Article from ejc/recDigital Divide in the Netherlands
Volume 12 Numbers 1 &
THE DIGITAL DIVIDE IN THE NETHERLANDS:
THE INFLUENCE OF MATERIAL, COGNITIVE AND SOCIAL RESOURCES
ON THE POSSESSION AND USE OF ICTS
Jos de Haan
Social and Cultural Planning Agency
Abstract. In the Netherlands, like in other
western countries, one observes social inequality in the possession and use of
information and communication technology (ICT) and in digital skills. This
digital divide between the information rich (such as whites, those with higher
incomes, those more educated, and dual-parent households) and the information
poor (such as certain minorities, those with lower incomes and lower education
levels, and single-parent households) is part of a diffusion process. Some
people take the lead, others follow. This paper addresses the mechanisms behind
different kinds of digital inequality within households (possession, skills and
use). The explanation of the differences between sections of the population
focuses on the possession of three types of resources: material, social and
cognitive resources. Differences in ICT access and use between population
groups can sometimes be explained completely and sometimes onlypartly. Some resources primarily influence possession, while
others mainly influence ICT use or digital skills.
Introduction: ICT and social
The rise of the information society is
facilitated by technological changes. The spread of PC ownership, innovations
in the fields of media and telephony made it possible to communicate in new
ways and to transport information through different channels. Those who possess
and use the new information and communication technology (ICT) equipment enjoy
benefits that others may lack. The question can be raised whether the rise of
new ICT increases social inequality. The fear of new social inequality has
given rise to alarming concepts such as the digital divide. This article will
contribute to the discussion about this digital divide by analysing in detail
the social backgrounds of the ‘haves’ and ‘have-nots’ and of the users and
non-users of ICT.
According to the OECD (2001), the term ‘digital divide’ refers to the gap between
individuals, households, businesses and geographic areas at different
socio-economic levels with regard both to their opportunities to access
information and communication technologies (ICTs) and to their use of the
Internet for a wide variety of purposes. The digital divide can be measured in
different ways, but is most often associated with PC and Internet access. PC
diffusion shows the usual logistic curves for the diffusion of new
technologies, although it is slower than some other technologies. The spread of
Internet access is more rapid, because necessary complementary investments in
PC equipment and the personal and business skill base have already been made (OECD, 2000). PC use in households is increasing in all OECD
countries, with the highest rates in Nordic countries (Denmark, Finland), the
Netherlands and English-language countries (Australia, Canada, United States).
Internet access from home shows similar patterns (see Figure
Figure 1: PC access
and Internet access in households in selected OECD countries (most recent year)
of both PC and Internet use show that more affluent and well-educated citizens are
the first to acquire ICTs. Besides on income and education, the digital divide
among households depends on variables such as household size and type, age,
gender, racial and linguistic backgrounds and location (OECD, 2000). These
differences are very consistent across countries. Although computer ownership
and Internet access rates have increased rapidly in all OECD countries and
among nearly all groups, several studies have indicated that the diffusion of
ICTs has given rise to growing inequality among the populations in the
countries studied (NTIA 1999,
2000; Perillieux et al., 2000; Van Dijk and
Hacker, 2002). The digital divide between the information rich (such
as whites, those with higher incomes, those more educated, and dual-parent
households) and the information poor (such as certain minorities, those with
lower incomes and lower education levels, and single-parent households) is
growing. This paper addresses the mechanisms behind the social inequality in
the possession and use of ICTs within households. It addresses the following
question. How can the differences in the possession and use of ICT products
between different sections of the population be explained?
The research was conducted in the Netherlands, one of the leading
countries in the world concerning the diffusion of ICTs in households as figure 1 shows.As
the patterns of diffusion are very similar across countries, the explanation of
differences that is offered here probably also holds for other countries. In
the Netherlands, we have witnessed a quick diffusion of PC's in households.
After a slow start in the first half of the eighties, the penetration of PCs
grew rapidly in the 1990s of the 20th century, with the number of
households having a computer rising from 9% in 1985 to 58% in 1998. In the
autumn of 1998 56% of PC-owners had a modem, 34% an e-mail connection and 37%
access to the Internet. Furthermore, 18% of the Dutch population had a fax
machine connected to the telephone network. The number of Internet connections
has increased fivefold in three years: from 4% of the population in 1995 to 21%
in the autumn of 1998 (Van Dijk et al., 2000).
This study of social inequalities in the possession and
use of ICTs is not restricted to PC use and Internet access, but also concerns
innovations in the fields of media, electronic payments and telephony. In
recent years more and more providers of mobile telephony services have appeared
on the market, and the number of mobile telephones has increased rapidly.
Mobile telephony is now one of the fastest growing and most visible new forms
of communication. Payment methods have also been digitised in the past decade.
Increasingly, people’s wallets are filled with cash/debit cards, credit cards
and/or ‘chipcards’ (‘electronic purses’). In the Netherlands, the penetration
of cash/debit cards is particularly high, with 94% of the population having
such a card in the autumn of 1998. Compared with the cash/debit card, the
penetration of credit cards – which have been around for longer – is modest
(35% of the population in the autumn of 1998).
As in other countries, the spread of ICT innovations in
the Netherlands is proceeding more rapidly in some sections of the population
than in others. Certain groups crop up time and again when it comes to
non-possession and non-use of the facilities described. The following are the
groups that are falling behind in the access to these facilities, arranged in
the order of how far behind they are on average: people in low-income
households (who on average lag furthest behind); (single) women; people
over-65; people with a lower (secondary) education level; the unemployed.
In order to explain the differences in the possession and
use of ICT a theory on the possession of resources is discussed in paragraph 2.
Subsequently, key concepts from this theory are measured in paragraph 3. In
this paragraph the data collection is also described in some detail. The
results of the multivariate analyses are presented in paragraph 4. In the final
paragraph these results are interpreted in the light of the discussions about
the digital divide.
explanation: unequal possession of different types of resources
explain the differences in the possession and use of modern ICT facilities,
alignment was sought with earlier studies on the diffusion of innovations.
According to this research tradition, the decision to accept a (technological)
innovation is dependent on different characteristics of products, on personal,
social and economic characteristics of consumers and on channels of information
that provide relevant knowledge on new products. Here we present a theoretical
framework that specifies the relationship between rational decisions of
consumers and a mix of product and consumer characteristics. Our framework not
only draws from literature on the diffusion of innovations, but also from
social network analysis and from research on information processing theory.
innovations we studied are classified as relatively complex consumer goods.
Besides complexity, we distinguished four other characteristics, following
Rogers (1995: 207 ff.): compatibility, testability,
visibility and the relative benefit. The rate of spread of different products
offers some information on the influence of product characteristics. The spread
of colour television was more rapid than that of the VCR, which in turn
penetrated the community faster than the PC. These differences cannot be
attributed entirely to average price levels: colour TV was an expensive product
in the 1970s. Other product characteristics play an important role here. The
relative benefit of colour television as compared to black and white sets was
obvious for large sections of society. The user-unfriendliness of the PC, by
contrast, contributes to the relatively slow spread of the personal computer.
that the acceptance of ICT products is the result of choices made by consumers.
In their decision-making process they are confronted with constraints for their
choices. This constraint-driven approach assumes that the adoption of ICT
products can be explained by differences in constraints between individuals.
People are constrained in their possession of resources. Differences in this
regard result not only fromthe
quantity of these resources, but also from the type of resources. A distinction
is drawn between material, cognitive and social resources. This distinction
draws on the work of Bourdieu (1984) and (Coleman, 1990). Instead of cognitive resources, they refer
to cultural resources or human capital. In order to stress that ICT skills are
mental capabilities, we propose to use the term cognitive resources.
resources in the strict sense include the financial budget of households but
because in some senses time is money, the available number of leisure hours
falls under this category too. Monetary and time constraints have been proven
to be a part of the decision-making process associated with the adoption of new
ICT products. Studies on consumer behaviour have shown that income and the
costs of ICT products affect the purchase of these products. People with higher
incomes spent a relatively large part of their financial budget on luxury consumer
goods (Linder, 1971). It is very likely that this also
holds true for ICT products. This relationship between individual or household
income and consumer goods will be stronger if the prices of the goods are
higher. This applies both to purchases of new goods and to replacements of old
time is an incentive to buy and use ICT products. It has been shown that the
amount of leisure time affects media consumption. Retired people who on average
have most leisure time are the greatest consumers of television programmes (Rubin and Rubin, 1981; Knulst and Kalmijn,
1988). On the other hand, people with paid jobs and parents with young
children have less free time and can be expected to have relatively little
opportunity for using time-consuming ICT products.
Cognitive resources, also called human capital,
can be defined as the ability to deal with symbols and information. In our
study three types of cognitive resources are distinguished: literacy, numeracy and informacy. Which cognitive resources are
primarily important? For a long time the ability to process written information
was the most important skill for communication and processing information.
These classic skills are called literacy, meaning the ability to use
information from books, newspapers and magazines. Later came the ability to handle
quantitative information, called numeracy or quantitative literacy (OECD, 1995). Nowadays another type of skills is becoming
increasingly important, i.e. the skills to handle information and communication
technologies. These digital skills or informacy refer to specific knowledge of
and ability to deal with new technologies as well as with previous experiences.
with a higher level of informacy will be better able to use ICTs. Digital
skills enlarge the opportunity to use computers for a variety of applications
such as word processing, programming and searching files (Rossom,
1984; Vincente et al., 1987; Czaja
et al., 1998). The need for those skills increases if the ICT products or
software become more complex (Steyaert, 2000). Thus
once again consumer characteristics are related to the product characteristics.
is often used as an indicator of informacy. This can be easily understood
because the school is the most important location were cognitive resources are
acquired. This holds true for literacy, which is primarily attained during
one’s early educational career. In higher forms of education more attention is
paid to informacy. Cognitive resources (literacy, numeracy and informacy) are
strongly related to the educational level people reach (OECD,
1997). During professional careers these skills will be used and further
developed. Especially the use of a PC at the workplace enhances a positive
attitude towards having and using a PC at home (De Haan,
the importance of education, it is relevant to know in which period people went
to school. Persons born after 1960 have a far greater chance to acquaint
themselves with ICT during their educational career than those born before
1960. The generation born after 1960 has already been called the 'technical
generation' (Sackmann and Weihmann, 1994) and the 'net
generation' (Tapscott, 1998). However, there are more
reasons why older people might use new media less often than young people.
Older people less often followed higher types of education, they left the
labour market before they could learn to work with computers, and their
physical, sensory and cognitive skills decrease as they become older (OECD, 1997; Van Rijsselt en Weijers,
1997; Freudenthal, 1999). For this combination of
reasons older people can be expected to have more difficulty processing complex
information and using modern technology. For several reasons, there may also be
a distinction between men and women. On average, women are lower educated, have
participated less often in the labour market and may have acquired less
affinity with technology during their gender specific socialisation (Rijken, 1999; Van Dijk and De Haan,
Social resources consist of types of
access people have to other people’s sources of help (Flap,
1987). In the field of ICT they are the number of people in someone’s
social setting who themselves possess new ICT products, the skills (in
particular informacy) to be addressed
in this way, and the degree to which these persons are in a position to provide
information on ICT. Someone will be more likely to buy and use new ICT products
if more persons in his personal network can provide information on them and if
they are able and willing to give advice. Computer use, for example, is
stimulated in a social environment providing much support (Kling
and Gerson, 1977).
can help reduce uncertaintiesoften
accompanying the purchase ofnew
consumer goods (Bettman, 1979). Uncertainty may relate
to the cost and quality of a product but also to the acceptance of that product
in a social community. Products that can be tested, that fit within existing
norms and values and that are more visible bring less uncertainty. People with
more social resources will be better able to reduce their uncertainty. Again
product characteristics (testability, compatibility and testability) are
related to consumer characteristics (social resources which may decrease
Social contacts not only provide
information but can also be a source of social approval and social distinction.
In general, people tend to adopt the kind of behaviour that is expectedto give them affirmation from the
group in which they live and especially from the people they consider to be
important (Burt, 1987). Positive feedback on the
acquisition of ICT can provide them with social well-being. Possessing visible
luxury goods they can also distinguish themselves from others. These products
can give them a certain status. More than the cultural elite, the economic
elite will try to attain this status of material ownership (cf. Bourdieu, 1984).
In this paragraph a distinction is
drawn between material, cognitive and social resources. We will analyse to what
extent the different resources are able to explain differences in possession
and use of ICT between different sections of the population (see Figure 2). For example, to what extent can they explain
differences between men and women and between different educational or
different age groups.
Figure 2: Explaining
differences in ICT possession, use and skills
Data were drawn from the Use of New
Communication Resources (UNCR) Survey, carried out in the Netherlands in the
autumn of 1998. This national survey not only allowed a detailed description of
the Dutch situation in the autumn of 1998, but also enabled a close examination
of the explanatory mechanisms behind the social inequality in the possession
and use of ICT. In November 1998, mail questionnaires were sent to 6000
addresses taken from the administration of the national telephone company. Of
these addresses, 109 could not be used. Within every household one person was
asked to complete the questionnaire, being the person who was the first to have
his/her birthday after a certain date (every quarter of the addresses had a different
date: 1 March, 1 June, 1 September or 1 December). Ten days and three weeks
after the distribution of the questionnaires reminders were sent to those
households not having responded yet. In the end, we received 2538 completed
questionnaires representing a 43% response.
The data were weighted for a number
of variables known to have a distribution different from the distribution of
the population at large. These variables were: gender, age, voting behaviour in
1998, registration in the telephone directory, residence in one of the four
largest cities, and marital status. The weights were constructed using
interactive proportional fitting on the distribution of marginal frequencies.
The introduction has already shown
that there are strong similaritiesin
the social distribution of the possession and use ofa whole range of ICT products. Our data show that the possession
of each of eleven ICT products (television with teletext, VCR, cordless
telephone, mobile telephone, fax, debit card, credit card, personal computer,
laptop, Internet access and printer) is nearly always positively correlated to
the others (table 1). People who have one product often
also possess other ICT products. For this reason the explanation will not be
sought for individual products. Instead we will use an additive score for all
of them. In the scores concerning possession, three products from table
1 are not counted. Both the debit card and the television with
teletext are present in nearly all households and do therefore notconstitute a distinctionbetween social groups. Access to
Internet is not counted either because measuring it produced a high number of
Table 1 Correlations
between the possession of ICT products, persons of 18 years of age
and older, 1998
The possession of ICT products does
not necessarily mean that these products are used. The UNCR survey contains
information on the extent to which six products are used (teletext, VCR, debit
card, credit card, computer and Internet). Based on these variables, a new
variable was constructed for the use of ICT products.
Below, we will use the cumulative variables of possession
and use of ICT products and not deal with single products. As a result, we will
not go into the influence of different product characteristics. The focus is
completely on the influence of consumer characteristics. Before we can assess
the impact of the different types of resources, we need to measure material,
cognitive and social resources.
measured in two ways. First, respondents were asked if three persons
(significant others) of their personal network possessed seven ICT products. In
the questionnaire the respondents were asked to give the names of these three persons(see note in table 2).
Subsequently, more information was requested about these persons. Asking the
respondents to supply such information for more than three persons was
considered too demanding on them. We assume that the data on three persons
gives a reliable picture of ICT possession in personal networks.
Table 2 Operational
definition of social resources: possession of ICTs in the social network of
the respondents (in percentages) a
a Survey question: Most people discuss important subjects with someone
else from time to time. Here, we are interested in everything that is
important for people. Can you think of three persons with whom you frequently
discuss important subjects? (...) Can you indicate whether these persons
possess the following products?
Source: UNCR (1998)
Secondly, we measured the extent to
which the social network is able to offer help in handling the personal computer.
Here, it is important how much advice can be expected from persons in one’s
direct social surroundings. Questions were posed regarding eight types of
problems of computer use (table 3).
Table 3 Operational
definition of social resources: number of network members that can give
advice about computers (in percentages) a
question: If you have questions with regard to computers (at home or at
work), how many relatives, friends, acquaintances or colleagues will be able
to help you?
Source: UNCR (1998)
Adding the positive answers created
two scales for social resources. We expected that possession in the social network
would be positively related to both ICT possession and ICT use among the
respondents. The capacity to give advice was considered to influence mostly the
use of ICT.
We measured cognitive resources
(literacy, numeracy and informacy), independently of educational level.
Literacy and numeracy were measured by calculating the degree of understanding
and application of written and numeric information. The literacy scale was
based on answers to questions about the difficulties encountered in using a
phone book, a dictionary, a television guide, filling out a tax form,
understanding a legal contract, different kinds of newspaper articles and
information brochures (see table 4).
Table 4 Operational
definition of cognitive resources: literacy (in percentages) a
Cannot do it
With much difficulty
Look up a telephone number in a phone book
Look up a word in a dictionary
Find a programme in a tv guide
Fill out a tax form
Read a contract
Read a front page article
Read an information brochure on taxes
question: Can you indicate, for each action, how much trouble it gives you?
Source: UNCR (1998)
Numeracy was measured on the basis of
the capacity to read and understand tables and graphs in newspapers and
magazines, to roughly estimate the total price of groceries and to convert
Dutch to foreign currency (table 5).
Table 5 Operational
definition of cognitive resources: numeracy (in percentages) a
Cannot do it
With much difficulty
Read graph and tables
Estimate price of groceries
Convert Dutch to foreign currency
question: Can you indicate, for each action, how much trouble it gives you?
Source: UNCR (1998)
Informacy or ICT skills refer to the
way information is retrieved (Internet or cd-rom versus telephone, telephone
directory, etc.) and different kinds of ICT products are handled (television,
VCR, personal computer) (see table 6).
Table 6 Operational
definition of cognitive resources: informacy (in percentages)
Yes / done by PC
Have you done the following things (without the aid of others)?
programme frequencies on VCR
record a tv programme with VCR
programme the VCR
put telephone no. in memory
connect answering machine
connect a fax
adjust voice mail
Nowadays there are many ways to get information. We want to ask you
where you look for information.
Suppose: You have to go by train from Utrecht to Amsterdam, but you do
not know at what time the train leaves. Which information source do you
look for departure time in computerised version of timetable or on
railway home page
If you need a someone’s phone number, where do you look first?
look up local phone number at WWW or in cd-phone guide
look up trunk phone number at WWW or in cd-phone guide
E-mail: do you know how to do the following? (e-mail owners)
make a distribution list
Television: Have you installed the tv channels yourself?
install tv channels
Did you install your PC yourself (PC owners)
If you need information from the Internet, can you find it easily?
find information on Internet
Source: UNCR (1998)
In order to estimate the extent to
which people have different types of cognitive resources, the positive answers to
the questions in the tables were added.
Material resources refer to both the
financial budget and the time budget. The household income is an important
indicator of the financial budget. However, income has already been included in
the set of background characteristics used to describe social inequality as
regards ICT. It is therefore difficult to measure material resources
separately. In order to arrive at an independent measurement we counted a
number of luxury goods (non-ICT products) in the household (table 7). This scale expresses the
ability and willingness to invest in expensive goods. Persons with the same
income may still have different amounts of money left after paying fixed
charges. They may also have different preferences and opinions where it
concerns spending money. Unfortunately, it was not possible to construct a
similar scale for the amount of leisure time on the basis of the UNCR survey.
Table 7 Operational definition
of material resources (in percentages) a
Owns the product
question: Do you possess any of the following?
Source: UNCR (1998)
On average, the more income people have,
the higher the investment in luxury goods. The correlation between the
possession of luxury goods and household income is 0.44 and between the
possession of luxury goods and ICT products 0.45.
We expected that people who possess
many luxury goods are more able and willing to buy ICT products. On the other
hand, we didnot expect that people
who invest heavily in luxury goods also use their ICT products more often than
Results: the impact of resources
differences between households and persons as regards the extent to which they
own and use ICT products. Both for ownership and for use, scales have been
constructed. These scales do not have the same range or the same mean. To
compare the distribution of ownership with that of use, the scores on the
scales have been expressed in percentiles. These scores are orders of ranking
with a fixed mean (50, the median) and a fixed standard deviation (26). Someone
with a score of 10 belongs to the lowest 10% of the distribution and someone with
a score of 90 to the highest 10% of the distribution. This score is very well
suited to illustrate inequality in the distribution of different
characteristics between groups.
The first column
of table 8 shows the distribution of ICT possession among different population
groups. The numbers reflect the differences already mentioned in the
introduction. Socio-economic factors (such as income, level of education and
marital status) are correlated with home ICT adoption. Men more often possess
ICT products than women. The differences between age groups can be seen as
well. Older people posses fewer ICT products than young people. Higher
education groups and higher income groups possess relatively many ICT products.
seem to be greater than theincome-relateddifferences. These differences reflect
those found in other countries (OECD, 2000). In the
United States noticeable differences were also found between different racial
and ethnic groups, between single and dual-parent families, between those with
and without disabilities and between rural areas and households nationwide (NTIA, 2000).
Table 8 includes not only the scores
regarding possession and use but also those concerning informacy. Given the
importance of digital skills in the information society, it seemed worthwhile
to present these scores in some more detail and subsequently analyse the
differences. Again, in this respect men and young people turn out to be more
skilful than women and elderly people. As expected, there is a strong
relationship between educational level and informacy. Income is less strongly
but still positively connected to digital skills.
Table 8 Degree of
possession and use of ICT products and informacy, according to background
characteristics, persons 18 years of age and older, 1998 (in percentiles)
Married/living together without child
Family with child > 14 years
Family with youngest child 14 years
65 years and older
Pre-university and senior vocational education
Higher professional and university education
Income 1st quartile
Source: UNCR (1998)
To a certain extent the presented
background characteristics overlap. People with a higher education often also
enjoy a high income. And, on average, the 18-34 year group earns less than the
35-49 year group. It is therefore possible that the differences we found
regarding one characteristic can in fact be attributed to another. In order to
control forthe influence of other
variables and estimate the net effect of a variable, multivariate regression
analyses have been conducted. The results are shown in table 9.
Table 9 Regression
analysis of ICT possession, ICT use and informacy regarding background
characteristics, persons of 18 years and older, 1998
The multivariate analysis
shows that the factor household income
is of primary importance for variation in access to ICT. It should be pointed
out that the income differentials are concentrated around the possession of a
given facility: once someone has access to a piece of hardware or
functionality, their income has little further effect on the actual use of that
hardware or functionality. The differences in use of facilities already
acquired, and the extent to which this use correlates with the background
variables considered, are however markedly smaller than the differences in
terms of acquisition and possession.
Where there are systematic
differences in use, one striking conclusion is how strongly these differences
are determined by the sex of the respondent. Women across the board have less
access to ICT than men, and also make less use of it. The elderly (in
particular the over-65s) are also at a systematic disadvantage when it comes to
the use of new ICT facilities. Educational differences come only fourth in the
list of important determinants of ICT inequality. Finally, evidence was found
here and there that unemployed or disabled people also sometimes participate
less in ICT usage. Given the importance of contacts at work, this is not very
ICT skills (informacy) are most
strongly related to age and gender. Older people and women have far fewer ICT
skills than young people and men. The education level also has a substantial
influence on informacy. The influence of income is small but significant.
Possession of ICT
To what extent can social, cognitive
and material resources explain the differences between the possession and use
of ICT? To test these influences, the constructed scales of the three types of
resources in table 10 have been
added tothe regression models in table 9. In table 10 on ICT possession, the base
model from table 9 is shown again as
model I. The next column presents a model with the separate resources only
(model II). In the following models the resources have step by step been
included in base model I: social resources (model III), cognitive resources
(model IV) and material resources (model V). To estimate the influence of the
work situation on the household, the use of a PC at work has been added to
Table 10 Regression analysis
of ICT possession (0-100) regarding background characteristics and resources
(beta and t-values)
To some extent, the difference in ICT
possession between those in work and the unemployed can be attributed to
divergent social resources. The correlation between income and ICT possession
also reduces when allowance is made for the difference in social resources. The
same applies to the relation between education levels and ICT possession.
Higher status groups evidently have access to well-informed networks so that,
irrespective of their own skills, they are more likely to acquire ICT products.
Access to material resources also influences the possession of ICT facilities.
Using a PC at work hardly influences the possession of a wide range of ICT
products. However, other research shows that this influence on having a PC at
home is indeed substantial(De Haan, 2001). Ethnic minority groups not only have less
PC and Internet access at home, but they also use a PC at work less often (Hoffman en Novak, 1999).
The high percentage of explained variance
(40%) in model II indicates that the resources strongly determine the
possession of ICT. Especially the degree of informacy, ICT possession in the
personal network and the possession of many luxury consumer goods prove to be
strong determinants. More informacy, more ICT in the network and more consumer
goods increase the likelihood of ICT possession.
The increase in the explained
variance (R2 ) in model III to V compared to model I indicates that
the three types of resources have additional explanatory power compared to the
background characteristics. Furthermore, the decrease in the betas referring to
social characteristics in these models indicates the degree to which the
resources explain the relationship between ICT possession and social characteristics.
For example: after adding the social resources in model III, the beta of
education (.02) is lower than the beta of education in model I (.06).
Furthermore, the beta of education in model III no longer has a significant
effect on the possession of ICT. This implies that the differences between
educational groups can largely be attributed to the social support the higher
educated can get from their social network.
Use of ICT
The use of ICT is less strongly
determined by social background than ICT possession. Only 12% of the variance
in ICT use is explained (table 9 and
table 11, model I). The resources
prove to have more explanatory power than social background characteristics
since their explained variance is 19% (model II).
The cognitive resources (informacy)
have the strongest influence onthe
use of ICT. The explained variance increases from 12% to 20% and the cognitiveresources are largely responsible for
differences in use between different educational groups. To a lesser extent,
differences in use between age groups and between men and women also relate to
differing access to cognitive resources. The level of informacy explains
approximately half of these differences.
The social resources have little
influence on ICT use. After controlling fordifferences in social resources, the relationshipbetween age groups and ICT use slightly reduces.
to material resources has no effect on the social distribution of the use
of ICT facilities.
The use of a PC at work stimulates
the use of ICT, independent from the influence of resources(model VI). A small part of the influence of informacy can be
attributed to PC use at work. Evidently, people acquire digital skills at the
workplace, which in turn increases the frequency of ICT use at home or for
Table 11 Regression
analysis of ICT use (0-100) regarding background characteristics and
resources (beta and t-values)
The level of informacy strongly determines
both the possession and the use of ICT. Of course, we cannot conclude from our
data that high levels of informacy are the reason why ICT products are
acquired. It is just as likely that ICT owners have learned to work with their
products after purchasing them. To a certain extent this also holds true for
the relationship between ICT use and informacy. Yet, it is more likely that the
frequency, as well as variety, of use will increase if people have more digital
skills. If the diffusion of ICT among larger sections of the population
continues, possession may become less of a distinction between social groups.
In the future, the level of informacy may become a key determinant of ICT use
and may become mainly responsible for differences in participation in the
information society. Therefore, we will consider the determinants of informacy
in detail. Table 12 shows the
results from our analysis.
The same procedure has been followed
as in the previous tables. Base model I explains 29% of the differences in
informacy between social groups. This means that the level of informacy is
strongly related to the social characteristics.
Table 12 Regression
analysis of informacy (1-100) regarding background characteristics and
resources (beta and t-values)
Informacy positively relates to
other types of resources, most strongly to literacy and social resources.
However, these relationships are not very strong (R2=.16). On
average, people who handle written information well, also know how to use digital
information and communication devices. And they find themselves surrounded by
people who show similar capabilities. Remarkably, numeracy is not positively
related to informacy. Being able to make calculations does not coincide with a
better command of ICT.
Social resources do not contribute
to the explanation of informacy. However, coefficients of background variables
change after adding social resources to the model. The effect of income disappears
and the effects of age and educational level are somewhat less. Furthermore,
when social resources are controlled there is no longer any difference in
informacy between people who are retired or do household work and those who are
in paid employment. The cognitive resources (literacy) are largely responsible
for differences in ICT skills between different educational groups. Access to
material resources does not influence differences in ICT skills between social
Using a PC at work strongly
influences informacy (model VI). The explained variance increases by another 3
percent points and the standardised effect (beta) of a PC at work (0,25) is
also high. By adding this variable to the model, the differences between age
groups and educational groups further decrease, indicating that young people
and higher educated people acquire digital skills at work and also use these
skills outside the workplace.
Summary and discussion
The analyses of ICT possession and
use and of informacy show that individual resources influence existing social
differences. Differences in ICT access and use between population groups can
sometimes be explained completely and sometimes onlypartly. Some resources primarily influence possession, while
others mainly influence ICT use or digital skills (informacy). However, the
explanation based on the different types of resources is not adequate for all
sections of the population. This applies in particular to the wide difference
between the sexes. In the area of skills, the differences between men and women
hardly reduce at all when allowance is made for divergent access to resources.
With regard to possession and use of ICT, more than half the differences remain
unexplained. The explanation for these differences must therefore be sought in
factors other than the resources measured. The differences between age groups
in all three domains also largely remain after control for the influence of
resources. Lack of experience with modern technology on the labour market plays
an important role here, but an anxious attitude towards unknown products and
few opportunities for use could also be important. The absence of local
information on the Internet can be an additional reason why the elderly are not
on line (Lazarus en Mora, 2000).
In the debate on the digital divide
there are two positions. According to some, there is a growing divide between
the information rich and the information poor (NTIA, 1999). Others do not see a
permanent divide between population groups, but rather a stage difference in
the same process of diffusion (Van Dijk et al., 2000).
Is the digital divide a matter of lasting social inequality?
The existence of a stable divide
assumes a gulf that is difficult to bridge. Although the different rates of
possession of ICT products in the different sections of the population have increased
since their market introduction, this does not mean that these differences are
here to stay. The chance of ownership for separate individuals is not fixed;
someone who today is a “non-possessor” may not be tomorrow. In general, new
products spread in accordance with the ‘trickle-down’ principle: the affluent
and well educated are among the early adopters and the less affluent and lower
educated follow in time. If the spread of existing ICT products continues under
the influence of falling prices and increasing user-friendliness, the PC and
mobile telephone will eventually become everyday possessions.
An updated snapshot of the digital
divide in the Netherlands already points to decreasing differences in PC
possession. Judging by a comparison ofdata
from 1995 and 2000, these differences decline between men and women. As for
education, persons who did not finish higher education have slowly but surely
been gaining upon the higher educated since 1995 (De Haan
and Huysmans, 2001). Possession among the elderly still lags far behind
that of young people, but there has been a strong increase in possession among
the 50-64-year olds. Many of the working people of this age group have gathered
experience with PC use at the workplace. The principle of cohort replacement
combined with the continuation of the diffusion process is expected to lead to
a further spread of PC possession among the elderly (De Haan, 2001).
in the diffusion process has also been witnessed in the United States. The
General Accounting Office (GAO, 2001) finds that the
‘digital divide’ is shrinking as more Americans gain access to the Internet.
Particularly usage gaps in gender and geography have notably declined. However,
the National Telecommunications and Information Administration (NTIA, 2000) still concludes that a
digital divide remains or has expanded slightly in some cases, even while
Internet access and computer ownership are rising rapidly for almost all
groups. Yet, the change in terminology in the latest ‘Falling through the Net –
toward digital inclusion’ study also indicates that NTIA foresees shrinking
divides in the time to come. NTIA recognises that groups that have
traditionally been digital ‘have-nots’ are now making ‘dramatic’ gains.
It is likely that several groups,
even if they have equal access to ICT products, will make differing use of them
in the future. Groups which are already over represented among the possessors
also make more use of the facilities in question. This study has shown that
usage differences are smaller than the differences in possession. Nonetheless,
this finding suggests that differences in usage (frequencies and above all type
of usage) remain once the ICT products described have become very widely
distributed. These differences will probably be strongly related to digital
skills (informacy). And differences in digital skills are likely to remain.
This calls for a more complex picture of the digital divide than a mere look at
access. We have done this by systematically comparing differences in access,
use and digital skills. Van Dijk and Hacker (2002) make a comparable distinction:
digital experience, possession of computers and network connections, digital skills
and usage opportunities. New inequalities are not expected to arise when
digital skills are taken into account. The literate elite will gradually change
into an elite, which is capable of handling new technologies.
The differences in computer use and
digital skills will be related to needs for digital information. The
significance of the possession of modern ICT facilities for the acquisition of
other scarce goods should not be overestimated. The labour market needs more
and more people with computer skills; a person with such skills has an
advantage in this situation. However, a substantial proportion of jobs still do
not require computer training. And a large section of the population, such as
retired people, has no labour market ambitions. For this group, the possession
or lack of computer skills is therefore not instrumental in the generation of
income. Once again the research in the Netherlands and that in the United
States correspond: in both countries there is quite a large group of information
want nots. Early research on the adoption of the PC showed that nearly one
fifth of families had quit using the PC entirely within two years of purchase (NSF, 2000). When asked why they are not
online, almost half of the Dutch and one third of the American non-users say
that they are ‘not interested’ (Van Dijk et al., 2000;
UCLA, 2000); many surfers went back to the beach (Wyatt, 2000)
Finally, consideration needs to be
given to the extent to which government intervention is desirable to ameliorate
ICT inequality, as well as the related question of what type of policy would
then be most appropriate. The recommendations can be relatively short. The
greatest inequality, which relates to the possession of ICT facilities, can be
attributed primarily to income differentials, and to some extent these are the
easiest to repair (through income and price measures). Is it desirable to do
so, however? The above description of diffusion processes suggests that the
answer is no. It is after all reasonable to assume that the consequences of
income differentials are ephemeral in nature. Higher income groups lead the
field, but they will not be able to retain this lead. In particular, falling
prices and increasing user-friendliness mean that the spread of these products
among those who do not currently have them will continue. Moreover, among those
who do not possess the various products are many people who do not see any
opportunities for using them. Encouraging the possession of ICT products would
therefore not appear to be a task for the government. It is likely that market
forces will lead to the further spread of these products among people who stand
to gain from possessing them.
Once a product has become widespread
in society, it is primarily the differences in use and utilisation that count.
And it is precisely these differences that, according to our study, are not
exceptionally large; they are in any event smaller than the differences in
possession. Differences in frequency of use, types of use and skills will
however continue to exist. These differences are not connected to income and
can therefore not be tackled through income and prices policy. Here again, the
question can be asked as to whether there is a task for the government in
stimulating the acquisition of user skills, and if so, how the government
should set about this.
Government policy with respect to
social inequality in the use of modern ICT facilities in the Netherlands
focuses primarily on education, where large-scale operations and catching-up
exercises are being implemented in order to ‘get everyone in front of a
computer’. There is a relatively strong belief that instruction via new ICT
technology offers major advantages and that it is necessary for the collective
well-being to train the population in this way. It is however debatable whether
this belief is correct, or whether it is based primarily on an unreasonable
fear of missing the connection to the electronic superhighway.
Not all consequences of
technological development can be tackled through education policy. Forms of
social exclusion among the elderly cannot be resolved at all in this way. Here
again, however, it is unclear to what extent people with few digital skills
perceive this as a disadvantage. It is quite plausible that older people can
manage perfectly well without a great deal of modern apparatus. However, the
commotion surrounding the closure of ‘physical points of contact’ is an
indication that keeping open traditional channels for communication and
information transfer deserves attention, as long as there is a group of
citizens who do not possess modern technology or are unable to use it
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