A Critical Analysis of Current Indexes for Digital Divide Measurement

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A Critical Analysis of Current Indexes for Digital Divide MeasurementGiuseppe Brunoa; Emilio Espositoa; Andrea Genovesea; Kholekile L. Gwebub

a Department of Business and Management Engineering, University of Naples “Federico II”, Naples,Italy b Whittemore School of Business and Economics, University of New Hampshire, Durham, NewHampshire, USA

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The Information Society, 27: 16–28, 2011Copyright c© Taylor & Francis Group, LLCISSN: 0197-2243 print / 1087-6537 onlineDOI: 10.1080/01972243.2010.534364

A Critical Analysis of Current Indexes for DigitalDivide Measurement

Giuseppe Bruno, Emilio Esposito, and Andrea GenoveseDepartment of Business and Management Engineering, University of Naples “Federico II,”Naples, Italy

Kholekile L. GwebuWhittemore School of Business and Economics, University of New Hampshire, Durham,New Hampshire, USA

Several composite indexes grouping several variables into a sin-gle figure have been proposed for measuring the digital divide. Inthis article, the authors analyze shortcomings of extant indexes.Using multivariate analysis tools, they show that in the definitionof composite indexes it is possible to individuate a limited set ofmore influencing and significant variables. This finding suggeststhe possibility of replacing some redundant variables with otherdimensions that include other aspects of the digital divide that arenot considered in extant measures.

Keywords composite indexes, digital divide, principal componentanalysis

The rapid development of information and communi-cation technologies (ICTs) and their pervasive role in eco-nomic activities can be considered one of the fundamentalfactors fueling growth and transforming the way businessis conducted in many countries (Mansell and Wehn 1998;Mansell 1999; Cette et al. 2000; Jorgenson 2001; Rao2001; Colecchia and Schreyer 2002). However, there isa striking and alarming disparity in ICTs access and us-age among economies. The term “digital divide” has beencoined to describe this phenomenon and to draw atten-tion to the existing gap in ICT access and in the abilityof individuals and economies to participate in the global

Received 15 September 2008; accepted 10 August 2010.Address correspondence to Andrea Genovese, University of

Naples “Federico II,” Business and Management Engineering De-partment, Piazzale Tecchio 80, 80125 Napoli (NA), Italy. E-mail:[email protected]

information society (readers interested in the origins ofthe term “digital divide” are encouraged to read Gunkel2003). In this context, different questions have surfacedabout the dynamic evolution of the digital divide and theidentification of its main determining factors. We focus onthe emerging need for performance evaluation and bench-marking tools to assess the magnitude and the evolutionof digital divide.

Institutions and organizations such as the InternationalTelecommunication Union (ITU) have proposed variouscomposite indexes that aggregate several indicators intoa single number to capture the complexity of the digitaldivide (ITU 2009). The first significant index released bythe ITU was the “Digital Access Index” (DAI), which wasdeveloped to measure the overall ability of individuals ina country to access and use ICT (ITU 2003). At about thesame time, a network of hundreds of communication lead-ers from academia, the media, business, and governmentcircles (Orbicom) developed a conceptual framework fordigital divide measurement that formed the basis of the“Infostate Index” (Orbicom 2003). In 2005 ITU and Or-bicom decided to merge the DAI and the Infostate Index tocreate the “ICT Opportunity Index” (ICT-OI) (ITU 2005)to avoid a duplication of indexes with similar characteris-tics. In the same year the ITU also proposed the “DigitalOpportunity Index” (DOI), later updated in 2007, to mea-sure the potential opportunities of countries to benefit fromaccess to ICTs (ITU 2007). Although the two indexes (DOIand ICT-OI) differ in terms of indicators and the method-ology used, an effort has been made to develop a singleindex because statistical analysis has demonstrated thatthe two indexes were closely correlated (Abuqayyas andAudin 2008). The need for a single index has also beenaffirmed during various international summits on ICTs

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DIGITAL DIVIDE INDEXES 17

development (ITU 2003, 2005, 2009). Accordingly, ITUrecently released a new index, the “ICT Development In-dex” (IDI), which represents a synthesis of the previousITU indexes (2009).

The development of an assortment of indexes in sucha short period suggests that the digital divide index isstill a work in progress. Moreover, it highlights the dif-ficulty in synthesizing a composite index for a complexconcept that relies on aggregation of indicators with dif-ferent characteristics. From a methodological viewpoint,the indexes are calculated by a linear or nonlinear com-bination of the indicators using a set of weights. Even ifcomposite indexes are widely exploited as useful tools inpolicy analysis, caution should be exercised in their designand implementation to avoid possible misrepresentation ormisinterpretation, as highlighted in Organization for Eco-nomic Cooperation and Development (OECD; 2008). Forthis reason, a debate has arisen about the reliability of thedigital divide indexes.

In this article, we discuss digital divide measurementthrough an assessment of two existing indexes (ICT-OI andIDI), based on principal component analysis, and high-light their shortcomings. The analysis focuses on thesetwo indexes because they are likely to be used exten-sively in years to come by a wide array of stakeholders—policymakers, academics, and the public—to measure dig-ital divide. Here are some important points to consider:� The IDI is the most recent index and represents

the evolution and the synthesis of the previousindexes suggested by the ITU.

� The ICT-OI is the result of the merging of twoindexes, the DAI proposed by the ITU and theInfostate developed by the Orbicom.

� The ICT-OI is also representative of the DOIsince the two indexes are strongly correlated(Abuqayyas and Audin 2008).

� The ICT-OI (employing the geometric average)and the IDI (utilizing a weighted sum) are repre-sentative of two different aggregation methodolo-gies.

This analysis is relevant since an index, whose aim is tomeasure digital divide among different countries, shouldbe an operative tool for international institutions and localgovernments. From this standpoint, a good index shouldbe both efficient and effective (Jollands et al. 2004).

Efficiency refers to the ability to represent the phe-nomenon without using redundant variables, which canconfuse policymakers. Effectiveness refers to the abilityto interpret the different ways in which the phenomenonoccurs and thereby provide policymakers with an opera-tive and concrete opportunity to intervene. The trade-offbetween the need to reduce the number of used variablesand the necessity to include variables representative of

the phenomenon as a whole is the focus of this analysis.In particular, this analysis seeks to verify the potentialpresence of a limited set of more influencing and signif-icant indicators. If such indicators are indeed present, itcould open the possibility of replacing some redundantindicators with other dimensions that allow inclusion ofother aspects of the digital divide, not considered in extantmeasures.

The remainder of the article is organized as follows.The next section provides a brief overview on the defi-nitions proposed for the digital divide. Subsequently, themethodologies used to build the proposed indexes are de-scribed with particular reference to the ICT-OI and the IDI,along with the main comments and criticisms in the liter-ature. Thereafter the methodology used for the analysis isillustrated and the results are presented. Finally, findingsare discussed and possible avenues for future research areoutlined.

THE DIGITAL DIVIDE CONCEPT ANDDEFINITIONS

The digital divide is a complex, dynamic, multifacetedconcept. Consequently, different streams of research of-ten define it in different ways. For example, in one streamof research, which focuses on the crucial role of techno-logical resources, Mehra et al. (2004) simply define digitaldivide as the gap between individuals who use comput-ers and the Internet and individuals who do not. Otherauthors in this stream suggest that it refers to the distinc-tion between the information haves and have-nots (Dewanand Riggins 2005; Ida and Horiguchi 2008; Belanger andCarter 2009). Along similar lines, Dewan and Riggins(2005) suggest that the digital divide refers to the divisionamong persons who have access to digital ICTs and thosewho do not. Moreover, they consider three different levelsof digital divide: individual, organizational, and global.Campbell (2001) and James (2008) describe the digitaldivide as the division between those who have access toICT and are using it effectively, and those who do not.

A second stream of research highlights the determi-nants of the digital divide, emphasizing that both techno-logical and nontechnological factors are important. Norris(2001) believes that the digital divide should be viewedas consisting of three distinct dimensions—global divide,social divide, and democratic divide. The global divideis the difference in Internet access among industrializedand nonindustrialized nations; the social divide is the gapbetween those with and those without information accesswithin a country; and the democratic divide representsthe gap between individuals who do and individuals whodo not utilize digital resources to engage and mobilizein the public life. Other authors also highlight that fac-tors such as the availability of digital resources (relevant


18 G. BRUNO ET AL.

content in diverse languages), human resources (literacyand education), and social resources (institutional struc-tures) contribute to the digital divide (Warschauer 2003;Bertot 2003; Cuervo and Menendez 2006). Mossbergeret al. (2003) see the digital divide as the disparity in in-formation technology due to various factors such as race,ethnicity, gender, and income. Chinn and Fairlie (2006),through a statistical analysis based on a sample encom-passing 161 developed and developing countries, highlightthe importance of per capita income, of the telecommu-nications infrastructure, and of the quality of regulationin explaining the gap in computer and Internet use. Theyalso affirm that education has a low impact on Internet ac-cess. Using data on 118 countries during the 1997–2001period, Guillen and Suarez (2005) argue that the globaldigital divide is the result of the economic, regulatory, andsociopolitical characteristics of countries and their evolu-tion over time.

Examining a panel of 40 countries from 1985–2001,based on data from three distinct generations of informa-tion technology (IT; mainframe, personal computer, andInternet), Dewan et al. (2005) find that information tech-nology (IT) penetration is positively associated with percapita income, years of schooling, and size of the tradesector, while it is negatively related to telecommunica-tion costs and size of urban population. In fact, accordingto Van Dijk (2006, 223), the urgent question is: “Whatis exactly new about the inequality of access to and useof Information and Communication Technology as com-pared to other scarce material and immaterial resourcesin society?” Finally, Fuchs (2008) criticizes approachesthat attempt to explain the digital divide primarily basedon economic factors. Accordingly, he suggests that otheraspects should be taken into account, such as motivationalaccess, skills access, usage access, the degree to whichusers benefit from usage, and the degree to which tech-nologies enable political participation.

A third stream of research tries to deal with the criti-cisms that have emerged from the second research streamand suggests more comprehensive definitions. In line withrequests for a sophisticated depiction of the phenomenon,ITU and Orbicom propose a more comprehensive def-inition that sees the digital divide as “the relative dif-ference in Infostate among economies” (Sciadas 2005)based on the concept of Infostate, an aggregation of info-density (including ICTs infrastructure and skills) and info-use (including ICTs uptake and intensity of use). Fuchs(2009, 46) proposes the following comprehensive defini-tion, which strives to address the already-mentioned crit-icisms, stating that the digital divide represents “unequalpatterns of material access to, usage capabilities of, andbenefits from computer-based information and communi-cation technologies that are caused by certain stratificationprocesses that produce classes of winner and losers of the

information society, and of participation in institutionsgoverning ICTs and society.”

The preceding discussion highlights two main points:� There exists a heated ongoing debate among prac-

titioners and academic circles about the definitionof the digital divide.

� The digital divide is an evolving concept, whichis shifting from a preoccupation with mere accessto technological resources to a multidimensionalunderstanding of inequality (e.g. global, social,and democratic divides).

The liveliness of the discussion has also influenced themeasurement methodologies, as they reflect the variousconceptual frameworks proposed for underlying the digi-tal divide concept. The body of literature on theoretical andempirical studies in this area is significant (e.g., Corrocherand Ordanini 2002; Sciadas 2002; Cuervo and Menendez2006; ITU 2005, 2006, 2007). In any case, even if the par-ticipation in the debate on the definition of digital dividedefinition is beyond the scope of this article, the variety ofpositions seems to confirm, as noted by Fuchs (2008), thatits measurement it cannot be limited to economic factorsbut should be more comprehensive in scope.

DIGITAL DIVIDE MEASUREMENT

Comparing country performances to identify evolutionarytrends and establish benchmarks is a common practice ina wide range of fields (e.g. environment, economics, andtechnological development). Such comparisons are oftenperformed by introducing composite indexes calculatedthrough the aggregation of individual indicators, repro-ducing quantitative or qualitative measures of factors withthe aim of representing the relative position of a countryon a conceptual space (OECD 2008). Indicators can begrouped into categories in accordance with their meaning.The aggregation of indicators and/or categories is per-formed according to an underlying mathematical model.Hence composite indexes mainly differ on the basis ofthe selected indicators and the aggregation methodology.The proposed digital divide indexes are characterized bydifferent numbers and types of indicators and methodolo-gies. The choice of the indicators to be considered dependson the conceptual framework assumed to describe and torepresent the process of ICTs penetration and diffusionamong countries.

The first index considered here, the DAI (“Digital Ac-cess Index”), uses eight indicators, which are grouped intofive categories (infrastructure, affordability, knowledge,quality, and actual usage of ICTs). Indicator values arenormalized with reference to upper values limits and thenaveraged to obtain category scores, and, finally, averagedto calculate the overall index value. The next index, the



TABLE 1Characteristics of the released indexes for the digital divide measurement

Index Year Institution No. categories No. indicators Methodology

DAI 2003 ITU 5 8 Average of category scores which are averages of therespective indicators

Infostate 2003 Orbicom 2 21 Geometrical mean of category scores which are geometricalmeans of the respective indicators

DOI 2005 ITU 3 11 As for the DAIICT-OI 2005 ITU-Orbicom 2 10 As for the InfostateIDI 2009 ITU 3 11 As for the DAI

Notes. DAI = Digital Access Index; DOI = Digital Opportunity Index; ICT-OI = Information and Communication Technologies–OpportunityIndex; IDI = Information and Communication Technologies Development Index; ITU = International Telecommunication Union.

Orbicom Infostate index, introduces two categories (info-density and info-use) with a total of twenty-one indicators.Although many indicators are aggregated, others are pre-liminarily combined to form intermediate-level compositeindicators. After the normalization, the aggregation is per-formed through the geometric mean (for a more detailedexplanation, see Sciadas 2005).

The DOI (“digital opportunity index”) includes elevenindicators grouped into three categories (opportunity, in-frastructure, use). The category scores are calculated asarithmetic averages of the respective indicators, while theDOI is the arithmetic average of the three category scores.The ICT-OI (“ICT opportunity index”) assumes the sameframework, main categories (info-density and info-use),and mathematical model used in the definition of the In-fostate. The total number of adopted indicators is reducedto ten, with many overlapping with the ones introducedfor the definition of the DAI.

The last and most recently developed index, the IDI(“ICT development index”), considers eleven indicatorsorganized into three categories (access, use, and skills)and the calculation is similar to the one of the DOI. Table 1summarizes the characteristics of the described indexes.

In the following subsections, a more detailed descrip-tion of recently proposed indexes (ICT-OI and IDI) isprovided.

The ICT-OI

The ICT-OI was designed to monitor the global digitaldivide and to track a country’s progress over time incomparison to other countries of similar income levels.Based on the Orbicom Infostate conceptual framework,the ICT-OI includes two categories—info-density (withsubcategories networks and skills) and info-use (with sub-categories intensity and uptake). This combined frame-work brings together a total of ten indicators. Table 2

shows the ICT-OI indicators and their groupings into cat-egories and the aggregation schema used to calculate theindex.

As mentioned earlier, the ICT-OI adopts most of theindicators from the DAI, reducing them from seventeen to

TABLE 2Indicators used for the calculation of Information and

Communication Technologies–Opportunity Index (ICT-OI)

IndicatorCategory Subcategory symbol Indicator

Info-density Networks DN1 Main telephone lines per100 inhabitants

DN2 Mobile cellular subscribersper 100 inhabitants

DN3 International internetbandwidth(kbps/inhabitant)

Skills DS1 Adult literacy rates (Source:UNESCO)

DS2 Gross enrolment rates(Source: UNESCO)

Info-use Uptake IU1 Internet users per 100inhabitants

IU2 Proportion of householdswith a TV

IU3 Computers per 100inhabitants

Intensity II1 Total broadband internetsubscribers per 100inhabitants

II2 International outgoingtelephone traffic(minutes) per capita


20 G. BRUNO ET AL.

ten. In this way, the ICT-OI can be calculated for a largernumber of countries (183) compared to its predecessor,the Infostate Index (139 countries).

The ICT-OI for a given country i(ICT-OIi) is computedas follows:

ICT-OIi =√Inf oDensityi

∗Inf oUsei

in which:

InfoDensityi =√

Networksi∗Skillsi

InfoUsei =√

Intensityi∗Uptakei

Networksi = 3√

DN1i∗DN2i

∗DN3

Intensityi =√

II1i∗II2i

Skillsi =√

DS1i∗DS2i

Uptakei = 3√

IU1i∗IU2i

∗IU3i

In practice, each category score is calculated as thegeometric average of the respective indicators.

The IDI

For several years, the ITU has been publishing two in-dexes to track digital divide (the ICT-OI and DOI) amongcountries. In 2007 it started considering the possibility ofmerging the two indexes and creating a single index. Theprocess ended in 2009 with the publication of the IDI.The IDI includes three categories (Access, Use, Skills)and eleven indicators according to the schema illustratedin table 3. The table also shows the indicators and their

groupings into categories and the weights assigned to eachindicator and category.

The IDI for a given country i(IDIi ) is computed asfollows:

IDIi = 0.4∗(ICT Access)i + 0.4∗(ICT Use)i

+ 0.2∗(ICT Skills)i

in which:

(ICT Access)i = 0.2∗(ICT A1i + ICT A2i

+ ICT A3i + ICT A4i + ICT A5i)

(ICT Use)i = 0.33∗(ICT U1i + ICT U2i

+ ICT U3i)

(ICT Skills)i = 0.33∗(ICT S1i + ICT S2i

+ ICT S3i)

Each category score is calculated as the arithmetic av-erage of the respective indicators. Then the three categoryscores are aggregated utilizing a weighted sum method.

Shortcomings of the Extant Indexes

Using synthetic composite indexes to represent a complexphenomenon provides, in general, advantages and limi-tations. Saisana and Tarantola (2002) propose a list ofpros and cons summarized in table 4, also cited in OECD(2008).

As previously mentioned, shortcomings of compositeindexes have been highlighted in the literature. One of themost severe critiques is by Van Dijk (2006), who argues

TABLE 3Indicators used for the calculation of Information and Communication Technologies Development Index (IDI)

Category Category weight Indicator symbol Indicator Indicator weight

ICT access 0.40 ICTA1 Fixed telephone lines per 100 inhabitants 0.20ICTA2 Mobile cellular telephone subscriptions per 100 inhabitants 0.20ICTA3 International Internet bandwidth (bit/s) per Internet user 0.20ICTA4 Proportion of households with a computer 0.20ICTA5 Proportion of households with Internet access at home 0.20

ICT use 0.40 ICTU1 Internet users per 100 inhabitants 0.33ICTU2 Fixed broadband Internet subscribers per 100 inhabitants 0.33ICTU3 Mobile broadband subscribers per 100 inhabitants 0.33

ICT skills 0.20 ICTS1 Adult literacy rate 0.33ICTS2 Secondary gross enrolment ratio 0.33ICTS3 Tertiary gross enrolment ratio 0.33

Note. ICT = information and communication techonology



TABLE 4Pros and cons in using composite indexes (OECD, 2008)

Pro Cons

Can summarize complex,multidimensionalphenomena with a view tosupporting decision makers.

May send misleading policymessages if poorlyconstructed ormisinterpreted.

Are easier to interpret than abattery of many separateindicators

Can assess progress ofcountries over time.

Reduce the visible size of a setof indicators withoutdropping the underlyinginformation base.

Thus make it possible toinclude more informationwithin the existing size limit.

Place issues of countryperformance and progress atthe centre of the policyarena.

Facilitate communication withgeneral public (i.e., citizens,media) and promoteaccountability.

Help to construct or underpinnarratives for lay and literateaudiences

Enable users to comparecomplex dimensionseffectively.

May invite simplistic policyconclusions.

May be misused, for example,to support a desired policy,if the construction processis not transparent or lackssound statistical orconceptual principles.

The selection of indicatorsand weights could be thesubject of political dispute.

May disguise serious failingsin some dimensions andincrease the difficulty ofidentifying proper remedialaction, if the constructionprocess is not transparent.

May lead to inappropriatepolicies if dimensions ofperformance that aredifficult to measure areignored.

that attempts to keep track of and measure the digital di-vide suffer from a lack of adequate theoretical frameworks.Indeed, most of the proposed indexes emphasize income,education, age, sex, and ethnicity, while not fully address-ing the deeper social, cultural, and psychological causesbehind access inequalities. Additionally, there tends to bea lack of conceptual elaboration and definition of the indi-cators used in composite indexes (e.g., computer literacy,Internet use). Statements supported by theory and validoperational definitions for empirical research could con-siderably help in building reliable indexes. Also, Fuchs(2009) criticizes the choice of the indicators in the currentindexes as they reduce the role of socioeconomic, political,cultural, and social factors.

From a methodological perspective, Braithwaite’s(2007) critique is that existence and use of too manyindicators makes data collection difficult, especially for

developing countries. It results in missing data, which ul-timately has an adverse effect on the rankings producedby composite indexes. Indexes including fewer indica-tors will help mitigate such problems. Menou and Taylor(2006) point out that bias is introduced by data standard-ization and normalization operations. They also note thatnational average measures are not very meaningful un-less they can be disaggregated at a more detailed level.Barzilai-Nahon (2006) criticizes indexes for only measur-ing the digital divide at the national and international levelwhile ignoring community level inequalities.

Barzilai-Nahon (2006) and James (2007) point out thatthe choice of the aggregation methodology of individualindicators is responsible for significant biases. This aspectis crucial in the definition of weights when linear and/orgeometric aggregations are performed (Munda and Nardo2005). In particular, since indicators are often weightedaccording to equal values, statistical or empirical evidenceexplaining that all indicators are “worth” the same shouldbe provided (OECD 2008). Thus, Vehovar et al. (2006)suggest the use of statistical tools (such as multivariateanalysis) to understand the interactions among indicatorsto avoid data redundancies.

METHODOLOGY

A composite index is intended to describe how an observedphenomenon (e.g., the digital divide) is simultaneously de-termined by several factors (indicators). In the definition ofsuch an index, indicators should be selected while consid-ering the interrelationships (correlations) among them, toavoid the presence of some overweighted factors (Saisanaet al. 2005). This way it could be possible to obtain almostthe same results using a reduced number of indicators thatbetter represent the influence on the phenomenon. Also,the OECD (2008) suggests that minimizing the numberof indicators in an index may be desirable for promotingtransparency and parsimony.

With regard to the most recently released digital di-vide composite indexes, we propose a methodology forselecting the most significant indicators and minimizingredundancies based on appropriate statistical tools. LetIdx be an index calculated through the aggregation of aset I of n indicators (Idx = f (I1, . . . , In)). We calculateand analyze the correlation among each pair of indicatorson a reliable data set of adequate size. In the presenceof significant correlations, a principal component analysisis performed. The steps entailed in the methodology aredescribed next.

The Principal Component Analysis

Within a data set, the principal component analysis method(Kim and Mueller 1978a, 1978b; Stevens 1986) detects a


22 G. BRUNO ET AL.

set of variables (principal components) able to signifi-cantly represent the phenomenon. In practice consideringthe set I of n indicators and a data set, it is possible todefine n principal components Ck(k = 1, . . . , n) as:

Ck = bk1I1 + · · · + bkj Ij + · · · + bknIn

for which the generic bkj is a weight associated with theindicator j of the component k and is “optimally” calcu-lated through appropriate algorithms. The principal com-ponents are then ranked in descending order according totheir significance in the explanation of the phenomenon(in terms of variance within the data set). Methodologiesfor the selection of principal components are described inJoliffe (2002) and OECD (2008). In this context we se-lect the first p < n components such that the cumulativepercentage of the total variance is at least equal to a giventhreshold.

Indicators Selection

The performed principal components analysis is used toreduce the number of indicators. However, principal com-ponents do not have physical meaning, as they representsome sort of artificial variables. For this reason, we cor-relate each indicator and each of the p selected principalcomponents. Then we individuate the indicators with thehighest values of correlation for each principal compo-nent. This way we select a subset I′ = ik: k = 1, . . . , p ofp indicators that are more strongly correlated with each ofthe principal components. By doing this, a good approx-imation of the principal components is obtained throughthe use of some of the original indicators.

Verification Process

On the basis of the obtained results, we can define a “re-duced index” Idxreduced = f (i1, . . . ,ip) in which the set Iof n original indicators has been replaced by the reducedset I′ of p < n indicators. To verify whether the reducedindex is a good synthesis of the original index we calculatethe correlation index between Idxreduced and the originalIdx. In the presence of a significant value of correlation,indeed, we can affirm that the reduction of indicators doesnot produce a significant loss in the representation of thephenomenon.

EMPIRICAL ANALYSIS

The methodology described in the previous section wasemployed using 2005 data for the ICT-OI and 2007 datafor the IDI, which are the most recent available data setsfor the two indexes (ITU 2009). The data sets consist of

TABLE 5AComposition of the Information and Communication

Technologies–Opportunity Index (ICT-OI) sample basedon the Human Development Index (HDI)

High Medium LowVariable HDI HDI HDI Total

Countries in the data set 61 69 19 149Percentage (data set) 40.94 46.31 12.75 100.00Percentage (all countries) 41.42 45.56 13.02 100.00

observations from 149 countries for the ICT-OI indicatorsand from 154 countries for the IDI indicators.

To show the general characteristics of the data set,tables 5A and 5B indicate the distribution of the avail-able countries on the basis of the Human Development In-dex provided by the United Nation Development Program(UNDP 2003). Following Efron and Tibshirani (1993),countries are classified as high, medium, and low withrespect to the Human Development Index. The tables re-veal that the distribution of the data set is similar to thedistribution of the total number of countries. Thus, we canaffirm that the sample is not affected by biases due to anonhomogeneous selection of the countries.

In tables 6A and 6B the correlation matrix for the ICT-OI and IDI is reported. These matrices reveal that there issignificant correlation among the indicators. In particular,for the ICT-OI, the indicator “Main telephone lines per100 inhabitants” has a correlation coefficient greater than0.70 with five other indicators. Indicators “mobile cellu-lar subscribers per 100 inhabitants,” “computers per 100inhabitants,” and “total broadband subscribers per 100 in-habitants” show the same high correlation levels with theother components. The degree of correlation among theskills indicators (“literacy rate,” “enrollment rate”) andall the others indicators is quite low (0.11 < rij < 0.29).This result reveals that the relationship between educa-tion levels and technological opportunities is weak. Fur-thermore, the two skills indicators are strongly correlated(rij = 0.79) with each other; this could suggest that the

TABLE 5BComposition of the Information and Communication

Technologies Development Index (IDI) sample based onthe Human Development Index (HDI)

High Medium LowVariable HDI HDI HDI Total

Countries in the data set 62 71 21 154Percentage (data set) 40.26 46.10 13.64 100.00Percentage (all countries) 41.42 45.56% 13.02 100.00



TABLE 6ACorrelation matrix for indicators of Information and Communication Technologies–Opportunity Index (ICT-OI)

Indicator DN1 DN2 DN3 IU1 IU2 IU3 II1 II2 DS1 DS2

Main telephone lines per 100 inhabitants DN1 1.00Mobile cellular subscribers per 100 inhabitants DN2 0.83 1.00International internet bandwidth (kbps/inhabitant) DN3 0.60 0.53 1.00Adult literacy rates (Source: UNESCO) IU1 0.89 0.82 0.66 1.00Gross enrolment rates (Source: UNESCO) IU2 0.72 0.75 0.30 0.65 1.00Internet users per 100 inhabitants IU3 0.87 0.74 0.73 0.88 0.53 1.00Proportion of households with a TV II1 0.84 0.70 0.75 0.85 0.46 0.90 1.00Computers per 100 inhabitants II2 0.54 0.38 0.27 0.44 0.31 0.56 0.48 1.00Total broadband internet subscribers per 100 inhab. DS1 0.20 0.21 0.12 0.20 0.20 0.17 0.21 0.12 1.00Intern.outgoing telephone traffic (minutes) per capita DS2 0.25 0.26 0.23 0.28 0.24 0.22 0.29 0.11 0.79 1.00

skills category can be described using only one of the twoindicators.

With regard to the IDI, the indicator “fixed telephonelines” correlates significantly (rij ≥ 0.70) with all the indi-cators except the “international Internet bandwidth”,” the“mobile broadband subscribers,” and the “tertiary grossenrollment ratio.” There is a very high correlation be-tween “household with a computer” and “household withInternet access,” which means that users working on a per-sonal computer (PC) also use the Internet. In addition, ahigh correlation can be observed among some ICT use in-dicators (i.e. “Internet users”,” “fixed broadband Internet

subscribers”) and some ICT access indicators (“House-hold with a computer,” “household with Internet access”),as can be expected.

The significance level for these calculated correlationcoefficients is shown in tables 7A and 7B. According toa t-Student test, p values less than or equal to .005 allowus to reject the null hypothesis (that the two variables arenot correlated). For the ICT-OI, all the correlation coeffi-cients between technological indicators (networks, uptake,and intensity) are associated with very low p values. Thecorrelation between the two skills indicators is also signif-icant (p value of 5.00E-32). Regarding the IDI, Table 7B

TABLE 6BCorrelation matrix for indicators of Information and Communication Technologies Development Index (IDI)

Indicator ICTA1 ICTA2 ICTA3 ICTA4 ICTA5 ICTU1 ICTU2 ICTU3 ICTS1 ICTS2 ICTS3

Fixed telephone lines ICTA1 1.00Cellular subscriptions ICTA2 0.72 1.00Internet bandwidth ICTA3 0.16 0.12 1.00Households with a

computerICTA4 0.89 0.72 0.17 1,00

Households withInternet at home

ICTA5 0.87 0.68 0.18 0.97 1.00

Internet users per 100inhabitants

ICTU1 0.88 0.71 0.18 0.94 0.94 1.00

Fixed broadbandInternet subscribers

ICTU2 0.87 0.64 0.18 0.92 0.94 0.92 1.00

Mobile broadbandsubscribers

ICTU3 0.64 0.52 0.27 0.71 0.72 0.67 0.68 1.00

Adult literacy rate ICTS1 0.74 0.71 0.07 0.71 0.66 0.71 0.62 0.43 1.00Secondary gross

enrolment ratioICTS2 0.72 0.62 −0.06 0.69 0.67 0.71 0.67 0.42 0.77 1.00

Tertiary grossenrolment ratio

ICTS3 0.55 0.53 0.07 0.49 0.43 0.48 0.40 0.26 0.70 0.55 1.00


24 G. BRUNO ET AL.

TABLE 7ASignificance levels for correlation coefficients of Information and Communication Technologies–Opportunity Index

(ICT-OI) indicators

DN1 DN2 DN3 IU1 IU2 IU3 II1 II2 DS1 DS2

DN1 0.0000DN2 0.0000 0.0000DN3 0.0000 0.0000 0.0000IU1 0.0000 0.0000 0.0000 0.0000IU2 0.0000 0.0000 0.0002 0.0000 0.0000IU3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000II1 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000II2 0.0000 0.0000 0.0005 0.0000 0.0001 0.0000 0.0000 0.0000DS1 0.0100 0.0060 0.0820 0.0090 0.0095 0.0239 0.0080 0.0740 0.0000DS2 0.0010 0.0009 0.0030 0.0004 0.0022 0.0038 0.0003 0.0950 0.0000 0.0000

confirms that “International Internet bandwidth per Inter-net user” indicator is not at all related to all the other ones,as p values are extremely high.

Principal Component Analysis and ComponentsSelection

The presence of significant correlations among pairs ofindicators of ICT-OI and IDI necessitates the use of tech-niques such as principal component analysis to reducethe redundancy. Table 8A shows the extracted principalcomponents for the ICT-OI sorted in decreasing order ofthe percentage of explained variance for ICT-OI. The tablesuggests that 58.01 percent of the variance in the ICT-OI isexplained by component 1, while the first two componentstogether explain 74.30 percent of the variance. Similarly,table 8B suggests that 66.45 percent of the variance inthe IDI data set is explained by component 1, while the

TABLE 8APrincipal component extraction for Information andCommunication Technologies–Opportunity Index

(ICT-OI) indicators

Principal % explained % cumulative explainedcomponent variance variance

1 58.01 58.012 16.29 74.303 8.67 82.974 7.72 90.695 2.74 93.436 1.98 95.427 1.81 97.238 1.16 98.389 0.87 99.25

10 0.75 100.00

TABLE 7BSignificance levels for correlation coefficients of Information and Communication Technologies Development Index

(IDI)

ICTA1 ICTA2 ICTA3 ICTA4 ICTA5 ICTU1 ICTU2 ICTU3 ICTS1 ICTS2 ICTS3

ICTA1ICTA2 0.0000ICTA3 0.0261 0.0620ICTA4 0.0000 0.0000 0.0187ICTA5 0.0000 0.0000 0.0127 0.0000ICTU1 0.0000 0.0000 0.0132 0.0000 0.0000ICTU2 0.0000 0.0000 0.0117 0.0000 0.0000 0.0000ICTU3 0.0000 0.0000 0.0005 0.0000 0.0000 0.0000 0.0000ICTS1 0.0000 0.0000 0.2007 0.0000 0.0000 0.0000 0.0000 0.0000ICTS2 0.0000 0.0000 0.2364 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000ICTS3 0.0000 0.0000 0.1958 0.0000 0.0000 0.0000 0.0000 0.0007 0.0000 0.0000



TABLE 8BPrincipal component extraction for Information andCommunication Technologies Development Index

(IDI) indicators

Principal % explained % cumulative explainedcomponent variance variance

1 66.45 66.452 11.20 77.663 8.02 85.674 3.79 89.475 3.36 92.836 2.85 95.687 1.69 97.378 1.23 98.609 0.62 99.22

10 0.62 99.8311 0.17 100.00

first two components together explain 77.66 percent of thevariance.

The number of components that could help explain asignificant portion of the variance in the ICT-OI was se-lected by fixing an optimal threshold of 90 percent for thecumulative variance to be explained and individuating thenumber of components that allows obtaining an amountof cumulative variance as close as possible to 90 percent.Consequently, for either the ICT-OI or for the IDI, fourcomponents turn out to be relevant. For the ICT-OI theyexplain 90.69 percent of the total variance, while for theIDI they explain 89.47 percent. This suggests the possi-bility of using a restricted number of indicators to obtain“similar” results to the one provided by the original ICT-OI and IDI.

Indicators Selection

Tables 9A and 9B show the correlation matrix betweeneach ICT-OI and IDI indicator and the four selected com-ponents. We observe that extracted components are oftenhighly correlated with indicators belonging to differentcategories. This result suggests that the grouping into cat-egories would require further considerations and attention.

Based on the findings detailed in table 9A, we selectedICT-OI indicators with the highest correlation levels toeach of the selected principal components:

� International Internet bandwidth per Internet user(DN3).

� Proportion of households with a televisions (IU2).� Adult literacy rates (DS1).� International outgoing telephone traffic per capita

(II2).

Similarly, with regard to the IDI, to explain the fourselected principal components, we considered:

� Fixed broadband Internet subscribers per 100 in-habitants (ICTU2).

� Tertiary gross enrolment ratio (ICTS3).� Mobile broadband subscribers per 100 inhabitants

(ICTU3).� International Internet bandwidth per Internet user

(ICTA3).

Verification Process

For the ICT-OI for each country i we calculated a reducedversion of the index as the geometric average of theselected indicators.

(ICT −OIreduced)i = 4√

DN3i∗IU2i

∗II2i∗DS1i

TABLE 9ACorrelation analysis among Information and Communication Technologies–Opportunity Index (ICT-OI) indicators

and selected principal components

Category Subcategory Indicator Component 1 Component 2 Component 3 Component 4

Info-density Networks DN1 0.601 0.672 0.094 0.319DN2 0.480 0.781 0.116 0.119DN3 0.923 0.099 0.084 −0.010

Skills DS1 0.032 0.093 0.943 0.071DS2 0.159 0.107 0.930 −0.001

Info-use Uptake IU1 0.696 0.600 0.115 0.199IU2 0.134 0.931 0.118 0.093IU3 0.782 0.423 0.069 0.352

Intensity II1 0.833 0.344 0.134 0.27II2 0.225 0.176 0.048 0.945


26 G. BRUNO ET AL.

TABLE 9BCorrelation analysis among Information and Communication Technologies Development Index (IDI) indicators and

selected principal components

Category Indicator Component 1 Component 2 Component 3 Component 4

Access ICTA1 0.788 0.435 0.259 0.070ICTA2 0.433 0.606 0.466 0.012ICTA3 0.084 0.018 0.089 0.987ICTA4 0.849 0.333 0.335 0.071ICTA5 0.883 0.254 0.328 0.084

Usage ICTU1 0.863 0.336 0.266 0.086ICTU2 0.905 0.213 0.255 0.092ICTU3 0.504 0.091 0.787 0.150

Skills ICTS1 0.496 0.774 0.113 −0.004ICTS2 0.669 0,575 −0.034 −0.164ICTS3 0.163 0.901 0.070 0.067

The same process can be applied to IDI, as stated next:

(ID Ireduced)i = (ICT U2i + ICT S3i

+ ICT U3i + ICT A3i)/4

Thus, we performed a linear regression between ICT-OIreduced and ICT-OI and between IDIreduced and IDI,whose results are highlighted in tables 10A and 10B, re-spectively. The presence of a high and significant corre-lation, in both the cases, between the original versions ofthe indexes and the reduced ones suggests that the current

TABLE 10ALinear regression analysis amongInformation and CommunicationTechnologies–Opportunity Index(ICT-OI) and ICT-OIreduced scores

rICT−OIreduced,ICT−OI 0.946R2

ICT−OIreduced,ICT−OI 0.896p (1-tailed) 0.0000Observations 149

TABLE 10BLinear regression analysis amongInformation and CommunicationTechnologies Development Index

(IDI) and IDIreduced scores

rIDIreduced,IDI 0.916R2

IDIreduced,IDI 0.839p(1-tailed) 0.0000Observations 153

TABLE 11ALinear regression analysis amongInformation and CommunicationTechnologies–Opportunity Index

(ICT-OI) and gross domesticproduct per capita scores

Variable Factor

rICT−OI,GDP 0.942R2

ICT−OI,GDP 0.887p(1-tailed) 0.0000Observations 139

versions of the indexes harbor redundancies in terms ofnumber of used indicators.

FURTHER RESULTS

The application of the methodology based on correlationevaluation and principal component analysis reveals sig-nificant redundancies in the structure of the two digital di-vide indexes under investigation. This evidence confirmsthe shortcomings that can affect digital divide measures,as already highlighted by Vehovar et al. (2006) and, moregenerally, by OECD (2008) best practices for constructingsynthetic indexes. To further verify the efficiency (abilityto represent the digital divide without using redundantvariables) of ICT-OI and IDI, further analysis was per-formed that evaluated the degree of correlation amongthe two indexes scores and gross domestic product (GDP)values for each country.

Empirical results show that both the indexes arestrongly and significantly correlated with the gross do-mestic product, as reported in tables 11A and 11B. In par-ticular, ICT-OI and IDI present a correlation coefficient



TABLE 11BLinear regression analysis among Information

and Communication TechnologiesDevelopment Index (IDI) and gross domestic

product per capita scores

Variable Factor

rIDI,GDP 0.921R2

ICT−OI,GDP 0.845p(1-tailed) 0.0000Observations 145

equal to .946 and .916, respectively; p values are, in bothcases, less than 10−5. Thus, even if digital divide is a stillevolving and complex concept, the main indexes seem todescribe a phenomenon that appears highly and signifi-cantly correlated to the difference in income among coun-tries. Therefore, besides presenting significant redundan-cies in their formulation, both the indexes seem to providea “reductionistic” approach to digital divide measurement(see for instance Van Dijk 2006; Fuchs 2009) that does notemphasize the role of socioeconomic, political,cultural,and social factors.

CONCLUSIONS AND DIRECTIONS FOR FUTURERESEARCH

As noted earlier, digital divide is no longer seen as thesimple divide in the access to technological resources,but increasingly it is being perceived as a multidimen-sional phenomenon that includes a set of complex divides(global, social, and democratic) caused by a variety offactors (digital resources, gender, income, etc.). In linewith this evolution, the literature has proposed numerousdefinitions, which range from simply the “gap between in-dividuals who use computers and Internet and individualswho do not” (Mehra et al. 2004) to the more compre-hensive definition that defines digital divide as “unequalpatterns of material access to, usage capabilities of, andbenefits from computer-based information and communi-cation technologies that are caused by certain stratificationprocesses that produce classes of winner and losers of theinformation society, and of participation in institutionsgoverning ICTs and society” (Fuchs 2008).

In this context, several composite indexes have beenproposed to measure the digital divide among countries.In this article we focused on two indexes: ICT-OI andIDI. The ICT-OI is the result of the merging of two in-dexes, DAI proposed by ITU and Infostate developed byOrbicom; IDI is the most recent index and represents theevolution and the synthesis of the previous indexes re-leased by ITU. In particular, we present an empirical in-

vestigation that employs principal component analysis tohighlight that both indexes present redundant indicators.This means that it is possible to increase their efficiencyby reducing the number of indicators and using the sametechnique of aggregation (average in the case of ICT-OIand geometric mean in the case of IDI). The reduction inthe number of indicators could also enlarge the sample ofcountries involved in the analysis.

Our analysis also showed that both indexes are stronglycorrelated with gross domestic product. This finding high-lights that although digital divide is a complex concept,main indexes describe a phenomenon that appears highlycorrelated with the difference in income among countries.Thus, the results of this analysis provide an analytic val-idation of critiques by Van Dijk (2005, 2006) and Fuchs(2009), who argue that current digital divide research isaffected by a “reductionistic” approach to measurementthat does not emphasize the role of factors other thantechnological access and use.

This study could represent a starting point for the defini-tion of digital divide composite indexes that shun indicatorredundancies and include ecological, socioeconomic, po-litical, and cultural factors that influence digital divide.Further analysis on the dynamic evolution of the indexesto evaluate their ability to capture differences among coun-tries across time could be also performed.

REFERENCES

Abuqayyas, A., and C. Audin. 2008. Measuring the information society.In Proceedings of ITU Regional Workshop on Universal ServiceFunding (Damascus, Syria). Geneva: ITU.

Barzilai-Nahon, K. 2006. Gaps and bits: Conceptualizing mea-surements for digital divides. The Information Society 22(5):269–78.

Belanger, F., and L. Carter. 2009. The impact of the digital divide one-government use. Communications of the ACM 52(4): 132–35.

Bertot, J. C. 2003. The multiple dimensions of the digital divide: Morethan the technology “haves” and “have nots.” Government Informa-tion Quarterly 20(2): 185–91.

Braithwaite, S. 2007. Measuring the information society: Proposals formeasuring the information society in Guyana and the wider world(Technical report). Georgetown, Guyana: University of Guyana.

Campbell, D. 2001. Can the digital divide be contained? InternationalLabour Review 140(2): 119–41.

Cette, G., J. Mairesse, and Y. Kocoglu. 2000. The diffusion of infor-mation and communication technologies in France. Measurementand contribution to economic growth and productivity. Economie etStatistique 339–40:73–91.

Chinn, M. D., and R. Fairlie. 2006. The determinants of the globaldigital divide: A cross-country analysis of computer and Internetpenetration. Oxford Economic Papers 59(1): 16.

Colecchia, A., and P. Schreyer. 2002. ICT investment and economicgrowth in the 1990s: Is the United States a unique case? A compar-ative study of nine OECD countries. Review of Economic Dynamics5:408–42.


28 G. BRUNO ET AL.

Corrocher, N., and A. Ordanini. 2002. Measuring the digital divide: Aframework for the analysis of cross-countries differences. Journalof Information Technology 17:9–19.

Cuervo, M. R. V., and A. J. L. Menendez. 2006. A multivariate frame-work for the analysis of the digital divide evidence for the EuropeanUnion—15. Information and Management 43:56−76.

Dewan, S., and F. J. Riggins. 2005. The digital divide: Current andfuture research directions. Journal of the Association for InformationSystems 6(12): 298–337.

Dewan, S., D. Ganley, and K. L. Kraemer. 2005. Across the digitaldivide: A cross-country multi-technology analysis of the determi-nants of IT penetration. Journal of the Association for InformationSystems 6(12): 409–32.

Efron, B., and R. Tibshirani. 1993. An introduction to the bootstrap.Boca Raton, FL: Chapman and Hall.

Fuchs, C. 2008. The implications of new information and communica-tion technologies for sustainability. Environment, Development andSustainability 10(3): 291–309.

———. 2009. The role of income inequality in a multivariate cross-national analysis of the digital divide. Social Science Computer Re-view 27(1): 41–58.

Guillen, M. F., and S. L. Suarez. 2005. Explaining the global digitaldivide: Economic, political and sociological drivers of cross-nationalInternet use. Social Forces 84(2): 681–708.

Gunkel, D. 2003. Second thoughts: toward a critique of the digitaldivide. New Media and Society 5(4): 499–522.

Ida, T., and Y. Horiguchi. 2008. Consumer benefits of public servicesover FTTH in Japan: Comparative analysis of provincial and urbanareas by using discrete choice experiment. The Information Society24(1): 1–17.

International Telecommunication Union. 2003. Measuring the in-formation society. Annual report of International Telecom-munication Union, Geneva, Switzerland. http://www.itu.int/ITU-D/ict/publications/

———. 2005. Measuring the information society. Annual reportof International Telecommunication Union, Geneva, Switzerland.http://www.itu.int/ITU-D/ict/publications/




James, J. 2007. Cumulative bias in the new Digital Opportunity Index:Sources and consequences. Current Science 92(1): 46–50.

———. 2008. Digital preparedness versus the digital divide: A confu-sion of means and ends. Journal of the American Society for Infor-mation Science and Technology 59(5): 785–91.

Joliffe, I. T. 2002. Principal component analysis. Berlin: Springer-Verlag.

Jollands, N., J. Lermit, and M. Patterson. 2004. Aggregate eco-efficiency indices for New Zealand: A principal components analy-sis. Journal of Environmental Management 73: 293–305.

Jorgenson, D. W. (2001). Information technology and the U.S. econ-omy. American Economic Review 91:1–31.

Kim, J. O., and C. W. Mueller. 1978a. Introduction to factor analysis:What it is and how to do it. Beverly Hills, CA: Sage.

———. 1978b. Factor analysis: Statistical methods and practical is-sues. Beverly Hills, CA: Sage.

Mansell, R. 1999. Information and communication technologies fordevelopment: Assessing the potential and the risks. Telecommunica-tions Policy 23(1): 35−50.

Mansell, R., and U. When, eds. 1998. Knowledge societies: Infor-mation technology for sustainable development. New York: OxfordUniversity Press for the United Nations Commission on Science andTechnology for Development.

Mehra, B., C. Merkel, and A. P. Bishop. 2004. Internet for empower-ment of minority and marginalized communities. New Media andSociety 6(5): 781–802.

Menou, M., and R. Taylor. 2006. A grand challenge: Measuring infor-mation societies. Information Society 22(5): 261–67.

Mossberger, K., C. J. Tolbert, and M. Stansbury. 2003. Virtual in-equality: Beyond the digital divide. Washington, DC: GeorgetownUniversity Press.

Munda, G., and M. Nardo. 2005. Constructing consistent compositeindicators: The issue of weights (EUR 21834 EN). Luxembourg:Office for the Official Publications of the European Communities.

Norris, P. 2001. Digital divide: Civic engagement, information povertyand the internet worldwide. New York: Cambridge University Press.

Organization for Economic Cooperation and Development, EuropeanCommission, Joint Research Centre. 2008. Handbook on construct-ing composite indicators: Methodology and user guide (OECD pub-lication code: 302008251E1). Paris: OECD.

Orbicom. 2003. Monitoring the digital divide and beyond. Ottawa:National Research Council of Canada.

Rao, P. M. 2001. The ICT revolution, internationalization oftechnological activity, and the emerging economies: Implica-tions for global marketing. International Business Review 10:571–96.

Saisana, M., and S. Tarantola. 2002. State-of-the-art report on currentmethodologies and practices for composite indicator development(EUR Report 20408 EN). Ispra, Italy: European Commission.

Saisana M., S. Tarantola, and A. Saltelli. 2005. Uncertainty and sen-sitivity techniques as tools for the analysis and validation of com-posite indicators. Journal of the Royal Statistical Society 168(2):1–17.

Sciadas, G. 2002. Monitoring the digital divide. Montreal: Orbicom.Sciadas, G. 2005. From the digital divide to digital opportunities:

Measuring infostates for development. Geneva: ITU.Stevens, J. 1986. Applied multivariate statistics for the social sciences.

Hillsdale, NJ: Erlbaum.United Nations Development Programme. 2003. Millennium develop-

ment goals: A compact among nations to end human poverty. NewYork: Author.

Van Dijk, J. A. G. M. 2005. Deepening digital divide: Inequality in theinformation society. Thousand Oaks, CA: Sage.

————. 2006. Digital divide research, achievements and shortcom-ings. Poetics 34:221–35.

Vehovar, V., P. Sicherl, T. Husing, and V. Dolnicar. 2006. Methodolog-ical challenges of digital divide measurements. Information Society22(5):279–90.

Warschauer, M. 2003. Dissecting the digital divide: A case study inEgypt. Information Society 19(4):297–304.


Documents

A Critical Analysis of Current Indexes for Digital Divide Measurement