Who Goes Online

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Who goes online? Evidence of internet use patterns fromrural Greece

Anastasios Michailidis n, Maria Partalidou, Stefanos A. Nastis,Aphrodite Papadaki-Klavdianou, Chrysanthi Charatsari

Aristotle University of Thessaloniki, School of Agriculture, Dept. of Agricultural Economics, 54124 Thessaloniki, Greece

a r t i c l e i n f o

Available online 17 March 2011

Keywords:

Adoption

Categorical variables

Diffusion

Internet

Multivariate analysis

Rural development

a b s t r a c t

This paper attempts to reveal the heterogeneity of Internet users in rural areas. Who is

really using the Internet? Is it the farmer or other members of the rural family? Can

rural areas be seen as a homogenous space or do different types of Internet users exist?

Research is based on both the descriptive statistics and multivariate analysis techniques

on a sample of 920 individuals in three administrative regions of rural Greece. Basic

findings suggest that less than one out of three rural residents go online. A set of

perceived potentials and pitfalls of Internet use are analyzed. Social networking and

e-mail are the principal uses of the Internet, classifying users into three distinct types.

For basic users, Internet access is influenced by income and gender while the socially

interactive users are influenced by the existence of a young member in the family. For

farm oriented users, Internet access is influenced by the digital divide between rural

and urban location and by farmers competency. The typology of users along with their

perceptions regarding Internet use can provide useful policy insights on the ways thatInternet access can contribute to diffusion of innovation and rural development.

& 2011 Elsevier Ltd. All rights reserved.

1. Introduction

In light of the European economic and social cohesion objective, great interest has been placed towards investments in

the development of Information and Communication Technologies (ICTs), since they are linked to productivity growth,

innovation and regional development (European Commission, 2009). It is true that all tasks can be accomplished without

using the Internet (Warren, 2007), but the Internet allows everything to happen more flexibly and cheaply ( Davidson & Yu,

2005). Especially in rural areas, the importance of Internet use to the quality of life is well documented. The main benefits

include reducing isolation and eliminating much of the hardships of rural living and the drawbacks of rural entrepreneur-ship (Akca, Sayili, & Esengun, 2007; Korsching, 2001; Sun & Wang, 2005). In particular, the Internet revolution has long

been deemed a catalyst of development and change in todays knowledge-based societies, reinforcing new forms of social

and business interaction, as well as allowing use of improved services and helping overcome digital inequalities. Hence, it

is considered one of the key drivers for social and economic welfare (Verdegem & Verhoest, 2009).

Previous research shows that rural areas are underserved and have lower Internet penetration rates (Bell, Reddy, &

Rainies, 2004) compared to urban areas. This situation is likely to persist in the future due to a number of factors, similar to

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Telecommunications Policy

0308-5961/$- see front matter & 2011 Elsevier Ltd. All rights reserved.doi:10.1016/j.telpol.2011.02.006

n Corresponding author. Tel.: 30 2310998783; fax: 30 2310998828.

E-mail addresses: [email protected] (A. Michailidis), [email protected] (M. Partalidou), [email protected] (S.A. Nastis),

[email protected] (A. Papadaki-Klavdianou), [email protected] (C. Charatsari).

Telecommunications Policy 35 (2011) 333343
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those that for a long time have limited telephone access to rural and low-income communities; customers are scattered

and often cannot afford high-priced Internet services. Nevertheless, some recent estimates indicate a near parity, in terms

of basic Internet access, albeit the broadband gap (IWS, 2010; Nielsen, 2009). Despite the fact that Internet diffusion in

rural areas is increasing, some believe that there will always be a part of the population that will not use it (Warren, 2007),

while others that it will hold both opportunities and threats (Sun & Wang, 2005).

In Greece, there are only 1.4 broadband subscribers per hundred inhabitants (2005 data). Hence, the OECD (2006) listed

Greece at the bottom of the OECD countries list. However, in terms of growth rates (from 2001 onwards), Greece was

ranked in the 10th place. In fact, according to the European Commission (2009), during the last four years there has beenan increase in DSL coverage (from 9% to 88%) in the whole country and an increase from 0% to 55% in rural areas. The

national broadband policy aims to cover 60% of rural territory and 90% of rural population by 2012. Even though Internet

availability is drastically increasing, there is still a lack of knowledge concerning Internet subscription rates, Internet

access of farms, the potentials and pitfalls of Internet development in rural areas and the factors that influence patterns of

Internet subscription and use.

Focusing specifically in rural areas (which are geographically dispersed and face great inequalities), mapping the users

profiles over different attitudes and characteristics (such as age, gender, education and geography/ruralurban location)

provides a better understanding of the current situation in rural Greece. Drawing from empirical data in Northern Greece,

this research can provide the basis for further discussion about the ways rural residents and entrepreneurs can evaluate

the gains (direct or indirect) from Internet use and have a better understanding of the outcomes of any digital revolution

that would affect the overall quality of life in rural areas.

The following section provides a brief overview of the diffusion theory, which is followed by a presentation of the case

study area and the research methodology (providing details on the survey data and the econometric model). Onwards, theresults are presented according to the basic rationale. In the final section conclusions and policy implications are

elaborated and future research is discussed.

2. Diffusion theory

As research on Internet diffusion grew, many aspects have been elaborated in an extensive body of literature. This

includes studies of the diffusion of ICTs amongst countries (Andres, Cuberes, Diouf, & Serebrisky, 2010), the role of

governments (Kim, Jeon, & Bae, 2008), technologies used (Lippert & Spagnolo, 2008), potentials and pitfalls (Malecki,

2003), ways people use the internet and success factors in Internet penetration.

Rural ICT development has been widely examined developing theories of broadband deployment ( Sawada, Cossette,

Wellar, & Kurt, 2006; Strover, 2003), digital divide (Norris, 2001, p. 112; Xia & Lu, 2008), e-government (Seifert & Chung,

2009; Thompson, 2002) and universal service (Blackman, 1995; Milne, 1998). A point made in the literature is that the

most important impact of ICTs is that they have lessened the attachment of the local community and the interdependenceamong people, especially during the recent European farm crisis (Moseley & Owen, 2008).

The present research is underpinned by the rationale that the Internet represents an innovation in rural areas that can

be used as a substitute for extension services provided on the field. In fact, according to Koutsouris (2006) the national

public agricultural extension system in Greece today has been trapped in a bureaucratic-administrative role, leading to the

provision of inadequate services to rural residents. Therefore, explaining how ideas and innovations (and in this case the

Internet) are spread and used in rural areas is of great importance for the agricultural sector as well.

Diffusion theory mainly seeks to explain the spread of new ideas and innovations. Although the origins of the theory

vary and span across multiple disciplines, six main traditions that impacted diffusion research have been identified

(Rogers, 1962, p. 137): anthropology, education, early sociology, rural sociology, medical and industrial sociology.

Diffusion theory posits that there are many qualities in people that make them accept (or not) a new idea or a new

product. In addition, there are many qualities of innovations that can make people to accept them with enthusiasm or not.

Generally there are five stages in the process of innovation adoption (Rogers, 1995, p. 83): knowledge, persuasion,

decision, implementation and confirmation. After adopting an innovation, the individual, based on his own personalexperience, makes a final decision on whether or not to continue using it. Diffusion theory is also concerned with the rate

at which innovations spread. Actually, some people hold out for a long time and continue using older methods while

others adopt an innovation immediately. The relative speed with which members of a social system adopt an innovation

depends on many factors and it is usually measured by the time required for a certain percentage of the members of a

social system to adopt an innovation (Rogers, 1962, p. 134).

The basic concept underlying diffusion theory is that adopters of an innovation do not adopt the innovation

independently, but instead influence each others adoption decisions (Rogers, 1995, p. 13). According to Rogers (1995,

p. 33), the influence of early adopters on later adopters is often called word of mouth communication, a term referring to

a much broader set of phenomena than adopters simply talking to each other.

3. Study area profile

Data were collected from three administrative regions in Northern Greece (NG): West Macedonia (RWM), CentralMacedonia (RCM) and East Macedonia-Thrace (REMT) (Fig. 1). The study area covers 42,878 km2, representing 32.6% of the

A. Michailidis et al. / Telecommunications Policy 35 (2011) 333343334


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countrys total land (NSSG, 2003). The landscape consists mainly of highlands (47.8%), forests (22.3%), rangelands (33.4%)

and cultivations or fallow land (26.0%), and the majority is considered rural ( NSSG, 2009). The study area has been chosen

since it is representative of rural Greece, in terms of Internet use. Moreover, according to data provided by NSSG (2010):

(a) RCM is the most developed Greek region in terms of economic development, (b) RWM is one of the three least

developed and (c) REMT expresses the mean values of all thirteen Greek regions. Thus, the study area is a mosaic of

different landscape and socioeconomic structures, rather representative of Greece, and results can therefore be

generalized.

4. Methodological framework

The survey was designed to collect information on several issues related to rural life and Internet use. Part of the survey

was designed to elicit data on respondents Internet use and their views on twenty-four prospective changes, desirable or

undesirable, all drawn from the literature (Moseley & Owen, 2008). To encourage participation and minimize the cognitive

burden on respondents, most questions were framed using Likert scale intervals. All respondents were heads of the rural

household, drawn from a sampling frame in each region provided by several official lists kept by the regional authorities.

These lists including members of rural cooperatives, municipal polls and lists of other rural residents were compiled from

several sources. Participants in each region were selected at random from the respective sampling frame. Data were

collected through a mail-out/telephone response format. All questionnaires were mailed out in batches of 30 per week

from January through July 2007. Respondents were then contacted by telephone and asked if they would participate. They

could either complete the forms on their own and return them by post, or respond over the telephone. By July 2007, 920

questionnaires had been collected from 2500 mailed, a response rate of 36.8%.

In this paper, both descriptive statistics and two multivariate analysis techniques were used using the Statistical

Package for Social Sciences (SPSS): Two-Step Cluster Analysis (TSCA) and categorical regression (CATREG). Although themethodology of the chosen empirical techniques is rather novel in telecommunication policy issues, they have been

selected due to their ability to optimally handle categorical variables. Indeed, much of the data that political scientists deal

with are qualitative in nature and most other data are at best ordinal ( Berry & Lewis-Beck, 1986, p 78). Political scientists

often analyze data as though they meet the criteria for an interval scale, even though they fail to meet the assumption

required for the methods used. However, with the categorical methods available for analyzing qualitative data, one does

not need to make the types of questionable assumptions that are often made.

In order to classify Internet users in discernible clusters, with similar adopting behavior, the TSCA was first employed as

a scalable cluster analysis algorithm designed to handle large datasets, revealing natural groupings within a data set that

would otherwise not be apparent (Siardos, 2002, p. 56). Traditional clustering methods are considered effective and

accurate on small datasets and usually do not scale up well to large datasets unless these datasets are first reduced into

smaller ones. Moreover, traditional clustering methods cannot optimally handle categorical variables, as well as attributes

most commonly found in sociological research surveys (Zhang, Ramakrishnon, & Livny, 1996). Although TSCA requires

only one data set, it follows a two-step procedure: the first step pre-clusters the cases into many small sub-clusters, andthe second step clusters the sub-clusters of the first step into the desired number of clusters. The algorithm can also

Fig.1. Administrative regions and prefectures of Greecestudy area.

A. Michailidis et al. / Telecommunications Policy 35 (2011) 333343 335


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automatically select the number of clusters. Since the number of sub-clusters is smaller than the number of original

records, traditional clustering methods can be used effectively (SPSS, 2007, p. 113).

Additionally, CATREG (Van der Kooij & Meulman, 1997) has been used in order to highlight possible relations between

Internet use and a set of other selected independent categorical variables. In fact, CATREG (one of the recent options in

SPSS ver.17) is a modern regression technique, much more holistic and effective than the multiple regression analysis and

the analysis of multiple regression with categorical variables. Actually, the CATREG model can deal more optimally with

both qualitative and quantitative data, as it works on two discrete and simple stages: firstly, the nominal and ordinal

variables are transformed to interval scales, in order to maximize the relationship between each predictor and thedependent variable, and secondly, multiple regression analysis is applied to the transformed variables ( SPSS, 2007, p. 188).

The main difference between CATREG and multiple regression analysis with categorical variables is the option for handling

how categorical variables are coded for parameterization of the coefficients: reference cell or effect cell parameterization.

CATREG tools provide the framework for choosing between reference cell and effect cell parameterization. This means that

categorical variables, or interaction terms that include categorical variables, will drop or add the entire variable or

interaction term and evaluate changes in model fit, rather than dropping one categorical level at a time. Comparatively,

even though CATREG is relatively complicated and sophisticated involving advanced statistical techniques such as optimal

scaling techniques for multivariate categorical data analysis, there are several advantages in using this model as well. The

main advantage is that categorical regression can be run with the least assumptions: (a) the normality assumption of the

predictor variables is relaxed, (b) factor levels are coded simultaneously into values, therefore sample sizes need not

necessarily be large, (c) only one coefficient is needed for a predictor variable and (d) non-linear associations can be

detected with these models.

Relative importance indicates the importance of each predictor, using Pratts measure (Pratt, 1987). This measure isroughly equivalent to the product of the regression coefficient and zero-order correlation. The Pratt index is primarily used

to uncover suppressor variables. That is, in the case that a predictor yields a relatively high beta but low importance, the

situation suggests that the variable may have been suppressed by other predictors. In addition, partial and part

correlations are similar to zero-order correlations, except that the effect of all other predictors has been controlled.

Finally, tolerance is utilized to identify multicollinearity. According to the econometrics literature (Pratt, 1987; Siardos,

2002, p. 109; SPSS, 2007, p. 212) relative importance measures are much more useful than the usual standardized beta

weights. In particular, relative importance indicates the percentage of explanation of the dependent variable while they

can also be used to predict the future values of the dependent one.

5. Results

The sample summary descriptive statistics (Table 1) show that the representative respondent in the study area is male,34 years old, married, with 11.5 years of education and has a household of 2.7 members. More than half (58.1%) of the

respondents are full-time farmers, with a median net monthly income of h1048 and farming is the source of 44.3% of total

household income. The average distance of the representative household from an urban center is 36.5 km and less than

one fourth of the sample (22.5% of the households or 207 cases) use the Internet. The later is the first interesting finding of

the analysis.

The majority of the sample (37.5%) does not have Internet access, neither at home nor at the office. Consequently,

further analysis regarding the causes of Internet use is based on the aforementioned 207 cases of those actually using the

Internet. Examining the profiles of those 207 cases, significant differences between Internet users are drawn in terms of

their income, age and residence (Table 2). High income households (more than h25,000 annually) are 3.25 times more

Table 1

Sample summary statistics (920 cases).

Personal characteristicsMale (%) 80.0

Average age (years) 34.0

Average years of education 11.5

Married (%) 55.4

Households characteristics

Average size (persons) 2.7

Median monthly income (h) 1048

Average total household income from farming (%) 44.3

Farming full time (%) 58.1

Average distance from urban center (km) 36.5

Internet use (%) 22.5

Within the regions

West Macedonia (%) 39.1

Central Macedonia (%) 43.5

East Macedonia-Thrace (%) 17.4



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likely to have Internet access at home or at work, whereas households with a member less than 18 years old and

households or businesses located near urban areas (less than 10 km) are 1.75 and 1.22 times more likely to have Internet

access, respectively. The aforementioned findings do not come as a surprise and are compatible to what Madden andCoble-Neal (2003) also note about Internet use increasing with household income; as well as to what Bell et al. (2004)

show, that middle and upper income households, in both rural and urban areas, are more likely to go online. In fact,

income differences play a major role in explaining the digital divide (Andres et al., 2010) and lower levels of income are

consistently shown to be associated with ICT inequalities (Verdegem & Verhoest, 2009).

It has also been argued in the literature that relative advantages of Internet use are accompanied by absolute

disadvantages (Warren, 2007). To verify this statement, users perceptions over the Internet, as a positive or negative

change driver in their household or their business, were investigated. Based on twenty four different probable changes

(Moseley & Owen, 2008) as a result of Internet development, this research highlights fifteen of them as rather positive

(potentials) and nine of them as rather negative (pitfalls).

In fact, there is a strong and positive relation between Internet development and six separate prospective and desirable

changes: (a) capacity for communication, (b) rural system change, (c) increased productivity, (d) social change, (e) home

based rural business and (f) change in recreation patterns (Table 3). Furthermore, respondents support the argument that

Internet development also enforces cultural change, agricultural economic growth and tourism development. Results alsodemonstrate some pitfalls of Internet development in Greek rural areas according to the ranking of undesirable Internet

Table 2

Profile of households with-without Internet access.

207 cases (%) 713 cases (%)

Net annual income

Less than h12,500 10.7 89.3

h12,501h25,000 19.8 80.2

More than h25,001 34.7 65.3

Resident aged under 18 years

Yes 43.4 56.6

No 24.7 75.3

Proximity to an urban center

Less than 10 km 18.7 81.3

More than 10 km 15.3 84.7

Table 3

Potentials and pitfalls of Internet development (920 cases).

Internet effectsn Mean: 1strongly disagree, 2disagree, 3neutral, 4agree, 5strongly agree

Over-information (1) 4.4 (S.D.0.5) Pitfall

Personal data loss (2) 4.2 (S.D.0.5) PitfallUnfamiliarity (3) 4.1 (S.D.0.7) Pitfall

Immoderately expectations (4) 3.8 (S.D.1.1) Pitfall

Target disorientation (5) 3.5 (S.D.1.2) Pitfall

Dependency (6) 2.5 (S.D.0.9) Pitfall

Increased cost (7) 1.8 (S.D.0.3) Pitfall

Time loss (8) 1.3 (S.D.0.4) Pitfall

Other negative effects (9) 1.2 (S.D.0.6) Pitfall

Capacity for communication (1) 4.5 (S.D.0.3) Potential

Rural system change (2) 4.3 (S.D.0.6) Potential

Increased productivity (3) 4.2 (S.D.0.7) Potential

Recreation (4) 4.1 (S.D.0.2) Potential

Home based rural business (4) 4.1 (S.D.0.5) Potential

Social change (4) 4.1 (S.D.0.7) Potential

Cultural change (5) 3.9 (S.D.0.8) Potential

Agricultural growth (6) 3.8 (S.D.1.0) Potential

Increased tourism (7) 3.5 (S.D.0.5) Potential

Personal mobility (8) 2.7 (S.D.1.4) Potential

Government policies (9) 2.6 (S.D.0.9) Potential

Time gain (9) 2.6 (S.D.1.4) Potential

e-education (10) 2.2 (S.D.1.1) Potential

Other positive effects (11) 1.8 (S.D.1.3) Potential

Demographic change (12) 1.3 (S.D.0.4) Potential

n Respondents were asked to indicate their agreement or disagreement to the prospective Internet effects giving an internal value for each one of

them. Numbers in parentheses indicate the ranking sequence according to the mean values.



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effects. The main pitfalls of Internet development according to subscribers are the following: (a) over-information,

(b) personal data loss, (c) unfamiliarity, (d) immoderate expectations and (e) target disorientation.

Regardless of what users believe of the Internet as a change driver, their access is driven by several factors ( Hasan &

Isaac, 2011). In addition, Internet use should not be considered simply a tool that makes life easier (Babar, Mahalle, Stango,

Prasad, & Prasad, 2010). Numerous studies have argued that Internet diffusion is different for each society; within a

complex process whereby social, economic, political and technological factors shape each other ( Jo, Hwang, & Lee, 2010;

Madden & Coble-Neal, 2003; Seifert & Chung, 2009; Xia & Lu, 2008). Table 4 presents the main reasons for Internet use,

based on previous literature and adapted to the specificities of the study area. According to the ranking of the reasons forusing the Internet, social networking holds the first position (80.2%), followed by e-mail use (69.6%), weather information

(29.9%), market information (24.6%) and educational use (20.8%).

To further support the arguments presented above and contribute to the debate of a typology of users, cluster analysis

was employed in order to reveal natural groupings within the given set of respondents. TSCA classified respondents into 3

different clusters according to the Bayesian Information (BAIC) criterion (SPSS, 2007, p. 95). The majority of respondents

(104% or 50.2%) were included in the second cluster, 72 (34.8%) of them were included into the first cluster and 31 (15.0%)

of them were included into the third one. Results show ( Table 4) that the first cluster constitutes households that use the

Internet mainly for e-mail, purchasing goods, online banking and maintaining their web site. In the second cluster,

households mainly use the Internet for social networking and in the third cluster households mainly use the Internet to

access information related to farming problems, such as weather uncertainty, market price data, technical problems,

knowledge inadequacy, etc. The first cluster can be designated as Basic services users, the second cluster as Socially

interactive users and the third cluster as Farm orientated users.

At this point, it is also important to analyze the non-users and their reasons for not using the Internet. According to theliterature (Madden & Coble-Neal, 2003; Nielsen, 2009), the main constraints of Internet subscription and use are peoples

beliefs: (a) the Internet is an expensive technology, (b) use of the Internet requires specific knowledge of computers and

(c) the Internet is not a necessary technology or the value of being online is limited. Although results confirm the

aforementioned constraints, the non-subscribers of the study area report some additional reasons ( Table 5). These are

mainly: Internet unfamiliarity, Internet unavailability, family disagreement over Internet use, time loss and general fear.

Onwards, reliability analysis (Bohmstedt, 1970; SPSS, 2007, p. 68) for eleven independent variables was used in order to

determine the extent to which these items are related to each other, to get an overall index of the internal consistency of

the scale as a whole, and to identify items that had to be excluded from the scale. In fact, none of the independent variables

Table 4

Distribution of observations within each cluster according to reasons for Internet use (frequencies and percentages).

Internet use (very often or often) Clusters

1st (72, 34.8%) 2nd (104, 50.2%) 3rd (31, 15.0%)

1. E-mail (144 respondents, 69.6%) 72/144 (50.0%) 50/144 (27.7%) 22/144 (15.3%)

2. Weather info (60 respondents, 29.9%) 7/60 (11.7%) 22/60 (36.7%) 31/60 (51.7%)

3. Technical info( 36 respondents, 17.4%) 1/36 (2.8%) 11/36 (30.5%) 24/36 (66.7%)

4. Market info (51 respondents, 24.6%) 15/51 (29.4%) 8/51 (15.7%) 28/51 (54.9%)

5. Educational use (43 respondents, 20.8%) 4/43 (9.3%) 16/43 (37.2%) 23/43 (53.5%)

6. Online banking (33 respondents, 15.9%) 20/33 (60.6%) 5/33 (15.2%) 8/33 (24.2%)

7. Social networking (166 respondents, 80.2%) 48/166 (28.9%) 101/166 (60.8%) 17/166 (10.3%)

8. Buying ( 35 respondents, 16.9%) 22/35 (62.8%) 3/35 (8.6%) 10/35 (28.6%)

9. Selling (12 respondents, 5.8%) 4/12 (33.3%) 1/12 (8.4%) 7/12 (58.3%)

10. Own web site (14 respondents, 6.8%) 9/14 (64.3%) 0/14 (0.0%) 5/14 (35.7%)

11. Other reasons (10 respondents, 4.8%) 5/10 (50.0%) 4/10 (40.0%) 1/10 (10.0%)

Sample: 207 out of the total 920 cases.

Table 5

Reasons for not subscribing to Internet (713 cases).

Reasons

Unfamiliarity 71.8%

Lack of computer literacy 67.3%

Not necessary technology 65.9%

Too expensive 53.6%

Limited value of being online 52.2%

Not available 30.0%

Family disagreement 21.9%

Time loss 18.1%

Fear 13.0%

Other reasons 3.6%



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were excluded. The value of Cronbachs alpha (a) reliability coefficient was found equal to 0.81 (SPSS, 2007, p. 70), thus

indicating that the Internet use scale is reliable. Friedmans two-way analysis of variance, with x22.6244 (a0.00) and

Hotellings T21.0609 (F30.14 and a0.00), indicated the significance in differences of item means. Having accepted the

consistency of the eleven items, the average rankings for each respondent were used as the numerical values of the

dependent variable Internet use, which, along with the categories of fourteen other independent variables, are shown in

Table 6.

Investigating further the decision of Internet use in order to reveal how Internet use is influenced by personal

characteristics of the respondents, a CATREG model was employed in the dataset of each cluster ( Table 7). The modelyielded goodness-of-fit (R2) values ranging from 0.738 (3rd cluster) to 0.867 (2nd cluster) indicating moderate relation

between the Internet use/access and the group of selected predictors. However, since R240.70, it is indicated that more

than 70% (from 73.8% to 86.7%) of the variance in the Internet use rankings is explained by the regression of the

optimally transformed variables used. The aforementioned high values of R2 are not unexpected as CATREG usually

maximizes the strength of the relation between the dependent variable and the elected predictors. For instance, when the

survey samples are large, the yielded R2 are usually higher in comparison to multiple regression. In addition, the F-statistic

values (from 7.16 to 7.98) with corresponding a0.00 indicate that this model is always performing well.

The relative importance measures (Pratt, 1987) of the independent variables show that the most influential factors

predicting the Internet use decision in the first cluster correspond to income (accounting for 31.6%), followed by

price (22.8%), gender (19.6%) and PC (11.8%). The relative importance measures of the independent variables, which

are reported in the second cluster, are higher for the variables young residents, education and age. Finally, the

relative importance of the independent variables in the third cluster are high for the variables region, distance,

employees and training. The total percentage of the internet use decision, which is explained by the estimated threeor four independent variables, in each cluster, is calculated in the last column of Table 7. The additive importance of the

estimated independent variables account for about 85.8%, 88.3% and 93.8% for the first, second and third cluster,

respectively.

Although the relative importance measures can predict the contribution of each independent variable on the dependent

one, they cannot indicate the direction of this interpretation. Transformed plots, of the main independent variables,

presenting the higher relative importance measures can give a better prediction of Internet use (Fig. 2). The original

Table 6

Selected independent variables.

Ind. variables Type Categories (description) Mean S.D.

Price Numeric Cost of internet access (monthly estimations) h28.32 7.61

Region Nominal 1RCM, 2RCM and 3REMT

Gender Nominal 1male, 2female

Distance Numeric Distance between respondent residence and the

nearest urban place (in km)

36.54 km 8.73

Income Ordinal 1less than h15,000, 2h15,001h25,000,

3h25,001h35,000, 4h35,001h45,000 and

5more than h45,001 (annual base)

h12,576 6128

Education Ordinal 1six or less years, 2from 7 to 9,

3from 10 to 12, 4more than 12

years and 5University education

(years of education)

11.51 3.42

PC Numeric Number of personal computers installed 1.26 0.19

Persons Numeric Number of persons residing in the households 2.73 1.18

Tertiary Nominal 1degree qualified, 2otherwise

Training Nominal 1vocational qualification, 2otherwise Young residents Numeric Number of residents aged under 18 years 0.67 0.32

Marital Status Nominal 1married, 2not married

Age Numeric Respondents age 34.02 12.88

Employees Numeric Number of persons employed full time 0.73 0.27

Table 7

Relative importance measures.

Cluster N R 2 F Relative importance measures Total explanation

1st 72 0.781 7.16 Income (0.316) Price (0.228) Gender (0.196) PC (0.118) (85.8%)

2nd 104 0.867 7.98 Young residents (0.386) Education (0.278) Age (0.219) (88.3%)

3rd 31 0.738 7.66 Region (0.312) Distance (0.274)e Employees (0.188) Training (0.164) (93.8%)

Dependent variable: Internet use (very often or often).



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category values are displayed on the x-axis, and the obtained category quantifications on the y-axis. The higher

quantification received by the original category, the greater the contribution of this category in the interpretation of

the dependent variable (Internet use). Within the first cluster, the most influential factors predicting the Internet use

decision are annual income (more than h35,000), price (more than h25), gender (male) and PC (2 or 3 PCs).

Although, this finding contrasts with the vast majority of the similar studies, it is rather predictable and prospective. In

Greece, the competition among Internet companies works well shaping a median monthly Internet access fee of more than

h25. Actually, only a few smaller companies offer lower prices but they are not providing reliable services. Thus, the fact

that high prices correspond to better service levels helps explains this contradiction. The most influential factors predictingthe Internet use decision, in the second cluster, are the number of young residents (one or more), education (more

than 7 years) and age (less than 46 years old). Finally, in the third cluster, the most influential factors predicting the

Internet use decision are region (RWM), distance (more than 10 km from urban areas), employees (more than 4) and

training (vocational qualification).

Fig. 2. Transformed plots.



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6. Conclusions and discussion

Information and Communication technologies are considered, among other things, as rural development indicators for

a region, linking innovation and socioeconomic cohesion. The EU emphasizes the contribution of ICTs to the economy,society and personal quality of life with the framework Digital Agenda 2010, which aims at getting more people online.

Evidence from Greece provided by this study shows that this policy challenge will be faced for a long time in rural areas,

where more than three quarters of households still do not have Internet access.

Limited Internet penetration rates exist because of technical constraints, due to lack of infrastructure, and because of

information constraints, due to lack of skills and limited perceived value of being online. The experience to date has not

been encouraging, on many fronts. Contemporary limited levels of Internet use in rural Greece may be accounted for by a

number of reasons found amongst users and non-users, both in terms of the demand-side and the supply-side. It seems

that Internet availability will not be a problem in the future, since attempts are being made and figures show that the

urbanrural divide and lack of infrastructure are dealt by national initiatives. The question raised, however, is why

penetration rates remain low despite all infrastructural development in rural Greece. The answer can be only found when

elaborating into demand side issues, as examined in this research.

An indicatory dataset has been analyzed using TSCA, CATREG models and descriptive statistics in order to classify the

subjects and to determine possible relations between the decision to use the Internet and several personal or householdcharacteristics. The results overall indicate three discrete clusters of respondents with different Internet use behavior. The

Fig. 2. (Continued)



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prevailing cluster is the one using the Internet for socializing, and is very much influenced by the construction of the rural

family and especially the existence of a youngster who is in fact the one using the Internet access subscription of another

family member. Almost half of the sample falls into this category, raising issues of disseminating successful forums and

social groups within the Internet in other sectors of the local economy in rural areas (for example rural tourism) or other

rural areas worldwide in order to further stimulate all rural inhabitants.

Today, it is well documented that the diffusion of ICTs has an influence on social networking and is influenced by the

same; one of the most important reasons for higher Internet penetration rates, both in rural and urban areas. Actually,

social networking through sites such as Facebook, Twitter, etc. is not restricted only to younger users but rather to thosemore sophisticated and skilled. Therefore, policies must pay attention to social capital analysis and theories, which can

help towards specific measures and actions. Implementing projects and initiatives aiming at the less skilled and familiar to

the use of such social networking groups could increase Internet penetration.

Increasing Internet penetration in rural areas can be based on diffusion of best practices and models found elsewhere,

either in other areas or other sectors of the economy. Forums and blogs for farmers, for example, have emerged in many

countries run by farmers or other bodies related to agriculture. In Greece, though not so advanced, a farmer can

communicate with other farmers or administrative bodies using Facebook, Twitter, through sites of newspapers, TV

channels, agricultural suppliers, etc. The challenge for policy makers in this case would be not merely disseminating such

forums but rather to create the desire and need for the non-users to finally use services provided through the Internet. If

farmers realize the added value of the Internet and social networking models to their everyday life, then there is a greater

chance that they will be involved. In any case, such policies must always place a great importance in finding the most

progressive audience, which means work in the field with existing networks in local communities, as well as finding and

using facilitators at a local level.This paper underlines that rather than looking at who has access and who has not, the focus on policy implications

should be on the added value of the use of Internet to peoples lives. Besides, awareness over the potentials of Internet use

must be raised to both users and non-users since findings from this research indicate that even those that go online are not

fully aware of the benefits Internet development offers. Limited value of Internet use and time loss, for example, were

statements from both users and non-users that were unexpected in this research. An attempt to stimulate the non-users

that might be intimidated by either the cost of Internet subscription or their lack of skills has been made by providing free

Internet hot spots (in parks and squares) in some of the countrys marginalized peripheries. Such initiatives provide

grounds for building awareness and might be considered as a policy tool for the future. Educational programs in rural areas

must be aiming at communicating the advantages of Internet use and at familiarizing people with its use. The latter is

useful, since those with Internet access quoted in this research that they often find themselves lost in translation by the

abundance of information provided through the Internet. Therefore, the support of vocational training in rural areas must

be taken under consideration as a communication tool for local stakeholders in order to reach the rural population and

contribute to further development of the Internet and ICTs in general.Rural residents are still latecomers in using the Internet for their business. They use the Internet for finding information

regarding farm techniques and educative purposes, replacing in a way farm extensions, which in Greece at the time being

are under restructure and reorientation. In any case, the effectiveness of Internet diffusion amongst farmers is very much

still related to the ruralurban divide. As presented in the findings within the farm oriented cluster, distance from an urban

center and the overall infrastructure influences their use patterns. Therefore, policy measures at a local level must cope to

the EU future goal of Internet for all by finding answers in terms of actions on how to make the Internet accessible and

affordable in all rural areas. Less important for the time being is the dissemination of social networks web blogs and other

online groups as an information pool, since results showed that they are less appealing to farmers.

It is still too early in the diffusion process to estimate the full future potential of the new ICTs and especially the

Internet. Further research is needed specifically targeted on non-users in order to fully understand the situation and

provide an overall future policy framework.

Although several studies have been conducted in Greece aiming at measuring ICTs adoption parameters, this study

applies non-linear methodologies in order to handle and accommodate categorical variables as well as attributes. Theinnovative use of non-linear methodologies for decision-making presented in this paper provides a methodological

framework suitable for a number of applications. Thus, a combinational use of CATREG model with a TSCA can be very

practical for future research, in the examination of any human decision where some variables are not multiple nominal.

However, as a first systematic attempt, the study was limited to a rather small area and a cross-section of observations.

Therefore, due to the small number of subjects and due to the indefinable number of potential Internet users (population),

results should be seen with certain caution when used for generalizations. In any case, findings could benefit from a

follow-up qualitative research within clusters, in order to elaborate to who is really online in rural Greece.

EU information policy to date has been influenced mainly by a strong technology dimension with an emphasis on the

installation of infrastructure and necessary equipment, which is understandable in the early stages of the diffusion

process. Besides, many rural areas find themselves at a disadvantage in terms of access to and the cost of sophisticated

infrastructure and advanced services; a drawback that is only perceived as official figures show efforts of overcoming

infrastructure inequalities. In addition, although Internet is no substitute for entrepreneurship or for well thought out

strategies for development, the potential that Internet use presents, within a more enlightened policy environment, shouldnot be underestimated. Despite the many disappointments, it would be a serious error to underestimate the potential that



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the Internet can contribute towards sustainable rural development within a more enlightened policy framework. No doubt

a different approach is required, which appreciates the fundamentally subordinate role that the Internet must play within

an integrated strategy, and much greater emphasis should be placed on enhancing human capital. Thus, the EU

information policy for rural areas should be reformulated in order to focus on the social dimension, as skepticism grows

about wasted resources, poorly thought out projects and false expectations.

References

Akca, H., Sayili, M., & Esengun, K. (2007). Challenge of rural people to reduce digital divide in the globalized world: Theory and practice. GovernmentInformation Quarterly, 24, 404413.

Andres, L., Cuberes, D., Diouf, M., & Serebrisky, T. (2010). The diffusion of the Internet: Across-country analysis. Telecommunications Policy, 34(56),323340.

Babar, S., Mahalle, P., Stango, A., Prasad, N., & Prasad, R. (2010). Proposed security model and threat taxonomy for the Internet of Things (IoT).Communications in Computer and Information Science, 89, 420429.

Bell, P., Reddy, P., & Rainies, L. (2004). Rural areas and the Internet. Rural Americans Internet use has grown, but they continue to lag behind others . PeeInternet & American Life Project, Retrieved from /http://www.pewtrusts.org/news_room_detail.aspx?id=17062S.

Berry, W. B., & Lewis-Beck, M. S. (1986). New tools for social scientists: Advances and applications in research methods. Beverly Hills, London, New Delhi:SAGE Publications.

Blackman, C. R. (1995). Universal service: Obligation or opportunity. Telecommunications Policy, 19(3), 171176.Bohmstedt, G. W. (1970). Reliability and validity assessment in attitude measurement. In G. F. Summers (Ed.), Attitude measurement(pp. 8099). Chicago:

Rand-McNally & Co.Davidson, A. P., & Yu, Y. (2005). The Internet and the occidental tourist: An analysis of Taiwans tourism websites from the perspective of western tourists.

Information Technology and Tourism, 7(2), 91102.European Commission (2009). European digital competitiveness. Report No. IP/09/1221, August 2009. Retrieved from /http://europa.eu/rapid/

pressReleasesAction.do?reference=IP/09/1221&format=HTML&aged=0&language=EL&guiLanguageS.Hasan, S. S., & Isaac, R. K. (2011). An integrated approach of MAS-CommonKADS, model-view-controller and web application optimization strategies for

web-based expert system development. Expert Systems with Applications, 38(1), 417428.IWS (2010). Internet world stats: Usage and population statistics. Retrieved from /http://www.Internetworldstats.com/stats2.htmS.

Jo, H. S., Hwang, M. S., & Lee, H. (2010). Market segmentation of health information use on the Internet in Korea. International Journal of MedicalInformatics, 79(10), 707715.

Kim, Y., Jeon, H., & Bae, S. (2008). Innovation patterns and policy implications of ADSL penetration in Korea: A case study. Telecommunications Policy, 32,307325.

Korsching, P. F. (2001). New technologies for rural America: Boon or bane. Technology in Society, 23, 7377.Koutsouris, A. (2006). ICTs and rural development: Beyond the hype. Journal of Extension Systems, 22(1), 4662.Lippert, S., & Spagnolo, C. (2008). Internet peering as a network of relations. Telecommunications Policy, 32, 3349.Madden, G., & Coble-Neal, G. (2003). Internet use in rural and remote Western Australia. Telecommunications Policy, 27, 253266.Malecki, E. J. (2003). Digital development in rural areas: Potentials and pitfalls. Journal of Rural Studies, 19, 210214.Milne, C. (1998). Stages of universal service policy. Telecommunications Policy, 22(9), 775780.Moseley, M. J., & Owen, S. (2008). The future of services in rural England: The drivers of change and a scenario for 2015. Progress in Planning, 69, 93130.Nielsen (2009). Internet Usage Statistics. Retrieved from /http://en-us.nielsen.com/content/nielsen/en_us/product_families/nielsen_netratings.html S.Norris, P. (2001). Digital divide: Civic engagement, information poverty, and the Internet worldwide. Cambridge, UK: Cambridge University Press.

NSSG (2003). Analytical data tables. National Statistical Service of Greece: Ekdoseis tis Statistikis Ipiresias. Retrieved from National Statistical Service ofGreece website: /http://www.statistics.grS.

NSSG (2009). Inventory of Greek agriculture. National Statistical Service of Greece: Ekdoseis tis Statistikis Ipiresias. Retrieved from National StatisticalService of Greece website: /http://www.statistics.grS.

NSSG (2010). E-stats: Society of information. National Statistical Service of Greece: Ekdoseis tis Statistikis Ipiresias. Retrieved from National StatisticalService of Greece website: /http://www.statistics.grS.

OECD (2006). OECD broadband statistics. Organization for Economic Cooperation and Development December 2005. Retrieved from /http://www.oecd.org/document/39/0,3343,en_2649_34225_36459431_1_1_1_1,00.htmlS.

Pratt, J. W. (1987). Dividing the indivisible: Using simple symmetry to partition variance explained. In T. Pukkika, & S. Puntanen (Eds.), Proceedings of thesecond international conference in statistics (pp. 245260). Tampere, Finland: University of Tampere.

Rogers, E. (1962). Diffusion of innovations ((1st ed.)). Glencoe: Free Press.Rogers, E. (1995). Diffusion of innovations ((4th ed.)). New York, NY: The Free Press.Sawada, M., Cossette, D., Wellar, B., & Kurt, T. (2006). Analysis of the urban/rural broadband divide in Canada: Using GIS in planning terrestrial wireless

deployment. Government Information Quarterly, 23(34), 454479.Seifert, J., & Chung, J. (2009). Using e-government to reinforce government citizen relationships: Comparing government reform in the United States and

China. Social Science Computer Review, 27(1), 323.Siardos, G. (2002). Methods of multivariate analysisSecond volume (1st ed.). Thessaloniki: ZITI Publications.

SPSS, G. (2007). SPSS 17.0 for windows. Users guide . Chicago: Inc.Strover, S. (2003). The prospects for broadband deployment in rural America. Government Information Quarterly, 20(2), 95106.Sun, Y., & Wang, H. (2005). Does Internet access matter for rural industry? A case study of Jiangsu, China. Journal of Rural Studies, 21, 247258.Thompson, C. S. (2002). Enlisting on-line residents: Expanding the boundaries of e-government in Japanese rural township. Government Information

Quarterly, 19(2), 173188.Van der Kooij, A. J., & Meulman, J. J. (1997). MURALS: Multiple regression and optimal scaling using alternating least squares. In W. Bandilla, &

F. Faulbaum (Eds.), Advances in statistical software 6 (pp. 99106). Stuttgart: Lucius & Lucius.Verdegem, P., & Verhoest, P. (2009). Profiling the non-user: Rethinking policy initiatives stimulating ICT acceptance. Telecommunications Policy, 33,

642652.Warren, M. (2007). The digital vicious cycle: Links between social disadvantage and digital exclusion in rural areas. Telecommunications Policy, 31,

374388.Xia, J., & Lu, T. J. (2008). Bridging the digital divide for rural communities: The case of China. Telecommunications Policy, 32(910), 686696.Zhang, T., Ramakrishnon, R., & Livny, M. (1996). BIRCH: An efficient data clustering method for very large databases. In H. V. Jagadish, & Inderpal Singh

Mumick (Eds.), Proceedings of the ACM SIGMOD conference on management of data (pp. 103114). Montreal, Canada: ACM Press.

http://www.pewtrusts.org/news_room_detail.aspx?id=17062http://europa.eu/rapid/pressReleasesAction.do?reference=IP/09/1221&format=HTML&aged=0&language=EL&guiLanguagehttp://europa.eu/rapid/pressReleasesAction.do?reference=IP/09/1221&format=HTML&aged=0&language=EL&guiLanguagehttp://europa.eu/rapid/pressReleasesAction.do?reference=IP/09/1221&format=HTML&aged=0&language=EL&guiLanguagehttp://europa.eu/rapid/pressReleasesAction.do?reference=IP/09/1221&format=HTML&aged=0&language=EL&guiLanguagehttp://europa.eu/rapid/pressReleasesAction.do?reference=IP/09/1221&format=HTML&aged=0&language=EL&guiLanguagehttp://europa.eu/rapid/pressReleasesAction.do?reference=IP/09/1221&format=HTML&aged=0&language=EL&guiLanguagehttp://www.internetworldstats.com/stats2.htmhttp://en-us.nielsen.com/content/nielsen/en_us/product_families/nielsen_netratings.htmlhttp://www.statistics.gr/http://www.statistics.gr/http://www.statistics.gr/http://www.oecd.org/document/39/0,3343,en_2649_34225_36459431_1_1_1_1,00.htmlhttp://www.oecd.org/document/39/0,3343,en_2649_34225_36459431_1_1_1_1,00.htmlhttp://www.oecd.org/document/39/0,3343,en_2649_34225_36459431_1_1_1_1,00.htmlhttp://www.oecd.org/document/39/0,3343,en_2649_34225_36459431_1_1_1_1,00.htmlhttp://www.statistics.gr/http://www.statistics.gr/http://www.statistics.gr/http://en-us.nielsen.com/content/nielsen/en_us/product_families/nielsen_netratings.htmlhttp://www.internetworldstats.com/stats2.htmhttp://europa.eu/rapid/pressReleasesAction.do?reference=IP/09/1221&format=HTML&aged=0&language=EL&guiLanguagehttp://europa.eu/rapid/pressReleasesAction.do?reference=IP/09/1221&format=HTML&aged=0&language=EL&guiLanguagehttp://europa.eu/rapid/pressReleasesAction.do?reference=IP/09/1221&format=HTML&aged=0&language=EL&guiLanguagehttp://europa.eu/rapid/pressReleasesAction.do?reference=IP/09/1221&format=HTML&aged=0&language=EL&guiLanguagehttp://europa.eu/rapid/pressReleasesAction.do?reference=IP/09/1221&format=HTML&aged=0&language=EL&guiLanguagehttp://europa.eu/rapid/pressReleasesAction.do?reference=IP/09/1221&format=HTML&aged=0&language=EL&guiLanguagehttp://www.pewtrusts.org/news_room_detail.aspx?id=17062

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