Int. J. Intercultural Information Management, Vol. 3, No. 1, 2012 15 A myriad of digital divides: a global comparison of internet and cellular divides Christine Bernadas* EM Normandie, 25 Boulevard de la République, 14800 Deauville, France E-mail: c.bernadas@em-normandie.fr *Corresponding author Jacques Verville School of Business, Auburn University Montgomery, Montgomery, AL 36117, USA Email: jvervill@aum.edu John Burton Faculty of Management, University of British Columbia Okanagan, 3333 University Drive, Kelowna, BC, V1V 1V7, Canada E-mail: John.Burton@ubc.ca Abstract: Digital divide refers to inequalities in access and use of information-communication technologies (ICT). This research classifies internet and cellular divides globally. Cluster analysis and a two-step classification process in organising data from 155 countries are used. Countries are classified by level of digital access then by digital access. This research shows that the importance of digital access is less pronounced for the internet than cellular use. Countries with lower access appear more homogeneous than those with a higher level. Finally, we show that the digital divide is relative to both the country and the type of technology studied. Keywords: digital divide; intercultural information management; cluster analysis; classification; internet; cellular; cross-country. Reference to this paper should be made as follows: Bernadas, C., Verville, J. and Burton, J. (2012) A myriad of digital divides: a global comparison of internet and cellular divides, Int. J. Intercultural Information Management, Vol. 3, No. 1, pp.15 44. Biographical notes: Christine Bernadas is the Chair of Information Systems Management programme and an Assistant Professor at Ecole de Management de Normandie, France. He has a number of articles published in conference proceedings and in journals including the Journal of Long Range Planning, International Journal of Technology Management, the International Journal of Enterprise Information Systems and Journal of Enterprise Information Management. Her current research interest is on ERPs, especially their maintenance and IT global issues. Copyright 2012 Inderscience Enterprises Ltd.
16 C. Bernadas et al. Jacques Verville is an Associate Professor and the Head Department of Information Systems and Decision Sciences at the School of Business of the Auburn University Montgomery. He holds degrees in Linguistics (BA), International Relations (MA), Information Resource Management (MSc) and a PhD in Organizational Information Systems. He has published in a number of journals including International Business Review, Decision Support Systems, IEEE Transaction of Professional Communications, Long Range Planning, Decision Sciences Journal of Innovation in Education, Industrial Marketing Management, Journal of Enterprise Information Management, International Journal of Technology Management, International Journal of Enterprise Information Systems, and Journal of Information Science. He is the co-author of Acquiring Enterprise Software: Beating the Vendors at their Own Game. John Burton is an Assistant Professor of Ethics and Management in the Faculty of Management at the Okanagan Campus of the University of British Columbia. He holds degrees in Law (LLB), Business (MBA), Divinity (MDiv), and Ethics (PhD). His research focuses on developing a stewardship theory of corporate social responsibility, understanding influences on ethical behaviour and the values underlying business activity. 1 Introduction Four words sum up today s telecommunication market: private, competitive, mobile and global. [ITU (2002), p.6] The majority of the world s population is not connected to any electronic network capable of providing information society services, and in most developing countries telecom fixed network connections remain available only to a small proportion of the population [Melody, (2004), p.21]. The world is in situation of digital divide; a situation of inequality of access to and use of information and communication technologies (ICT). This research presents a classification of two digital divides at a global level: the internet divide and cellular divide. A two-step classification process, using cluster analysis techniques, was used to classify the hundred and fifty-five countries studied. Because ICT could have a huge impact on the development of a country, this type of classification is a vital reference for governments, international development agencies, non-governmental organizations and the private sector to assess national conditions in information and telecommunications technologies [ ] It could also help the countries to identify their relative strengths and weaknesses (ITU, 2003a). Cluster analysis allows us to systematically assign the panel countries first to different groups based on similarities in access in a broad sense, and then into different sub-groups based on similarities in cellular and internet density characteristics. Furthermore, it has permitted us to discuss some distinguishing features of those sub-groups. The second section of this paper will discuss the digital divide: its definition, how it has been studied generally and at the global level. Its measurement principally in terms of access but also in terms of usage will be examined in the third section. Section 4 is a methodological section, where the analysis method (the cluster analysis) with the research design and the sample are presented. From this, different clusters are formed. They are presented, briefly discussed and compared in Section 5. Finally, a conclusion
A myriad of digital divides 17 will summarise the findings of this research, present its limits, and some examples of future research. 2 Digital divide The majority of research pertaining to digital divide deals with the internet divide in the USA. It is often seen as a problem of access to the technology. Others see, the digital divide as large, multifaceted, and, in some ways not shrinking [Chen and Wellman, (2003), p.155]. For Van Dijk and Hacker (2003, p.315), it is a complex and dynamic phenomenon. For others, the digital divide is a source of inequalities (Notten et al., 2009; Cheneau-Loquay, 2007; Elie, 2001). Molina (2003, p.138) refers to the nature and magnitude of the challenge of digital divide especially in terms of definition and measure. Figure 1 Study levels for the digital divide Nation-state (e.g. digital divide in Egypt) Community level (e.g. digital divide among low income adults in the US) Individual level Global level (e.g. digital divide in the world) Table 1 Examples of studies of the digital divide at different levels Level of study Examples of what is studied Example(s) of studies Individual Barriers to use of ICT by an individual (e.g., fears) Community The disparate availability of resources among sectors of the population Tekinarslan (2008) about computer anxiety Verma (2003) examines whether the digital phenomenon can be used to address and resolve the issues caused by economic divide. The differences in usage Jackson et al. (2004) explore factors influencing the social impact of the internet use on low income adults Nation-state The disparities at the country scale (between communities) Global The access to ICT in a very broad meaning The adoption of ICT (causes of digital divide) Van Dijk and Hacker (2003) Hoffman et al. (2000) ITU (2003a) with the first global ICT ranking Caselli and Coleman (2001) with a study of the diffusion of computers in the OCDE
18 C. Bernadas et al. We have adapted Chen and Wellman s (2003, p.155) definition removing its specific focus on the internet. For us, digital divide refers to the inequalities in access to and use of ICT, at all levels; global, national, community, and individual. Chen and Wellman s definition was interesting because, it put access and use together as the measure of the divide. By removing that specificity, the digital divide now includes the broadest range of ICTs, including television, the computer, the phone, or the internet (Agarwal et al., 2009; Fuchs and Horak, 2008). Table 2 Source Countries/regions studied or mentioned inside some cross-countries recent research pertaining to digital divide [Bass02] [Corr02] [ITU02] [Bec03] China X X Finland X X France X X Germany X X x X Italy X X X Japan X X X Korea X X Mexico X X Netherlands X X Norway X X Spain X Sweden X X UK X X X USA X X X x X Canada X X Europe X Australia, Japan, NZ X X Developing Asia-Pacific X LAC X X Africa X OECD X X X X Non-OECD X Visegrad countries X X Europe transition X Asia-pacific rim X World X X X X Notes: X = studied, x = mentioned [bass02] = Bassanini and Scarpetta (2002); [corr02] = Corrocher and Ordanini (2002); [ITU02] = ITU (2002); [Bec03] = Beccheti and Adriani (2003); [Che03] = Chen and Wellman (2003); [Mol03] = Molina (2003); [Van03] = Van Dijk and Hacker (2003) 1 ; [IUT03a] = ITU (2003b); [Ban03] = Banerjee and Ros (2004); [ITU03b] = ITU (2003a); [chi04] = Chinn and Fairlie (2004); [Poo04] = Poók and Pence (2004) [Che03] [Mol03] [Van03] [ITU03a] [ITU03b] [Ban03] [Chi04] [Poo04]
A myriad of digital divides 19 Another interest of Chen and Wellman s definition was to situate the digital divide at different levels, which represents also the levels where the digital divide is studied (see Figure 1). Table 1 presents some examples of research about the digital divide at different levels. It does not appear in the table, but the majority of the research is at the nation-state level, perhaps because of availability of data. The research in this article is set at the global level where many international organisations have an interest. The International Telecommunication Union (ITU), was charged with providing the indicators to help measure the information and communications targets of the Millennium Declaration 2. The three indicators chosen to measure ICT availability are: number of telephone subscribers per 100 inhabitants [teledensity], personal computers per 100 inhabitants [computer density] and the internet users per 100 inhabitants [the internet density] [ITU, (2003b), p.71]. We note that the study employs measures of usage to measure access. The assumption appears to be that if a person is able to use a technology, they have access to it. Table 2 shows the countries/regions studied in some of the recent research about the digital divide at the global level. As we can see, not a lot of research has been done at that level. The literature tells us that in many countries, cellular phones are used as substitute for fixed telephones. In others they are complementary tools (ITU, 1998, 1999; Banerjee and Ros, 2004). Thus, the ratio of telephone use between mobile and fixed differs greatly between countries. The use of cellular phones is not an accurate indicator of the internet use even though the technology now allows the internet activity with cellular phones. On the contrary, we believe that access to and usage of the internet is different than the access to and the usage of cellular phones in many countries. This will be our first hypothesis. Hypothesis 1 The digital divide between countries changes when the technology changes. 3 Digital divide measure = digital access and digital use measures From the literature on digital divide, two obstacles to the measure of information inequality emerged: 1 unclarity about what counts as access or use of a technology 2 the ambiguous relationship between access and use as measurements. When searching for a way to measure the digital divide in various countries, we noticed that the concept of access was often used without acknowledging that it could be understood in many different ways. Van Dijk and Hacker (2003), identify four barriers to access: 1 Material access: No possession of computers and network connections. 2 Mental access: Lack of elementary digital experience caused by lack of interest, computer anxiety, and unattractiveness of the new technology.
20 C. Bernadas et al. 3 Skills access: Lack of digital skills caused by insufficient user friendliness and inadequate education or social support. 4 Usage access: Lack of significant usage opportunities. The authors indicate that the most common meaning refers to the first. Usage access is interesting because it places the use of a technology in relation with all possible uses of this technology. For example, the internet can be used to search for information, pay bills, chat, read news, play games, listen to music, etc., however, if a person only has access to the search capabilities of the internet, this person is in a usage access divide in relation to one who has access to the full capabilities of the internet. It is difficult to measure the usage dimension of access. A survey could provide an approximation of the perception of usage access at the community level (e.g., the different usage access in Laredo, Texas), if enough persons are questioned, but it seems difficult to have a valid measure at the nation-state level. Therefore, the cross-country analysis of this dimension appears compromised. Chen and Wellman (2003) break the digital divide into four categories, which overlap some of the access categories of Van Dijk and Hacker (2003). These categories are: 1 technological access (ICT infrastructure: hardware, software, bandwidth) 2 technological literacy (technological skills; social and cognitive skills) 3 social access (economic, organisational and cultural factors, for example the affordability) 4 social use (information seeking, resource mobilisation, social movements, civic engagement, social inclusion). They also split the digital divide into two broad categories: access (household possession of computers, skills with applications) and use (types of usage of PC). It is difficult to implement their scheme at the global level due to the lack of data at the country level particularly that relating to social use. Figure 2 Impact of the different types of access on usage (see online version for colours) Knowledge Infrastructure Usage Quality Affordability Source: Adapted from ITU (2002)
A myriad of digital divides 21 Corrocher and Ordanini (2002) propose a model for measuring the digital divide. They create and test an index (synthetic index of digitalisation) and they define the digital divide as the dispersion in the distribution of this index of digitalisation. Their index includes a number of variables organised into six categories: markets, diffusion, infrastructure, human resources, competitiveness and competition. They use thirty-six elementary indicators to calculate the index. They tested their index in ten countries where the data was available. The quantity of elementary indicators assumes that, outside these ten countries, the missing data will be important especially for developing countries, reducing the interest of this index. ITU (2003a) has also developed an Index, which measures access in a broad sense. Their index has five dimensions: infrastructures (technical access), affordability (economic access), knowledge (e.g., the skill of a person in using the technology) and finally quality. 3 ITU (2002, p.6) indicates that new gaps are emerging, notably in terms of access to the internet. At the heart of this index is the simple reality that if you don t have a technology to use or the skills to use it or if you can t even afford to use it, then you don t use it. Thus it is clear that access is essential to use. We want to know if this true for the internet and for cellular phones as well, thus our second hypothesis. Hypothesis 2 Use is strongly and positively correlated with access. For us, use and access are two different concepts: having access to a technology does not necessarily imply its use, but if access is difficult, it follows that it will be difficult to use that technology. Integration within a single index of these two concepts will not, in our opinion, reflect the digital divide well. From our point of view this major limitation of the ITU index leads to the need to create a new digital access index (DAI). In order to calculate this new index a strong understanding of the calculation of the ITU s DAI to which it is quite similar was necessary. The first step was to recalculate this index with the information given by ITU. ITU indicates that the rank ordering is based to the third decimal of the DAI scores, but that does not appear to be the case, because a simple sort of the scores calculated doesn t give the same rankings. Another problem with the ITU s DAI is bias toward the internet. It is not a DAI it is an internet access index. Our study is oriented not only towards the internet, but also to cellular telephones. Table 3 presents the changes made in the ITU s DAI to create what we call the new DAI. It could be interesting to compare the ITU s ranking (with the correction) to the ranking given by this new index. Two variables from the original DAI have been removed and two added. The two removed are those used in our study as dependent variables (measure of the internet and cellular usage). The two variables added are the percentage (%) of households with televisions (as another indicator of the digital infrastructure) and the cellular monthly subscription as a percentage of gross national income (GNI) per capita (as an indicator of the affordability of the cellular telephone and as a way to compensate for the bias toward the internet). During the work on this measure of access and use, two hypotheses were tested as characteristics of the groups of countries sorted by access. The hypotheses are: Hypothesis 3 Low access countries form a more homogeneous group than the other groups of countries in terms of usage. Hypothesis 4 High access countries form the most heterogeneous group in terms of usage.
22 C. Bernadas et al. This is simply another way to qualify the link between access and usage. Countries with high access may well be assumed to have a high rate of usage. We believe, on the contrary, that in the case of high access countries, all types of usage can occur. For low access countries, there is less variety in terms of usage, because the opportunities are restricted by lack of access. Table 3 DAI vs. new DAI Access dimension Infrastructure Affordability Knowledge Quality Usage DAI variables New DAI variables Indicator Fixed phone lines subscriber per 100 inhabitants Mobile subscribers per 100 inhabitants Internet tariff in % of GNI per capita Fixed phone lines subscriber per 100 inhabitants (FL) % households with television (HT) Internet tariff in % of GNI per capita (IT) Cellular monthly subscription in % of GNI per capita (CT) FL/60 HT/100 100-IT/100 or 0 if negative Adult literacy (AL) Adult literacy (AL) AL/100 Combined primary, secondary and ternary school enrolment International internet bandwidth per capita Broadband subscribers per 100 inhabitants Internet users per 100 inhabitants Combined primary, secondary and ternary school enrolment (SE) International internet bandwidth per capita (IP) Broadband subscribers per 100 inhabitants (BS) Excluded for the access measurement 10-HT/10 or 0 if negative SE/100 (Log (IP) log (0.01)) / (log (10,000) log (0.01)) or 0 if IP = 0 BS/30 DAI or new DAI = Sub-index calculation Weight of 1/2 given to each indicator in this group and sum Weight of 1/2 given to each indicator in this group and sum Weights: 2/3 for AL and 1/3 for SE Weight of 1/2 given to each indicator in this group and sum Average of the sub-indexes 4 Research methodology 4.1 Cluster analysis Cluster analysis refers to a wide variety of techniques used to group entities into homogeneous subgroups (Lorr, 1983; Aldenderfer and Blashfield, 1984; Romesburg, 1984; Hair et al., 1995). Clustering techniques have been applied to a wide variety of research problems. Even if they are relatively dated, Hartigan (1975) and Romesburg (1984) both provide a summary of many published studies reporting the results of cluster analyses. For example, in the field of medicine, clustering diseases, cures for diseases, or
A myriad of digital divides 23 symptoms of diseases can lead to very useful taxonomies (e.g., Markey et al., 2003). Or in the context of cross-countries analysis, cluster analysis allows the creation of classifications, maps, etc. (e.g., Manrai et al., 2001). Hair et al. (1995, p.481) declare that the most traditional use of cluster analysis has been for exploratory purposes and the formation of a taxonomy an empirical classification of objects, which is exactly the goal of this research. Cluster analysis is a multivariate method not often published in business research, 4 perhaps because of its exploratory nature. In the Management Information System (MIS) field research using this method is published in particular in studies pertaining to the development of technologies. Table 4 presents some examples of such research. Obtaining homogeneous groups as well as the classification and characterisation of the sample among the groups seems to be the main goal. We note that globally few quantitative results are reported and they are often descriptive in nature. Three techniques are available in SPSS. One hierarchical and two non-hierarchical (k-mean and two-step). We followed the process recommended by Hair et al. (1995). The hierarchical technique helped us to determine a possible number of acceptable configurations which then were tested by the two non-hierarchical techniques. For the hierarchical technique, a centroid method with the Euclidean distance as similarity measure has been used. According to Hair et al. (1995, p.486), the Euclidean distance is the most common distance measure, it is less sensitive to outliers and it works well when the scale for the two dependant variables is the same. In all cases, we chose to standardise the data, even if the scale for usage is the same for the internet and cellular telephones. In the cluster analysis, each variable is weighted equally, but the multicollinearity acts as a weighting process not apparent to the observer, but affecting the analysis nevertheless [Hair et al., (1995), p.491]. This becomes problematic with more than two variables, because the group of variables with a high multicollinearity will weigh more than the other(s) and then has more likelihood of affecting the similarity measure. The outliers have been removed from the analysis then added back in because they did not create overlap and were interesting to discuss. To select a cluster solution, we calculated the percentage of change in the clustering coefficient for each solution. Those with a high increase of change are more interesting [Hair et al., (1995), p.506]. In order to obtain mutually exclusive and exhaustive categories, some compromises have been made in the choice of the cluster solutions. The choice has always been to keep the most stable configuration without overlapping. By stable, we mean a configuration, which when the technique or the resemblance coefficient is changed gives the same number of groups and the same cases in each group. For example, if a hierarchical technique suggests three possible configurations: 2, 3 or 4 clusters, with a respective number of cases in each configuration, for example, (10, 30), (3, 7, 30) and (2, 8, 10, 20), the use of another technique with these three types of configuration gives respectively: (10, 30), (2, 8, 30), (4, 7, 14, 25). The solution with two clusters is the most stable (same number of cases in each clusters for the two techniques), but we suppose that it creates a lot of overlapping with the other existing clusters. Without the existence of a conceptual theory, which states the contrary, the next best solution will be the solution with three clusters, because the configuration shows little difference between the techniques (if it is a solution without overlapping of course).
24 C. Bernadas et al. Table 4 Research Huerta Arribas and Sánchez Inchusta (1999) Banerjee and Ros (2004) Poók and Pence (2004) Wallace et al. (2004) Example of cluster analysis researches in MIS Goal(s) of the research Studying how information technology (IT) evaluation is carried out among a group of Spanish companies, analysing the characteristics which unite or differentiate them and determining whether they can establish a typology of companies which uses similar evaluation criteria. Answering following question: are there patterns to the spread of mobile telephony, alongside conventional fixed telephony, in different parts of the world? Examining the developmental status of four of the next candidate countries information infrastructures for accession into the Europe Union. Exploring trends in risk dimensions across low, medium and high risk projects, and determining how project characteristics and strategy orientation of a project affect the risk. Goal(s) of the cluster analysis Establish niches with certain characteristics in common Confirm the preliminary findings and gain additional insights into the development of fixed and mobile telephony globally. Locate the V4 countries among peers that are most like each other on social development as well as NII indicators. Classify projects Sample and Main result(s) 20 companies belonging to diverse economic sectors with a variety of legal status Four groups of companies with distinguishing modalities + typology 61 countries four clusters? Four clusters of the social development indicators variable Cluster analysis: results presented Groups with characteristics and modalities with level of confidence T test and percentage of the companies who display modality and who are included in the group. *Hierarchical clustering Dendogram; Discussion choice of number of clusters; Descriptive statistics and characteristics of the groups. F statistic Cluster plots 507 project *K-mean cluster managers/ analysis companies Clusters means Three clusters for the six risk representing low, dimensions; medium, and high risk projects Risk star chart.
A myriad of digital divides 25 Figure 3 Steps in the digital divide classification Group of countries 1 Groups of countries with 2 various digital access Groups of countries with various digital divide 1st classification: digital access 2nd classification: digital use As shown in Figure 3, cluster analysis allows us to systematically assign the panel countries first to different groups based on similarities in access in a broad sense, and then into different sub-groups based on similarities in cellular and internet density characteristics. It s what we call a two-step classification process. Figure 4 Sample by language, economic development, principal religion, and region (see online version for colours) Official/ principal language Portuguese 3% French 10% Arabic 10% German 2% Other 40% Economic development (WordBank) High 26% Low Medium 28% Spanish 12% English 23% Upper medium 18% Low 28% Buddhist 7% Principal religion Hindu 3% Christian 56% Other 4% Muslim 24% Indigeneous believes (Indbel) 6% Europe 23% Asia 26% Regions Oceania 5% Africa 26% The Americas 20%
26 C. Bernadas et al. 4.2 Sample One hundred fifty five countries form this sample. It is 87% of the sample used by ITU for its DAI. The difference is due to the lack of data necessary for the calculation of the new DAI. This panel of countries differs not only in terms of our dependent variables: cellular use (cellular density: mobile cellular subscribers per 100 inhabitants) and the internet use (internet density: internet users per 100 inhabitants), but also in terms of region, language, religion, and especially socio-economic development. Appendix A presents the detail of the data used for this research and Figure 4 presents the sample sorted by our different variables: economic development, geographic region and culture (religion and language). The pie charts show the diversity of the sample. Data regarding economic, geographic and cultural dimensions is secondary and used only to indicate the composition of the sample or the clusters. The origin of this data is diverse, but the United Nations and the World Bank are the principal providers. The data used to make the classification (New DAI, internet density and cellular density) comes from the same source: ITU, which avoids the problem of combining heterogeneous databases. 5 Results and discussion 5.1 Use vs. access The first result is about the link between use and access. The scatter plots by New DAI scores (Figure 5), allow us to assess differences between cellular and internet usage. This visual observation is not a scientific proof, but a priori the cellular divide and the internet divide appear to follow different patters. The scatter plots also give an idea of the correlation between use and access for the two technologies. Figure 5 shows that the observations for cellular are spread without apparent order indicating the correlation between the variables is weak or non-existent. The internet seems to follow a logarithmic curve suggesting a correlation between the variables. Table 5 presents the partial correlations between use and access for the two technologies. Table 5 Correlations between access and usage Cellular use Internet use Access 0.311* 0.757* Note: *Correlation is significant at 0.01 (one-tail). These correlations confirmed the observation made from the scatter plots, that use is positively correlated with access, but though this link is strong for the internet that is not the case for the cellular telephone. Perhaps internet use is a good proxy for measuring the availability of the internet, but not for the cellular telephone (which could be problematic for the conclusion in terms of access for cellular in the Declaration). Thus our second hypothesis is partially rejected.
A myriad of digital divides 27 Figure 5 Scatter plots of cellular and internet use by the new DAI scores (see online version for colours) 12000 10000 8000 6000 4000 2000 0-2000 Cellular Internet 7000 0.0.2.4.6.8 1.0 Access code 3 2 1 6000 5000 4000 3000 2000 1000 0.0.2 New-DAI score New-DAI score.4.6.8 1.0 cellsubs per 10000 inhabitants 2003 (ITU2004) Internuser per 10000 inhabitants 2003 (ITU2004) High Medium Low -1000 0
28 C. Bernadas et al. 5.1.1 First classification with digital access The first cluster analysis uses the DAI scores to make the access clusters. In the clusterisation of their DAI, ITU (2003a) arrived at four categories: low, medium, upper and high. Even without addressing the ambiguity of the labelling between upper and high, we did not find the analysis confirmed these four groups. When we made a cluster analysis with the original DAI data, the three cluster solution appears more stable than the four cluster solution. This is why we presented three clusters in lieu of four. Table 6 presents the descriptive statistics for these three clusters and Table 7 the countries in each cluster. Table 6, shows the measures of dispersion (especially the range and the standard deviation) bolded, because they provide a measure of the homogeneity/heterogeneity of a group of observations. Hypothesis 3 supposes that the low access countries are a group more homogeneous than the other two groups. But this is only true for the internet. Hypothesis 4 states the heterogeneity of the high access countries in terms of usage. If we take the range as a measure of dispersion, this hypothesis is not validated for the cellular telephone, but it is validated with the standard deviation, which is a more powerful measure of dispersion than the range. Thus our fourth hypothesis is confirmed. The assumption that countries with high access will inevitably have a high usage seems to be wrong. Table 6 Descriptive statistics for access classification New DAI (new DAI scores) Cellular usage Internet usage High access N = 53 N = 53 (min = 0.703 max = 0.918) Min = 21 Min = 409.36 Max = 10,605 Max = 6,747.40 Mean = 4,509.25 Mean = 3,349.51 Range = 10,584 Range = 6,338.08 Std deviation = 3,405.19 Std deviation = 1,543.47 Variance = 11,595,292.3 Variance = 2,382,303.72 Medium access N = 64 N = 64 (min = 0.432 Min = 10 Min = 97.63 max = 0.696) Max = 10,176 Max = 3,440.95 Mean = 2,589.08 Mean = 789.75 Range = 10,166 Range = 3,343.32 Std deviation = 2,764.032 Std deviation = 710.36 Variance = 7,639,872.68 Variance = 504,617.84 Low access N = 38 N = 38 (min = 0.049 Min = 14 Min = 6.30 max = 0.383) Max = 11,084 Max = 709.22 Mean = 1,856 Mean = 106.36 Range = 11,070 Range = 702.92 Std deviation = 2,805.76 Std deviation = 144.98 Variance = 7,872,285.95 Variance = 21,019.42
A myriad of digital divides 29 Table 7 Countries by access groups Low access countries (38) Medium access countries (64) High access countries (53) Angola Mali Albania Lebanon Antigua and Barbuda New Zealand Bangladesh Mozambique Algeria Malaysia Argentina Norway Poland Benin Nepal Armenia Maldives Australia Portugal Qatar Burkina Faso Nicaragua Belarus Mauritius Austria Russia Burundi Niger Belize Mexico Bahamas Singapore Cambodia Nigeria Bolivia Mongolia Bahrain Slovak Republic Cameroon Pakistan Bosnia Morocco Barbados Slovenia Central African Rep. Papua New Guinea Botswana Namibia Belgium Spain Côte d Ivoire Rwanda Brazil Oman Brunei Darussalam St. Kitts and Nevis Djibouti Senegal Bulgaria Panama Paraguay Canada Sweden Ethiopia Solomon Cape Verde Peru Chile Switzerland Islands Gambia Sudan China Philippines Croatia Taiwan Ghana Tajikistan Colombia Romania Cyprus Trinidad and Tobago Guinea Tanzania Costa Rica Samoa Denmark United Arab Emirates Kenya Togo Czech Republic Saudi Arabia Estonia UK Lao P.D.R. Uganda Dominica Seychelles Finland USA Lesotho Vanuatu Dominican South Africa France Uruguay Rep. Madagascar Yemen Ecuador Sri Lanka Germany Greece Malawi Zambia Egypt Suriname Grenada El Salvador Swaziland Hong Kong Fiji Syria Hungary Gabon Georgia TFYR Macedonia Iceland Guatemala Thailand Ireland Guyana Tunisia Israel Honduras Turkey Italy India Ukraine Japan Indonesia Uzbekistan Korea (Rep.) Iran (I.R.) Venezuela Latvia Jamaica Viet Nam Lithuania Jordan Zimbabwe Luxembourg Kazakhstan Macao Kuwait Malta Netherlands
30 C. Bernadas et al. Figure 6 Economic development and regions of the different clusters of access (see online version for colours) Economic development - Medium access countries (%) Economic development - High access countries (%) UM: upper medium H: High L: Low UM: Upper medium LM: Low medium H: high LM: Low medium Regions - High access countries (%) Regions - Medium access countries (%) North America South Asia Middle East and North Africa Africa sub-sahara East Asia & Pacific Middle East and North Africa Latin America and Caribbean East Asia & Pacific Africa sub-sahara Latin America and Caribbean Europe and central Asia Europe and central Asia f Economic development - Low access countries (%) LM: Low medium L: Low Region - Low access countries (%) South Asia Middle East and North Africa Latin America and Caribbean Europe and central Asia East Asia & Pacific
A myriad of digital divides 31 Figure 6 categorises the countries in the three access clusters. From left to right the pairs of pie charts present information about the low, medium and high access countries respectively. Economic development seems an important indicator/cause of the level of access: most low access countries have a low level of economic development, most medium access countries have a low-medium level of economic development and most high access countries have a high level of economic development. No low economic development country has a high digital access. The upper-medium economic development countries can be found in relatively the same proportions in the medium and high digital access clusters. This tends to confirm Verma s (2003, p.28) point of view that the economic divide precedes [the] digital divide. European countries are mainly in the cluster of high digital access, while African countries are mainly in the low digital access cluster. Middle Eastern countries can be found mainly in the medium access digital cluster. No South Asian countries are in the high digital access cluster. Finally, Latin America and Caribbean countries are well represented in the medium and high access clusters. This classification is used to make the second step cluster analysis in order to sub-divide the three clusters in regard to the level of usage. 5.1.2 Second classification with digital use The second step gives us two different configurations for the digital divide: a cellular divide and an internet divide. Table 8 and Table 9 present the descriptive statistics and Table 10 and Table 11 present the classification with the countries. Table 8 Descriptive statistics after classification with cellular use Cellular use High (25) C03 C06 C09 N = 4 N = 6 N = 15 Min = 7,854 Min = 8,434 Min = 7,195 Max = 11,084 Max = 10,176 Max = 10,605 Mean = 8,949.5 Mean = 9,360.33 Mean = 8,689.07 Std. dev. = 1,845.80 Std. dev. = 600.96 Std. dev. = 3,178.79 Medium (40) C02 C05 C08 N = 4 N = 17 N = 19 Min = 2,604 Min = 2,636 Min = 3,287 Max = 6,482 Max = 6,937 Max = 6,842 Mean = 3,743.25 Mean = 4,103.12 Mean = 5,107.53 Std. dev. = 1,845.80 Std. dev. = 1,446.06 Std. dev. = 1,188.69 Low (90) C01 C04 C07 N = 30 N = 41 N = 19 Min = 14 Min = 10 Min = 21 Max = 2,434 Max = 22,564 Max = 1,913 Mean = 658.57 Mean = 970.39 Mean = 611.11 Std. dev. = 744.38 Std. dev. 783.48 Std. dev. = 553.28 Access Low (38) Medium (64) High (53)
32 C. Bernadas et al. Table 9 Descriptive statistics after the classification with internet use Internet use Very high (17) I08 N = 17 Min = 4,056.87 Max = 6,747.40 Mean = 5,167.69 Std. dev. = 1,697.66 High (19) I04 I07 N = 2 N = 17 Min = 2,682.67 Min = 2,719.85 Max = 3,440.95 Max = 3,906.29 Mean = 3,061.81 Mean = 3,324.66 Std. dev. = 536.18 Std. dev. = 371.72 Medium (32) I03 I06 N = 16 N = 16 Min = 1,039.33 Min = 1,060.32 Max= 2,308.16 Max = 2,675.59 Mean = 1,572.83 Mean = 1,955.08 Std. dev. = 440.89 Std. dev. = 567.70 Low (87) I01 I02 I05 N = 38 N = 46 N = 3 Min = 6.30 Min = 97.63 Min = 409.32 Max = 709.22 Max = 844.14 Max = 1,023.39 Mean = 106.36 Mean = 418.58 Mean = 624.36 Std. dev. = 144.98 Std. dev. = 216.77 Std. dev. = 345.92 Access Low (38) Medium (64) High (38) Countries classified as low level cellular users are from almost all regions, but the biggest group comes from Sub-Saharan Africa (31.1%), and from the lower economic development groups, with 68.9% in the lowest two groups. Countries in the medium use level come from all levels of economic development, but mainly two: high (35%) and low-medium (32.5%). They are also in all regions except North America principally Europe and Central Asia (32.5%) and Latin America and the Caribbean (25%). The countries in the high level of cellular telephone usage are distributed in all regions, but mainly in Europe and central Asia (40%). Surprisingly, 24% of the countries in this group have a low level of economic development. Most countries have a high level of economic development (48%), but all levels of economic development are represented. If we analyse cluster by cluster, we can remark that the low economic development countries are in a majority in C01 (low access, low use) and are absent after C06 (medium access, high use). Latin America countries are located essentially in C04 (medium access, low use) and C08 (high access, medium use). Clusters C03, C04, C05 group mainly countries with a low-medium economic development.
A myriad of digital divides 33 Table 10 Cell High use Medium use Cellular divide (classification of the digital divide for cellular phones) Ghana Czech Republic Australia Madagascar India Austria Solomon Islands Jamaica Belgium Tajikistan Oman Denmark Sri Lanka France Syria Grenada Hungary Israel Italy Macao Qatar Slovak Republic Switzerland UK USA Nicaragua Albania Antigua and Barbuda Papua New Guinea Bosnia Bahamas Senegal Brazil Bahrain Togo Bulgaria Barbados Dominican Rep. Brunei Darussalam Fiji Canada Gabon Chile Jordan Croatia Kuwait Cyprus Lebanon Estonia Maldives Iceland Mexico Japan Peru Luxemburg Romania New Zealand Seychelles Portugal Swaziland Russia Tunisia Slovenia Spain Uruguay
34 C. Bernadas et al. Table 10 Cellular divide (classification of the digital divide for cellular phones) (continued) Cell Low use Angola Algeria Argentina Bangladesh Armenia Finland Benin Belarus Germany Burkina Paso Belize Greece Burundi Bolivia Hong Kong, Cambodia Botswana Ireland Cameroon Cape Verde Korea (Rep.) Central African Rep. China Latvia Côte d Ivoire Colombia Lithuania Djibouti Costa Rica Malta Ethiopia Dominica Netherlands Gambia Ecuador Norway Guinea Egypt Kenya El Salvador Poland Lao P.D.R. Georgia Singapore Lesotho Guatemala St. Kitts and Nevis Malawi Guyana Sweden Mali Honduras Taiwan, China Mozambique Indonesia Trinidad and Tobago Nepal Iran (I.R.) United Arab Emirates Niger Kazakhstan Nigeria Malaysia Pakistan Mauritius Rwanda Mongolia Sudan Morocco Tanzania Namibia Uganda Panama Vanuatu Paraguay Yemen Philippines Zambia Samoa Thailand Turkey Ukraine Uzbekistan Venezuela Viet Nam Zimbabwe Access Low Medium High
A myriad of digital divides 35 Table 11 Internet divide (digital divide classification for internet) Internet usage Very high High Czech Republic Malaysia Australia Austria Canada Denmark Finland Germany Hong Kong Iceland Japan Korea (Rep.) Latvia Netherlands New Zealand Singapore Sweden UK USA Bahrain Barbados Belgium Chile Cyprus Estonia France Ireland Israel Italy Luxembourg Malta Norway Slovenia Switzerland Taiwan United Arab Emirates
36 C. Bernadas et al. Table 11 Internet divide (digital divide classification for internet) (continued) Internet usage Medium Low Belarus Antigua and Barbuda Belize Argentina Bulgaria Bahamas Costa Rica Croatia Dominica Greece Guyana Grenada Jamaica Hungary Kuwait Lithuania Lebanon Macao Mauritius Portugal Mexico Qatar Oman Slovak Republic Philippines Spain Romania St. Kitts and Nevis Seychelles Trinidad and Tobago Thailand Uruguay Angola Albania Brunei Darussalam Bangladesh Algeria Poland Benin Armenia Russia Burkina Paso Bolivia Burundi Bosnia Cambodia Botswana Cameroon Brazil Central African Rep. Cape Verde Côte d Ivoire China Djibouti Colombia Ethiopia Dominican Rep. Gambia Ecuador Ghana Egypt Papua New Guinea El Salvador Rwanda Fiji Senegal Gabon Solomon Islands Georgia Guatemala Sudan Honduras Tajikistan India Tanzania Indonesia Togo Iran (I.R.)
A myriad of digital divides 37 Table 11 Internet divide (digital divide classification for internet) (continued) Internet usage Low Uganda Vanuatu Yemen Zambia Jordan Kazakhstan Maldives Mongolia Morocco Namibia Panama Paraguay Peru Samoa Saudi Arabia South Africa Sri Lanka Suriname Swaziland Syria TFYR Macedonia Tunisia Turkey Ukraine Uzbekistan Venezuela Vietnam Zimbabwe Access Low Medium High Countries with a low level of internet use are mainly from the low-medium economic development group (71.4%), and from the low economic development group (14.3%). Saudi Arabia is the only country with a high level of economic development in this group. In the medium level of use group, Latin American countries and European and Central Asian countries, and Middle Eastern and East Asia-Pacific countries are equally represented (respectively 43.8% and 6.3%). They are in a majority in the upper-medium group of economic development (62.5%) and in the high level of economic development (37.5%). The countries in the high internet use group come from the same groups of economic development as the medium use countries, only the proportions change: 88.2% for high economic development and 11.8% for upper-medium. They represent countries from four regions: Europe and Central Asia (64.7%), Middle East and North Africa (17.6%), Latin America and Caribbean (11.8%), and East Asia and the Pacific region (5.9%). Finally, the countries at the very high level of internet use almost all have a high level of economic development (94.1%) and they come from three regions: Europe (Nordic countries mainly) (47.1%), East Asia and the Pacific region (42.2%) and North America (11.8%).
38 C. Bernadas et al. Looking cluster by cluster, shows that countries with upper-medium economic development are in a majority in I06 (high access, medium use), and low economic development countries cannot be found after the third cluster (medium access, medium use) and are mainly in I01 (low access, low use). African countries are mainly inside the first two clusters (low use, and low and medium access). European countries are in all clusters, which indicates a great deal of heterogeneity in this region in terms of digital divide. Latin American countries are principally in I02 (medium access, low use) and I06 (high access, and medium use). As for the cellular divide, a really deep understanding of the clusters (and the classification) require a study at the country level, where we would try to understand why a country is in a specific cluster. Table 12 Countries which present a difference between internet and cellular in the classification Countries equivalents in terms of use of internet and cellular (88) Countries where internet usage is better than cellular usage (18) Countries where cellular usage is better than internet usage (49) Canada Albania Madagascar Finland Antigua and Barbuda Maldives Germany Bahamas Mexico Hong Kong, Belgium Nicaragua Iceland Bosnia Oman Ireland Brazil Papua New Guinea Japan Brunei Darussalam Peru Korea (Rep.) Bulgaria Portugal Latvia Croatia Qatar Malaysia Czech Rep. Romania Malta Dominican Rep. Russia Netherlands Fiji Senegal New Zealand France Seychelles Norway Gabon Slovak Republic Singapore Ghana Solomon Islands Sweden Grenada Spain Taiwan Hungary Sri Lanka United Arab Emirates India Swaziland Israel Switzerland Italy Syria Jamaica Tajikistan Jordan Togo Kuwait Tunisia Lebanon Uruguay Macao
A myriad of digital divides 39 Figure 7 Countries with different use with regard to their original cluster in the two classifications (see online version for colours) Internet use- Countries where Internet more used than Cell I04: medium access High use I07: high access High use I08: High acc. Very high use Internet Use- Countries where Cell more used than Internet I07: High access High use I01: Low access low use I06: high acc. Medium use I05: high acc. Low use I04: medium Acc. High use I03: medium access medium use I02: medium access low use Cellular use- Countries where Internet more used than Cell C08: high access medium use C04: Medium access Low use C07: High access medium use Cellular use- Countries where Cell more used than Internet C09: High access high use C02: Low access Medium use C03: Low access High use C08: High access Medium use C06: Medium access high use C05: Medium Access Medium use
40 C. Bernadas et al. 5.2 Comparison of the classification for the two technologies The two classifications are different: the internet divide is not the cellular divide (support Hypothesis 1), but the fact that the scales (numbers by 100 of inhabitants and DAI scores) are identical in the two classifications allows us to merge the two and briefly compare them. In regard to the values of use, the low and medium levels of the internet use are comparable to the low level of cellular use, the high level of internet use is comparable with the medium level of cellular use, and, finally the very high level of the internet use is comparable with the high level of cellular use. Hopefully, very little overlap occurs (observations which could not be placed precisely to one of the new categories). Table 12 presents the countries which present a difference in terms of use between the two technologies. As we can see, around 57% of the sample is equivalent in terms of the internet or cellular use (i.e., countries stay in the same cluster for the two technologies), 12% have a better internet usage than their cellular usage, and 31% have a better cellular usage that their internet usage. To try to understand which are the countries with better results with internet than cellular and vice versa we turn to the original classification and look at the cluster from which the countries in the two new groups (in the merged classification) come (see Figure 7). The countries with a better use of the internet (pies row at the top) mainly come from the high access cluster and they have a high level of use of the internet (clusters I07 and I08) and a medium level of use of cellular (clusters C07 and C08). We can add that they are at 88.9% in the high economic development group. No Latin American or African or South Asia countries are in this group. The countries with a better use of cellular (pies row at the bottom) form a more heterogeneous group than the previous one. They are principally medium access countries, with low use of the internet and medium use of Cellular. They belong to all the groups of economic development with slightly greater representation from the low-medium group (30.6%). All regions are represented, except North America. The countries of Europe and Central Asia are mainly located in Central Asia. Finally, Table 13 presents a summary of the results for the four hypotheses tested. Table 13 Summary of the results about the hypotheses Hypothesis Hypothesis 1 The digital divide between countries changes when the technology changes Hypothesis 2 use is strongly and positively correlated with access Hypothesis 3 Low access countries form a more homogeneous group than the other groups of countries in terms of usage Hypothesis 4 High access countries form the most heterogeneous group in terms of usage Result Validated. Partially validated in the case of the internet. For cellular, the relation is positive but not strong. Partially validated in the case of the internet and not at all for the cellular. Validated.
A myriad of digital divides 41 6 Conclusions The data used for classification come from the same source: ITU, which avoids the problem of heterogeneous databases. ITU itself is quite cautious about its data and informs users of the problems related with it, for example, in the collection of the data. ITU figures that they are probably the best data available for this topic. The use of cluster analysis allows us to classify 155 countries on the two dimensions of the digital divide: access and use. A cellular divide and an internet divide have been found. A new DAI has been calculated. Subsequent work on this index is necessary. As for use; access in term of cellular is perhaps not the same as access in terms of the internet. Perhaps an access index specific to the technology will be necessary and its application to the classification could perhaps change the classification obtained. As already mentioned to insure a certain level of validity of the final classification results, different cluster analysis techniques and the application of a second hierarchical analysis have been made. Usually, the K-mean method provides the result of an F test for the solution created, but it is not a reliable test because it is always significant (by principle, the cluster analysis has created groups which are different). However, different factorial analysis has been performed to test the validity of the clusters created. This analysis was possible because the cluster analysis done was essentially inside the access variable (it gave us vertical groups: e.g., C01, C02, C03). If we look at the clusters horizontally (in terms of use), their average must not differ inside a row (e.g., C01, C04, C07). They belong to the same group of use, if the cluster analysis is correct. All the tests (three factorial analysis for cellular and three for the internet) were significant, except for I01 5 (low access and low internet usage), but when we return to the original solution (without the outliers), the test was not significant any more. The comparison of the rankings for the top ten economies in terms of mobile penetration for 2001 and 1998 (ITU, 2002) shows the rapid rate of change in the diffusion of technology in terms of access and use; and thus in terms of digital divide. It indicates that digital divide is a relative concept not only in terms of countries and technologies, but also that it evolves with the time. Future research could be done using the classification to follow this evolution. As already mentioned, a deep understanding of the classification needs to move from the global level of the digital divide to the country level (just to understand the position of a country). It was not the objective of this research, but it could also easily be done by region for comparison. Finally, the majority of the research pertaining to digital divide tried to determine the causes and tried to provide solutions/advice to solve the consequences of this divide. It could be interesting to put in parallel all those causes and consequences with the classification, which can allow the comparison of countries in the same state of digital divide and then control for this dimension in order to see 1 what caused this divide 2 its consequences. For example a change in this type of telecommunication policy for countries with a low usage of the internet and low access will have a positive impact in the gap reduction, because the countries which have already implemented this policy are not in this cluster. Figure 8 summarises the principal result obtained by this research: the classification.