natural choice also because both MSN and Yahoo! use topical classifi-cation to differentiate graphically and conceptually among different kinds of content.

Excluding rare exceptions, most content could be sorted into those seven cate-gories. Each category had structured and detailed rules regarding the inclusion of

content in it. For example, links to purchase products and services would be cate-gorized under the ‘‘shopping’’ category. A particular challenge was to define the rulesof inclusion and exclusion for the category ‘‘news,’’ which can be relevant for many

different topics, such as economics or sports. In order to maintain the consistency ofthe classification process, it was decided to count as news only content that deals with

politics and society in general.All local homepages were translated by means of online dictionaries into English.

Since local homepages inMSNandYahoo! consist of very short sentences and are oftenalready graphically divided into topics, the classification of content into the seven

broad categories was straightforward. Additionally, a research student was employedto validate the online translation of the Japanese, Chinese, and Korean homepages.

Apart from the general number of links from each category, content was further

subdivided by its location and function. Thus content variables could be, for exam-ple, the number of news, economics, or sports headlines, or the number of enter-

tainment or shopping banners (see Appendix B for the full item list).To conclude, 44 different attributes of content and form were analyzed monthly

over a period of six months in 33 countries for MSN and 23 countries for Yahoo!.In order to explore the diversity of content and form, three indices were developed

(General Diversity Index, Difference Diversity Index, and National Similarity Index)


Dow


ic.oup.com/jcm


and a structured statistical analysis was employed (factor and cluster analyses). Bothindices and statistical analyses examined the diversity of local homepages of the same

parent site and its trends over time; thus they complemented and supported eachother.

Three Diversity Indices

The first index, the General Diversity Index (GDI), was designed to explore the

diversity of the content and form of local homepages. It is based on calculatingthe coefficient of variation of the form and content variables through three steps:

1. Compute the average and standard deviation across all homepages for each inde-pendent variable.

2. Compute the scattering index of the variable (standard deviation/average), alsoknown as the coefficient of variation.

3. The first index, the GDI, is derived from the average scattering index of thevariables of form and content separately.

Table 1 demonstrates how to calculate the GDI index for a partial sample takenfrom actual observations of MSN in June 2003.

The second index, the Difference Diversity Index (DDI), was designed to explorethe diversity of the absolute differences between local homepages and the parent U.S.

homepage. Similar to the GDI, it is based on calculating the coefficient of variationthrough the following steps:

Table 1 First index (GDI)—Sample from MSN, June 2003

Country Banners Active Banners Background Colors Pictures

U.S. 0 0 4 6

Arabia 2 2 4 8

Italy 0 0 5 8

Netherlands 2 1 3 15

Norway 2 1 4 14

Spain 5 0 3 9

U.K. 3 1 4 6

Brazil 2 2 4 8

Canada (English) 0 0 4 6

Mexico 6 2 3 12

India 3 3 5 4

Korea 1 1 4 10

Singapore 4 2 4 7

Australia 1 0 3 5

Taiwan 4 2 4 0

Average 2.33 1.13 3.87 7.87

Standard Deviation 1.84 0.99 0.64 3.85

Scattering Index 0.79 0.87 0.17 0.49 0.58


Dow


ic.oup.com/jcm


1. Compute the absolute differences between each local homepage and the U.S.homepage for each observation.

2. Compute the average and the standard deviation among all homepages for eachindependent variable.

3. Compute the scattering index of the variable (standard deviation/average), alsoknown as the coefficient of variation.

4. The second index, the DDI, is derived from the average scattering index of the

variables of form and content separately.

A trend of decline in the second index does not indicate that the local homepagesbecome similar to the U.S. homepage. It only indicates that the absolute differencesbecome more homogenous. The full meaning of the second index will be further

discussed in the analysis of the results.The first and the second indices engender a dimensionless number, which is

meaningful only if compared over a period of time to track diversity trends. Sincethe calculation of indices is based on the coefficient of variation, it can be imple-

mented easily in other fields to explore the scattering level and its trends.The third index, the National Similarity Index (NSI), is different from the first

two in the sense that it does not describe the level of diversity among all localhomepages of the same parent site, but rather indicates the level of similarity of each

local homepage to the parent U.S. homepage (MSN or Yahoo!). The steps to calcu-late the third index are as follows:

1. Compute the ratio between each local homepage and the U.S. homepage for eachobservation.

2. If the ratio is greater than 1, use the reciprocal value (absolute ratio), so that it willbe possible to intersect the ratio’s average per each local homepage.

3. The third index, the NSI, is derived from the average of those ratios among all

independent variables for each local homepage.

Table 2 demonstrates how to calculate the third index for a partial sample takenfrom actual observations of MSN, June 2003.

The third index indicates the percentage of similarity for each local homepage to

the U.S. homepage. The NSI value ranges from 0 to 1, where a value of 0 indicatesa large difference from the U.S. homepage, while a value of 1 designates the highest

possible similarity to the U.S. homepage. The NSI applies to both form and contentattributes.

Factor and Cluster Analyses

In addition to the three indices, we use statistical analysis to validate and support thenew metrics of indices and to examine the scatter of local homepages in detail. This

was performed through a sequence of three stages:

1. Factor analysis was used to identify correlated variables and replace them with

fewer components that explain the content and form.


Dow


ic.oup.com/jcm


2. Cluster analysis was used in order to group local homepages according to similarcharacteristics. The homepages in each cluster share common factors of content

or form. The distance between clusters can be measured either by the least squaresmethod, or by the Ward method based on variation analysis. The Ward (1963)

method was found more adequate for this study, as will be later argued.3. The common factors in each cluster were traced. Since the clustering process was

based on the new components that the factor analysis generated, we could indi-cate the common factors of homepages in the same cluster.

All stages of the diversity analysis were carried out each month for six months oneach site for the content and form variables separately, thus enabling us also to

construct a trend component to the study. The observation item list contained44 variables (10 of form, 34 of content) that were sampled over 332 homepages

(33 MSN1 23 Yahoo!3 6 months). Consequently, 14,608 observations were collected.

Results

Analysis of the Three Indices

General Diversity Index (GDI)

Figure 1 does not indicate a clear trend in the general diversity of local homepages.

However, interestingly, form and content have similar trends in MSN, and in October,the homepages were relatively more similar to each other. If October is regarded as

Table 2 Third index (NSI)—Sample from MSN, June 2003

Country Background Color

Absolute Ratio

Picture

Absolute

Ratio

Menu

Absolute

Ratio

Search Box

Size Absolute

Ratio

Average

U.S. 1.00 1.00 1.00 1.00 1.00

Arabia 1.00 0.75 0.88 0.74 0.84

Italy 0.80 0.75 0.91 0.80 0.82

Netherlands 0.75 0.40 0.88 0.88 0.73

Norway 1.00 0.42 0.85 0.97 0.81

Spain 0.75 0.67 0.95 0.74 0.78

U.K. 1.00 1.00 0.94 0.80 0.94

Brazil 1.00 0.75 0.91 0.88 0.89

Canada (English) 1.00 1.00 0.91 0.81 0.93

Mexico 0.75 0.50 0.68 0.76 0.67

India 0.80 0.67 0.88 0.82 0.79

Korea 1.00 0.60 1.00 0.72 0.83

Singapore 1.00 0.86 0.95 0.76 0.89

Australia 0.75 0.83 0.73 0.46 0.69

Taiwan 1.00 0.00 0.88 0.74 0.66

Average 0.80


Dow


ic.oup.com/jcm


an exception, it is possible to observe a slight increase in the general diversity level

both in form and content. The sampling covered a relatively short period, however;thus a further statistical investigation is required to support this trend.

Similarly, it is difficult to trace general trends in the diversity of local homepages

in Yahoo! over the observation period. Figure 2 indicates that form and content havedifferent trends in Yahoo!. The best-fit line of the GDI in the content only implies

a slight trend of decline. Although six months may be a relatively short period todraw a conclusion about the GDI trends in MSN and Yahoo!, those slight trends will

gain further support by the other indices and the statistical analysis and therefore areworth mentioning.

Difference Diversity Index (DDI)

The second index describes the level of homogeneity of the absolute differences

between the sampled homepages and the main U.S. homepage.Figure 3 portrays the trends of the DDI in MSN, indicating a general trend of

decline mainly in content. This suggests that the absolute differences became similarto each other. In Yahoo! it was difficult to trace a general trend for the DDI, and

again content and form had opposite trends. The main power of the DDI is shownwhen it is compared with the first index (GDI), as demonstrated in the followingexample.

In this example it is assumed that in month A there were 10 pictures in eachhomepage (including the U.S. homepage), except for one local homepage that had

Figure 2 Yahoo! GDI trends

Figure 1 MSN GDI trends


Dow


ic.oup.com/jcm


only five pictures in its homepage. Assuming that in the next month another localhomepage had five pictures, the first index increased, while the second index decreased.

This indicates that some homepages become similar to one another, but differentfrom the U.S. homepage. This phenomenon is called in this research ‘‘the alternative

block,’’ since homepages that become similar to one another suggest alternatives tothe form and content of the U.S. homepage.

Comparing the trends of the first and the second indices therefore indicates that

the alternative block in MSN is getting stronger over the period of six months. Inother words, localization forces appear to be stronger than globalization forces. This

phenomenon will receive further support from the cluster analysis.In Yahoo! the trends of the first and the second indices are similar during five out

of the six months both in form and content variables, and therefore there is noreason to discuss the alternative block and its trends.

National Similarity Index (NSI)

The third index indicates the degree of proximity (similarity) of local homepages to

the U.S. homepage in terms of form and content. Its value ranges from 0 (leastsimilarity) to 1 (greatest similarity).

Figure 3 MSN DDI trends

Table 3 Comparison of the first and the second indexes (GDI and DDI)

Month A Month B

Number of

Pictures

Absolute

difference

Number of

Pictures

Absolute

difference

Homepage 1 10 0 5 5

Homepage 2 10 0 10 0

Homepage 3 5 5 5 5



Average 9 1 8 2

Standard Dev. 2 2 2.4 2.4

GDI = 0.22 DDI = 2 GDI = 0.3 DDI = 1.2


Dow


ic.oup.com/jcm


Figure 4 portrays the trends of the third index over a period of six months inMSN. It indicates that there was a general trend of decline in form and content,

which means that local homepages become more different from the U.S. homepagein terms of form and content over a period of six months. These findings are

consistent with the trends of the first index that implied an increase of heterogeneityand with the growth of the alternative block that the second index revealed.

Figure 5 shows that in Yahoo!, unlike in MSN, the NSI increases in content only.

There are no clear trends in form in the period of the observation, although it mightbe suggested that the content of local homepages in Yahoo! becomes more similar to

the main U.S. homepage. These findings conform to the trend of homogeneity incontent that the GDI previously suggested.

Another interesting result provided by the third index is the level of similarity ofeach homepage to the U.S. homepage. Table 4 summarizes this proximity for MSN,

June 2003, where values close to 1 indicate great similarity and values close to0 indicate great difference from the U.S. homepage.

Interestingly, Table 4 (June 2003) and the results for the next five months (July-

December 2003) display the same patterns over a period of time for form andcontent attributes. Local homepages in MSN that were similar to the U.S. homepage

in form (e.g., the U.K., Canada, Germany, Belgium, and France) were often similar incontent as well. Local homepages in MSN that were different from the U.S. home-

page in form (e.g. China, Slovakia, South Africa, Finland, Denmark, The CzechRepublic, Austria, and Switzerland) were often different in content as well. A further

analysis will reveal the reasons behind those trends.

Figure 4 MSN—Trends of the NSI

Figure 5 Yahoo!—Trends of the NSI


Dow


ic.oup.com/jcm


In Yahoo! it was difficult to find a pattern over time. There was no significantconsistency in homepage ranking order for form or content.

Results of the Statistical Analysis

Factor Analysis

In addition to the three indices, statistical tools were exploited to further analyze thefactors behind the diversity of the homepages’ form and content. By means of

principal component analysis the correlated variables were extracted and a linearcombination that could explain them was found.

Table 5 displays the percentage of explained variation accounted for by the newcomponents. Each of the four new components represents a group of correlated

variables. The first component represents the ‘‘number of links,’’ the ‘‘number ofmain links,’’ and the ‘‘number of menus.’’ Those highly correlated variables togethercomprise the form of the content. Similarly, the second new component represents the

‘‘size of the search box,’’ the ‘‘number of background colors,’’ and the ‘‘number offrames,’’ which together comprise the page layout or the pattern. The third compo-

nent represents the ‘‘number of pop-up windows’’ and the ‘‘number of active ban-ners,’’ which together comprise the activity. Finally, the fourth component represents

the ‘‘number of photos’’ and ‘‘number of banners’’ that comprise colorfulness. Appar-ently, each month the same form variables were consistently correlated and grouped

into the same new components that comprise four aspects of the form: form ofcontent, patterns, activity, and colorfulness. Those components will be used later

for the cluster analysis.

Table 4 NSI in MSN, June 2003

Ranking Form Diversity (MSN) Ranking Content Diversity (MSN)

1 U.S. 0.819 India 1 U.S. 0.466 Belgium (French)

0.937 Canada 0.810 Hong Kong 0.707 Netherlands 0.461 Norway

0.914 Germany 0.805 Spain 0.701 U.K. 0.45 Korea

0.908 Brazil 0.759 Taiwan 0.701 Japan 0.427 Sweden

0.888 Malaysia 0.746 Australia 0.673 Arabia 0.423 Spain

0.882 France 0.736 Sweden 0.669 Germany 0.403 Hong Kong S.A.R.

0.874 U.K. 0.726 Israel 0.641 Canada 0.361 Taiwan

0.873 Italy 0.683 Denmark 0.606 France 0.354 Austria

0.866 Latin America 0.668 Czech Republic 0.598 Italy 0.352 Czech Republic

0.865 Korea 0.659 New Zealand 0.593 Brazil 0.337 Singapore

0.861 Belgium 0.642 Mexico 0.554 New Zealand 0.28 India

0.86 Norway 0.519 Switzerland 0.554 Israel 0.279 Denmark

0.852 Arabia 0.499 China 0.536 Latin America 0.249 Switzerland (German)

0.846 Japan 0.495 Finland 0.519 Malaysia 0.187 Finland

0.842 Netherlands 0.467 South Africa 0.498 Australia 0.118 South Africa

0.840 Singapore 0.463 Austria 0.492 Mexico 0 China


Dow


ic.oup.com/jcm


In Yahoo! the factor analysis of the form shows similar patterns. The onlydifference is that local homepages in Yahoo! are more similar to one another. The

variable ‘‘number of background colors,’’ for instance, always gets the value of 4, andthere were hardly any pop-up windows. This is why the factor analysis process inYahoo! generates only three new components that describe three aspects of the form

(the colorfulness and activity variables were reduced into one factor). Those threenew components appeared consistently in all six months with no variation.

The factor analysis for content was less consistent. The correlation matrix showedsome unreasonable relations among the 34 independent variables. The unreasonable

relations appeared mostly within variables that usually get a zero value and thereforecreate ‘‘noise’’ (e.g., the ‘‘number of sports banners’’). After filtering those variables,

the correlation matrix of the 20 content variables that were left made much moresense.

Table 6 presents the new groups of correlated variables after filtering the varia-bles that create ‘‘noise.’’ The variables in each group are reasonably correlated, sincethey deal mostly with the same subject. Interestingly, the variables that deal with

economics and shopping were highly correlated in almost every month and thereforewere replaced by a single new component. This may indicate that MSN content

editors in many countries have similar attitudes and preferences as regards thoseparticular topics. Entertainment does not attain representation as an independent

component, but rather it is reflected in some components in combination with otherissues.

Unlike the factor analysis of form, the factor analysis of content in both MSN andYahoo! creates slightly different groups of variables for each month. Variables of thesame subject were almost always correlated and therefore were replaced by a single

new component.

Table 5 New form components—MSN, June 2003

Note: Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 6 iterations.


Dow


ic.oup.com/jcm


Cluster Analysis and Tracing the Factors

The cluster analysis is the second stage in the diversity analysis process. It uses thefactor analysis components as input and groups together local homepages with

similar patterns of form and content.Figure 6 delineates the results of a cluster analysis of form in MSN, June 2003.

The horizontal axis shows the distance between each cluster using the Ward method

(Ward, 1963). This method was found to be most adequate, since it creates a smallnumber of clusters with relatively more homepages. We decided to focus on clusters

whose distance was between 5 and 10 units from each other, entailing six mainclusters. In this way, we could trace the common factors for all homepages within

the same cluster and observe the trends of those six clusters each month.Figure 6 and Table 7 show that the U.S. homepage was located in the biggest

cluster (no. 1) and that MSN Israel was located in a separate cluster (no. 6). This isbecause the form of MSN Israel is significantly different from the form of other localhomepages in MSN. Homepages that have relatively less content and therefore

a simple form, like MSN Austria and Finland, are located together in the same cluster(no. 3).

Figure 7 displays screenshots of four representative local homepages in MSN. Itclearly indicates that MSN U.S. and MSN Canada, which are also located in the same

cluster (no. 1), have very similar page layouts as well as a number of backgroundcolors and photos. By contrast, MSN Israel has a very different design and a greater

number of photos. Finally, MSN Finland has a relatively simple layout and lesscontent.

Table 7 shows how the composition of each form cluster varies over time inMSN. It shows that the biggest cluster contains MSN U.S., together with other

Table 6 New content components—MSN, June 2003

Notes: Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.


Dow


ic.oup.com/jcm


Figure 6 Form cluster analysis—MSN, June 2003


Dow


ic.oup.com/jcm


Table

7MSN

Form

clustersandcommonfactors

overtime

June2003

July2003

August2003

September

2003

October

2003

November

2003

Patterns,Form

ofContent

Patterns,Form

ofContent

Patterns

Patterns,Form

ofContent

Patterns,Colorfulness,

Form

ofContent

Form

ofContent,

Patterns,

U.S.

U.S.

U.S.

U.S.

U.S.

U.S.

Arabia

Arabia

Italy

Belgium

Belgium

Belgium

Belgium

Belgium

Spain

France

France

France

Czech

Re

France

Sweden

Italy

Netherla

United

K

France

Germany

United

KUnited

KNorw

ayBrazil

Italy

Italy

Latin

Am

Brazil

Spain

Canada

Netherlands

Netherlands

China

Canada

Sweden

Latin

Norw

ayNorw

ayJapan

Latin

Am

United

KIndia

Spain

Spain

Malaysia

Japan

Brazil

Japan

United

KSw

eden

Singapore

Korea

Canada

Korea

Brazil

United

KTaiwan

Taiwan

Latin

Am

Singapore

Canada

Brazil

Japan

Taiwan

Latin

Am

Canada

Singapore

India

Latin

Am

Taiwan

Japan

Japan

Korea

India

Malaysia

Korea

Singapore

Malaysia

Taiwan

(con

tin

ued

)


Dow


ic.oup.com/jcm


Table

7C

onti

nu

ed

June2003

July2003

August2003

September

2003

October

2003

November

2003

PoorForm

PoorForm

PoorForm

PoorForm

PoorForm

PoorForm

Austria

Austria

Austria

Austria

Austria

Austria

Finland

Finland

Czech

Re

Czech

Re

Czech

Re

Czech

Re.

Switzerland

Switzerland

Finland

Finland

Finland

Finland

South

Africa

South

Africa

Slovakia

Slovakia

Slovakia

Slovakia

Switzerland

Switzerland

Switzerland

Switzerland

South

Africa

South

Africa

South

Africa

Latin

Am.

China

Malaysia

South

Africa

Patterns,active

Patterns,active

Colorfulness

Patterns

Patterns,Colorfulness,active

Colorfulness,Patterns

Denmark

Czech

Re

Netherlands

Denmark

Mexico

Denmark

Sweden

Denmark

Norw

ayNetherla

China

Italy

Mexico

Mexico

Spain

Netherlands

China

China

Sweden

Norw

ay

Singapor

China

Spain

India

Sweden

Mexico

Colorfulness,active

Active,Patterns

Active,Patterns

Active

Active,Patterns

Active

Germany

HongKong

Arabia

Arabia

Arabia

Arabia

HongKong

Denmark

Germany

Denmark

Germany

Taiwan

Germany

Norw

ayGermany

HongKong

Mexico

Mexico

Italy

(con

tin

ued

)


Dow


ic.oup.com/jcm


Table7

Con

tin

ued

June2003

July2003

August2003

September

2003

October

2003

November

2003

HongKong

HongKong

HongKong

India

Singapore

India

Korea

Korea

Malaysia

Colorfulness,active,

Form

ofContent

Colorfulness,

active,Form

ofContent


Form

ofContent

Colorfulness,

Form

ofContent


Form

ofContent

Colorfulness,active

Israel

Israel

Israel

Israel

Israel

Israel

Form

ofContent

Form

ofContent

Form

ofContent

Form

ofContentForm

ofContent

Form

ofContent

Australia

Australia

Belgium

Australia

ustralia

Australia

New

Zealand

New

Zealand

France

New

Zealand

New

Zealand

New

Zealand

Brazil

Canada

Australia

New

Zeal


Dow


ic.oup.com/jcm


countries that appear consistently over the observation period such as the homepagesof the U.K., Brazil, Canada, Italy, Japan, and France. This is consistent with the

analysis of the third index (NSI). The common factor that makes them belong tothe same cluster is the similarity in the pattern of the form (number of frames and

size of the search box). Table 7 indicates that this cluster becomes smaller overa period of six months (from 19 to 12 homepages), supporting previous findings.The heterogeneity of local homepages in MSN grows, and they become more dif-

ferent from the U.S. homepage over time.Another cluster consists of MSN Austria, the Czech Republic, Finland, Slovakia,

Switzerland, and South Africa. The common factor for this cluster is the limitedcontent and therefore the simple form. This cluster grows over the period of time

(from 4 to 9 homepages). It therefore supports the findings of the second index(DDI), which indicate the growth of an ‘‘alternative block.’’ The cluster analysis

reveals exactly what this ‘‘alternative block’’ is, and what the variables that make itdistinctive are.

Using the same method, a cluster analysis was employed to analyze the content

diversity of homepages based on the content components in MSN. It was found thatsimilar to the form clusters, the biggest content cluster contains MSN U.S., together

with the local homepages of the U.K., Brazil, Canada, Japan, and France. This finding

Figure 7 Screenshots of MSN’s local homepages, July 2003


Dow


ic.oup.com/jcm


is consistent with the analysis of the third index (NSI), in which those countriesranked first as the most similar to MSN U.S. in both content and form. The common

factor that makes them belong to the same cluster is the relatively frequent appearanceof economics and shopping-related content. Similar to the main form cluster, the main

content cluster shrinks over a period of six months (from 14 to 5 homepages), sup-porting previous findings. The heterogeneity in content grows as well, and local home-pages in MSN become more different from the U.S. homepage over time.

Similarly, cluster analysis was used to explore the diversity of form in Yahoo!’slocal homepages over time. It was found that the biggest cluster contains Yahoo!

U.S., together with the homepages of Asia, Brazil, India, Mexico, and Canada. Thecommon factor of this cluster is the similarity in the patterns of the content (number

of links and menus). There is no clear trend for this cluster to become smaller orbigger over time. These findings are consistent with the third index analysis.

Interestingly, the composition of countries in each cluster is consistent over time.One cluster consists of Yahoo! Catalan only, which provides little content andtherefore has a simple form; another cluster consists of the homepages of Scandina-

vian countries: Denmark, Sweden, and Norway, all of which have relatively colorfuland active homepages. Another cluster consists of the homepages of European

countries: France, Germany, and Italy, all of which have a relatively high numberof links in their homepages; another cluster consists of the Asian countries home-

pages: Japan, Hong Kong, and Taiwan, each of which has a relatively small number oflinks in their homepages; and finally there is a cluster for Yahoo! China, which has

a relatively colorful, active, and content-full homepage.The composition of factors was further explored in each content cluster over

time in Yahoo!. It was found that the cluster that contains Yahoo! U.S. has no stablecomposition and therefore no stable common factor. The cluster that contains theU.S. homepage grows over the period of six months (from 2 to 13 homepages). This

supports the results of the first and third indices analyses. The homogeneity incontent increases and the homepages become more similar to the U.S. homepage

over time.

Discussion: Towards A Dictated Heterogeneity?

This article presented new methods, indices, and metrics to explore cultural diversityonline and implemented them to measure the similarities among homepages of MSNand Yahoo! in a large number of countries over time. The tools and methodology

proved useful in measuring content and form proximities of affiliated websites. Sinceprevious comparative studies in the field were limited to a small number of countries

and often used Hofstede’s (1997) framework to analyze content and form, we hopeto encourage further use of these new analytical tools and approaches to explore

online trends of heterogeneity and localization on a large scale.The study posed two main research questions, the first about cultural diversity

online and the second about trends toward homogeneity or heterogeneity over time.


Dow


ic.oup.com/jcm


Despite the relatively short observation period, the findings clearly implied that MSNtended to increase the customization of form and content in its local homepages.

Additionally, it was found that local homepages in MSN tended to differ from theU.S. homepage over time in terms of both content and form.

Conversely, Yahoo!’s analysis identified a trend toward homogeneity of content.The resemblance of the local homepages to the U.S. homepage grew in term ofcontent. However, in terms of form, the cluster analysis indicated the distinctive

differentiation of local homepages by their region: America, Europe, Scandinavia,and the Far East. The relatively high activity and colorfulness of Asian homepages

in both MSN and Yahoo! support previous studies, which found similar trendsin websites of transnational corporations and e-businesses as a reflection of

high-context cultures (Becker, 2002; Cyr, Bonanni, Bowes, & Ilsever, 2005; Wurtz,2005).

In general, the hypothesis that assumed trends toward homogeneity was rejected.MSN, an American brand and popular content provider, displayed heterogeneoushomepages and trends toward localization. These findings may therefore support

similar studies that found cultural heterogeneity and localization of web design andusability (Badre, 2000; Becker, 2002; Cyr & Trevor-Smith, 2004; Lo & Gong, 2005;

Marcus & Gould, 2000; Sheridan, 2001; Singh & Baack, 2004; Singh, Xhao, et al.,2003; Singh, Furrer, et al., 2004; Sun, 2001; Tsui & Paynter, 2004; Wurtz, 2005;

Zhao et al., 2003). Moreover, our study found a growing diversity of content inMSN local homepages, suggesting a new perspective for further investigation in the

field.These trends may be also explained as a result of the business model of MSN that

focuses on direct subscription and production of customized cultural artifacts (VanCouvering, 2004). Heterogeneity and localization trends do not imply less controlby the parent site over the local sites. On the contrary, these may be analyzed as a

stronger leverage to control users’ preferences. Through addressing local content,big portals and content providers strengthen their grip on users, attracting

attention that non-American users might not otherwise pay to more global contentand form.

In contrast to MSN, the relative homogeneity in the content of Yahoo!’s localhomepages and their similarity to the U.S. parent site imply that Yahoo! takes less

account of cultural issues. A few studies (Kang & Corbitt, 2001; Schmied, 2003) maysupport this trend; they propose a certain level of standardization in websites of trans-national (and particularly U.S.-based) corporations. These findings are consistent

with the different business model of Yahoo!, which is based mainly on advertisement(Van Couvering, 2004) rather than on the provision of customized information

products.To conclude, the increasing diversity of local homepages in MSN and the

regional division of form clusters in Yahoo! clearly indicate trends of localizationand cultural diversity in cyberspace and a gradual departure from their U.S. sources.

Consequently, we may expect further customization of local homepages, especially in


Dow


ic.oup.com/jcm


MSN, in the Far East and Latin America, as MSN adds new ethnic cultures to itscustomer base. This is probably why during the observation period MSN launched

another homepage entitled ‘‘Latin.’’ This homepage is culture and Spanish-languageoriented, rather than geography oriented.

It may be argued that the customization process of content and form proceeds inphases, starting with geographical and national distinctions and progressing towardsa focus on ethnographic differences. A reason for that could be that it is technically

easier for a corporation to conduct a geographical customization than an ethno-graphic and more specific one.

Future research into the increasing customization of local homepages, especiallyin popular and influential U.S.-based companies such as MSN and Yahoo!, may

further contribute to the understanding of cultural globalization (Appadurai,1996; Basch, Schiller, & Blanc, 1994; Foster, 1991; Friedman, 1990). In this sense,

our findings support previous studies that highlighted the centrality of customiza-tion and cultural diversity (Anderson, 2006; Caves, 2000; Shapiro & Varian, 1999;Sunstein, 2001). Additionally, our findings suggest that future studies of this diver-

sity should increasingly focus on ethnographic as well as geographical differences.

Acknowledgments

The research for this article was supported by the faculty of management, Tel AvivUniversity. We would like to thank the administration staff, and especially Mrs. Orit

Aviv-Prihed, who helped us to carry out this research. We would like to thankDr. Glenn McGregor and Dr. Yaojun Li of Birmingham University for their contri-

bution to designing the methodology of this research. A special thanks to ProfessorRobert E.F. Smith for his comments on an earlier version of this article.

Note

1 Information search plays a prominent role in websites such as MSN and Yahoo!, and

therefore the size of the search box was considered a notable element of form (see also

Cyr & Trevor-Smith [2004], who compared the search layout of various national

websites). The size of the search box was measured in pixels and ranged between 140px,

which is relatively small (3.7cm), and 620px, which is relatively large (16.3cm).

References

Adorno, T. W. (1991). The Culture Industry: Selected Essays on Mass Culture. London:

Routledge.

Ahituv, N. (2001). The open information society. Communications of the ACM, 44(6), 48–52.

Anderson, C. (2006). The Long Tail. London: Random House Business.

Appadurai, A. (1996). Diversity and disciplinarity as cultural artifacts. In C. Nelson &

D. P. Gaonkar (Eds.), Disciplinarily and Dissent in Cultural Studies (pp. 23–36).

New York: Routledge.


Dow


ic.oup.com/jcm


Badre, A. N. (2000). The effects of cross-cultural interface design orientation on World Wide

Web user performance. GVE Research Technical Reports. Retrieved June 22, 2007 from

ftp://ftp.cc.gatech.edu/pub/gvu/tr/2001/01-03.html

Bagdikian, B. (2004). The New Media Monopoly. Boston, MA: Beacon Press Books.

Barabasi, A. (2001). The physics of the Web. Physics World, 14(7), 33–38.

Barber, W., & Badre A. N. (1998, June).Culturability: The merging of culture and usability. In

Proceedings from the 4th Conference on Human Factors and the Web. Retrieved March 21,

2006 from http://research.microsoft.com/users/marycz/hfweb98/barber/index.htm

Barzilai-Nahon, K. (2006). Gatekeeping, virtual communities, and their gated:

Multidimensional tensions in cyberspace. International Journal of Communications, Law

and Policy, 11, Autumn. Retrieved July 15, 2007 from http://projects.ischool.washington.

edu/karineb/html/pub/IJCLPGatekeeping.pdf

Basch, L. G., Schiller, N. G., & Blanc, C. S. (1994). Nations Unbound: Transnational Projects,

Postcolonial Predicaments, and Deterritorialized Nation-States. Amsterdam: Gordon and

Breach.

Becker, S. A. (2002). An exploratory study on web usability and the internationalization of

U.S. e-business. Journal of Electronic Commerce Research, 3(4), 265–278.

Callahan, E. (2005). Cultural similarities and differences in the design of university websites.

Journal of Computer-Mediated Communication, 11(1), article 12. Retrieved July 15, 2007

from http://jcmc.indiana.edu/vol11/issue1/callahan.html

Castells, M. (1996). The Information Age: Economics, Society and Culture (Vol. 1.): The Rise of

the Network Society. Oxford: Basil Blackwell.

Caves, R. (2000). Creative Industries: Contracts between Art and Commerce. Cambridge:

Harvard University Press.

Cyr, D., Bonanni, C., Bowes, J., & Ilsever, J. (2005). Beyond trust: Website design preferences

across cultures. Journal of Global Information Management, 13(4), 24–52.

Cyr, D., & Trevor-Smith, H. (2004). Localization of web design: An empirical comparison of

German, Japanese, and United States web site characteristics. JASIST, 55(1), 1199–1208.

DiMaggio, P., Hargittai, E., Neuman, W. R., & Robinson, J. P. (2001). Social implications of

the Internet. Annual Review of Sociology, 27, 307–336.

Foster, R. J. (1991). Making national cultures in the global ecumene. Annual Review of

Anthropology, 20, 235–260.

Friedman, J. (1990). Being in the world: Globalization and localization. In M. Featherstone

(Ed.), Global Culture: Nationalism, Globalization and Modernity (pp. 311–328).

London: Sage.

Global Reach. (2004). Global Internet statistics: Sources & references. Retrieved June 22, 2007

from http://global-reach.biz/globstats/refs.php3

Hall, E. T., & Hall, M. R. (1990). Understanding Cultural Differences. Yarmouth, ME:

Intercultural Press Inc.

Hofstede, G. (1997). Cultures and Organizations: Software of the Mind. New York:

McGraw-Hill.

Kang, K. S., & Corbitt, B. (2001). Effectiveness of graphical components in web site

e-commerce application: A cultural perspective. The Electronic Journal Systems in

Developing Countries, 7(2), 1-6. Retrieved September 22, 2006 from http://

www.ejisdc.org/ojs/include/getdoc.php?id=35&article =35&mode=pdf

Kellerman, A. (2002). The Internet on Earth: A Geography of Information. London: Wiley.


Dow


ic.oup.com/jcm


Lash, S. (2002). Critique of Information. London: Sage.

Leonardi, P. M. (2002). Cultural variability in web interface design: Communicating U.S.

Hispanic cultural values on the Internet. In F. Sudweeks & C. Ess (Eds.), Proceedings of

Cultural Attitudes towards Communications and Technology (pp. 297–315). Murdoch,

Australia: Murdoch University.

Lo, B. W. N., & Gong, P. (2005). Cultural impact on the design of e-commerce websites:

Part I: Site format and layout. Issues in Information Systems, VI(2), 182–189. Retrieved

March 7, 2006 from http://www.jcis-online.org/iis/2005_IIS/PDFs/Lo_Gong.pdf

Marcus, A., & Gould, E.W. (2000, June). Cultural dimensions and global web user-interface

design: What? So what? Now what? In Proceedings from the 6th Conference on Human

Factors and the Web. Retrieved March 5, 2006 from http://www.amanda.com/resources/

hfweb2000/AMA_CultDim.pdf

Mattelart, A. (1991). Advertising International: The Privatization of Public Space. London:

Routledge.

Neuman, W. R., McKnight, L. W., & Solomon, R. J. (1998). The Gordian Knot: Political

Gridlock on the Information Highway. Cambridge: MIT Press.

Nielsen/NetRatings. (2004). Audience measurement service. Retrieved March 17, 2004 from

http://www.nielsen-netratings.com/press.jsp?section=pr_netv&nav=3

Norris, P. (2004). The bridging and bonding role of online communities. In P. N. Howard &

S. Jones (Eds.), Society Online: The Internet in Context (pp. 31–43). Thousand Oaks,

CA: Sage Publications.

Robbins, S. S., & Stylianou, A. C. (2003). Global corporate websites: An empirical

investigation of content and design. Information and Management, 40(3), 205–212.

Schmied, J. (2003). Cultural discourse in Internet business. In E. H. Oleksy &

B. Lewandowska-Tomaszczyk (Eds.), Research and Scholarship in Integration Processes:

Poland-USA-EU (pp. 273–283). Lodz: Lodz U.P.

Shapiro, C., & Varian, H. (1999). Information Rules: A Strategic Guide to the Network

Economics. Boston: Harvard Business School Press.

Sheridan, E. F. (2001). Cross-cultural web design: Considerations for developing and

strategies for validating locale appropriate on-line content. Multilingual Computing &

Technology, 12(7), 53–56.

Shils, E. (1963). The theory of mass society. In P. O. Glencoe (Ed.), America as a Mass Society

(pp. 30–50). Glencoe, IL: Free Press.

Singh, N., & Baack D. W. (2004). Web site adaptation: A cross-cultural comparison of U.S.

and Mexican web sites. Journal of Computer-Mediated Communication, 9(4). Retrieved

September 20, 2006 from http://jcmc.indiana.edu/vol9/issue4/singh_baack.html

Singh, N., Furrer, O., & Ostinelli, M. (2004). To localize or standardize on the Web: Empirical

evidence from Italy, India, Netherlands, Spain, and Switzerland. Multinational Business

Review, 12(1), 69–87.

Singh, N., Xhao, H., & Hu, X. (2003). Cultural adaptation on the Web: A study of American

companies’ domestic and Chinese websites. Journal of Global Information Management,

11(3), 63–80.

Sun, H. (2001). Building a culturally-competent corporate web site: An explanatory study of

cultural markers in multilingual web design. In Proceedings from SIGDOC 2001

(pp. 95–102). Retrieved June 22, 2007 from http://delivery.acm.org/10.1145/510000/


Dow


ic.oup.com/jcm


501536/p95-sun.pdf?key1=501536&key2=2727352811&coll=GUIDE&dl=GUIDE&

CFID=22084028&CFTOKEN=51919415

Sunstein, C. (2001). Republic.com. Princeton: Princeton University Press.

Tsui, T. W. C., & Paynter, J. (2004). Cultural usability in the globalisation of news portal.

Lecture Notes in Computer Science, 3101, 501–510.

Van Couvering, E. (2004, July). New media? The political economics of Internet search

engines. In Proceedings from the Annual Conference of the International Association of

Media & Communications Researchers, Porto Alegre, Brazil.

Ward, J. H., Jr. (1963). Hierarchical grouping to optimize an objective function. Journal of the

American Statistical Association, 58(301), 236–244.

Waxman, J. (2000). The Old 80/20 Rule Takes One on the Jaw. Internet Trend Report 1999,

Review. San Francisco: Alexa Research.

Wurtz, E. (2005). A cross-cultural analysis of websites from high-context cultures and

low-context cultures. Journal of Computer-Mediated Communication, 11(1), article 13.

Retrieved May 15, 2006 from http://jcmc.indiana.edu/vol11/issue1/wuertz.html

Zhao, W., Massey, B. L., Murphy, J., & Liu, F. (2003). Cultural dimensions of website design

and content. Prometheus, 21(1), 75–84. Retrieved March 7, 2006 from http://

web.biz.uwa.edu.au/staff/jmurphy/Cultural_Dimensions.pdf

Appendix A

Table 8 Full list of local homepages in MSN and Yahoo!

MSN Yahoo!

U.S. Sweden U.S. Italy

Canada (English) Italy U.K. & Ireland Korea

Japan Malaysia Australia & NZ Canada

Brazil Korea France Spain

Israel Taiwan Brazil Catalan

Latin America India Germany China

France Mexico Argentina

Belgium (French) Hong Kong S.A.R. Norway

Germany Denmark Hong Kong

Spain Czech Republic Denmark

United Kingdom South Africa Taiwan

Singapore Switzerland (German) Mexico

Australia Austria Sweden

New Zealand Finland Asia

Norway Slovakia Japan

Arabia China Singapore

Netherlands Sweden India


Dow


ic.oup.com/jcm


Appendix B

Appendix C

Table 9 Full list of content and form elements

Content Form

News Local Sport Frames

Main (headline) News Sport Banner Banners

Local News Keyword Sport* Active

News Banner Shopping Banners

Keyword News* Main (headline) Shopping Background Colors

Economics Shopping Banner Photos

Main (headline) Economics Keyword Shopping* Main Links

Local Economics Technology Links

Economics Banner Main (headline) Technology Search Box Size

Keyword Economics* Technology Banner Menus

Entertainment Keyword Technology*

Main (headline) Entertainment General Information

Local Entertainment Main General Info.

Entertainment Banner General Info. Banner

Keyword Entertainment* Keyword General Info.*

Sport Study English

Main (headline) Sport Keyword Study English*

Note: *Within the top popular search queries

Table 10 Top 10 parent companies worldwide (Home Panel February 2004)

Parent Name UniqueAudience (000) Reach % Time Per Person

United States

Microsoft 93,672 66.25 01:32:48

Time Warner 82,530 58.37 03:59:38

Yahoo! 79,603 56.30 01:57:25

France

Microsoft 9,190 66.68 02:09:19

Wanadoo 8,624 62.58 00:50:20

Google 6,591 47.82 00:23:13

Iliad - Free 5,995 43.50 00:27:30

Time Warner 4,635 33.63 02:50:51

Yahoo! 4,333 31.44 00:42:34

Hong Kong

Yahoo! 2,068 76.61 01:41:05

(continued)


Dow


ic.oup.com/jcm


About the Authors

Elad Segev is a doctoral candidate at Keele University, a lecturer of Media andCommunications at Aston University, and a former research student at Tel Aviv

University in the Faculty of Management. His research deals with technology ingeneral and the Internet in particular, and its social, political, and cultural

implications.Webpage: http://www.eladsegev.comAddress: School of Politics, International Relations and Philosophy (SPIRE), Keele

University, Keele, Staffordshire ST5 5BG, UK

Table 10 Continued

Parent Name UniqueAudience (000) Reach % Time Per Person

Microsoft 1,964 72.75 01:34:02

Spain

Microsoft 6,460 77.54 04:30:54

Google 5,231 62.79 00:27:14

Terra Lycos 3,161 37.94 00:23:26

Wanadoo 3,142 37.71 00:23:09

Yahoo! 2,935 35.23 00:38:01

Sweden

Microsoft 3,423 74.27 01:50:05

Aftonbladet Hierta 1,827 39.64 00:48:11

Eniro 1,553 33.70 00:15:42

TeliaSonera 1,400 30.37 00:20:14

Bonnierforlagen 1,315 28.54 00:24:02

Google 1,309 28.41 00:15:38

Forenings 1,213 26.32 00:47:08

Sparbanken

Yahoo! 1,093 23.71 00:12:19

United Kingdom

Microsoft 14,652 69.92 02:05:05

Google 9,762 46.59 00:18:31

Yahoo! 7,899 37.70 01:01:50

Switzerland

Microsoft 2,163 71.90 01:24:03

Google 1,630 54.18 00:20:37

Bluewin 1,501 49.87 00:39:06

Yahoo! 794 26.37 00:49:27

Source: Nielsen//NetRatings, retrieved May 13, 2004 (http://www.nielsen-netratings.com).

Note: The data about the popularity of MSN and Yahoo! were retrieved near the observation

period. For more recent statistics, see http://www.nielsen-netratings.com.


Dow


ic.oup.com/jcm


Niv Ahituv is the Marko and Lucie Chaoul Chair for Research in InformationEvaluation and the Academic Director of the Institute of Internet Studies of Tel

Aviv University. His research deals with information economics, information systemstrategy and management, and social and business implications of the Internet.

Webpage: http://recanati.tau.ac.il/Index.asp?ArticleID=76&CategoryID=164Address: Faculty of Management, Tel Aviv University, Tel Aviv, 69978, Israel

Karine Barzilai-Nahon is Assistant Professor in the Information School at the Uni-versity of Washington. Formerly she held senior positions in Research and Devel-

opment in hi-tech industry. Her research deals with social and business aspects of theInternet and telecommunications.Webpage: http://projects.ischool.washington.edu/karineb/

Address: The Information School, University of Washington, Mary Gates Hall,Seattle, WA 98195, USA


Dow


ic.oup.com/jcm


mapping diversities and tracing trends of cultural

Documents