The evolution of the digital divide: The digital divide turns to inequality of skills and usage

Abstract

The digital divide can be understood as inequalities in four successive types of access: motivation, physical access, digital skills and different usage. It isclaimed that the divide has shifted from the first to the last-called types of accessin the last ten years. The current, mainly European situation of all four accesstypes is amply described. This is done against the explanatory background ofresources and appropriation theory, a materialist and relational theory that emphasizes positions and relations instead of individual attributes. The effects of unequal access on unequal participation in society are summarized.

Full text

The digital divide shifts to
differences in usage
Alexander JAM van Deursen
and Jan AGM van Dijk
University of Twente, The Netherlands
Prepublication Draft; definitive publishing:
New Media & Society
2014, Vol. 16(3) 507–526
© The Author(s) 2013
Reprints and permissions:
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DOI: 10.1177/1461444813487959
nms.sagepub.com
Abstract. In a representative survey of the Dutch population we found that people with low
levels of education, and unemployed and disabled people are using the Internet for more hours
a day in their spare time than higher educated, employed, and student populations. To explain
this finding, we investigated what these people are doing online. The first contribution is a
theoretically validated cluster of Internet usage types: information, news, personal
development, social interaction, music and video, commercial transaction, and gaming. The
second contribution is that, based on this classification, we were able to identify a number of
usage differences, including those demonstrated by people with different gender, age,
education and Internet experience, that are often observed in the digital divide literature of the
last fifteen years. The general conclusion is that when the Internet matures, it will increasingly
reflect known social, economic and cultural relationships of the offline world, including
inequalities.
Keywords: digital divide, usage gap, online activities, digital inequality.
Number of words: 7891

1. INTRODUCTION
This article reports an observation in a recent Dutch survey of Internet use and tries to explain
and frame this observation. We found that people with a low level of education use the
Internet more hours a day in their spare time than people with medium and higher education
levels. Furthermore, disabled people use the Internet more hours a day in their spare time than
employed people. This finding is interesting because it is not in accordance with general
results of digital divide research. In the first three decades of its history, the Internet was
completely dominated by people with a high or medium level of education, both inside and
outside work and school. Today, lower educated and disabled people are considered as
digitally falling behind (e.g., Dutton, Helsper, and Gerben, 2011). It is often shown that they
are less likely to use the Internet overall, in any environment, than people that are employed
or high educated. With recent observations such as the one above, one might argue that the
digital divide has finally closed. This makes it interesting to report on this development in
detail and to frame it in digital divide theory.
Several conceptualizations of the digital divide exist (e.g., DiMaggio and Hargittai,
2001; Gunkel, 2003; Katz and Rice, 2002; Mossber, Tolbert and Stansbury, 2003; Norris,
2001; Warschauer, 2003; Witte and Mannon, 2010). Most conceptualizations generally
identify four areas of importance: attitudes, access, skills, and types of usage. Usage access is
the focus of this study and encompasses the purpose of the whole process of technology
appropriation. Having sufficient motivation, physical or material access, and skills to apply
digital media are necessary but not sufficient conditions for actual use (Van Dijk, 2005). Even
if differences in terms of physical access have diminished, significant differences may remain
in terms of differential skills and the nature of Internet use (e.g., Brandtzæg, 2010; Chen and
Wellman, 2005; DiMaggio et al. 2004; Hargittai and Hinnant, 2008; Selwyn 2004; Van Dijk,
2005; Zillien and Hargittai, 2009).
Internet usage has its own grounds or determinants. It can be defined in terms of
content (broadband or narrowband, active and creative or consumptive), frequency, length of
time the Internet is used and the type of activities performed. To address the observation
described, we focus on the amount and type of usage. More specifically, we are interested in
why the lower educated have become the most frequent users of the Internet in terms of hours
of use in spare time and what lower educated people are doing on the Internet. These interests
lead to the first two research questions:

1. How do socio-demographic variables relate to the amount of Internet use?
2. How do socio-demographic variables relate to types of Internet usage?
To answer the second research question, a classification of different usage activities is
required. Therefore, we will propose a distinction of seven types of usage types, validated in
previously established Uses and Gratifications Theory. Countries with high levels of Internet
access, such as the Netherlands, provide the best setting for these types of analytic distinctions
because here, Internet access and use are maturing and social distinctions of Internet use are
articulating. It becomes possible to identify the most likely categories for Internet usage.
After proposing the classification of usage activities, several demographic groups can
be investigated. As there is some variation to the scale of difference, the segments of the
population that are most likely to differ in their Internet use can be defined in terms of gender,
age, education, Internet experience, employment status, income, and residence (Section 2.3).
2. THEORETICAL BACKGROUND
2.1 From Knowledge to Usage gap
Although our research questions are descriptive, they have a theoretical relevance that can be
found in the so-called knowledge gap and the usage gap hypotheses. The knowledge gap
hypothesis is a forty-year-old theory of media that mainly considers traditional media but is
often observed as a forerunner to the digital divide concept. Tichenor, Donohue, and Olien
(1970) suggest that when the infusion of mass media information into a social system
increases, segments of the population with higher socio-economic statuses tend to acquire this
information at a faster rate than lower-status segments, adding the value judgment that more
information is better. It is not possible to apply the knowledge gap directly to the Internet. The
use of the traditional mass media—on which the knowledge gap focuses—is relatively
straightforward and uniform compared to Internet use (Bonfadelli, 2002). The latter requires a
broad range of skills enabling navigation through a vast amount of information rather than
simply reading newspapers or watching television. Relative to print media and television,
Internet usage requires not only enabling technologies but also users with sufficient skills to
use the Internet (Bonfadelli, 2002). The characteristics of traditional media (e.g., low potential
of selectivity and accuracy of information) create relative passivity in its use (Stern, 1995). In
this respect, traditional media usage is different from predominant Internet use (e.g., Stern,
1995). While traditional media enables active mental processing, the Internet requires users to

interact with interfaces, frequently cited as the main distinguishing attribute of the Internet
(Leckenby and Lee, 2000). A minimum level of active engagement with the medium is
required, and interactions, transactions, and interpersonal communication are made possible.
However, the difference in functionality of print media, radio, television, and
telephone is small compared to the Internet. Therefore, the Internet may create a usage gap
that is different from the knowledge gap. While the knowledge gap is about the differential
derivation of knowledge from the mass media, the usage gap is a broader thesis that
potentially is more relevant for society with regard to differential uses and activities in all
spheres of daily life, not just the perception and cognition of mass media. The background of
the usage gap lies in a combination of societal tendencies and technological characteristics.
The social tendencies are sociocultural differentiation or individualization in (post)modern
society, rising socioeconomic income inequality, employment and property worldwide,
commercialization (privatization and liberalization) of formerly public information and
communication facilities that increase conditional access that may be costly. Technological
characteristics include the complexity, expensiveness, and multi-functionality of computer
and Internet technology, which invite different uses (Van Dijk, 2005).
Behind the concept and thesis of a usage gap a clear normative account comes
forward. The assumption is that some Internet usage activities are more beneficial or
advantageous for Internet users than others. Some activities offer users more chances and
resources in moving forward in their career, work, education, and societal position than others
that are mainly consumptive or entertaining (e.g., DiMaggio et al., 2004; Hargittai and
Hinnant, 2008; Kim and Kim, 2001; Mossberger et al., 2003; Van Dijk, 2005; Wasserman and
Richmond-Abbott, 2005; Zillien and Hargittai, 2009). In terms of capital and resources theory,
inspired by Bourdieu (1984), one could also say that users build more economic, social, and
cultural capital and resources. The same normative background could also be found in the
knowledge gap thesis; knowledge was considered more important than other benefits, such as
consumption and entertainment. Zillien and Hargittai (2009, p. 278) concluded that “the
knowledge-gap theory and digital divide research provide a theoretical basis that points to a
relationship between social status and patterns of media use.”
2.2 Usage classifications
Proper observation of differences in usage requires a classification of Internet usage types
derived from the most important contemporary Internet activities. There are several
candidates for such a classification. Some are based on a particular theory, while others use a

descriptive and inductive approach deriving classifications from factor analyses of the steadily
growing list of Internet activities. Most theoretical classifications take the uses-and-
gratifications approach (Katz, Blumler, and Gureitch, 1974) as a starting point. The first step
of this approach is an examination of a medium to derive a list of motivations and
gratifications inherent in its use. The uses and gratifications approach and the related
Expectancy-Value Model (Palmgreen and Rayburn, 1979) explain the way people adopt and
use communication media as a function of their psychological needs. For example, some
gratifications found are problem solving, persuading others, relationship maintenance, status
seeking, and personal insight (Flanagin and Metzger, 2001).
Other potential theoretical backgrounds include the Technology Acceptance Model
(Davis, 1989, Davis and Venkatesh, 1996) and Social Cognitive Theory, the latter of which
has, among others, produced the Model of Media Attendance (LaRose and Eastin, 2004). The
first model posits perceived usefulness as an important explanatory variable for use but has
not yet produced a list of perceived useful Internet applications. The second claims that
expected outcomes are a direct cause of web usage: activity outcomes (playing games,
entertainment, cheering-up, monetary outcomes (shopping and prizes), novel outcomes (news
and information), social outcomes (talk and support), self-reactive outcomes (pass time and
relaxation), and status outcomes (improve life prospects and familiarize oneself with new
technology) (LaRose and Eastin, 2004).
Then, there are studies that account for differences in usage by grouping Internet users
into use typologies (e.g., Brandtzæg, 2010; Livingstone and Helsper, 2007; Ortega Egea,
Menéndez, and González, 2007). These studies utilize descriptive and inductive research to
identify categories of usage types (Kalmus, Realo, and Siibak, 2011). The result is a variety of
classifications that can be advanced to plot Internet usage. Kalmus et al. (2011) suggest that
classifications can be used to differentiate between the use of online social, leisure, and
information services (Amichai-Hamburger and Ben-Artzi 2000), between social, leisure, and
academic Internet use (Landers and Lounsbury 2006), between technical, information
exchange, and leisure motives (Swickert et al. 2002) or between ritualized and instrumental
use (Papacharissi and Rubin 2000). Kalmus et al. (2011) evaluated the number of motives for
Internet use from a list of Internet applications using exploratory factor analysis. They
clustered their motivational items in two groups: social media and entertainment, as well as
work and information. These researchers correlated these clusters not only with socio-
demographic variables but also with personality traits and indicators of habitus and lifestyle,
trying to explain Internet use at large. Their aim was broader than ours, as we focus solely on

socio-economic variables and on differences in usage. Furthermore, we take an approach in
which we clarify the distinction between motives and actual use, which are two different
concepts. We use theoretical accomplishments in uses and gratifications research to propose
classifications of usage activities. This is further explained in Section 3.3. The purpose of this
operation is to relate validated usage clusters with socio-demographic variables to investigate
whether differences in usage exist.
2.3 Socio-demographic categories and Internet usage
There are several socio-demographic variables that explain individual differences in Internet
use. Several studies suggest gender differences (e.g., Jackson et al., 2001; Fallows, 2005;
Meraz, 2008; Subrahmanyam et al., 2001; Valkenburg and Peters, 2006; Wasserman and
Richmond-Abbott, 2005; Zillien and Hargittai, 2009). There is, for example, evidence that
adult females are more likely to use the Internet’s communication tools, whereas adult males
are more likely to use the Internet for information, entertainment, commerce (Jackson et al.,
2001; Subrahmanyam et al., 2001; Valkenburg and Peters, 2006; Zillien and Hargittai, 2009),
and online gaming (e.g., Schumacher and Morahan-Martin, 2001).
Age appears to be one of the most significant variables that effect Internet use (e.g.,
Bonfadelli, 2002; Fox and Madden, 2005; Zillien and Hargittai, 2009). Presently, it appears
that young adults take the lead with the use of communication tools such as instant messaging
and chatting and are more likely to pursue entertainment and leisure activities such as
downloading music or surfing for fun (Fox and Madden, 2005; Dutton et al., 2009; Jones and
Fox, 2009). In contrast, buying products online, emailing, and searching for health-related
information are more popular among older users (Jones and Fox, 2009).
In addition, socio-economic status indicators have a significant impact on Internet use
(Zillien and Hargittai, 2009). Dimaggio et al. (2004) argued that persons of higher socio-
economic status employ the Internet more productively and to greater economic gain than
their less privileged, but nonetheless connected peers. There is evidence to suggest that people
with lower levels of socioeconomic status tend to use the Internet in more general and
superficial ways (Van Dijk, 2005). Here, socioeconomic status is considered as a multi-faced
concept incorporating educational level of attainment, employment status, and income.
The traditional knowledge gap hypothesis and most versions of the usage gap
hypothesis suggest that education is the most important predictor for explaining the types of
online activities a person will pursue (Robinson et al., 2003; Van Dijk, 2005). Howard, Rainie
and Jones in 2001 already revealed that people with higher levels of education use the Internet

for health information, financial transactions, and research, while people with a lower level of
education use the Internet for casual browsing, playing games, or gambling online. Madden
(2003) discovered that people with a higher level of education are less likely to download
music or use instant messaging but that they are more likely to use the Internet for news,
work, travel arrangement, and product information. Hargittai and Hinnant (2008) found that
those with higher levels of education use the Internet for ‘capital-enhancing’ activities, which
includes seeking political or government information, exploring career opportunities, and
consulting information about financial and health services. Helsper and Galacz (2009)
revealed that the lower educated are least likely to use the internet for educational and
economic purposes, even when they have similar levels of Internet access and skills.
With regard to employment status, we will find in this contribution that disabled
people use the Internet for longer periods of spare time daily than people at work or in school.
Obviously, the employed and students use them more at work and at school. Still, the finding
is remarkable because it is often shown that students and workers are more likely to use the
Internet overall, in any environment, than people that are disabled.
Income is a variable with a strong correlation to educational level attained. However,
there are studies that show an independent effect of income on, for example, physical and
material Internet access (e.g., Katz and Rice, 2002). Concerning types of online activities,
Madden (2003) revealed that those with a higher household income are less likely than those
with less income to use instant messaging or download music. However, they are more likely
to seek news and product information or arrange for travel online and typically use the
Internet for work.
Internet experience is often mentioned as a direct competitor to the effect of education
in predicting Internet usage types (e.g., Eastin and LaRose, 2000; Gil-Garcia, Helbig, and
Ferro, 2006; Hargittai and Hinnant, 2008; Livingstone and Helsper, 2007). Length of
experience appears to be a useful predictor of which activities people engage with online
(Howard et al., 2001; Zillien and Hargittai, 2009). People most experienced with the Internet
are most likely to engage in personally advantageous activities.
Since Internet patterns mirror aspects of social structures (Graham, 2008; Van Dijk,
2005), the final factor accounted for is residency. People in rural areas have less Internet
access given their lower levels of education and income and lower levels of access to
broadband connections (Hale et al., 2010). However, few researchers have examined
residency differences concerning type of activities.

3. METHOD
3.1 Sample
We relied on a data set collected in September 2011. Sampling and fieldwork were done by
PanelClix in the Netherlands. Respondents were recruited from their online panel which
includes 108.000 people and is believed to be a largely representative sample of the Dutch
population, although migrants are slightly underrepresented. Members of the panel receive a
small incentive of a few cents for every survey they participate in. Panel members are invited
to participate in a study by being sent an e-mail explaining the topic of the survey and how
much time it will take to complete. In total, a sample of 2,850 people were randomly selected
to reach a sample of about 1,200 persons. During the data collection, amendments were made
to be sure to represent the Dutch population in the final sample.
Several measures were taken to increase response rate. The time needed to answer
survey questions was reduced to approximately 15 minutes. The online survey used specific
software that checked for missing responses even when users were prompted to answer them.
Pretesting of the survey was conducted with ten Internet users in two rounds. Amendments
were made at the end of every round based on provided feedback. The ten respondents in the
second round gave no major comments and the survey was deemed ready for posting. The
survey lasted for two weeks.
Background variables of the respondents are compared with the latest data from
Statistics Netherlands. Given that our final sample is drawn from a representative sample, and
that amendments were made to be sure to represent the Dutch population in the final sample,
analyses showed that the gender, age, and formal education of our respondents did match
official statistics. As a result, only a very small correction was needed post hoc.
3.2 Measures
Amount of Internet use was measured as the number of hours in a day respondents spent
online in their free time.
The respondents were asked to indicate to what extent they use the Internet for several
activities. In total, 20 popular activities that regularly appear in recent scientific and market
research of Internet applications were added to the survey. Respondents were asked with what
frequency they engage in the activities, by using a five-point scale ranging from ‘never’ to
‘daily’ as an ordinal-level measure.

Motivations for using the Internet were comprised of 24 items. Respondents indicated
their level of agreement with reasons for accessing the Internet. Possible responses ranged
from (1) strongly disagree to (5) strongly agree. Items included in the study cover motivations
that can be directly related to types of usage. The motivational items included in the study are
based on motivations relating to information seeking (Papacharissi and Rubin, 2000; Song et
al., 2004), career (Charney and Creenberg, 2001), personal development (Parker and Plank,
2000; Choi, Dekkers, and Park, 2004), transaction, leisure-related activities such as
entertainment and passing time (Papacharissi and Rubin, 2000), and items based on constructs
of more interpersonally oriented needs (Papacharissi and Rubin, 2000; Song et al., 2004).
To measure age, respondents were asked for their year of birth, which was then
transposed to a continuous age variable. Gender was included as a dichotomous variable. Data
on education were collected by degree. These data were subsequently divided into three
overall groups of low, medium, and high educational levels attained. Internet experience was
measured as the number of years that people have been using the Internet. Employment status
was coded as dummy variables of the following groups: employed, retired, disabled,
housemen or –wives, unemployed and students. Income was measured as total family income
in the last twelve months, in eight categories of ten thousand Euros and 80.000 Euros or more.
Finally, urban residency was included as a dichotomous variable, urban and rural.
3.3 Data analysis
We took three steps to create a validated classification of Internet usage activities that can be
used to identify usage gaps. In the first step, we created a list of 20 Internet activities and
subsequently used principal component analysis with varimax rotation to identify the
underlying clusters. Factor loadings were used at 0.5 and above for each item (Hair et al.,
2006). All items were used for the factor analysis, which extracted seven factors. It was
observed that two items were not loaded on any of the factors. These items were deleted from
the original list. Factor analysis was repeated using 18 items (Table 4). There were no items
that loaded on two factors. Seven factors showed eigenvalues above the acceptable 0.7
(Jolliffe, 1972) and were retained. Internal consistency of the factors for each usage cluster
reveal a reliable factor solution. Cronbach’s α coefficients ranged from 0.64 to 0.75.
Second, we conducted a confirmatory factor analysis of motivational items, derived
from Uses and Gratifications Theory. In Uses and Gratification studies, respondents are
typically asked to indicate for what purpose they use the Internet. A confirmatory principal
component analysis with varimax rotation was used to identify the underlying motivations for

Internet use. All 24 items were used for the factor analysis and confirmed eight motivational
clusters, with eigenvalues above the acceptable 0.7. Internal consistency of the factors for
each motivation cluster ranged from 0.66 to 0.87.
Third, we compared the results of the confirmatory factor analysis of motivations for
using the Internet with new clusters of actual usage derived from the exploratory factor
analysis of Internet activities. Here, the goal is to prove the assumption that high measures of
statistical association exist between neutrally labeled usage activities and clusters labeled with
motivations derived from established theory. Therefore, the correlations between the
confirmed motivation clusters and the newly created usage clusters were determined. If the
Pearson’s correlations are highest among related clusters, then we appear to have created a
usage classification that is validated by the previously established Uses and Gratifications
Theory.
The purpose of the three steps described above is to relate the validated usage activity
clusters with socio-demographic variables to investigate differences in usage. To decipher
what exactly may be the cause of the association of people’s background characteristics and
the frequency of several Internet activities people engage in, we performed linear regression
analyses with newly created usage clusters as dependent variables. The regression models
included the independent variables of gender, age, education, Internet experience,
employment status, income, and residency.
4. FINDINGS
4.1 Respondents
The final response rate was 52%. A total of 1,488 responses were received, of which seven
were rejected due to incomplete responses. Hence, a total of 1,481 responses were used for
data analysis. For education, age, and gender, our findings are consistent with the
segmentations provided by the official statistics of the Netherlands. Table 1 summarizes the
demographic profile of the respondents. The mean age of the respondents was 48.2 years
(SD=17.4), with age ranging range from 16 to 87. Almost all respondents had been born in the
Netherlands (95%). The average years of Internet experience of the respondents is 11.8
(SD=4.6). The amount of Internet use is high, with an average of 3.1 (SD=3.2) hours a day in
spare time.

Table 1. Demographic profile
N %
Gender
Male 771 52.1
Female 710 47.9
Age
16-29 271 18.4
30-49 460 31.2
50-64 426 28.9
65+ 316 21.3
Education
Low 504 34.0
Middle 570 38.5
High 387 26.1
Employment status
Employed 723 48.8
Unemployed 63 4.3
Disabled 88 5.9
Retired 371 25.1
Housemen / -wife 104 7.0
Student 132 8.9
Residency
Urban 877 59.2
Rural 604 40.8
4.2 Classifying Internet usage activities
Table 2. Rotated factor matrix of usage activities (How often do you use the Internet for…)
Factors Items
Factor
loadings
Reliability
(Internal
consistency)
1: Personal development Finding online courses and training 0.792 0.77
Following online courses 0.781
Find vacancies/applying for jobs 0.688
Independent learning 0.680
2: Music and video Downloading music / video 0.777 0.67
Hobby 0.523
Free surfing 0.501
3: Commercial transaction Using sites such as E-bay 0.820 0.71
Acquiring product information 0.687
Shopping or ordering products 0.679
4: Social interaction Using social network sites 0.775 0.69
Chatting 0.725
Sharing photos / videos 0.491
5: Information Using search systems 0.808 0.63
Searching information 0.732
6: News News services 0.875 0.67
Newspapers and online magazines 0.774
7: Gaming Playing online games 0.882
Loadings greater than .50 are shown. The items are sorted by the size of their factor loadings on a respective
factor.

To investigate which usage gaps exist on the Internet, we first need to classify usage activities.
As described in Section 3, we took three subsequent steps to create such a classification. In
the first step, we investigated several Internet usage activities using an exploratory principal-
component factor analysis. In total, 18 items were retained in a seven factor structure together
accounting for 66.0% of the total variance, which is considered acceptable for research in the
social sciences (Hair et al., 1995). The resulting seven-factor solution and the factor’s labels
are shown in Table 2. The factor labeled “gaming” is poorly defined since only one item loads
on it. However, the exploratory nature of this study warranted using “Playing online games”
as the only item for the subsequent analysis. Two factors contain two items which is
acceptable since both items are strongly correlated.
Table 3. Rotated factor matrix of motivational items (My reason to use the Internet is….)
Factors Items
Factor
loadings
Reliability
(Internal consistency)
1: Information To find information 0.856 0.66
To discover things 0.798
To investigate things 0.696
2: Career To make a career for myself 0.872 0.75
To improve my chances in the work field 0.842
To get a promotion at work 0.777
3: Personal development To stimulate my creativity 0.763 0.71
To learn new things 0.531
Develop myself 0.428
4: Shopping To order something quickly 0.846 0.87
To buy a product I heard of 0.818
To purchase something 0.751
5: Entertainment To entertain myself 0.828 0.71
To have fun 0.751
To find information for amusement 0.727
6: Relaxation To feel less hurried 0.805 0.71
To release stress 0.802
To come at ease 0.723
7: Relationship maintenance To maintain contact with friends 0.823 0.66
To have contact with my friends 0.759
To send people I know messages 0.741
8: Social interaction To participate in chat sessions 0.812 0.71
To make new contacts 0.589
To connect with a group 0.587
Loadings greater than .50 are shown. The items are sorted by the size of their factor loadings on a respective
factor.
In the second step, we conducted a confirmatory principal-component factor analysis
for the 24 motivational items. The eight factors together accounted for 75.8% of the total

variance. All the items were retained for the factor analysis, and all items loaded on the
factors obtained. Thus, the factor analysis confirmed the eight motivations for using the
Internet. The coefficient alphas reveal a reliable factor solution. The results are shown in
Table 3.
Table 4. Validation Of Factor Analysis Of Application Clusters In Terms Of Drivers Of Applications By Factor
Analysis Of Actual Motives
Usage activity clusters
Information
Personal
development News
Music and
video
Social
Interaction
Commercial
transaction
Gaming
Motivation Information .388*** .093** .240*** .207** .093** .249** .038
Clusters Career .145*** .399*** .140*** .251*** .295*** .246*** .167***
Relationship maintenance .195*** .180*** .173*** .204*** .454*** .193*** .202***
Shopping .294*** .183*** .214*** .284*** .198*** .469*** .135***
Entertainment .318*** .193*** .257*** .419*** .354*** .285*** .303***
Relaxation .102*** .223*** .100*** .235*** .369*** .242*** .287***
Social interaction .089*** .290*** .127*** .267*** .558*** .251*** .255***
Personal development .322*** .285*** .246*** .320*** .263*** .306*** .180***
*significant at the 5% level, **significant at the 1% level, ***significant at the 0.1% level
In the third step, the seven usage factors are validated by measuring Pearson’s
correlations with the motivation factors. For content, we would expect there to be a significant
relationship between the motivation and usage factor “information,” between the motivation
“career” and the usage cluster of “personal development,” between the motivations “social
interaction” and “relationship maintenance” with the usage cluster “social interaction,”
between the motivation “shopping” and the usage cluster “commercial transaction,” between
the motivations “entertainment” and “relaxation” and the usage clusters “music and video”
and “gaming.” All expected relations are confirmed, since the correlations are highest
between the related clusters (see Table 4). This suggests that we obtained a classification of
usage activities that is validated by established uses and gratifications theory and can be used
to reveal which socio-demographic variables usage gaps exist.
4.3 Investigating Differences in Usage
Using the validated classification of seven types of Internet usage, we investigate how these
types relate to differences in socio-demographic variables. For all categories of usage listed,
regression analyses are summarized in Table 5. First, we investigated how differences
between gender, age, levels of education attained, internet experience, income, employment
status, and residency are significant when considering amount of use as a dependent variable.
Here, the finding addressed in the introduction of this article is shown: in their free time,
lower educated individuals use the Internet for longer periods of time than those who are
medium and higher educated. The same can be observed regarding employment. People that

Table 5. Multiple linear regression analysis with six usage activities as dependent variables
Amount of Internet
use
Information News Personal
development
Music and video Gaming Social interaction Commercial
transaction
Explanatory variables B (Std. error) B (Std. error) B (Std. error) B (Std. error) B (Std. error) B (Std. error) B (Std. error) B (Std. error)
Constant 3.364(0.499)*** 4.020(0.108)*** 3.173(0.204)*** 2.108(0.116)*** 3.087(0.146)*** 3.130(0.229)*** 3.224(0.163)*** 2.990(0.133)***
Sex
Male 0.602(0.208)** 0.049(0.045) 0.278(0.085)*** 0.072(0.048) 0.492(0.061)*** -0.216(0.095)* -0.071(0.068) 0.061(0.055)
Age (reference: 16-29)
30-49 0.095(0.350) -0.156(0.076) -0.086(0.143) -0.414(0.081)*** -0.296(0.102)** -0.412(0.161)* -0.561(0.115)*** -0.083(0.093)
50-64 -0.802(0.372)* -0.327(0.081)*** -0.542(0.152)*** -0.704(0.086)*** -0.891(0.109)*** -1.128(0.171)*** -1.198(0.122)*** -0.429(0.099)***
65+ -0.610(0.521) -0.580(0.113)*** -0.807(0.213)*** -0.816(0.121)*** -1.152(0.152)*** -1.057(0.239)*** -1.326(0.170)*** -0.616(0.138)***
Educational level (reference:
low educational level)
Medium educational level -0.583(0.242)* 0.126(0.052)* 0.096(0.099) 0.025(0.056) -0.070(0.071) -0.196(0.111) -0.114(0.079) 0.017(0.064)
High educational level -0.816(0.284)** 0.245(0.064)*** 0.196(0.120) 0.220(0.068)*** 0.030(0.086) -0.507(0.135)*** -0.211(0.096)* -0.047(0.078)
Internet experience 0.053(0.022)* 0.021(0.005)*** 0.030(0.009)*** 0.000(0.005) 0.027(0.007)*** 0.002(0.010) 0.008(0.007) 0.003(0.006)
Household Income -0.113(0.059) 0.054(0.013)*** 0.078(0.024)*** -0.001(0.014) 0.036(0.017)* -0.027(0.027) -0.010(0.019) 0.031(0.016)*
Employment status
(reference: Employed)
Unemployed
Disabled
Retired
Housemen / -wife
Student
-0.113(0.532)
1.505(0.422)***
0.078(0.082)
0.151(0.466)
0.301(0.569)
-0.062(0.115)
0.154(0.091)
0.041(0.018)
-0.031(0.101)
0.242(0.123)*
-0.126(0.217)
0.305(0.172)
0.086(0.033)
-0.060(0.190)
-0.152(0.232)
0.213(0.123)
-0.100(0.098)
-0.034(0.019)
-0.071(0.108)
0.334(0.132)*
0.106(0.156)
0.197(0.124)
-0.016(0.024)
0.011(0.136)
0.357(0.166)*
0.149(0.244)
0.743(0.194)***
-0.029(0.037)
-0.164(0.214)
0.047(0.261)
-0.248(0.147)
0.282(0.134)*
-0.011(0.027)
-0.004(0.152)
0.367(0.186)*
-0.037(0.141)
-0.076(0.112)
0.016(0.022)
-0.094(0.124)
-0.010(0.151)
Community (Reference: Rural)
Urban 0.478(0.201)* 0.021(0.044) 0.100(0.082) 0.041(0.047) 0.085(0.059) -0.062(0.092) 0.137(0.066)* 0.010(0.053)
R, R2,
adj R2
0.252, 0.064,
0.050
0.389, 0.151,
0.139
0.303, 0.092,
0.079
0.467, 0.218,
0.207
0.532, 0.283,
0.273
0.385, 0.148,
0.136
0.489, 0.339,
0.230
0.275, 0.076,
0.063
F4.816*** 12.648*** 7.166*** 19.730*** 27.984*** 12.328*** 26.074*** 5.798***
*significant at the 5% level, **significant at the 1% level, ***significant at the 0.1% level

are disabled use the Internet more hours a day than people who are employed. Also, the
results show that people living in urban areas use the Internet for longer periods of time than
people living in rural areas.
From Table 5, we conclude that the most prominent differences relate to age. For all
usage clusters, age is an important contributor. Considerable differences for education are also
observable. Lower educated people make less use of information than medium and high
educated people. They also make less use of the Internet for personal development than the
higher educated. Conversely, the lower educated use the Internet more for gaming and social
interaction than the higher educated. Table 5 also reveals differences over gender, favoring
men concerning the activities of news and music and video. Women use the Internet more for
online gaming.
Employment status reveals that people who are disabled are more likely to use the
Internet for gaming and for social interaction than people who are employed. Students are
more likely to use the Internet for information, personal development, social interaction, and
music and video than the employed.
There are also relatively small, but nevertheless significant, differences in usage
regarding Internet experience and income. People that have been using the Internet for longer
periods of time are more likely to use the Internet for news, information, and music and video.
The same goes for people with higher income levels. They also are more active in online
shopping. Finally, there is one small difference regarding residency; People living in urban
areas make more use of social interaction than people living in rural areas.
5. DISCUSSION
5.1 Main findings
In the last decade, attention in digital divide research has shifted from inequalities of access to
digital skills and usage, pointing out the limitations of digital divide research in the beginning
of the 21st century that mainly considered binary classifications of haves and have nots.
Furthermore, the descriptive inventories of Internet activity use by the most important
demographic categories made in the last ten years now evolve into more analytic
considerations. Our analysis of the data from a representative population survey revealed and
validated seven clusters of Internet usage: information, news, personal development,
commercial transaction, music and video, social interaction, and gaming. This classification is
used to answer the research questions. We investigated how and by who the Internet is used to
explain the observation that currently in their spare time, at least in the Netherlands, people

with a low level of education use the Internet more frequently and for more hours a day than
people with medium and high levels of education. Low educated people seem to engage more
in social interaction and gaming which both are very time-consuming activities. Besides
education, age and gender are the most salient predictors for differences in Internet usage,
whereas Internet experience, income, and residency seem to be less relevant than expected.
Age appears as the most important factor in Table 5. It is important to emphasize that
both the knowledge gap and the usage gap thesis are framed in terms of knowledge and usage
inequalities related to levels of education. It is a plausible statement that the age gap partly is
a temporary phenomenon, not only because the contemporary young will grow old, but also
because increasingly present day older generations adopt Internet activities such as music and
video, gaming and social media. Partly the same could occur with the gender gap that also
strongly comes forward in Table 5. This could happen when internet activities become more
equally shared among the sexes after some time. As with both age and gender a particular
share of inequality will remain that is derived from relatively permanent socio-cultural
preferences. However, it is also a plausible statement that inequalities related to different
levels of education are longer lasting as they are deeply engrained in the fabric of our
information or knowledge society. Therefore the suggestion for discussion can be made that
ultimately the education gap might be more permanent than the age and gender gaps.
Although, at least in the Netherlands, low educated Internet users spent more time
online in their spare time, the findings reveal that those with higher social status use the
Internet in more beneficial ways. Similarly, Zillien and Hargittai (2009, p. 287) concluded that
“those already in more privileged positions are reaping the benefits of their time spent online
more than users from lower socioeconomic backgrounds.” The findings suggest that as the
Internet becomes more mature, its usage reflects traditional media use in society; Internet use
increasingly reflects known social, economic, and cultural relationships present in the offline
world, including inequalities (e.g., Golding, 1996; Mason and Hacker, 2003; Van Dijk, 2005;
Witte and Mannon, 2010; Zillien and Hargittai, 2009). For example, people with lower
education and lower income also tend to watch more TV, or read fewer books and
newspapers. Such parallels support the comparison between the knowledge gap hypothesis
regarding the use of mass media and the usage gap hypothesis regarding the use of the
Internet (e.g., Bonfadelli, 2002; Van Dijk, 2005; Zillien and Hargittai, 2009). The effect of
education conforms to the thesis of the usage gap, and to previous assumptions that defended
the knowledge gap.

Similarities between participation in the offline and online world are often topic of
debate in discussions concerning social inequality. A decade ago, Compaine (2001) compared
the diffusion of television, radio, and telephone with the diffusion of the Internet, and
concluded that the digital divide is a temporary problem. Most scholars have moved away
from such conclusions, but comparing the knowledge gap hypothesis with the usage gap
hypothesis might lead to another misinterpretation, namely that differences in education have
always been one of the causes of differences in society and opportunities in life, and thus, the
Internet is just the next advancement in communication technology with its usage determined
by education. The intensive and extensive nature of Internet use among the well-off and well-
educated suggests an elite life-style from which those with less capital are marginalized (Van
Dijk, 2005; Witte & Mannon, 2010; Zillien and Hargittai, 2009). Although inequalities within
society have always existed, the Internet created an even stronger division; the higher status
members increasingly gain access to more information than the lower status members. The
Internet is not only an active reproducer of social inequality, but also a potential accelerator
(Witte and Mannon, 2010). Rather than equalization, the Internet tends to reinforce social
inequality and lead to the formation of disadvantaged and excluded individuals (Golding,
1996; Van Dijk, 2005). Wei and Hindman (2011), for example, found that socioeconomic
status is more strongly related to the informational use of the Internet than with that of the
traditional media, and that the differential use of the Internet is associated with a greater
knowledge gap than that of the traditional media. They therefore suggested that the digital
divide matters more than its traditional counterpart. After all, the Internet has more functions
than traditional media have.
Information and network society theory both acknowledge the importance of the
Internet as a vital resource in society. In political, social, cultural, health, and economic
domains, more and more information and services are provided online, and often, it is
expected that they will be used by all. The results of this and other recent studies reveal that
within several domains, current policy directions should be evaluated. There are strong
indications that parts of the population will be excluded from several Internet activities. The
results of the current investigation suggest that overcoming digital divides is a rather complex
challenge that goes beyond improving access or Internet skills. Clearly, this article among
others has shown that they are related to individual motivations and socio-cultural
preferences. In a free society, these preferences can only partly be changed by policy such as
governmental social and cultural policies in education and community building. Internet
activities related to information, career and personal development could be made more

attractive for larger parts of the population. Finally, the improvement and spread of positions
in education and on the labor market (actually following school or adult education and having
an appealing job) might show the most positive contributions to the reduction of the usage gap
as described here.
5.2 Shortcomings and future research
In this article, we propose seven categories of usage activities. Our classification made a
distinction between motives and actual use, which are different concepts. The usage
categories are validated by using motivational categories present in Uses and Gratifications
theory. In future contributions, the identified usage clusters can be further improved, for
example, by adding more items to the gaming factor.
The validated usage clusters are used to explain the finding that people with low levels
of education use the Internet for more hours a day than people with high levels of education.
One might argue that high educated populations have less spare time, however, the results do
reveal that they use this time online differently. Future studies should investigate whether high
educated and employed people use the Internet at work also for private purposes. And if they
do, what this private use looks like. This would be to investigate whether the higher educated
compensate at work for the activities that the lower-educated perform in their leisure time.
Considering the assumed advantages of serious uses of the Internet it has to be shown
that that they actually create more benefits in terms of different types of resources and capital
than entertainment uses. This is hard to measure. In fact, this article only shows evidence of
unequal use that might have societal results. It would also have to be demonstrated that
Internet use increasingly reflects and perhaps even reinforces inequalities in society.
Furthermore, it is not fully clear what the exact implications of the difference between
the knowledge and usage gap hypotheses are. Further research should investigate the
similarities and differences between usage of the traditional mass media and the Internet, by
for example including comparable types of usage in mass media other than the Internet. A
comparison of the results could show whether the use of the Internet actually makes a
difference, the underlying assumption of all digital divide research. Related to such
investigations, is the current debate around the skills needed for online and traditional media.
While some argue that the skills needed to seek online information require ever less
capabilities, others argue that benefiting from the Internet requires a growing number of
capabilities as compared to traditional media (Van Deursen, 2010).

In this study, we have revealed that differences in usage exist. Structural usage
differences appear when particular segments of the population systematically and over longer
periods of time take advantage of the serious Internet activities they engage in, while others
only use the Internet for everyday life and entertaining activities. Future research should also
determine whether there is a growth or a reduction of the multiple differences distinguished in
this article in a longitudinal perspective. As suggested, gender and age differences might
partly disappear when the technology matures and spreads further across the population, while
educational differences increase. Finally, this study should be replicated in other countries
with increasing popular use of the Internet for all everyday activities. Will the same trends of
popularization and increasingly unequal use appear as in the Netherlands? Here again,
longitudinal replications are required to determine whether the differences discussed are
growing or decreasing.
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