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Revisiting the digital divide in Canada: the impact of demographic factors
on access to the internet, level of online activity, and social networking
site usage
Michael Haight
a
*, Anabel Quan-Haase
b
and Bradley A Corbett
c
a
Department of Sociology, Social Science Centre, The University of Western Ontario, London, Ontario,
Canada N6A5C2;
b
Faculty of Information and Media Studies/Sociology, The University of Western Ontario,
North Campus Building, London, Ontario, Canada N6A 5B7;
c
Ivey Business School, The University of
Western Ontario, 1151 Richmond Street, Room 1030 SSC, London, Ontario, Canada N6A5C2
(Received 1 October 2013; accepted 29 January 2014)
The present study relies on the 2010 Canadian Internet Use Survey to investigate differences in
people’s access to the internet and level of online activity. The study not only revisits the digital
divide in the Canadian context, but also expands current investigations by including an analysis
of how demographic factors affect social networking site (SNS) adoption. The findings
demonstrate that access to the internet reflects existing inequalities in society with income,
education, rural/urban, immigration status, and age all affecting adoption patterns.
Furthermore, the results show that inequality in access to the internet is now being
mimicked in the level of online activity of internet users. More recent immigrants to Canada
have lower rates of internet access; however, recent immigrants who are online have
significantly higher levels of online activity than Canadian born residents and earlier
immigrants. Additionally, women perform fewer activities online than men. People’s use of
SNSs differs in terms of education, gender, and age. Women were significantly more likely
to use SNSs than men. Interestingly, high school graduates had the lowest percentage of
adoption compared to all other education categories. Current students were by far the group
that utilized SNSs the most. Canadian born, recent, and early immigrants all showed similar
adoption rates of SNSs. Age is a strong predictor of SNS usage, with young people relying
heavily on SNSs in comparison to those aged 55+. The findings demonstrate that the digital
divide not only persists, but has expanded to include inequality in the level of online
activity and SNS usage.
Keywords: digital divide; level of online activity; digital inequality; social media; social
networking sites
Introduction
Internet penetration rates have increased significantly in North America since the early 2000s.
Even though nearly every social group in society is showing increased levels of internet utiliz-
ation, the rate at which some are lagging behind is cause for concern (Epstein, Nisbet, & Gillespie,
2011). In 2008, as many as 10% of American users continued to have dial-up internet connection
at home (Horrigan, 2009), with affordability and the rural/urban divide identified as the key bar-
riers to equal broadband access in the United States. According to a 2013 Pew Internet and
© 2014 Taylor & Francis
*Corresponding author. Email: mhaight@uwo.ca
Information, Communication & Society, 2014
Vol. 17, No. 4, 503–519, http://dx.doi.org/10.1080/1369118X.2014.891633
American Life report, as many as 70% of Americans have a broadband internet connection at
home, which facilitates access to digital resources, social networking, and information sharing
(Zickuhr, 2013). Issues of the digital divide have typically been phrased in terms of the possibi-
lities that technology has to overcome or exacerbate existing inequalities (Chen, 2013; Witte &
Mannon, 2009).
According to Norris (2001),
digital networks have the potential to broaden and enhance access to information and communications
for remote rural areas and poorer neighbourhoods, to strengthen the process of democratization
under transitional regimes, and to ameliorate the endemic problems of poverty in the developing
world. (p. 6)
Consequently, questions around the digital divide are of major concern to all citizens because the
economic, cultural, and social possibilities of individuals and nations depend on their ability to
leverage digital technologies and participate in the information age (Quan-Haase, 2013). The con-
sequences for these groups can be dramatic. Wellman, Quan-Haase, Witte, and Hampton (2001)
note that the ‘Internet is becoming normalized as it is incorporated into the routine practices of
everyday life’(p. 1). Hence, lack of access to the internet can significantly undermine efforts
to obtain employment, access current news and debates, and secure online government services.
While researchers agree on the importance of studying the digital divide, much controversy
still surrounds its definition (Epstein et al., 2011; Stevenson, 2009; Vehovar, Sicherl, Husing,
& Dolnicar, 2006). Studies conducted in the 1990s were primarily concerned with issues sur-
rounding access, where access was measured in terms of having a computer at home that connects
to the internet. DiMaggio and Hargittai (2001) were two of the first scholars to propose a shift in
the definition of the digital divide by examining differences in internet usage among those who are
connected. This change highlights the growing concern that although internet diffusion has accel-
erated since the mid-1990s, the benefits derived from this access are not universally experienced
by all users (Attewell, 2001; Howard, Busch, & Sheets, 2010; Ono & Zavodny, 2007). Hence,
measuring individuals’online engagement and the range of activities that users perform is criti-
cally important for understanding how those who are connected take advantage of the opportu-
nities afforded by the internet (Quan-Haase, 2013; Witte & Mannon, 2009). In the present
study, we examine the range of online activities performed by connected Canadians to better
understand how these vary across different demographic characteristics.
One important consideration that often has been neglected in discussions of the digital divide
is the changing nature of the internet (Quan-Haase & Wellman, 2004). While most internet
activity still consists of searching the web for content, an increasing percentage of internet
users’time online is spent on social networking sites (SNSs) (Pew Research Center, 2013). Face-
book has grown rapidly and it reports having approximately 1.19 billion monthly active users as
of October 2013 (Facebook, 2013); in 2013, Twitter reports having over 200 million active users
(Twitter, 2013), and also in 2013, Tumblr reports hosting 138.2 million blogs (Tumblr, 2013).
Recent research has demonstrated that participating in SNSs is tied to several positive outcomes,
such as identity management, informational access, and the creation of social capital through net-
working opportunities (Ellison, Steinfield, & Lampe, 2007 2011). Despite the growing evidence
of the benefits of SNSs, the digital divide literature tends to disregard involvement on these sites
(for an exception see: Hargittai & Hsieh, 2010). To fill this gap, the present paper examines and
contrasts three indicators of the digital divide: (1) access to the internet; (2) level of online
activity; and (3) SNS usage. The comparison of these three indicators will provide a broader
picture of how Canadians are connected and permit a more comprehensive analysis of the
digital divide.
504 M. Haight et al.
The present paper investigates the digital divide in Canada by employing the 2010 Canadian
Internet Use Survey (CIUS). Researching the digital divide in Canada is important because the
majority of the current literature on this topic utilizes data from the United States and other devel-
oped countries (Eurostat, 2011; Zickuhr, 2013). This limitation in our understanding of the digital
divide in the Canadian context is concerning because of the implications and consequences of
digital inequality within a society that has become dependent on the internet to the point of indis-
pensability (Hoffman, Novak, & Venkatesh, 2004). Moreover, Canada is unique in terms of the
geographic challenges it has to overcome. One key challenge is the low population density in
relation to its geographic size. As a result, there are limited economic incentives for commercial
carriers to provide rural or remote areas with high-speed internet services. Collins and Wellman
(2010) write in their analysis of Chapleau, a rural Northern Ontario community, that it was pri-
marily remote and rural areas of Canada that lacked high-speed internet in 2005. Until recently,
it was also nearly impossible to create the appropriate infrastructure in Northern Canada because
of the technical challenges presented by the remoteness and geographic characteristics of
Canada’s North. A second important challenge is Canada’s high level of immigration. Recent
data show that in some of Canada’s largest cities as much as half of the population is foreign
born. For instance, half of Toronto’s population (1,237,720) in 2013 was foreign born, up from
48% in 1996 (City of Toronto, 2006) and Statistics Canada reports that in 2001 39% of the popu-
lation in Vancouver was foreign born (Citizenship and Immigration Canada, 2010). This creates
unique challenges in terms of understanding how immigration is linked to internet usage, levels of
online activity, and SNS adoption. As other countries –the United States, Germany, and Singa-
pore –also experience comparable levels of immigration, the findings from the present study will
provide a baseline for comparative research and future analysis on immigration and the digital
divide, an as yet much neglected topic. Overall, this paper presents a renewed look at the
access divide and it expands current analyses by providing an investigation of Canadians’
level of online activity and SNS usage.
Literature review
Theoretical framework and background
The central theoretical framework for the present study is the research on the digital divide. In this
section, we examine three perspectives on the digital divide: (1) internet access, (2) level of online
activity, and (3) SNS usage. Each view conceptualizes the digital divide and its implications from
a different angle.
The first digital divide: internet access
There is no question that penetration rates of the internet have increased significantly in recent
years, yet real differences in internet access continue to persist not only in developing nations,
but also in developed nations, such as Canada. Internet access in Canada has been on an
upward trend: from 51% to 80% of the population using the internet in 2000 and 2009, respect-
ively (Figure 1).
This divide often reflects existing inequalities and persists among a number of segments
within the population (Hargittai, 2010; Ono & Zavodny, 2007). Ono and Zavodny (2007)
address this specifically in their multinational study of digital inequality when they comment
that ‘countries with higher high school completion rates have smaller education gaps in computer
ownership’(p. 1150).
A 2011 Eurostat report found that 92% of individuals in Europe with high levels of education
use the internet when compared to only 71% of those with lower levels of education (Eurostat,
Information, Communication & Society 505
2011).
1
In the United States, the Pew found that those individuals with less than high school edu-
cation and less than $30,000 yearly income have the lowest rate of internet access (Zickuhr,
2013). Consistently studies find that the most central predictors of internet access are education
and income (Beunte & Robbin 2008; Goldin & Katz, 2008; Hale, Cotten, Drentea, & Goldner,
2010). By contrast, research on gender and internet access has produced inconsistent results.
For instance, Bimber (2000) found that a significant difference existed in access to the
internet between men and women. However, this difference was a result of other factors, with
the most important being socioeconomic differences between men and women. He suggested
that eventually ‘this gap will narrow of its own accord, because educational and income differ-
ences between men and women are slowly shrinking’(p. 874). His assertion appears to have
been correct because recent scholarship in this area has demonstrated little to no difference in
online access to the internet between men and women in developed countries (Ono &
Zavodny, 2003; Wasserman & Richmond-Abbott, 2005), suggesting that the gender gap has
closed.
Differences in access also exist between those of different ages, rural/urban dwellers, and
immigrants. Research shows that younger individuals are significantly more likely to be online
than seniors (Fox, 2004; Hale et al.,2010; Hargittai & Hinnant, 2008; Madden 2006). As individ-
uals who did not grow up with computers, this population finds it harder to fully immerse them-
selves into the digital realm. Comparing rural and urban dwellers, geographic location continues
to impact access to the internet (McKeown, Noce, & Czerny, 2007; Veenhof, Wellman, Quell, &
Hogan, 2008; Wasserman & Richmond-Abbott, 2005). Collins and Wellman (2010) explain that
‘[t]he Chapleau experience did not reflect rural Internet users’[sic] becoming the same as urban
and suburban Internet users’(p. 1363). Rural users primarily embed the internet into their every-
day lives instead of changing their practices and habits. Finally, immigration seems to also impact
internet access. Using data from the Current Population Survey and the US 2000 Census, Ono and
Zavodny (2008) observed lower rates of computer ownership and internet access among immi-
grants in the United States than those born in the United States.
While the gap between the ‘haves’and the ‘have nots’has continued to narrow since the
1990s, it is also evident that there is a proportion of the population that continues to lag
Figure 1. Internet access in Canada, 2000–2007 (Source: CIUS data).
506 M. Haight et al.
behind. We argue that access to the internet reflects existing inequalities in society, even in devel-
oped nations, with key social and demographic factors affecting adoption patterns. If this is the
case, it would be of great importance to identify along which dimensions the gap continues to
exist in order to develop appropriate policy and programmes to remedy this social problem.
This leads to our first research question:
RQ1: Do differences in access to the internet exist in the Canadian context along key demographic
factors, such as age, gender, income, education, rural/urban location, and immigration status?
The second digital divide: gaps in the level of online activity
The term second digital divide has recently drawn considerable attention from academics and
policy-makers, who view it as central for understanding ‘a person’s ability to perform tasks effec-
tively in a digital environment’(Jones & Flannigan, 2006, p. 9). Understanding the range of
activities a person performs when online is important because it can help users take full advantage
of the internet as a resource for locating information, getting a job, and connecting with friends
and family (Wellman et al., 2001). The concept of the second digital divide has, however, eluded a
universally accepted definition (van Deursen & van Dijk, 2010; Epstein et al., 2011; Eshet-
Alkalai, 2004)‘as some restrict [it] to the technical aspects of operating in digital environments,
while others apply it in the context of cognitive and socio-emotional aspects of work’(Eshet-
Alkalai, 2004, p. 103). Still other scholars tend to focus on the range of activities a person per-
forms when online (Hargittai & Hinnant, 2008; Zickuhr & Smith, 2012). The importance of
measuring a person’s online activity level is evident in that users’online skills enable them to
engage with the internet in ways that are useful and meaningful to their specific needs (Hargittai,
2002; Hargittai & Hinnant, 2008).
Limited research has focused on the second digital divide in the Canadian context, whereas in
the United States and Europe a number of studies have emerged in the past 10 years. For instance,
Papastergiou and Solomonidou (2005) found in their study of Greek high school students that
men were more likely than women to use the internet for recreational and content creation. By
contrast, Gross (2004) found that teenage boys’and girls’online activities were fairly similar.
In a study by Teo (2001), education had ‘little effect on messaging, downloading and purchasing
activities’(p. 134). By contrast, Zickuhr and Smith (2012) observed several demographic factors
affecting online activities (i.e. to search, email, buy products, and do online banking), including
age, income, education, and gender. Overall, there is little consistency in the literature as to what
key factors affect online activities. Moreover, many studies tend to focus on one to three activities,
instead of assessing a wide range of activities to obtain a broader understanding of internet
engagement.
In the present study, we examine the level of online activity in which users engage as a means
for understanding what they do when they are online. This follows previous work by Wellman
et al. (2001) and Hargittai and Hinnant (2008) and provides a useful conceptualization of the
second digital divide as it moves away from problems associated with relying on measures of
self-efficacy. That is, rather than attitudinal questions measuring perceptions of expertise, this
is a more robust approach and asks respondents directly about activities that they have recently
performed when online (DiMaggio & Hargittai, 2001; Hargittai & Hsieh, 2012; Livingstone,
Bober, & Helsper, 2005). Moreover, Hargittai and Hinnant (2008) propose that ‘the way in
which people utilize the Internet is at least in part driven by their online skills’(p. 605).
Hence, determining the level of online activity of an individual also provides a means of accessing
how well they can engage with and navigate the online sphere. To identify differences in the level
of online activity among Canadians, we propose the following research question:
Information, Communication & Society 507
RQ2: Do differences in the level of online activity exist in the Canadian context along key demo-
graphic factors, such as age, gender, income, education, rural/urban location, and immigration status?
The third digital divide: leveraging connectivity on SNSs
Not all uses of the internet have the same kinds of benefits and social consequences (Wellman
et al., 2001). Dystopian arguments proposing that internet use has led to social isolation (Kraut
et al., 1998) have often neglected to consider that browsing the web does not afford the same
kinds of social benefits as emailing, Skyping, or updating Facebook. An impressive body of
work is providing growing evidence that specific uses of the internet can help with the creation,
maintenance, and growth of social capital (Ellison et al., 2007,2011). Even though the evidence
suggests a wide range of benefits derived from SNS usage, differences in use of these sites by
different social groups in society has not been studied in sufficient detail.
According to a 2013 Pew Internet and American Life report on social networking, women are
no longer more likely to use SNSs than men in the United States (Duggan, 2013). The gap in SNS
use by gender is closing from a difference of 15% (68% of women and 53% of men) in 2010 to
just 4% (74% of women and 70% of men) in 2013, which is a result that is no longer statistically
significant. Hargittai and Hsieh (2010) examined SNS usage among first-year university students
and found a significant gender difference with 90% of women and 86% of men utilizing SNSs,
respectively. The Pew report further found a significant difference in SNS use by age, with 89% of
those aged 18–29 using an SNS compared to only 60% of those aged 50–64 (Duggan, 2013). In
the present paper, we investigate the extent to which inequality in access to the internet and level
of online activity mirror differences in the usage of SNSs in Canada.
RQ3: Do differences in SNS usage exist in the Canadian context along key demographic variables,
such as age, gender, income, education, rural/urban location, and immigration status?
Methods
The CIUS was analysed to understand the demographic trends in internet access, level of online
activity, and usage of SNSs in Canadian society. The CIUS was collected in the fall of 2010 and
includes responses from 22,623 residents of Canada aged 16 years and older. The population-
based sample was selected from Canada’s Labour Force Survey (LFS) and is, in 2013, the most
current data on Canadians’use of the internet. Data access was granted through the Western Uni-
versity’s Statistics Canada Research Data Centre. The survey sample is representative of the Cana-
dian population and sampling weights have been applied to account for design effects. Even though
the survey is representative of the Canadian population, it excludes residents of the territories of
Canada and those residing on aboriginal reserves and in institutions, such as prisons and residential
care facilities. Full-time members of the military were also excluded from this survey. Due to the
CIUS being attached to the LFS, very few cases have missing data. For instance, the demographic
variables had no missing values; the only exception was the income variable. To address this
problem, Statistics Canada utilized a series of imputations for missing income values.
Measures
Dependent variables
Internet access. Internet access is measured by asking respondents whether or not they had
accessed the internet for various types of personal use in the past 12 months. This measure
includes access to the internet from any location (e.g. home, public library, friend’s house).
508 M. Haight et al.
Level of online activity. The dependent variable in the second model is a generated scale
measuring the level of online activity of the respondents. The scale was developed from a sum
of 23 dichotomous items that were scored as 1 if the individual indicated they had participated
in the activity and 0 if they had not (see the appendix). This variable has been derived from
items asked only of individuals who used the internet in the previous 12 months and as such
excludes non-internet users. The scale includes activities such as communicating with others
(e.g. email and instant messenger), information seeking (e.g. researching community events
and browsing for information on goods or services), and entertainment (e.g. downloading or
watching TV online and playing online games). Those individuals who engaged in more of
these activities are considered to have a higher level of online activity.
Usage of SNSs. This dichotomous variable measures whether or not the respondent used any SNS
in the previous 12 months. Only respondents who have used the internet in the past 12 months
were included in this analysis.
Independent variables
The following explanatory variables were included in the analysis: household income, highest
level of education, gender, rural/urban status, immigration status, and age. Household income
was included as a categorical variable: $25,000 and lower, $25,000–41,000, $41,000–65,000,
$65,000–100,000, and $100,000 and greater. Those respondents reporting incomes shared by
two categories were randomly assigned to either of the two, which is standard practice with
Statistics Canada data. Another key explanatory variable is the highest level of education
completed by the respondent. The categories of the highest level of education attained
include less than high school, high school graduate, some post-secondary education, univer-
sity graduate, and current student. For the education variable, those individuals who indicated
that they were students during the data collection period were included as a separate category
labelled current student. Following the methodological suggestions of Hargittai (2002)this
helps to avoid bias from placing young individuals who are still obtaining their education
into low education categories. Furthermore, rural/urban status is measured following Statistics
Canada’s(2009) dichotomous categorization, with those individuals living in areas of less
than 1000 individuals being categorized as rural.
2
Immigration status of the respondent is
also included in the models. Respondents born in Canada and those who immigrated
during or prior to 2004 were grouped into a category labelled Canadian born/earlier immi-
grants. Respondents who arrived in Canada during or after 2005 comprise the recent immi-
grants category. This follows previous work that has identified differences in internet
access and use among immigrants depending on the length of time they have been in a
country (Veenhof et al., 2008). Age is presented in the descriptive statistics as a categorical
variable with two groups, those aged 16–54 and those 54 and older, and in the multivariate
models as a continuous variable.
Data analysis
To examine our research questions, a number of models were run in Stata 12. Table 1 presents the
cross-tabulated descriptive statistics. In the first model, the explanatory variables are regressed on
the dependent variable internet access using logistic regression (Table 2). The second model
employs ordinary least-squares (OLS) regression with the dependent variable being level of
online activity and all explanatory variables included (Table 3). The final model investigates
Information, Communication & Society 509
the dependent variable usage of SNSs, utilizes logistic regression, and includes all of the
explanatory variables (Table 4).
Results
The descriptive statistics in Table 1 show that 60% of those in the lowest income quintile reported
access to the internet in the previous month, compared to 95% of those in the highest quintile.
This difference between income levels in access to the internet is also mimicked in the online
activity level of internet users. Individuals in the highest income quintile compared to those in
the lowest income quintile performed nearly two more activities online. By contrast, no difference
was observed in the usage of SNSs between income levels.
Access rates were 8% higher for individuals who completed at least some education beyond
high school compared to high school graduates. The difference increased to 46% when individ-
uals who completed at least some education were compared to those with less than high school
education. This divide was also observed in terms of the online activity level of internet users.
University graduates perform 4.6 more activities online than people with less than high school
education. Education did not have a large effect on the usage of SNSs. However, current students
were by far the group that utilizes SNSs the most, with 87% usage compared to 48% for those
with less than high school.
Table 1. Descriptive statistics for explanatory variables by internet access, activity level online, and SNS
usage (N= 22,623).
Variables
Percentage
within category
Percentage
internet access
Average number of
online activities
(0–23)
Percentage
SNS usage
Household income (dollars)
25,000 and lower 15 60 10.8 59
25,000–41,000 18 66 10.5 58
41,000–65,000 20 82 11.0 59
65,000–100,000 22 88 11.7 57
100,000 and over 25 95 12.6 57
Highest level of education
Less than high school 15 39 8.6 48
High school graduate 18 77 9.8 51
Some post-secondary 33 85 11.1 55
University graduate 22 93 12.8 53
Current student 12 99 13.9 87
Gender
Women 49 82 11.8 54
Men 51 79 11.4 62
Rural/urban
Rural 11 70 10.2 54
Urban 89 82 11.8 58
Immigration status
Recent immigrants 5 88 13.4 71
Born in Canada/earlier
immigrants
95 80 11.5 57
Age
16–54 56 92 12.3 67
55 and older 44 56 9 26
510 M. Haight et al.
Internet access
RQ1 examines differences in internet access along key demographic variables (Table 2). The
reference group in this analysis is families with incomes below $25,000 per year. Families
making between $25,000 and $40,999 are 40% more likely to access the internet when compared
to individuals with family incomes below $25,000 (β= 0.34; SE = 0.12; p< .01). Persons with
family incomes between $41,000 and $65,000 are more than twice as likely to access the internet
in comparison to the reference group. Compared to respondents in the lowest income category,
respondents in families with incomes between $65,000 and $100,000 (β= 1.03; SE = 0.31;
p< .01) and those with incomes of $100,000 and higher (β= 1.62; SE = 0.69; p< .01) are 2.80
times and 5.4 times more likely to access the internet, respectively. These findings show large dis-
crepancies in internet access by income.
Education is also a significant predictor of internet access. Individuals with higher education
levels are more likely to access the internet. In this model, the reference group is the respondents
who are no longer students and have a high school education.
3
The group with less than a high
school education is 67% less likely to access the internet compared to those with a high school edu-
cation. Current students are, on average, over 4.63 times more likely to use the internet compared to
the group with a high school education (β= 1.53; SE = 1.27; p< .01). People living in urban areas
are 51% more likely to access the internet compared to those in rural areas (β=0.41; SE=0.11;
p< .01). Canadian born/earlier immigrants are 68% more likely to access the internet compared
to recent immigrants (β= 0.52; SE = 0.39; p< .05). As age increases, the likelihood of a person
having access to the internet drastically decreases (β=−0.06; SE = 0.002; p<.01).
Table 2. Logistic regression predicting internet access in the past 12 months (N= 22,623).
Variables
Model 1
βSE e
B
Household income (dollars)
25,000 and below (omitted) –––
25,000–41,000 0.34** 0.12 1.40
41,000–65,000 0.78** 0.20 2.17
65,000–100,000 1.03** 0.31 2.80
100,000 and over 1.62** 0.69 5.40
Highest level of education
High school graduate (omitted) –––
Less than high school −1.11** 0.03 0.33
Some post-secondary 0.43** 0.14 1.54
University graduate 1.27** 0.53 3.56
Current student 1.53** 1.27 4.63
Gender
Men (omitted) –––
Women 0.02 0.07 1.02
Rural/urban
Rural (omitted) –––
Urban 0.41** 0.11 1.51
Immigration status
Recent immigrants (omitted) –––
Born in Canada/earlier immigrants 0.55* 0.42 1.74
Age
−0.06** 0.002 0.94
*p< .05.
**p< .01.
Information, Communication & Society 511
Level of online activity
RQ2 investigates if there are differences in the level of online activity along key demographic
factors (Table 3). This analysis restricts the sample to those respondents who have had internet
access in the past 12 months (N= 16,606). Families with household incomes below $25,000
per year are used as the reference group in this analysis. No difference was observed when com-
paring families earning $25,000–41,000 with the reference group. Families with incomes between
$41,000 and $65,000 completed on average 0.48 more activities than families in the reference
group (β= 0.48; SE = 0.21; p< .05). In comparison to the reference group, respondents in families
with incomes between $65,000 and $100,000 and those in families with incomes of $100,000
and higher completed an average of 0.89 (β= 0.89; SE = 0.19; p< .01) and 1.4 (β= 1.4; SE =
0.20; p< .01) more activities, respectively.
Education was also a significant predictor of the level of online activity (Table 3). The refer-
ence group for this model is high school education. Respondents with less than high school edu-
cation completed 0.62 fewer activities online than those with a high school education (β=−0.62;
SE = 0.26; p< .05). Current students, on average, completed nearly two more online activities
than those with a high school education (β= 1.86 SE = 0.24; p< .01). University graduates com-
pleted 2.78 more activities on average than those with a high school education when holding con-
stant all other variables in the model (β= 2.78; SE = 0.19; p< .01). Not surprisingly, education is a
strong predictor of the level of online activity.
In the multivariate model, women engaged in 0.41 fewer activities than men (β=−0.41; SE =
0.11; p< .01), supporting previous findings on gender differences in the level of engagement with
Table 3. OLS regression predicting the level of online activity in the past 12 months (N= 16,606).
Variables
Model 2
ΒSE
Household income (dollars)
25,000 and below (omitted) ––
25,000–41,000 0.18 0.22
41,000–65,000 0.48* 0.21
65,000–100,000 0.89** 0.19
100,000 and over 1.40** 0.20
Highest level of education
High school graduate (omitted) ––
Less than high school –0.62* 0.26
Some post-secondary 1.20** 0.18
University graduate 2.78** 0.19
Current student 1.86** 0.24
Gender
Men (omitted) ––
Women −0.41** 0.11
Rural/urban
Rural (omitted) ––
Urban 0.89** 0.14
Immigration status
Recent immigrants (omitted) ––
Born in Canada/earlier immigrants −0.67* 0.32
Age
−0.12** 0.004
*p< .05 ± Unstandardized coefficients.
**p< .01.
512 M. Haight et al.
the internet. We found a difference in the level of online activity between individuals living in
urban and rural locations. Those living in urban areas completed just under 1 activity more
than those living in rural areas (β= 0.89; SE = .14; p< .01). It is apparent that rural/urban differ-
ences continue to exist in the utilization of the internet.
Canadian born/earlier immigrants participated in a significantly lower number of activities
online than recent immigrants (β=−0.67; SE = 0.32; p< .05). Recent immigrants completed
0.67 more activities, on average, than Canadian born/earlier immigrants. For each year increase
in a person’s age, they completed 0.12 fewer online activities (β=−0.12; SE = 0.004; p< .01).
Usage of SNSs
RQ3 examines if there are differences in the usage of SNSs along key demographic factors
(Table 4). The sample for this analysis is limited to respondents who have had internet access
in the past 12 months (N= 16,606). Income had no effect on SNS usage in this model. Individuals
with higher education levels were more likely to use SNSs. One exception did however emerge,
with those respondents with less than high school education having 42% higher odds of using an
SNS than high school graduates (β= 0.35; SE = 0.17; p< .01). Current student respondents were
91% more likely to use SNSs compared to those with a high school education (β= 0.65; SE =
0.27; p< .01). Gender was a strong predictor of SNS usage after controlling for all other variables
in the model, with women having a 58% higher likelihood of using an SNS than men (β= 0.46;
SE = 0.09; p< .01). Lastly, age was negatively associated with the likelihood of using an SNS
Table 4. Logistic regression predicting SNS usage in the past 12 months (N= 16,606).
Variables
Model 3
βSE e
B
Household income (dollars)
25,000 and below (omitted) –––
25,000–41,000 0.17 0.13 1.19
41,000–65,000 0.10 0.12 1.10
65,000–100,000 −0.04 0.10 0.96
100,000 and over −0.12 0.09 0.89
Highest level of education
High school graduate (omitted) –––
Less than high school 0.35** 0.17 1.42
Some post-secondary 0.26** 0.11 1.29
University graduate 0.29** 0.13 1.34
Current student 0.65** 0.27 1.91
Gender
Men (omitted) –––
Women 0.46** 0.09 1.58
Rural/urban
Rural (omitted) –––
Urban 0.05 0.07 1.05
Immigration status
Recent immigrants (omitted) –––
Born in Canada/earlier immigrants −0.03 0.15 0.97
Age
−0.07** 0.002 0.93
*p< .05.
**p< .01.
Information, Communication & Society 513
(β=−0.07; SE = 0.002; p< .01): young people are more likely to use an SNS than those of older
generations.
Discussion
The present paper makes two major contributions to the digital divide literature. First, it expands
current definitions by arguing that SNS usage needs to be included as a third important dimension
in investigations of the digital divide to supplement and expand findings on people’s internet
access and level of online activity. Examining SNS usage seems particularly relevant in the
context of current discussion on the value of social networking for the transmission of infor-
mation, the production of knowledge and identity, and the formation and maintenance of social
capital. Second, we provide a renewed look at the digital divide in Canada –a nation that is geo-
graphically and culturally unique. This renewed look has direct bearing on policy, as it demon-
strates which populations within Canada need to be targeted for increased internet access,
improved internet utilization, and social connectivity.
The central finding of our investigation is that the digital divide continues to exist in Canada
along a number of key demographic factors. Differences exist not only in access to the internet,
but also in Canadians’level of online activity and usage of SNSs. The present study finds that
80% of Canadians aged 16 and older are online. This contrasts with 73% in 2007, 68% in
2005, 64% in 2003, and 51% in 2000 (Figure 1). These numbers mirror American data, where
15% of Americans in 2013 have no internet access (Zickuhr, 2013). The fact that 20% of Cana-
dians continue to remain unconnected has serious implications and requires strategic policy inter-
ventions. Closing this gap is not an easy task because other factors play an important role –such
as socioeconomic status, education, immigration status, and age.
Individuals in the highest income category compared to the lowest have nearly five times
higher odds of accessing the internet. This difference in internet access among categories of
income parallels the findings for the online activity level. Individuals in the highest income quin-
tile completed 1.40 more activities online than those in the lowest quintile. Income was identified
early on in digital divide research as a key source of inequality and a decade later continues to be a
key determinant of not only internet access, but also online activity level (Attewell, 2001; Hargit-
tai & Hinnant, 2008). Respondents with household incomes above $41,000 are much more likely
to have higher levels of internet utilization when controlling for the other variables in the model
than those earning less than this amount. These results are concerning, as DiMaggio and Boni-
kowski (2008) found ‘significant positive associations between Web use and earnings growth,
indicating that some skills and behaviors associated with Internet use were rewarded by the
labor market’(p. 227). The implications for those of low income who lack adequate internet
access or internet utilization can be substantial. Consistent with the findings from the 2013
Pew report on SNS adoption, no variation in SNS usage by income level was observed
(Duggan, 2013). Once individuals overcome the barriers to access, income does not predict the
use of SNSs: individuals in all income categories use SNSs to the same extent. These findings
are noteworthy, particularly due to the social capital implications of SNS usage (Alkalimat & Wil-
liams, 2001; Chen, 2013; Frank, Zhao, & Borman, 2004; Wellman et al., 2001).
The education divide continues to persist in terms of internet access, level of online activity,
and SNS usage. The literature on the relationship between internet access and internet utilization
is extensive, and the findings confirm the trends observed in the United States and elsewhere:
higher levels of education are associated with greater rates of internet access and individuals par-
taking in more activities online. These results differ from recent Pew data that found no diver-
gence in SNS use by education level. Also observed is significantly higher SNS use by those
with less than high school education compared to high school graduates. Those respondents
514 M. Haight et al.
with less than high school education had 42% higher odds of using an SNS than high school
graduates.
A surprising finding to emerge from this study is that recent immigrants to Canada are signifi-
cantly less likely to have internet access; however, among those online they have a higher level of
online activity than earlier immigrants and Canadian born residents. A myriad of possible expla-
nations for this exists. Looking at the trends in immigration to Canada provides some clarification
for the seemingly counterintuitive result stemming from these analyses. While immigration into
Canada has been steadily increasing over the last two decades, the official criteria for entry into
the country have changed considerably. The percentage of economic immigrants (e.g. skilled
workers, persons in the business sector) to Canada, who ‘are selected for their skills and
ability to contribute to Canada’s economy’, has risen substantially from 38% in 1986 to 70%
in 2010, while the percentage of refugees during that same period has declined substantially
from 23% to 9% (Citizenship and Immigration Canada, 2010). Another explanation for this
finding can be drawn from research done by NetLab at the University of Toronto. The interviews
conducted in this study found that among new immigrants who used the internet keeping in touch
with family back home was important (Kayahara, Wellman, Boase, Hogan, & Kennedy, 2005).
Future research should further examine this finding and investigate what it is about recent immi-
grants to Canada that makes them less likely to use the internet but have a higher level of online
activity if they do use it.
Although the access divide ceases to exist between men and women, a statistically significant
difference does remain in terms of activities performed online. The results of this analysis indicate
more online activities completed by men, which is a surprising finding as it suggests that the alleged
gender gap within prior digital divide literature, whereby men had higher activity levels online, con-
tinues to exist (Fallows, 2005; Wasserman & Richmond-Abbott, 2005). This aligns with a 2005
Pew report that found men to be much more active online and complete significantly more activities
than women (Fallows, 2005). SNS use among women is significantly higher, which is a result that is
consistent with the literature (Duggan, 2013; Hargittai & Hsieh, 2010).
The consequences for the 20% of Canadians who are not yet online are far-reaching, as the
ability to operate a computer at an adequate level is fast becoming a prerequisite for participating
in Canada’s emerging digital economy. For one to fully participate both socially and economically
in Canadian society, access to the internet is increasingly important. Barth and Veit (2011) high-
light the significant paradox that exists between massive efforts to digitize many government and
public sector services without sufficient attention directed towards ensuring that internet access
and use does not lag behind. Thus, it is those individuals who do not have internet access as
well as those who engage in a limited number of activities online who are unable to fully partici-
pate in a digital society and take full advantage of resources, information, and social contacts. In
particular, Illinois’s 2000 Eliminate the Digital Divide Act posits that citizens who have both
access to the internet and the necessary skills to take advantage of ‘the tools of the new digital
technology’have ‘benefited in the form of improved employment possibilities and a higher stan-
dard of life’, as opposed to those who have inadequate access and limited online engagement and
are becoming ‘increasingly constrained to marginal employment and a standard of living near the
poverty level’(Illinois General Assembly, 2000).
Katz and Rice (2002) have examined the small subset of people who consistently declined to
adopt the internet despite affordability of access. This bears the question whether the remaining
digital divide in many Western countries is largely the result of inequality, or preference (Katz &
Aspden, 1997). While it is beyond the scope of this paper to provide a definitive answer, our
results suggest that differences in access, level of online activity, and SNS adoption are all influ-
enced by key demographic factors –factors that reflect existing inequalities in society. Moreover,
our preliminary analysis shows that the only factor that is associated with no need or interest in
Information, Communication & Society 515
using the internet in our study is income: non-internet users in the $41,000–65,000 and $65,000–
100,000 income categories were significantly more likely to report ‘no need or interest’as one of
the reasons why they did not use the internet. This suggests that for all other groups other reasons
exist for their non-adoption, such as cost, lack of skills, personal barriers, and limited literacy. The
CIUS data set does not include data from residents of the territories of Canada and those residing on
First Nations reserves. First Nations people who live off-reserve resemble the Canadian population;
whereas little is known about First Nations who live on reserves. This much neglected population
requires further attention. This paper demonstrates the significant inequality that exists in access to
the internet, level of online activity, and SNS usage among Canadians. The findings presented in this
paper suggest that further research is necessary to fully understand the impact of the digital divide on
immigrants (both early and recent) in Canada. The digital divide gap may not be as wide as it has
historically been; however, real and impactful differences continue to persist.
Notes
1. Those with a high level of education had undergone tertiary education, whereas those with a medium
level of education had upper secondary/post-secondary education.
2. Statistics Canada (2009)defines an urban area as an area with a population of at least 1000 people and a
density of no fewer than 400 individuals per square kilometre.
3. Note that current students were separated into their own category because they have not yet reached the
end of their formal education.
Notes on contributors
Michael Haight graduated from The University of Western Ontario with an MA in Sociology. Currently he is
a PhD student at The University of Western Ontario in Sociology researching internet use over the life
course. Present research is on the second digital divide, investigating internet utilization, engagement, and
social inequality. [email: mhaight@uwo.ca]
Anabel Quan-Haase is an Associate Professor at the Faculty of Information and Media Studies and the
Department of Sociology, The University of Western Ontario and the director of the SocioDigital Lab.
Dr. Quan-Haase holds a MSc in Psychology from Humboldt University, Berlin, and a PhD in Information
Studies from the University of Toronto. Her interests lie in the area of technology and social change, with
a particular focus on social networks, social media, and scholarship. Her book Technology and society:
Inequality, power, and social networks was published in 2013 by Oxford University Press. [email: aqua-
n@uwo.ca]
Bradley A. Corbett earned a PhD in Education Studies (2008) from the University of New Brunswick.
Current research activities include an analysis of income inequality within Canada using data from the Cana-
dian Census. Corbett also studies digital skills and digital literacy in the context of the growing shift towards
E-Government and E-Business in the new E-economy. Early studies into the Digital Divide focused on
access to Information and Communication Technology (ICT). His research now focuses on the divide
between those who do and do not have the skills to use ICT. [email: bcorbet@uwo.ca]
References
Alkalimat, A., & Williams, K. (2001). Social capital and cyberpower in the African-American community: A
case study of a community technology centre in the dual city. In L. Keeble & B. D. Loader (Eds.),
Community informatics: Shaping computer-mediated social networks (pp. 177–204). London:
Routledge.
Attewell, P. (2001). The first and second digital divides. Sociology of Education,74(3), 252–259. Retrieved
from http://www.jstor.org/stable/2673277
Barth, M., & Veit, D. J. (2011, February). How digital divide affects public e-services: The role of migration
background. Paper presented at the meeting of Wirtschaftinformatik, Zurich. Retrieved from http://aisel.
aisnet.org/wi2011/118
516 M. Haight et al.
Beunte, W., & Robbin, A. (2008). Trends in internet information behavior 2000–2004. Journal of the
American Society for Information Science and Technology,59(11), 1743–1760. doi:10.1002/asi.20883
Bimber, B. (2000). Measuring the gender gap on the internet. Social Science Quarterly,81(3), 868–876.
Retrieved from http://www.dleg.state.mi.us/mpsc/electric/workgroups/lowincome/internet_gender_
gap.pdf
Chen, W. (2013). The implications of social capital for the digital divides in America. The Information
Society,29(1), 13–25. doi:10.1080/01972243.2012.739265
Citizenship and Immigration Canada. (2010). Immigration overview: Permanent and temporary residents.
Ottawa: Government of Canada. Retrieved from www.cic.gc.ca/english/resources/statistics/facts2010/
permanent/01.asp
City of Toronto. (2006). Toronto’s racial diversity. Retrieved from City of Toronto website http://www.
toronto.ca/toronto_facts/diversity.htm
Collins, J. L., & Wellman, B. (2010). Small town in the internet society: Chapleau is no longer an island.
American Behavioral Scientist,53(9), 1344–1366. doi:10.1177/0002764210361689
van Deursen, A., & van Dijk, J. (2010). Internet skills and the digital divide. New Media & Society,13(6),
893–911. doi:10.1177/1461444810386774
DiMaggio, P., & Bonikowski, B. (2008). Make money surfing the web? The impact of Internet use on the earn-
ings of US workers. American Sociological Review,73(2), 227–250. doi:10.1177/000312240807300203
DiMaggio, P., & Hargittai, E. (2001). From the ‘digital divide to ‘digital inequality’: Studying internet use as
penetration increases (Working Paper #15). Retrieved from Princeton University website https://www.
princeton.edu/∼artspol/workpap/WP15%20%20DiMaggio%2BHargittai.pdf
Duggan, M. (2013). It’s a woman’s (social media) world. Washington, DC: Pew Internet & American Life
Project. Retrieved from http://www.pewresearch.org/fact-tank/2013/09/12/its-a-womans-social-media-
world/
Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook ‘friends’: Social capital and
college students’use of online social network sites. Journal of Computer-Mediated Communication,
12(4), 1143–1168. doi:10.1111/j.1083-6101.2007.00367.x
Ellison, N. B., Steinfield, C., & Lampe, C. (2011). Connection strategies: Social capital implications of
Facebook-enabled communication practices. New Media and Society,16(6), 873–892. doi:10.1177/
1461444810385389
Epstein, D., Nisbet, E. C., & Gillespie, T. (2011). Who’s responsible for the digital divide? Public perceptions
and policy implications. The Information Society,27(2), 92–104. doi:10.1080/01972243.2011.548695
Eshet-Alkalai, Y. (2004). Digital literacy: A conceptual framework for survival skills in the digital era.
Journal of Educational Multimedia and Hypermedia,13(1), 93–106. Retrieved from http://leadidf.
edu.haifa.ac.il/files/final-test-articles/2013/9-Digital-literacy-JEMH.pdf
Eurostat. (2011). Internet access and use in 2011: Almost a quarter of persons aged 16–74 in the EU27 have
never used the internet. Luxembourg: Eurostat Press Office. Retrieved from http://europa.eu/rapid/press-
release_STAT-11–188_en.htm
Facebook. (2013). Newsroom: Key facts. Retrieved from http://newsroom.fb.com/Key-Facts
Fallows, D. (2005). How women and men use the internet. Washington, DC: Pew Internet & American Life
Project. Retrieved from http://www.pewinternet.org/Reports/2005/How-Women-and-Men-Use-the-
Internet.aspx
Fox, S. (2004). Older Americans and the internet. Washington, DC: Pew Internet & American Life Project.
Retrieved from http://www.pewinternet.org/Reports/2004/Older-Americans-and-the-Internet.aspx
Frank, K. A., Zhao, Y., & Borman, K. (2004). Social capital and the diffusion of innovations within organ-
izations: The case of computer technology in schools. Sociology of Education,77, 148–171. doi:10.
1177/003804070407700203
Goldin, C., & Katz, L. (2008). The race between education and technology. Cambridge: Harvard University
Press.
Gross, E. F. (2004). Adolescent Internet use: What we expect, what teens report. Journal of Applied
Developmental Psychology,25(6), 633–649. doi:10.1016/j.appdev.2004.09.005
Hale, T. M., Cotten, S. R., Drentea, P., & Goldner, M. (2010). Rural-urban differences in general and health-
related internet use. American Behavioral Scientist,53(9), 1304–1325. doi:10.1177/0002764210361685
Hargittai, E. (2002). Second-level digital divide: Differences in people’s online skills. First Monday,7(4).
doi:http://dx.doi.org/10.5210%2Ffm.v7i4.942
Hargittai, E. (2010). Digital na(t)ives? Variation in internet skills and uses among members of the ‘net gen-
eration’.Sociological Inquiry,80(1), 92–113. doi:10.1111/j.1475-682X.2009.00317.x
Information, Communication & Society 517
Hargittai, E., & Hinnant, A. (2008). Digital inequality: Differences in young adults’use of the internet.
Communication Research,35(5), 602–621. doi:10.1177/0093650208321782
Hargittai, E., & Hsieh, Y. P. (2010). Predictors and consequences of differentiated social network site uses.
Information, Communication and Society,13(4), 515–536. doi:10.1080/13691181003639866
Hargittai, E., & Hsieh, Y. P. (2012). Succinct survey measures of web-use skills. Social Science Computer
Review,30(1), 95–107. doi:10.1177/0894439310397146
Hoffman, D. L., Novak, T. P., & Venkatesh, A. (2004). Has the internet become indispensable? Empirical
findings and model development. Communications of the ACM,47(7), 37–42. doi:10.1.1.94.4505
Horrigan, J. (2009). Home broadband adoption 2009 report: Broadband, mobile, communities. Washington,
DC: Pew Internet & American Life Project. Retrieved from http://www.pewinternet.org/Reports/2009/
10-Home-Broadband-Adoption-2009.aspx
Howard, P. N., Busch, L., & Sheets, P. (2010). Comparing digital divides: Internet access and social inequal-
ity in Canada and the United States. Canadian Journal of Communication,35(1), 109–128. Retrieved
from http://www.cjc-online.ca/index.php/journal/article/view/2192
Illinois General Assembly. (2000). An act to eliminate the digital divide. Springfield: State of Illinois.
Retrieved from http://www.ilga.gov/legislation/ilcs/ilcs5.asp?ActID=574&ChapterID=7
Jones, B. R., & Flannigan, S. L. (2006). Connecting the digital dots: Literacy of the 21st century.
EDUCAUSE Quarterly,29(2), 8–10. Retrieved from http://net.educause.edu/ir/library/pdf/eqm0621.pdf
Katz, J. E., & Aspden, P. (1997). Motives, hurdles and dropouts: Who is on the internet and why?
Communications of the ACM,40(4), 97–102. doi:10.1145/248448.248464
Katz, J. E., & Rice, R. E. (2002). Social consequences of Internet use: Access, involvement, and interaction.
Cambridge, MA: The MIT Press.
Kayahara, J., Wellman, B., Boase, J., Hogan, B., & Kennedy, T. (2005). Canadians, culture, and computers.
Ottawa: Heritage Canada. Retrieved from http://homes.chass.utoronto.ca/∼wellman/publications/
canadians_culture_and_computers/canadians_culture_and_computers.pdf
Kraut, R., Patterson, M., Lundmark, V., Kiesler, S., Mukophadhyay, T., & Scherlis, W. (1998). Internet
paradox: A social technology that reduces social involvement and psychological well-being?.
American psychologist,53(9), 1017. doi:10.1037/0003-066X.53.9.1017
Livingstone, S., Bober, M., & Helsper, E. (2005). Internet literacy among children and young people:
Findings from the UK Children Go Online project. London: LSE Research Online. Retrieved from
http://eprints.lse.ac.uk/399/
Madden, M. (2006). Internet penetration and impact. Washington, DC: Pew Internet & American Life
Project. Retrieved from http://www.pewinternet.org/Reports/2006/Internet-Penetration-and-Impact.aspx
McKeown, L., Noce, A., & Czerny, P. (2007). Factors associated with internet use: Does rurality matter?
Ottawa: Statistics Canada. Retrieved from http://www.statcan.gc.ca/pub/21–006-x/21-006-x2007003-
eng.htm
Norris, P. (2001). Digital divide: Civic engagement, information poverty, and the internet worldwide.
New York: Cambridge University Press.
Ono, H., & Zavodny, M. (2003). Gender and the internet. Social Science Quarterly,84(1), 111–121. doi:10.
1111/1540-6237.t01-1-8401007
Ono, H., & Zavodny, M. (2007). Digital inequality: A five country comparison using microdata. Social
Science Research,36(3), 1135–1155. doi:10.1016/j.ssresearch.2006.09.001
Ono, H., & Zavodny, M. (2008). Immigrants, English ability and the digital divide. Social Forces,86(4),
1455–1479. doi:10.1353/sof.0.0052
Papastergiou, M., & Solomonidou, C. (2005). Gender issues in Internet access and favourite Internet activi-
ties among Greek high school pupils inside and outside school. Computers & Education,44(4), 377–
393. doi:10.1016/j.compedu.2004.04.002
Pew Research Center. (2013). What internet users do online. Washington, DC: Pew Internet & American
Life Project. Retrieved from http://pewinternet.org/Trend-Data-(Adults)/Online-Activites-Total.aspx
Quan-Haase, A. (2013). Technology and society: Inequality, power, and social networks. Don Mills: Oxford
University Press.
Quan-Haase, A., & Wellman, B. (2004). How does the internet affect social capital? In M. Huysman & V.
Wulf (Eds.), Social capital and information technology (pp. 151–176). Cambridge, MA: The MIT Press.
Statistics Canada. (2009). Urban areas (UA). Ottawa: Government of Canada. Retrieved from http://
www12.statcan.gc.ca/census-recensement/2006/ref/dict/geo049-eng.cfm
Stevenson, S. (2009). Digital divide: A discursive move away from the real inequities. The Information
Society,25(1), 1–22. doi:10.1080/01972240802587539
518 M. Haight et al.
Teo, T. S. (2001). Demographic and motivation variables associated with Internet usage activities. Internet
Research,11(2), 125–137. doi:10.1108/10662240110695089
Tumblr. (2013). Tumblr at a glance. Retrieved from http://www.tumblr.com/about
Twitter. (2013). Celebrating #Twitter7. Retrieved from https://blog.twitter.com/2013/celebrating-twitter7
Veenhof, B., Wellman, B., Quell, C., & Hogan, B. (2008). How Canadians’use of the internet affects social
life and civic participation. Ottawa: Statistics Canada. Retrieved from http://www.statcan.gc.ca/pub/
56f0004 m/56f0004m2008016-eng.htm
Vehovar, V., Sicherl, P., Husing, T., & Dolnicar, V. (2006). Methodological challenges of digital divide
measurements. The Information Society,22, 279–290. doi:10.1080/01972240600904076
Wasserman, I., & Richmond-Abbott, M. (2005). Gender and the internet: Causes of variation in access, level,
and scope of use. Social Science Quarterly,86(1), 252–270. doi:10.1111/j.0038-4941.2005.00301.x
Wellman, B., Quan-Haase, A., Witte, J., & Hampton, K. (2001). Does the internet increase, decrease, or sup-
plement social capital? Social networks, participation, and community commitment. American
Behavioral Scientist,45(3), 436–455. doi:10.1177/00027640121957286
Witte, J. C., & Mannon, S. E. (2009). The internet and social inequalities. New York: Routledge.
Zickuhr, K. (2013). Who’s not online and why. Washington, DC: Pew Internet & American Life Project.
Retrieved from http://pewinternet.org/Reports/2013/Non-internet-users.aspx
Zickuhr, K., & Smith, A. (2012). Digital differences. Washington, DC: Pew Internet & American Life
Project. Retrieved from http://pewinternet.org/∼/media/Files/Reports/2012/PIP_Digital_differences_
041312.pdf
Appendix. Level of online activity (N= 16,606)
Did you use the internet in the previous 12 months to perform any of the
following activities?
Percentage who responded
yes
Send an email 93
Use security software 81
Browse for information about goods and services 74
Electronic banking 68
Read or watch news 68
Travel or travel arrangements 65
Medical/health information 64
Visit government websites 64
Backup files on computer 62
SNSs 58
Research community events 54
Order goods and services online 51
Download or watch movies/videos 47
Use instant messenger 47
Obtain or save free/paid music 45
Education, training, school 37
Listen to the radio online 36
Obtain/save free/paid software 35
Play games online 33
Download or watch TV 32
Make telephone calls 24
Contribute to discussion boards or blogs 19
Sell goods or services online 19
Information, Communication & Society 519
