Digital Divide and e-Readiness - Semantic Scholar

30 International Journal of E-Adoption, 5(3), 30-75, July-September 2013

Digital Divide and e-Readiness: Trends and Gaps

Mohammad Reza Hanafizadeh, Research Institute for ICT, ITRC, Tehran, Iran Payam Hanafizadeh, School of Management & Accounting, Allameh Tabataba’i University, Tehran, Iran Erik Bohlin, Department of Technology Management and Economics, Chalmers University of Technology, Gothenburg, Sweden

ABSTRACT This paper reviews the literature on digital divide and e-readiness in different fields with an eye to identifying trends and gaps in prominent research areas. In this study, 411 articles, conference papers, master’s and doctoral dissertations, textbooks, and working papers on digital divide and e-readiness are classified and elaborated and their results are presented. Drawing upon this literature review and analysis of digital divide and e-readiness, several important research areas surrounding digital divide and e-readiness are discussed and examined from a critical standpoint. In the paper, a comprehensive list of references is presented and, to the best of the authors’ knowledge, this is the most complete study of digital divide and e-readiness, even in the field of IT, in terms of its references. This paper reviews the literature on the digital divide and e-readiness from three perspectives with the purpose of identifying trends and gaps in this field: definition, methodology and scale. This review reveals that most modelers do not take sound theoretical and policy concerns into consideration, rather they tend to provide an empirical summarized measure for digitalization. Also, they develop digital divide and e-readiness models by building static composite indexes from individual indicators and tend to apply dynamic models to a lesser degree. Finally, there is a lack of research in the micro level vis-à-vis macro level that the authors attempt to compensate for. Keywords:

Composite Index, Digital Divide, E-Readiness, Information Society, Trends

1. INTRODUCTION The origin of the term digital divide goes back to an unknown American source in the mid-1990s, though it has come to be attributed to some US professionals and bureaucracy. It initially emerged in media and government reports; for example, “Falling Through the Net”, “A Nation

Online” and “US Department of Commerce’s National Telecommunications and Information Administration (NTIA)” (1995, 1998, 1999, 2000, 2002, 2004). After this the digital divide became a common term in the US and it has been used widely by bureaucrats, legislators, activists and scholars since the mid-1990s. In the meantime,

DOI: 10.4018/ijea.2013070103 Copyright © 2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

International Journal of E-Adoption, 5(3), 30-75, July-September 2013 31

the development of the information society has also become an important priority for many other countries, including those of the European Union (EU). Eventually, digital divide achieved recognition as an English colloquial term in dictionaries such as ‘The Australian Concise Oxford Dictionary 4th edition’ and ‘The Penguin English Dictionary 2nd edition’. It has taken its place in the ICT and development literature and is coming of age. It is now generally assumed that bridging the digital divide is inextricably intertwined with social, economic and political development (Mutula, 2008). However the first step in any approach to the digital divide problem is to consider a country’s ability or “readiness” to integrate information technology (IT) and e-commerce in order to provide a baseline that can be used for global and regional comparisons and planning (Hanafizadeh, Hanafizadeh, & Khodabakhshi, 2009b). It is essential to understand what it means for a country or economy to be “e-ready” and to conduct an evaluation based on objective criteria in order to establish basic benchmarks (Hanafizadeh, Hanafizadeh, & Saghaei, 2011). Therefore, if a country is to narrow the digital divide, an understanding of where that country currently stands vis-à-vis the information society must be achieved, which is called “e-readiness” (Hanafizadeh et al., 2011). In this paper, we view the digital divide and e-readiness from three perspectives: definition, methodology and scale. In each perspective, research trends are reviewed and classified and, based on this review, gaps in them are identified. Consequently this paper wishes to address current, widely diffused, measurement instruments with the purpose of identifying trends and gaps in digital divide and e-readiness research.

2. RESEARCH METHODOLOGY This survey is based on a study of digital divide and e-readiness articles, conference papers, master’s and doctoral dissertations, textbooks, working papers and other valid and reputable documents. The literature search was

based on the descriptors “digital divide” and “e-readiness”. Considering the nature of the research on digital divide and e-readiness, it would be difficult to group the literature under any specific disciplines. The first scholarly papers focusing on digital divide and e-readiness appeared around 1997 (Katz & Aspden, 1997) and were followed by a growing series of publications (Vehovar et al., 2006). However before that date, researchers conducted research in this area not as studies of digital divide and e-readiness but as examinations of factors affecting the development of ICT. Nowadays digital divide and e-readiness are two of the major concerns of many scientific journals and conferences. However, it was found that no previous study had identified and ranked the published outputs of research on digital divide and e-readiness. Figures 1 and 2 show distribution of articles by journal and year, respectively. Figure 1 shows that Information Society includes by far the most articles related to digital divide and e-readiness. It is a quarterly journal specifically devoted to analyzing the impact, policies, system concepts and methodologies related to information technologies and changes in society and culture. Figure 2 indicates that the total number of articles published on digital divide and e-readiness is ascending. According to this figure, since 2002, researchers have paid special attention to these themes so that a significant increase in number of articles can be observed in this particular year. Digital divide and e-readiness journals can be found in four categories of subjects: • • • •

Business collection; Social and behavioral sciences; Electronics and telecommunications collection; Engineering, computing and technology (see Figures 3 and 4).

As seen in Figure 3, since digital divide and e-readiness are multidisciplinary subjects

Copyright © 2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.


Figure 1. Distribution of articles by journals

Figure 2. Distribution of articles by year



Figure 3. Digital divide and e-readiness in four categories of subjects

Figure 4. Distribution of digital divide and e-readiness by subject area

addressing the effects of access and use, or lack of access to and use, of ICT on society and business, the highest number of articles on this subject was related to Business, Management and Accounting and Social Sciences. Consequently, the online journal databases shown in Table 1 were searched to provide

a comprehensive bibliography of the digital divide and e-readiness literature. In this article, 1986 was selected as a starting date for surveying and examining documents filed for a period of 25 years. The full text of each document was reviewed in order to eliminate articles that were not really related



Table 1. Online databases for digital divide and e-readiness literature selected ABI/INFORM database

IGI Global

Scopus

Academic Search Elite

Interscience Publishers

Social Science Citation Index

ACM Digital Library

Ingenta Journals

Springer Link

Catch Word

Mendeley

Web of Knowledge

Compendex

SAGE Publications

Web of Science

EBSCO

Science Citation Index

Wiley InterScience

Emerald Full text

Science Direct

IEEE Xplore

Scirus

to digital divide and e-readiness. As a result of the search, 411 documents in were identified. Each document was carefully reviewed in terms of four aspects: (1) definitions of digital divide and e-readiness, (2) methodologies used in evaluating digital divide and e-readiness, (3) gaps and challenges of research in this field and (4) literature of digital divide and e-readiness from a micro perspective. Although this search was not exhaustive, it serves as a comprehensive base for an understanding of digital divide and e-readiness research.

3. DIGITAL DIVIDE: A DEFINITION PERSPECTIVE There has been a widespread debate about the definition of the digital divide and the empirical analysis of its components (Barzilai-Nahon, 2006). Literature on the digital divide describes intricate interactions between individuals, technology and society. It may, in fact, refer to several different phenomena. One, for example, is unequal Internet access and usage. Another phenomenon is unequal ability to make use of the Internet, due not only to unequal access but also to other factors (such as education, language, content, etc.) (Warschauer, in press). While the second definition is preferable to the first, it is still somewhat vague—makes use of the Internet to what end? Warschauer (in press) presents a wider definition: the digital divide refers to social stratification due to unequal ability to access, adapt and create knowledge

through ICT. Then, he parses concepts used in this definition as follows; first the term “stratification” indicates that the “divide” is not really a binary division at all but rather a continuum based on different degrees of access to information technology. Second, the adjective “social” is a welcome correction to the somewhat confusing term “digital.” The stratification that does exist regarding access to online information has very little to do with the Internet per se, but has everything to do with political, economic, institutional, cultural and linguistic contexts which shape the meaning of the Internet in people’s lives. Examining the evolutionary trend of the digital divide studies, definitions of the digital divide can be separated into three groups; (1) definitions that focus on ICT access, (2) definitions that take into account ICT use in addition to access, (3) definitions that look into reasons for and the purpose of use, or lack of use, and quality of access to and use of ICT instead of its quantity. Like this division, Bridges.org (2001) defined three dimensions of the digital divide as ICT access, ICT usage and ICT applications. Definitions of the first two groups that emphasize technologies may be viewed as the first generation type. However, recent literature expands on the scope of the term, thus pushing the phenomenon to the next (second generation) level and the third group forms. In a similar classification, Helbig et al. (2009) group influential factors on the digital divide into three levels (approaches) including (1) an access



digital divide, (2) multi-dimensional digital divide, and (3) an emergent multi-perspective digital divide (see Figure 5). Below we classify and review the reputable digital divide studies based on the development generation definitions.

3.1. Definition of Digital Divide from the First Generation Perspective The Organization for Economic Co-operation and Development (OECD) (2001) adopted a first generation perspective for defining the digital divide. They defined it as differences between individuals, households, companies or regions at different socio-economic levels related to access to, and use of, ICT. Orbicom (2005), the Net-

work of United Nations Educational, Scientific and Cultural Organization (UNESCO) Chair in Communications, advocated a framework for measuring the digital divide based on the same type of definition—he develops concepts such as information density (info-density) and information use (info-use) (Sciadas, 2005). In the same way, some researchers (such as Al-mutawkkil, Heshmati, & Hwang, 2009; Hanafizadeh, Hanafizadeh, & Saghaei, 2009c) have concentrated on the aspects of access to technology and have devised indices for analyzing the ICT infrastructure and telecommunication of a given area. However, nearly everyone can access computers and the Internet somewhere. Thus, at least in developed countries, what is considered to be

Figure 5. Different levels of digital divide (Helbig et al., 2009)



the original digital divide (refer to definitions of first generation) is largely resolved.

3.2. Definition of Digital Divide from the Second Generation Perspective The multidimensionality of the digital divide implies that, despite their importance, no single factor such as gender, age, race, education, income or geographical location alone (Dada, 2006; Dewan & Riggins, 2005; Korupp & Szydlik, 2005; Yu, 2006) can fully explain the first order (physical access) gap. The same is true for the second level (ability to use) digital gap (and the resulting ‘knowledge gap’) as they are also impacted by a variety of factors such as education, gender, autonomy of use, motivation, family structure, information technology literacy and IT skill (and experience), social support, type of employment, language, kinds of information (content), involvement in social/ civic pursuits, satisfaction, etc. (Cho, de Zuniga, Rojas, & Shah, 2003; Chen & Wellman, 2004; Dewan & Riggins, 2005; Gil-Garcia, Helbig, & Ferro, 2006; Selwyn, 2006; Hargittai & Hinnant, 2008). Consequently, today there is wider recognition that the digital divide must be understood as incorporating a broad range of variables. In the rest of this section, we modify a cumulative and recursive model of the second generation definition developed by van Dijk (2006) and finally identify common factors on the digital divide along with their references. This model was selected because it is one of the most complete and highly referenced models in the field. Van Dijk (2006) used a framework to reveal the main achievements of digital divide research. This model elaborates the succession of types of media or technology access as a process with many social, mental and technological causes and not as the single event of obtaining a particular technology. We added a stage to van Dijk’s model which is called “impact of usage access” and changes the route of recursion and achievement to the “next innovation” (the added

stage is shown in red in Figure 6). This stage has been taken into consideration in many studies as a factor widening or bridging the digital divide (Loveman, 1988; Iacovou, Benbasat, & Dexter, 1995; Jorgenson & Stiroh, 1999; Lee & Barua, 1999; Palmer & Markus, 2000; Anderson & Tracey, 2001; Howard, Rainie, & Jones, 2001; Goolsby, 2001; Budhiraja & Sachdeva, 2002; Davidson & Cotton, 2003; Lucas & Sylla, 2003; Dewan & Riggins, 2005; Hill, 2005; Kauffman & Kumar, 2005; Billon, Lera-Lopez, & Marco, 2009, 2010). In this model, material access is preceded by motivational access and succeeded by skills access, usage access and impact of usage access. When the full process of technology appropriation is completed according to this ideal schedule, new requirements are perceived and a new innovation for fulfilling them arrives and the process starts again, wholly or partially. In this study, Van Dijk’s (2006) definitions for stages of “material access” are adopted. Using definitions applied to “skill access” by Carvin (2000), Steyaert (2000), Mossberger, Tolbert, and Stansbury (2003), van Dijk and Hacker (2003), van Dijk (2006) and Warschauer (2010), a comprehensive definition is developed; the definition of “usage access“ has been manipulated and a definition of “impact of usage access” is proposed. As Figure 6 shows, prior to physical access comes the wish to have a computer and to be connected to the Internet. Many of those who remain on the ‘wrong’ side of the digital divide have motivational problems. The concept of material access (in Figure 6) comprises physical access and other types of access that are required to reach complete usage and connection such as conditional access (subscriptions, accounts, pay-per-view). In the improved model, the concept of skills access is divided into seven types of skills needed by a user in order to take full advantage of ICT; basic literacy (van Dijk, 2006) (the ability to read and write), functional literacy (van Dijk, 2006) (the ability to apply basic literacy to everyday tasks), occupational literacy (van



Figure 6. A cumulative and recursive model of successive types of access to digital technologies adapted from van Dijk (2006)

Dijk, 2006) (the skills necessary to succeed in a professional setting), technical competence (Mossberger et al., 2003) or technological literacy (Carvin, 2000) or instrumental skills (Steyaert, 2000) or operational skills (van Dijk & Hacker, 2003; van Dijk, 2006) (the ability and the capacity to use ICTs), electronic literacy (including computer literacy, information literacy, multimedia literacy and computer-mediated communication literacy) (Warschauer, 2010) or information skills (the ability to determine the quality of informational resources or the skills to search, select and process information in computer and network sources) (Carvin, 2000; van Dijk & Hacker, 2003; van Dijk, 2006) - two types of information skills are required; formal information skills (the ability to work with the formal characteristics of computers and the Internet, e.g. file and hyperlink structures) and substantial information skills (ability to find, select, process and evaluate information in specific sources following specific questions)- strategic

skills (Steyaert, 2000; van Dijk, 2006) (the capacity to use computer and network sources as the means for particular goals and for the general goal of improving the individual’s position in society), and adaptive literacy/skills (the ability to develop new skills) (Carvin, 2000). In Figure 6, usage access is applying (new) technology in businesses, organizations or society in order to benefit from its achievements. Impact of usage access is the final stage and ultimate goal of the process of technological appropriation in the shape of particular applications. At this stage, effects and results of the usage of new technology, especially financial and economic returns, appear and new requirements for emerging technological innovations are perceived. The list of commonly-identified factors that would facilitate or discourage access to and use of ICT and the Internet for certain social groups, as well as a description of its distribution throughout the population and between different global regions is presented in the Appendix.



As it can be seen, the emphasis of the studies is on access to ICT, the effective use of ICT, knowledge and technical skills, and content. Hence, the digital divide should be seen and addressed holistically in terms of these factors. There is now ample consensus that access to ICT is a factor contributing to the construction and preservation of social networks as well as to meaningful participation in the knowledge society, as it allows users to take advantage of educational, professional and leisure opportunities (Foster & Snider, 2000; DiMaggio, Hargittai, Coral, & Shafer, 2004; Brainin & Bar-Lev, 2005). Also, recent works have pointed out that the effective use of ICT is related to three dimensions: motivation, possession and digital skills (Valadez & Duran, 2007). Motivation refers to the willingness of individuals to use technology and to include it in their home, work and educational efforts. Possession includes physical access to computers and Internet and the ability to use the technology. Skills refer to the ability to use the technology (Valadez & Duran, 2007).

3.3. Gaps in the Definitions As the concept of the digital divide has been narrowly conceptualized as an access problem, it would be misleading to relate it solely to equipment purchasing. Such an access measure of digital divide has been criticized by many researchers in that it neglects what people are doing with ICT and what they are able to do with ICT (Attewell, 2001; DiMaggio & Hargittai, 2001; Light, 2001; Servon, 2002; Warschauer, 2002, 2008; Gunkel, 2003; Harper, 2003; van Dijk & Hacker, 2003; Selwyn, 2004). In other words, these modelers fail to address issues of use and quality of access that have become pertinent in an increasingly interconnected world. Consequently, in the last decade, the concept of the digital divide has been modified as a) not only a technological, but also a social problem i.e. a phenomenon reflecting broader social, economic, cultural and learning inequalities and b) concerns not only physical access but the required skills for using computers/the

Internet and what users do with them as well (Cho et al., 2003; Dewan & Riggins, 2005; Selwyn, 2006; Buente & Robbin, 2008; Fuchs, 2009). Following the usual demographics and the emphasis on physical access, there is a preponderance of sociological and economic research (van Dijk, 2006). Contributions from psychology and even from communication and education studies are relatively minor (Hacker & Mason, 2003; Bucy & Newhagen, 2004). However, the digital divide cannot be understood without addressing issues such as attitudes toward technology (e.g. technophobia and computer anxiety), the channels used in new media diffusion, educational views of digital skills and cultural analyses of lifestyles and daily usage patterns. Another important issue is also missing from current debate (and in most past debates too) about the digital divide is moral reasoning and the issue of ethics (Hacker & Mason, 2003). By building moral reasoning into the process of conducting digital divide research and linking such research to policy debates, it will be possible to accomplish more valid research, better and more focused debates about the significance of empirical findings and consequently policies that are less subservient to political ideologies and are oriented more towards serving collective goods. This new perspective has produced a lively debate on the criteria necessary to measure it (Bruce, 1999; DiMaggio, Hargittai, Neuman, & Robinson, 2001; de Haan, 2004; Warschauer, 2004; Livingstone & Helsper, 2007; Valadez & Duran, 2007). Most models (and the choice of the indicators behind them) are not driven by sound theoretical and policy concerns, rather they are driven by the simple willingness to provide an empirical measure for digitalization (Rizk, 2004; Ramayah, Yan, & Sulaiman, 2005). This approach by researchers has caused the next pitfall which is to consider the inappropriate variables for measuring the digital divide. In order to compare the countries with each other and measure the digital divide between them, as a first step there should be consensus on a set of indicators that can properly measure



the differences in the level of digitalization (Hanafizadeh, Hanafizadeh, & Khodabakhshi, 2009a; Çilan, Bolat, & Erman, 2009). In the development of the models, modelers often confuse “what is needed” with “what is available” in terms of data. They start their design process with variables and indicator levels and thereby enter into the “decision makers’ loop” in that they are trying to come up with factors that are measurable, and they overlook what is truly meaningful in any particular context. The main reason is that research on the digital divide suffers from lack of theory and consequently, lack of conceptual elaboration and definition.

4. DIGITAL DIVIDE AND E-READINESS: A METHODOLOGICAL PERSPECTIVE The construction of a digital divide and ereadiness model is not unsophisticated and in addition, the methodological challenges raise a series of technical issues that, if not addressed adequately, can lead to models being misinterpreted or manipulated. Hence, the focus here is on these essential methodological issues that are associated with any empirical research on the digital divide and e-readiness. Researchers have used two approaches for building digital divide and e-readiness models: static and dynamic. Reviewing the literature, it becomes clear that the number of static models exceeds that of the dynamic. These two groups of models are discussed in the following section.

4.1. Static Measurements Examining the efforts made to identify the digital divide and e-readiness, it is understood that the most widespread methodology used by scholars and practitioners for measurement is to construct a static composite index from indicators, for example the Internet Connectedness Index (Jung, Qiu, & Kim, 2001), Economist Intelligence Unit (EIU) (EIU, 2011), the Technology Achievement Index (TAI) (UNDP, 2001), Knowledge Economy Index (KEI) and

Knowledge Index (KI) (World Bank, 2009), Knowledge Assessment Methodology (KAM) (World Bank, 2011), the Executive Index of the Massachusetts Innovation Economy (Massachusetts Technology Collaborative, 2003), the National Informatization Quotient (NIQ) (Jin & Chengyu, 2002), the Information Society Index (ISI) (IDC, 2001), the Networked Readiness Index (NRI) (Dutta & Jain, 2004), the Global Digital Internet (GDI) (Wolcott, Press, McHenry, Goodman, & Foster, 2001), the Synthetic Index of Digitalization presented by Corrocher and Ordanini (2002), the Digital Access Index (DAI) (International Telecommunication Union (ITU), 2003), the ArCo Index (Archibugi & Coco, 2004), the eGovernment Index (EGI), the Digital Divide Index (DIDIX) (Husing & Selhofer, 2004) in Statistical Indicators Benchmarking Information Society (SIBIS), the Digital Opportunity Index (DOI) (ITU, 2005), the Infostate, which is a set of global indicators comprising two components, namely, Infodensity and Info-use (Sciadas, 2005),and two telecommunication and ICT infrastructure indices developed recently by Hanafizadeh et al. (2009c) and Al-mutawkkil et al. (2009). A composite index integrates different kinds of indicators, that is, qualitative parameters with quantitative parameters, and hard data with soft data. Consequently, the need to combine different indicators and dimensions measured on different scales in a meaningful manner is central to the construction of a composite index. This implies a decision as to which weighting model will be used and which procedure will be applied to aggregate the information (Nardo, Saisana, Saltelli, & Tarantola, 2005). Some modelers such as Sciadas never used weights for aggregation of their indicators and dimensions. He introduced the notion of a country’s “ICTization” or Info-state, as the aggregation of Info-density and Info-use. The Info-state was developed based on an unweighted geometric average of 21 indicators that assessed 192 countries over nine years (Sciadas, 2005). In contrast to this index, most researchers have employed weighted aggregations to build their indices.



In many composite indices, all the variables are given the same weight when there are no statistical or empirical grounds for choosing a different scheme. Equal weighting could imply the recognition of equal status for all indicators (e.g. when policy assessments are involved). Alternatively, it could be the result of insufficient knowledge of causal relationships, or ignorance about the correct model to apply or it could even stem from lack of consensus on alternative solutions. The DOI is an example of using this methodology for constructing its model. The DOI was devised, as the United Nations (UN) World Summit on the Information Society (WSIS) (2003) recognized an urgent need for improving measurement capabilities of ICT investment, adoption and impact (ITU, 2005). This composite index utilized a set of eleven indicators and assigned them equal weight in order to create a single value that can be compared to other countries. However, when using equal weighting, a variable might be counted double in the composite index by combining variables with high degrees of correlation. Sometimes, modelers have utilized the opinions of experts who are aware of policy priorities and theoretical backgrounds to reflect the multiplicity of stakeholders’ viewpoints and weight indicators and dimensions. For example, in July 2001 the Ministry of Information Industry of the People’s Republic of China built the national informatization quotient (NIQ) (Jin & Chengyu, 2002) based on the opinions of experts. Consequently, in this case assigning weights is also a subjective process. Moreover, the Economist Intelligence Unit (EIU) (2011) has developed a composite index to rank ereadiness in 60-70 countries annually since 2000 and has applied this method to determine how each of its six dimensions influences the overall e-readiness of countries. One of the main drawbacks of this method is that modelers may not have access to the necessary experts. In some cases there are very few experts, fewer than 30. Accordingly, the outcome of a composite index and ranking the countries in a benchmarking

exercise is not robust. It is worth mentioning that such a method is of limited use when the number of variables in the model, and consequently the number of questions, rises. Finally, it may be dubious as this weighting process is based on individual’s perceptions. In order to avoid these limitations, modelers use a regression approach that is suitable for a host of variables of different types. In such models (usually linear) multiple regression models are estimated to retrieve the relative weights of indicators. The review of literature on the digital divide revealed that some studies focus mostly on measuring and quantifying the divide; its magnitude, evolution and the speed at which it is happening (Corrocher & Ordanini, 2002; OECD, 2005) while others concentrate on explaining the determinants of ICT diffusion (Hargittai, 1999; Kiiski & Pojhola, 2002; Chinn & Fairlie, 2007). The latter group have made enormous efforts using the regression approach, such as the studies conducted by Hargittai (1999), Norris (2001), Caselli and Coleman (2001), Dasgupta, Lall, and Wheeler (2001), Gruber and Verbove (2001), Oxley and Yeung (2001), Robison and Crenshaw (2001), Guillen and Suarez (2001), Kiiski and Pojhola (2002), Wong (2002), Beilock and Dimitrova (2003), Quibria, Ahmed, Tschang, and ReyesMacasaquit (2003), Shih, Kraemer and Dedrick (2003), Tanner (2003), Pohjola (2003), Wallsten (2003), Bagchi (2005), Dewan et al. (2005), Andonova (2006), Guillen and Suarez (2006) and Martinelli, Serrecchia and Serrecchia (2006) to name but a few. Although this approach lends itself to numerous variables, it implies the assumption of linear behavior and requires the independence of explanatory variables. Indeed, if these variables are correlated, estimators will show high variance and as a result, parameter estimates will not be precise and hypothesis testing will not be powerful. In the extreme case of perfect collinearity among regressors, the model will not even be identified. This problem arises specifically as concerns ICT indicators which are very likely to lead to overlapping information. Such indicators would be hard to reconcile in the context of econometric



models and their estimates for the purposes of associational and causal explanations would not be of practical use. Unfortunately, the authors of almost all studies listed above apply correlated explanatory variables to evaluate IT penetration and the digital divide, so validity and accuracy of results declined. Examining a dataset of OECD countries in 1998, Hargittai (1999) concludes that, while GDP is a large driver of Internet connectivity, telecommunications policy can also exert a major effect that is correlated with the level of telephone density. Similarly, Oxley and Yeung (2001) studied 30 countries in the same year and found out that Internet host penetration was positively associated with physical communication infrastructure, rule of law and credit card use, while negatively correlated with telephone service costs. What was neglected in this model is the high correlation between variables of communication infrastructure and telephone service costs, which questions the results. Findings from empirical studies show the close links between ICT diffusion and economic development. Similar to previous innovations, economic wealth is a prerequisite for ICT diffusion and a primary determinant of the digital divide (Hargittai, 1999; Dasgupta et al., 2001; Norris, 2001; Robison & Crenshaw, 2001; Kiiski & Pohjola, 2002; Beilock & Dimitrova, 2003; Pohjola, 2003; Quibria et al., 2003; Shih et al., 2003; Dewan et al., 2004; Kraemer, Ganley, & Dewan, 2005; Andonova, 2006; Crenshaw & Robison, 2006; Chinn & Fairlie, 2007; Hanafizadeh et al., 2009c). Despite the significance of income, some studies show that disparities in ICT penetration rates are greater than that of GDP in the case of some types of technologies (Wong, 2002; Liu & San, 2006). Many researchers emphasize the multidimensional and complex nature of the digital divide and point to the role played by political, institutional, educational and cultural cross-country differences to explain the gap (Hargittai, 1999; Norris, 2001; Corrocher & Ordanini, 2002; Kiiski & Pohjola, 2002; van Dijk & Hacker, 2003; Tanner, 2003; Sciadas,

2005; Liu & San, 2006; Chinn & Fairlie, 2007; Zhao, Kim, Suh, & Du, 2007) and show that public policies and regulation play a significant role in promoting or inhibiting ICT diffusion (Hargittai, 1999; Kiiski & Pohjola, 2002; Guilen & Suarez, 2005; Andonova, 2006; Chinn & Fairlie, 2007). In addition to the above-mentioned factors, demographic factors such as population size, population density and urban population proportion are associated with the cross-country digital divide (Quibria et al., 2003; Bagchi, 2005; Kraemer et al., 2005; Chinn & Fairlie, 2007). A method similar to regression, particularly logistic regression, is Discriminant Analysis for analyzing the digital divide. Discriminant Analysis is the appropriate statistical technique when the dependent variable is qualitative and the independent variables are metric (Çilan et al., 2009). The single dependent qualitative variable in Discriminant Analysis turns into an independent variable in MANOVA (Hair, Anderson, Tatham, & Black, 1998). The objective of the analysis is to determine the variables that significantly define the groups and to describe the functions of these discriminating variables. In the analysis, the ability of the variables to discriminate between the groups is determined through testing and by considering the question of “whether the predicted discriminant function properly classifies the cases to their own groups” (Tacq, 1999). In this method, the correlations of variables are also used to analyze the digital divide. Çilan et al. (2009) used MANOVA to determine the existence of digital divide, and Discriminant Analysis to determine which factors are significant in creating different information society levels and a digital divide among the original 15 members of European Union, its new members and candidate countries. Regression analysis limits the analytical scope by excluding the communality (correlations) that may cross digitalization variables when they are examined separately. Also, the use of the correlated variables in regression models (or Discriminant Analysis) reduces the validity of the findings. Researchers tackle this problem in two ways. The first useful route is to use



factor analysis (FA) to determine the key constructs and to identify the redundant indicators (Corrocher & Ordanini, 2002; Al-mutawkkil et al., 2009; Hanafizadeh et al., 2009c). Another remedy may be found in associating principle components analysis (PCA) or in FA with regression analysis (Bagchi, 2005). The FA and the PCA were found to be useful techniques for exploring the underlying dimensions of the digital divide, as well as for dealing with the complexity of this issue (Cuervo & Menendez, 2006; Hanafizadeh et al., 2009c; Bruno et al., 2010). The weaknesses and strengths of countries in terms of access to, and use of, ICT can be analyzed by applying these methods. One of the famous studies in the field of digital divide is the composite index of digitalization presented by Corrocher and Ordanini (2002). They used PCA to combine factors influencing digitization into an index of digitization. Also, Al-mutawkkil et al. (2009) and Hanafizadeh et al. (2009c) constructed a composite index using Multi-Stage FA and Common FA, respectively, for measuring and analyzing the divide between countries in the fields of ICT infrastructure and access. Moreover, Bruno et al. (2010) used PCA for selecting the most significant indicators and minimizing redundancies and constructed the digital divide as a composite index. Recently, ITU (2011) developed the ICT Development Index (IDI) based on PCA. They used PCA for weighting indicators and sub- indicators and for examining the underlying nature of the data and exploring whether the different dimensions were statically well-balanced. Other researchers applied regression with FA to model the phenomenon (Bagchi, 2005). In this case, when the dependent variable is a synthetic index (usually the first factor obtained by factor analysis), the regression model allows an explanation of the common information shared by elementary variables included in the index only. This common information is the proportion of the total variability captured by the first factor. Canonical Correlation Analysis (CCA) resolves this defect. CCA allows an explanation of total variability, whether common or not, of

the set of representative variables of digitalization. CCA is an alternative to multivariate multiple regression analysis that incorporates correlations between variables into computation, so information about cross-functional relationships that a simple regression analysis would ignore is conveyed, along with the fact that CCA captures the different dimensions of ICT adoption into a single model. A single model is defined as alternative models not being used for each dependent variable, but the adoption of several technologies is explained using only one model. Billon et al. (2009, 2010) presented a cross-country study on the determinants of ICT diffusion using this method. This is the first study to use a single model to explain the digital divide and to capture its relative and multidimensional nature using data from 142 countries. Some researchers apply a cluster analysis after FA to obtain a classification of the levels of digital development (Cuervo & Menendez, 2006). This cluster analysis is performed on the factors identified by FA to look for groups of countries with similar levels of digital development.

4.2. Dynamic Measurements As was previously discussed, indices of the digital divide and e-readiness are often presented as static measurements. However, static measurements of disparities (e.g. percentage difference, ratio, Gini coefficient, Theil index, coefficient of variation etc.) are insensitive to changes in the corresponding absolute magnitude of the indicator growth rates. In order to overcome such a problem, an advanced time distance methodology (longitudinal research) was developed at conceptual and applied levels (Sicherl, 1973, 1978, 2003; Vehovar et al., 2006). This is a new statistical measurement in dynamic gap analysis (Sicherl, 2004) where the levels of variable(s) are used as identifiers and time is the focus of comparison. For instance, Lee, Gholami and Tong (2005) used time series analysis to examine the nexus between ICT and economic growth. The study showed that



ICT contribution to economic growth is only significant ‘‘in many developed countries and Newly Industrialized Economies (NIEs), but not in developing countries’’. Dewan et al. (2009) adopted a generalized diffusion model to study the cross-country diffusion of PCs and the Internet, and examined how diffusive interactions across these technologies affected the evolution of the global digital divide. They estimated the model on the data obtained from 26 developing and developed countries between 1991 and 2005. They found that the codiffusion effects between PCs and the Internet are complementary in nature and the impact of PCs on Internet diffusion is substantially stronger in developing countries compared to developed countries. This is one of only a few studies that conducted robustness checks to enhance their confidence in the results presented. Generally, the works done on the digital divide using this method can be separated into two groups. The first group is composed of studies that used cross-sectional time series for developing countries (Tanner, 2003; OyelaranOyeyinka & Lal, 2005), and the second group involves studies that applied them for a combination of developing and developed countries (Kiiski & Pohjola, 2002; Bagchi, 2005; Guillen & Suarez, 2006; Kraemer et al., 2005; Chinn & Fairlie, 2007). Concerning the data used in digital divide research, annual cross sections in time are common, but longitudinal data are scarce (van Dijk, 2006). They are only starting to appear now in regular or annually replicated survey research. In addition to static measurements being insensitive to change, they also have another problem. They are constructed based on correlations between variables. However, correlations between variables reflect the past behavior of a system - the construct that we wish to study and measure (Sterman, 2000). Correlations do not represent the structure of the system. If circumstances change, if previously dormant feedback loops become dominant and if new policies are tried, previously reliable correlations between variables may break down.

Modelers found system dynamic models (causal models) a valuable tool to deal with this limitation (Sterman, 2000). In these models, relationships between variables are causal, no matter how strong the correlation, how high the R2 (determinate coefficient), or how great the statistical significance of the coefficients in a regression may be. Arellano and Bond (1991) proposed a dynamic panel data estimator based on a generalized method of moments (GMM) methodology that optimally exploits the linear moment restrictions implied by the dynamic panel ICT model. The dynamic GMM estimator is an instrumental variable estimator that uses the lagged values of all endogenous regressors as instruments as well as lagged and current values of all strictly exogenous regressors. Equations can be estimated using the levels or the first differences of the variables. For the difference estimator, the variables are measured as first differences and the lagged values of the variables are used as appropriate instruments (Yartey, 2008). Among the dynamic models, the logistic model is widely used to explain and predict the diffusion of new products and innovations, and the Internet in particular. A study in which a logistic model was used is that by Kiiski and Pohjola (2001). They, using a Gompertz model of technology diffusion that is a special case of the logistic model, examined the diffusion of the Internet in 60 countries over the years 19952000. Likewise, Wolcott et al. (2001) presented a comprehensive framework for describing the diffusion of the Internet in a country through this model (Wolcott et al., 2001).

4.3. Gaps in Methodologies Normally, the above-mentioned digital divide and e-readiness measurements are based on Figure-of-Merit (FOM) calculation (Davidrajuh, 2007). A generalized FOM calculation features a series of indicators with corresponding weights (indicator-weights). The indicators are grouped into sectors which have different



weights too (sector-weights). The sectors are further grouped into blocks each of which being assigned a block-weight. Hence, an indicator is multiplied by up to three different weights (Davidrajuh, 2007). There are two major problems with these calculations: 1. Imprecise data: The first problem with the FOM calculation is that values provided for indicators are imprecise; 2. Heterogeneous data: The second problem with this calculation is that all the indicators are assumed to be homogenous taking scores on the same scale (e.g. 1- 5). However, in reality, indicators are not homogenous. For example, an indicator may require a Boolean answer (yes or no), or a multi-valued answer (within next twelve months are you planning to buy a personal computer, mobile phone, Internet access, any of these or all of these?) or linguistic quantifiers (such as many, most, at least, about etc.). Consequently, obtaining an overall score from a set of scores of heterogeneous indicators is not feasible with FOM calculations. In addition, FOM calculations are sensitive to the imprecise values provided for the indicators. Hence, the existing tools use only homogeneous indicators. Since fuzzy logic works well with imprecise data (Klir & Yuan, 1995) and heterogeneous data sets (Yager & Zadeh, 1991; Yager & Kacprzyk, 1997), it can be used to realize a tool for measuring the digital divide and e-readiness. Davidrajuh (2007) used this method for measuring the e-readiness of a country. Furthermore, the researchers have mostly used static models; both in arguments produced and in empirical data used and, as a result, a dynamic approach is lacking (van Dijk & Hacker, 2003). They usually assign equal weights or use regression, the PCA, and the FA for weighting and developing models, and are less inclined to apply dynamic models. Therefore, digital divide and e-readiness works suffer from the constraint of applying statistical methods to most of their

gap analyses. According to Rogers (1986), methods of statistical analysis are best suited to studying cause and effect, not the patterns of effects that occur across time, or the processes that create these patterns in social systems. The focus of social scientific research should be more on what causes the processes to happen, not simply whether a pattern exists or not. Rogers asserts that one of the most important tasks researchers are faced with is to understand the process and impacts of communication technology, which will require major changes in research methods. Statistics without theory cannot explain the order in which patterns occur or how each pattern influences the next. Thus, knowledge of an existing pattern, such as a current lack of digital skills among a particular group, does not go far enough; scientists must also seek to understand how that pattern may result in future inequities for that group. The primary reason for this trend is that time series data for ICT indicators are very limited and it is not possible to analyze them for several years. Also, since it is necessary in dynamic modeling (causal models) to investigate causal relations, this type of modeling is more complicated than static modeling. Hacker and van Dijk (2000) indicated that the static nature of many available divide reports and data sets do not fully illustrate nor explain the problems of the digital divide. For example, according to the trickle-down principle of diffusion theory, existing technologies such as personal computers and Internet connections will soon be available to all because they are becoming cheaper and easier to use by the day. Such reasoning seems dynamic, but actually it is not, because the fact that technology is changing quickly is forgotten and the people who first adopted it do not stop to obtain new technologies and skills. Finally there is a lack of qualitative research in this field. Most studies on the digital divide and e-readiness are based on quantitative data collection and attempt to describe the bigger picture of the problem. Although this produces vast amounts of correlations, it does not bring



about precise mechanisms explaining the appropriation and division of the technology concerned in everyday life (van Dijk, 2006).

5. DIGITAL DIVIDE AND E-READINESS FROM SCALE PERSPECTIVE The digital divide and e-readiness can be viewed and defined from two perspectives: macro and micro level. There is asymmetry of research on the different levels in this context. The literature on the digital divide and e-readiness reveals that while numerous studies have been conducted on the international and national level or country, government, policy levels the macro perspective - few have attempted to evaluate these issues from a micro perspective i.e. an assessment of sector, community, public system, enterprise, organization, institution and individual (consumer) levels (Hanafizadeh et al., 2011). Consequently, in this section an attempt has been made to examine the important studies on the digital divide and e-readiness at micro level that have been paid little attention in previous works. From a micro perspective, e-readiness is the level of preparedness pertaining to the ability of exploiting Internet technology for economic purposes through the rapid adoption of e-business (Jutla, Bodorik, & Dhaliwal, 2002). The digital divide on this level, for example among businesses, is defined as disparity between the effective uses of ICT for gains in productivity (Wielicki, 2007). Most studies done on micro level digital divide and e-readiness pertain to assessment of SMEs (e.g. e-readiness assessment of SMEs in Egypt, India, Korea, Iran, South Africa, Turkey and Malaysia). Other studies conducted on micro level deal with companies (firms) and financial organizations’ e-readiness assessment (Mutula, 2010). In the study by Hartman, Sifonis, and Kador (2000), net readiness is measured as a company’s preparedness to exploit the enormous opportunities available in the e-economy landscape. The success of the Internet initiatives

of a firm or enterprise depends not only on its own effort to digitize its value chain, but also on the readiness of its customers, suppliers and trading partners to engage in electronic interactions and transactions (Barua, Whinston, & Yin, 2000a, 2000b). Successful e-readiness practice requires readiness on the part of all the players in the value chain, and companies that adopt e-commerce or e-business must invest in increasing their trading partners’ readiness (Barua et al., 2000a, 2000b). In the maturity model, suggested by Grant (1999), a business is “ready” to implement an e-business and ecommerce strategy when business plans and expectations are clearly stated, there are no insurmountable obstacles impeding progress and any partners or professional support needed have been identified. The literature on the micro digital divide and e-readiness measurements reveals some common factors (factors on which the majority of researchers are in consensus) influencing the digital divide and e-readiness in the micro perspective. Table 2 illustrates these factors and their sources drawn from the numerous theoretical, empirical and summary attempts at defining and measuring the digital divide and e-readiness at micro level.

5.1. Digital Divide on Different Scales Research on the digital divide frequently emphasizes three fields or ‘‘structural contexts of opportunities’’ through which people access ICT: home, school and university and work (de Haan, 2004; Brainin & Bar-Lev, 2005; Hassani, 2006). Other authors include cyber cafes (Cilesiz, 2004) and libraries (Schement, 2003). Also, the digital divide has been studies on three scales: domestic (or national), regional and international (or global). The domestic (or national) digital divide refers to the digital disparities between groups in a particular country (Bikson & Panis, 1995; NTIA 1995, 1997, 1999, 2000; Selwyn, Gorard, & Williams, 2001; Sciadas, 2002; Warren, 2002, 2007; Looker & Thiessen, 2003; Warschauer, 2003, 2010; Bar-



Table 2. The common factors of measurements and their sources Factors

Sources

Infrastructure and Connectivity

Rizk (2004), Mutula and van Brakel (2006a), Jutla et al. (2002), Ramayah et al. (2005), Keen (1991), APEC (1999), Macintyre and Ramnarine (2003), Mehrtens, Cragg, and Mills (2001), World Bank (2004), Fink (1998), Kleindl (2000), Moodley (2001), Kotler (2003), Van Belle and Vosloo (2005), Kasraian (2007), Aminali (2007), Mutula and van Brakel (2006b), Fathian et al. (2008), Jerman-Blazic (2008).

Human Resources

Ramayah et al. (2005), Fink (1998), Doukidis, Smithson and Lybereas (1994), Attaran (2001), Hong (2002), Kwon and Zmud (1987), Jutla et al. (2002), Mutula and van Brakel (2006a), Minton (2003), Kasraian (2007), Aminali (2007), Fathian et al. (2008), Horrocks and Haines (2004).

Networked World Enablers

Norazah (2001), Goodwin (1991), Gupta (1995), Janes, Lambert, Pollett and Reid (1997), Tan and Teo (1998), Dr Sankaran (1999), Ainin and Rohana (2000), Jutla et al. (2002), World Bank (2004), Mutula and van Brakel (2006a), Grant (1999), Ramayah et al. (2005), Lawrence, Corbitt, Tidwell, Fisher and Lawrence (1998), Van Belle and Vosloo (2005), Kasraian (2007), Aminali (2007), Mutula and van Brakel (2006b), Fathian et al. (2008)

IT Applications

Evans and Wurster (1997), Barua et al. (2000a, 2000b), Barua and Lee (1997), Clark and Stoddard (1996), Drury and Farhoomand (1996), Hart and Saunders (1997), Iacovou et al. (1995), Massetti and Zmud (1996), Mukhopadhyay, Kekre, and Kalathur (1995), Premkumar and Ramamurthy (1995), Riggins and Mukhopadhyay (1994), Srinivasan, Kekre and Mukhopadhyay (1994), Zaheer and Venkatraman (1994), Jain (2005), Hashem (2001), World Bank (2004), Mutula and van Brakel (2006a), Kasraian (2007), Aminali (2007), Mutula and van Brakel (2006b), Fathian et al. (2008), Jerman-Blazic (2008).

ICT Use

Payne (1996), Jain (2005), Minton (2003), Macintyre and Ramnarine (2003), Rizk (2004), Gray and Lawless (2000), Ramsey, Ibbotson, Bell, and Gray (2003), World Bank (2004), Engler (1999), Fink (1998), Moodley (2001), Kotler (2003), APEC (1999), Van Belle and Vosloo (2005), Aminali (2007), Fathian et al. (2008), Jerman-Blazic (2008).

Barriers to ICT Use

Khader (2005), OECD (2000), Rizk (2004), World Bank (2004), Ramayah et al. (2005), Sulaiman and Jani (2001), Attaran (2001), Lawrence et al. (1998), APEC (1999), Moodley (2001), Mutula and van Brakel (2006b).

External Environment Readiness

APEC (1999), Mutula and van Brakel (2006a), Kasraian (2007), Jutla et al. (2002), Macintyre and Ramnarine (2003), Hong (2002), Fink (1998), Barua et al. (2000a, 2000b), Fathian et al. (2008).

roso & Martinez, 2004; Whaley, 2004; Rao, 2005; Kasusse, 2005; Mariscal, 2005; Blake & Tucker, 2005; Kaiser, 2005; Martinelli et al., 2006; Srinivas, 2005; Selwyn, 2006; Willis & Tranter, 2006; Behl, 2007; Hohlfeld, Ritzhaupt, Barron, & Kemker, 2008; Xia & Lu, 2008; Jin & Hin Cheong, 2008; Salinas & Sanchez, 2009; Chung, Li, & Chen, 2010; Koutsouris, 2010), the regional digital divide refers the digital gaps between the countries of a certain region

or continent (Hargittai, 1999; APEC Economic Comittee, 2002; Hong, 2002; Wong, 2002; Beilock & Dimitrova, 2003; Quibria et al., 2003; Tanner, 2003; Ifinedo, 2005; OyelaranOyeyinka & Lal, 2005; Cuervo & Menendez, 2006; Gebremichael & Jackson, 2006; Vicente & Lopez, 2006; Cheneau-Loquay, 2007; Fuchs & Horak, 2008; Mutula, 2008; Çilan et al., 2009; Lengsfeld, 2011), and the international digital divide refers to digital inequalities between



countries in different regions and continents (Dekimpe, Parker, & Sarvary, 2000; Dewan & Kraemer, 2000; Caselli & Coleman, 2001; Dasgupta et al., 2001; Norris, 2001; Oxley & Yeung, 2001; Corrocher & Ordanini, 2002; Kiiski & Pohjola, 2002; Tiene, 2002; Pohjola, 2003; Quibria et al., 2003; Wallsten, 2003; Archibugi & Coco, 2004; Chinn & Fairlie, 2007; Bagchi, 2005; Dewan et al., 2005, 2009; Dewan & Riggins, 2005; Yartey, 2008; Fuchs, 2009; Al-mutawkkil et al., 2009; Billon et al., 2009, 2010; Andres, Cuberes, Diouf, & Serebrisky, 2010; Shirazi, Ngwenyama, & Morawczynski, 2010; Stump, Gong, & Chelariu, 2010).

5.2. Gaps in the Scale The works conducted in the area of the digital divide can be classified into three general groups with respect to their geography. The first group, which constitutes the largest share, involves studies that have been conducted on measuring and analyzing the digital divide between developed and developing countries (Dewan & Kraemer, 2000; IDATE, 2000; Dasgupta et al., 2001; Kenny, 2001; Norris, 2001; Roller & Waverman, 2001; Kiiski & Pohjola, 2002; Beilock & Dimitrova, 2003; Quibria et al., 2003; Chen & Wellman, 2004; Bagchi, 2005; Dewan et al., 2005, 2009; Dewan & Riggins, 2005; Guillen & Suarez, 2006; Chinn & Fairlie, 2007; Pick & Azari, 2008; Andonova & Diaz-Serrano, 2009). The second group, less in number than the first group, consists of some researchers who have used cross-sectional data for a specific group of developed countries (Hargittai, 1999; Choi, 2000; Ricci, 2000; Selhofer & Mayringer, 2001; Sidorenko & Findlay, 2001; Corrocher & Ordanini, 2002; Cuervo & Menendez, 2006; Vicente & Lopez, 2006; Çilan et al., 2009). The last group of studies, which is smaller than the other two groups, includes a very few studies that have been conducted to identify the factors mostly contributing to the digital divide assessment for developing countries (Dasgupta et al., 2001; Wong, 2002; Quibria et al., 2003; Tanner, 2003; Al-Solbi & Mayhew, 2005; OyelaranOyeyinka & Lal, 2005; Dasgupta et al., 2005;

Mutula, 2008; Al-Kinani & Al-Besher, 2008). In contrast to progress in the developed world, the digital divide is widening and deepening within developing countries in spite of efforts to bridge it (Kubicek, 2004). Although developing economies (middle and low income) have increased ICT access and use in recent years, the gap between income groups still remains remarkable and varies according to the type of technology, with newer technologies (such as broadband Internet) being the most unequally distributed (Billon et al., 2009, 2010). The digital divide is widening in the sense that few people actively use the Internet, and deepening in the sense that the consequences of not being online may be greater when moving beyond a subsistence level. Therefore, considering the digital divide between these countries is essential for policy makers and an attractive issue for researchers. Furthermore, even among these scarce endeavors related to evaluating the digital divide between developing countries, most focus on African countries (Center for International Development and Conflict Management (CIDCM), 2001; Compaine, 2001a, 2001b; Lall & Pietrobelli, 2002; Zachary, 2002; Mutume, 2003; Oyelaran-Oyeyinka & Nyaki Adeya, 2004; Yau Yunusa, 2004; Oyelaran-Oyeyinka & Lal, 2005; Kasusse, 2005; Ifinedo, 2005; Blake & Tucker, 2005; Gyamfi, 2005; Masal, 2005; Gebremichael & Jackson, 2006; Cheneau-Loquay, 2007; Furuholt & Kristiansen, 2007; Johan Lor, 2007; Mutula, 2008; Fuchs & Horak, 2008; James, 2009; Lotriet, Matthee, & Mazanderani, 2009) and international organizations and researchers pay less attentions to other developing regions such as the Middle East and Eastern Europe.

6. CONCLUSION ICT is an amplifier of other social and economic factors and processes. It is thus correctly regarded as having the potential to help individuals, groups and even nations leapfrog over developmental stages (van Dijk, 2006). Yet, at the same time, infusions of ICT can amplify



existing inequalities, as the effective use of ICT requires new human and social resources and can magnify differences in their distribution. The rapid rate of ICT penetration throughout the world, coupled with dramatic advances in its use in business and society, has created a remarkable amount of effort spent on measuring ICT dissemination in various parts and the levels of the different sectors, communities and countries. Within this context, composite indicators (often called indices) are increasingly used by statistical offices and national or international organizations to convey information on the status of countries in the field of ICT development - what is entitled the country’s e-readiness. However, the construction of a composite indicator is not a straightforward process. It involves assumptions which have to be assessed carefully in order to avoid the creation of dubious analytic rigor. The construction of indices involves stages where judgments have to be made: the selection of indicators, the choice of a conceptual model, the weighting of indicators etc. All sources of subjective judgment will affect the message produced by the indices in a way that deserves analysis and corroboration. However, the temptation for stakeholders and practitioners to summarize complex and sometime elusive processes (e.g. sustainability or a single-market policy) into a single figure in order to benchmark a country’s performance for policy consumption seems irresistible. Consequently, to obtain robust and valid results and outcomes from assessments of the digital divide and e-readiness that simulate the real world as closely as possible, recognizing the drawbacks of existing assessments tools, researchers need to move towards more accurate and precise tools. In this article, perusing the extensive literature on digital divide and e-readiness assessments, their trends and gaps from the standpoints of conceptualization, methodological and scale were identified. Recognizing and classifying these strengths and weaknesses is essential since it can make valuable contributions for use by researchers or top-level decision-makers or construct digital divide and e-readiness tools. In

fact, this paper, as a guideline, can help modelers to avoid the pitfalls in their development of digital divide and e-readiness tools.

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APPENDIX Table 3. The common factors of the digital divide and their sources Factors

Income/GDP

Name of Author(s)

Year of Publication

Silver

1994

Bikson and Panis

1995

Rodgers Gore and Figueiredo

1995

NTIA

1995, 1998, 1999 2000, 2002

Atkinson and Hill

1998

Hargittai

1999

Lipke

2000

Norris

2001

Primo Braga, Kenny, Qiang, Crisafulli, Di Martino, Eskinazi, Schware and Kerr-Smith

2000

van Dijk, Liset, de Haan and Rijken

2000

Caselli and Coleman

2001

Dasgupta, Lall and Wheeler

2001

Eurobarometer/ European Commission

2001–2005/2006

Natriello

2001

OECD

2001

APEC Economic Comittee

2001

Horrigan and Rainie

2002a, 2002b

Kiiski and Pohjola

2002

Muffels, Tsakloglou and Mayes

2002

Servon

2002

Bellock and Dimitrova

2003

de Haan

2003

Mossberger, Tolbert and Stansbury

2003

Quibria, Ahmed, Tschang and ReyesMacasaquit

2003

Pohjola

2003

Tseng

2003a, 2003b

Van Dijk and Hacker

2003

Chinn and Fairlie

2007

Dewan, Ganley and Kraemer

2004

Dewan and Riggins

2005

Whaley

2004

Bagchi

2005

Kaiser

2005

continued on following page



Table 3. Continued Factors

Income/GDP

Position in society or social class/ inequality, occupation

Gender

Name of Author(s)

Year of Publication

Parayil

2005

Van Dijk

2005

Guillen and Suarez

2006

Martinelli, Serrecchia and Serrecchia

2006

Prieger and Hu

2006

James

2007

Fong

2009a, 2009b


2009

Billon, Lera-Lopez and Marco

2009


2010

Van Dijk

1999, 2000, 2003, 2004, 2005

Norris

2001

Attewell

2001

DiMaggio, Hargittai, Neuman and Robinson

2001

Howard, Rainie and Jones

2001

Menou

2001

Bonfadelli

2002

Horrigan and Rainie

2002a

Park

2002

Cho, de Zuniga, Rojas and Shah

2003

UCLA Center for Communication Policy Policy

2003

Warschauer

2003

Parayil

2005

Silver

1994


1995

NTIA

1995, 1998, 1999,2000 2000, 2002

Fletcher-Flinn and Suddendorf

1996

Makrakis and Sawada

1996

Janssen Reinen and Plomp

1997

Atkinson and Hill

1998

Bimber

2000


2000

OECD-reports

2000, 2001





Gender

Age

Name of Author(s)

Year of Publication


2001


2001

SCP

2001

Von and Nielsen

2001

Hargittai

2002, 2003, 2004

Horrigan and Rainie

2002a

Kelkar and Mathan

2002


2002

Randall, Reichgelt and Price

2002

Johnson

2003

Lee

2003

Colley and Comber

2003a, 2003b

the SIBIS-project

2003

UCLA Center for Communication Policy

2003

Wilson, Wallin and Reiser

2003

Finn and Inman

2004

Fan and Li

2005

Van Dijk

2005

Tien and Fu

2008

Vekiri and Chronaki

2008

Foteinou

2010

NTIA

1995, 1998, 1999, 2000, 2002

OECD-reports

2000, 2001


2000


2000


2001

Newberger

2001

Hargittai

2002, 2003, 2004

Horrigan and Rainie

2002a





Age

Education/literacy

Name of Author(s)

Year of Publication

the SIBIS Project

2003


2003

de Haan

2003

Chinn and Fairlie

2007

Singh

2004

Whaley

2004

Keil

2005

Van Dijk

2005

Chinn and Fairlie

2007


2009


2010

Bikson and Panis

1995

NTIA

1995, 1998, 1999, 2000; 2002

Hargittai

1999


2000


2000


2001

Eurobarometer/ European Commission

2001–2005/2006


2001

Natriello

2001

OECD

2001


2002

Hargittai

2002, 2003, 2004

Kiiski and Pohjola

2002

Horrigan and Rainie

2002a, 2002b

Servon

2002

Warschauer

2002

de Haan

2003


2003

Rogers

2003

Tanner

2003

Tseng

2003a, 2003b


2003

Van Dijk and Hacker

2003

Chinn and Fairlie

2007





Education/literacy

Geographic centrality and demographic factors (population size, population distribution and density and urban versus rural population)

Name of Author(s)

Year of Publication

Norris and Conceicao

2004

Whaley

2004


2005

Kaiser

2005

Crenshaw and Robison

2006


2006

Ono and Zavodny

2007

Pick and Azari

2008

Tien and Fu

2008


2009


2009


2010

NTIA

1995, 1998, 1999, 2000

Paisley and Richardson

1999

Strover

1999

Van Dijk

1999, 2000, 2003, 2004, 2005

Hindman

2000

Parker

2000

Rural Prosperity Task Force

2000


2001

Drabenstott

2001

Lentz

2000

Natriello

2001

OECD

2001

Ramirez

2001

Graham

2002


2002

Bonfadelli

2002

Crews and Feinberg

2002

Forman, Goldfarb and Greenstein

2002





Geographic centrality and demographic factors (population size, population distribution and density and urban versus rural population)

Name of Author(s)

Year of Publication

Graham

2002

Park

2002

Servon

2002

Cho, de Zuniga, Rojas and Shah

2003

Donnermeyer and Hollifield

2003

Mills and Whitacre

2003


2003

Tseng

2003a, 2003b


2003

Chinn and Fairlie

2007

Raju

2004


2005

Bagchi

2005

Kaiser

2005

Demoussis and Giannakopoulos

2006

Gil‐Garcia et al.

2006


2006

Prieger and Hu

2006

Willis and Tranter

2006

Akca, Sayili and Esengun

2007

Chinn and Fairlie

2007

Poncet and Ripert

2007

Warren

2007

Gilbert, Masucci, Homo and Bove

2007

Pick and Azari

2008

Fucks

2009

Koutsouris

2010





Ethnicity and race

Religion/beliefs/norms/ethics

Language (English proficiency/skills)

Lifestyle

Name of Author(s)

Year of Publication

Silver

1994

Bikson and Panis

1995


1995

Atkinson and Hill

1998

Hoffman and Novak

1998

Walton

1999


2000

U.S. Department of Commerce

2002


2001

Lawson Mack

2001

Newberger

2001

Horrigan and Rainie

2002a


2002

Fairlie

2003


2003

Van Dijk and Hacker

2003


2003

Fairlie

2004

Frehill, Benton-Speyers, and Cannavale

2004

Whaley

2004

Kaiser

2005

Korupp and Szydlik

2005

Lorence, Park and Fox

2006

Prieger and Hu

2006

van Dijk

2006

Tien and Fu

2008

Fulk, Flanigan, Kalman, Monge and Ryan

1996

Floridi

2002

Floridi and Sanders

2002

Hacker and Mason

2003

Kaiser

2005

Hargittai

1999


2000

Kiiski and Pohjola

2002

Chinn and Fairlie

2007

Rojas, Straubhaar, Roychowdhury and Okur

2004




Table 3. Continued Factors Family structure and size of family

Telecommunication infrastructure/ access capacity (narrowband vs. broadband)

Motivation (lack of interest), computer anxiety, fear, technophobia

Purpose of use

Name of Author(s)

Year of Publication


2000

Tien and Fu

2008

NTIA

1995, 1998, 1999, 2000, 2002

OECD-reports

2000, 2001

Corrocher and Ordanini

2002

Horrigan and Rainie

2002b


2003


2003

the SIBIS-project

2003

United Nations Statistics Division

2004

Oyelaran-Oyeyinka and Lal

2005

Van Dijk

2005

Andonova

2006

Guillen and Suarez

2006


2006

Vicente and Lopez

2006

Chinn and Fairlie

2007

Lee and Brown

2008

Turk, Blazic and Trkman

2008


2009

Brosnan

1998

ARD-ZDF

1999

Chua, Chen and Wong

1999

NTIA

2000

Stanley

2001

Katz and Rice

2002

Rockwell and Singleton

2002

Warschauer

2002

Lenhart, Horrigan, Rainie, Allen, Boyce, Madden and O’Grady

2003

Stanley

2003


2003

Rojas, Straubhaar, Roychowdhury and Okur

2004

Van Dijk

2006

DiMaggio and Hargittai

2001





Skills, capability, experience, competency, digital/electronic/IT literacy

Human resources (capital)

Autonomy of use

Foreign investment

Name of Author(s)

Year of Publication

van Dijk

1999, 2003, 2005, 2006

Steyaert

2000


2000


2001


2003

Vihera and Nurmela

2001

Gartner Group

2002

de Haan and Huysmans

2002

Hargittai

2002, 2003, 2004

Park

2002

de Haan

2003


2003

Rogers

2003

van Dijk and Hacker

2003

Livingstone

2004

Selwyn

2006

Ferro, Helbig and Gil-Garcia

2011

Caselli and Coleman

2001


2002

Pohjola

2003


2003


2005

Andonova

2006

Guille´n and Sua´rez

2006


2001

Hargittai

1999


2005

Crenshaw and Robison

2006

World Bank

2006

Chinn and Fairlie

2007

Pick and Azari

2008





Market structure, telecommunications policy, regulation, competition, trade openness

Internet users

Equipment (PCs, mobile phones)

Name of Author(s)

Year of Publication

Hargittai

1999

Caselli and Coleman

2001


2002

Nicholas

2003


2003

Wallsten

2003, 2005

Chinn and Fairlies

2004

Yau Yunusa

2004


2005


2005

Andonova

2006

Guillen and Suarez

2006

Prieger and Hu

2006

Chinn and Fairlie

2007

Fuchs and Horak

2008


2009


2009


2010

NTIA

1995, 1998, 1999, 2000

Cooper

2000

Tiene

2002

Wong

2002

Beilock and Dimitrova

2003

Tanner

2003

Bagchi

2005

Bradshaw, Fallon and Viterna

2005


2005

Guillen and Suarez

2006

Chinn and Fairlie

2007

Tien and Fu

2008

Caselli and Coleman

2001


2001

Wong

2002


2003


2003





Equipment (PCs, mobile phones)

ICT costs and prices

Social support

Content

Policy structure

Personality dimensions

Name of Author(s)

Year of Publication

Bagchi

2005


2005

Kaiser

2005

Selwyn

2006

Vicente and Lopez

2006

Chinn and Fairlie

2007

Pick and Azari

2008

Donner

2008


2009


2010

Hargittai

1999


2002

Kiiski and Pohjola

2002

Pohjola

2003

Andonova

2006

Demoussis and Giannakopoulos

2006

Vicente and Lopez

2006

Chinn and Fairlie

2007


2009


2010


2001


2006

Welling and Kubicek

2000

Stanley

2003

Norris

2001


2001


2002

Kiiski and Pohjola

2002

Bellock and Dimitrova

2003

Mossberger, Tolbert, and Stansbury

2003

Andonova and Diaz-Serrano

2009

Hudiburg

1999

Finn and Korukonda

2004

Van Dijk

2005
