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Volume 12 Numbers 1 &
2, 2002
THE DIGITAL DIVIDE IN THE NETHERLANDS: THE INFLUENCE OF MATERIAL, COGNITIVE AND SOCIAL RESOURCES ON THE POSSESSION AND USE OF ICTS 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 only partly. Some resources primarily influence possession, while
others mainly influence ICT use or digital skills. Introduction: ICT and social inequality 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
1). Figure 1: PC access
and Internet access in households in selected OECD countries (most recent year)
Source: OECD (2000) Patterns 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. An
explanation: unequal possession of different types of resources
To
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. The ICT
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. We assume
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 from the
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. Material resources
Material
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
ones. Available
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 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.
People
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. Education
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,
2001). Minding
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,
1999). Social resources 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). Acquaintances
can help reduce uncertainties often
accompanying the purchase of new
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
uncertainty). 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 expected to 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 and
operational definitions
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 similarities in
the social distribution of the possession and use of a 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 not constitute a distinction between social groups. Access to
Internet is not counted either because measuring it produced a high number of
missing values.
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. Social
resources were
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.
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).
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).
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).
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).
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.
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 did not expect that people
who invest heavily in luxury goods also use their ICT products more often than
others. Results: the impact of resources
There are
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.
The education-related differences
seem to be greater than the income-related differences. 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.
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 for the 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.
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