Journal of Information Technology & Politics
ISSN: 1933-1681 (Print) 1933-169X (Online) Journal homepage: http://www.tandfonline.com/loi/witp20
How App Are People to Use Smartphones, Search
Engines, and Social Media for News?: Examining
Information Acquisition Tools and Their Influence
on Political Knowledge and Voting
Maegan Stephens, Joseph Yoo, Rachel R. Mourao, Hong T. Vu, Brian Baresch
& Thomas J. Johnson
To cite this article: Maegan Stephens, Joseph Yoo, Rachel R. Mourao, Hong T. Vu, Brian Baresch
& Thomas J. Johnson (2014) How App Are People to Use Smartphones, Search Engines, and
Social Media for News?: Examining Information Acquisition Tools and Their Influence on
Political Knowledge and Voting, Journal of Information Technology & Politics, 11:4, 383-396,
DOI: 10.1080/19331681.2014.951137
To link to this article: http://dx.doi.org/10.1080/19331681.2014.951137
Accepted author version posted online: 07
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Date: 25 August 2016, At: 12:46
Journal of Information Technology & Politics, 11:383–396, 2014
Copyright © Taylor & Francis Group, LLC
ISSN: 1933-1681 print/1933-169X online
DOI: 10.1080/19331681.2014.951137
How App Are People to Use Smartphones, Search Engines,
and Social Media for News?: Examining Information
Acquisition Tools and Their Influence on Political
Knowledge and Voting
Maegan Stephens
Joseph Yoo
Rachel R. Mourao
Hong T. Vu
Brian Baresch
Thomas J. Johnson
ABSTRACT. The growing use of tablet and smartphone news applications, search engines, and online
social media for political and news information deserves attention because of the political implications.
Using data from a statewide Texas opt-in poll from February 2012, this study tests the direct versus the
differential hypothesis for each of the information acquisition tools with respect to political knowledge
and voting. Results indicate a direct effect for search engine use and political knowledge. Suggestions
for future research are provided in light of limitations of the current study and the possibility that
information acquisition tool use will continue to grow.
KEYWORDS. Applications, information acquisition tools, online social media, political knowledge,
search engines, voting
Maegan Stephens is a PhD candidate in the Department of Communication Studies at The University of
Texas at Austin. Her research interests include political language, political trust, and the role of emerging
technologies in news acquisition.
Joseph Yoo is a PhD student in the School of Journalism at The University of Texas at Austin. His
research interests include political communication, telecommunications, and the influence of communication
technology on human communications.
Rachel R. Mourao is a PhD student in the School of Journalism at The University of Texas at Austin.
Her areas of interest include political communication, international communication, new media, and Latin
American Studies.
Hong T. Vu is a PhD Candidate in the School of Journalism at The University of Texas at Austin. His
research interests include international communication, changes in newsrooms, and the development of mass
communication theories in the new media environment.
Brian Baresch is an editor with Portfolio Media. He is interested in the future of news and the nature of
information.
Thomas J. Johnson is the Amon G. Carter Jr. Centennial Professor in the School of Journalism at The
University of Texas at Austin. His research interests include the uses and effects of new media, such as social
network sites, in a political context.
Address correspondence to: Maegan Stephens, The University of Texas at Austin, Department of
Communication Studies, 2504A Whitis Ave. (A1105), Austin, TX 78712-0115 (E-mail: maegan@mail.
utexas.edu).
383
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JOURNAL OF INFORMATION TECHNOLOGY & POLITICS
Assessing the role of the Internet on political and civic outcomes has remained an oftenstudied but still contentious topic (Bimber,
2003; Bimber & Copeland, 2013; Boulianne,
2009; Johnson & Kaye, 2003a; Kenski &
Stroud, 2006; Mossberger, Tolbert, & McNeal,
2008; Sunstein, 2001; Tolbert & McNeal,
2003; Xenos & Moy, 2007). While studies
have found that Internet use positively predicts
various political outcomes (Boulianne, 2009;
Johnson & Kaye, 2003a; Xenos & Moy, 2007),
some researchers caution against adopting such
findings because of the lack of consistency
across studies (Bimber & Copeland, 2013), the
Internet’s harmful potential to isolate individuals (Sunstein, 2001), and the contingent role that
individual characteristics and social contexts
play in the relationship (Bimber, 2003; Xenos
& Moy, 2007). Still, scholars continue to investigate the relationship between online information and democratic outcomes for a number of
reasons.
First, the public’s increased use of Internet
news sources allows for more thorough theory building (Xenos & Moy, 2007). According
to Sasseen, Olmstead, and Mitchell (2013),
online news was the only news category to
see user numbers increase in 2012. As use
patterns increase and stabilize, researchers can
more fully assess the Internet’s impact on political and civic outcomes. A second reason that
studies of Internet effects continue is the additional, more nuanced options users have online
(Massanari & Howard, 2011). Individuals have
a variety of information acquisition tools (IATs)
that could impact political variables. For example, they can rely on applications (apps) that
continually provide updates about the news, they
can search for news themselves, or they can even
wait for others in their online social network
to share news stories of interest. Pew Research
discovered that more than half of smartphone
users rely on their device for news (Mitchell &
Rosenstiel, 2012). Another study (Pew Research
Center, 2012a) found an increase in news use
between 2010 and 2012 for those who received
news from their online social networks yesterday. A 2013 report found that nearly half of
all Facebook and Twitter users get news from
those online networks, accounting for roughly
30% of the adult U.S. population (Holcomb,
Gottfried, & Mitchell, 2013). Communication
researchers must continue to assess the impact
of such advancements in order to fully integrate
these new uses into theories and models.
The present study follows the tradition of
those assessing both the direct and the differential effects of the Internet in predicting political
outcomes and behaviors (Bimber, 2003; Xenos
& Moy, 2007), while introducing more specific
use measures to the discussion. Bimber (2003)
offers the direct-effect hypothesis, which suggests that Internet use will lead to increases in
political outcome variables such as knowledge
and voting. Alternatively, the differential-effects
hypothesis states that contingent explanations
(e.g., the interaction between political interest
and media use) are responsible for those outcomes. Specifically, this study uses data from
a February 2012 statewide Texas opt-in survey (n = 800) to investigate how news apps,
search engines, and social networks directly or
differentially impact political outcomes.1
DIRECT OR DIFFERENTIAL
INTERNET EFFECTS
At least two perspectives exist regarding individual-level effects on various political outcomes after interacting with technology
(Bimber, 2003; Xenos & Moy, 2007). Bimber
(2003) offers the direct effect, or instrumental
perspective, to suggest that the Internet’s lower
opportunity costs will lead to political engagement. This perspective, aligned with the rational
choice model of voting behavior (Downs, 1957),
proposes that the reduced time and cost barriers
in the online environment will lead individuals
to acquire more information than they would in
a traditional news environment. Unlike print and
television news sources, the Internet provides
relatively quick and easy access to voluminous
news information. For example, a user can turn
to his or her smartphone to access a news organization’s Web site for timely updates about
political information at his or her discretion.
More traditional options may require a news
consumer to wait for the news organization to
publish the print paper or air the news during
Stephens et al.
a nightly broadcast. Additionally, passive exposure to news appearing in one’s online social
network requires even less effort than actively
obtaining the news.
A number of studies have generally found
that online media serve as a positive predictor of political knowledge even after controlling
for multiple demographic and political variables
(Kenski & Stroud 2006; Mossberger et al., 2008;
Xenos & Moy, 2007). Political knowledge is
widely viewed as essential to a healthy democracy, and communication models regarding the
variable have evolved over time, using it as
a dependent (Hardy & Scheufele, 2009; Gil
de Zúñiga, Puig-i-Abril, & Rojas, 2009), independent (Price & Zaller, 1993), and mediating
variable (Jung, Kim, & Gil-de-Zúñiga, 2011).
Delli Carpini and Keeter (1996) defined political knowledge as “the range of factual information about politics that is stored in long-term
memory” (p. 10). More recent studies emphasize
the structure of knowledge and interconnections,
drawing a distinction between factual learning
and what Eveland, Cortese, Park, & Dunwoody
(2004) call knowledge structure density; that is,
the difference between learning larger amounts
of disconnected facts and learning about the
relationships between pieces of information.
The former has generally been found to be
greater among users of print news sources. The
structure of online knowledge, though, with
such tools as in-text hyperlinks, has been shown
to be less efficacious at transmitting bare facts
but produces denser interconnected knowledge
structures (Eveland et al., 2004). While there has
been considerable debate over the best way to
measure political knowledge, there is still support for the traditional “civics test” approach for
assessing respondents’ knowledge about government structure and current events as long as
there are differences in the degree of difficulty
in the questions. Multiple-choice questions are
preferred over true/false questions to minimize
guessing (Delli Carpini & Keeter, 1993; Strabac
& Aalberg, 2011).
Research has also revealed a direct effect with
respect to voting (Tolbert & McNeal, 2003).
Boulianne (2009) conducted a meta-analysis of
38 studies regarding Internet effects on engagement and found a positive relationship between
385
Internet use and civic and political engagement
over time. While political participation can refer
to many activities related to politics such as
donating to a campaign, signing petitions, or
organizing a protest, voter turnout is one of the
most important measures of democratic participation. Tolbert and McNeal (2003) found that
Internet use was associated with greater likelihood of voting in the 1996, 1998, and 2000 U.S.
elections. Studies on traditional and new media
suggest that the media can influence intention to
vote by providing information to the electorate,
as well as stimulating public debate (Tolbert &
McNeal, 2003; Weaver, 1996).
In a more nuanced explanation of Internet
effects, Bimber (2003) also offers a psychological approach that emphasizes other factors responsible for moderating the relationship
between use and political outcomes. This indirect explanation suggests that Internet effects
are contingent upon such considerations as the
context in which one finds oneself (e.g., highchoice media environment), the motivations one
has for attending to the media (Gil de Zúñiga
et al., 2011; Shah, Rojas, & Cho, 2009), the way
an individual uses the media (Gil de Zúñiga,
2009), and an individual’s level of sophistication
(Bimber, 2003). Liu, Shen, Eveland, and Dylko
(2013) suggest that variations in news content
over time result in differences in knowledge,
complicating the relationship between direct
exposure and outcomes tested at different times.
Several recent studies suggest that the findings for online news do not consistently carry
over to user-generated content and social media,
calling into question the presence of a direct
effect (Dimitrova, Shehata, Stromback, & Nord,
2014; Groshek & Dimitrova, 2011; Kaufhold,
Valenzuela, & Gil de Zúñiga, 2010); however,
Pasek, More, and Romer (2009) found that
Facebook users demonstrated greater political
knowledge, compared to their MySpace counterparts. Hence, the authors suggest that social
network effects are contingent upon different
Web sites and cannot be examined in the aggregate. Additionally, some studies have seen new
media as predominantly entertainment-oriented
and pointed out that high choice factors allow
people to avoid information and thus reduce
new media’s influence on political interest and
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JOURNAL OF INFORMATION TECHNOLOGY & POLITICS
attitudes (Baumgartner & Morris, 2010; Prior,
2007).
In terms of individual characteristics that may
be responsible for interacting with media use
and political outcomes, Bonfadelli (2002) found
a correlation between seeking news or information and socioeconomic status, with the affluent
tending to prefer news. Similarly, those with
prior motivation and interest in politics were
more likely to use the Internet (Kaye & Johnson,
2004; Xenos & Moy, 2007). An individual’s
level of political interest, which often stimulates various motivations and abilities, has been
shown to moderate the relationship between
online media use and electoral/civic participation (Bimber, 2003).
Because studies have found support for new
media’s direct and differential effects on political knowledge and voting (Xenos & Moy, 2007),
the present study tests the instrumental and
psychological hypotheses with respect to three
IATs: news applications, search engines, and
online social networks.
INFORMATION ACQUISITION TOOLS
Information acquisition tools, while providing opportunities for exposure to news and
political information online, have yet to be
fully explored in research. The direct-effects
approach might suggest that the mere use of
IATs would impact political outcome variables
such as knowledge and voting. On the other
hand, the differential-effects approach might
counter that the motivation or interest associated with clicking on a news application or
actively searching for news in a browser would
impact the relationship between Internet use and
political outcome variables. This study seeks to
understand the degree to which three IATs (news
applications, search engines, and online social
networks) predict political knowledge and intent
to vote (direct effect) or whether these effects are
contingent upon political interest for the various
IATs (differential effect).
News Applications
With respect to news application users, as of
January 2014, 58% of American adults owned
a smartphone (Pew Research Internet Project,
2014), and 42% owned a tablet (Zickuhr &
Rainie, 2014). Of these mobile news consumers,
28% and 23%, respectively, used news apps to
retrieve information. Additionally, Weiss (2013)
found that 66% of young adult smartphone users
accessed apps in order to get local news. Tablet
users are heavy news consumers and spend considerable time reading the news online, suggesting that they could be politically interested and
active (Mitchell & Rosenstiel, 2012). Despite
the increase in mobile news consumption and
the spread of smartphones and tablets, few
studies have investigated the effect of news
applications on political attitudes and behaviors.
We test the competing hypotheses with respect
to news apps:
H1: News app use will predict (a) political
knowledge and (b) voting frequency,
after controlling for demographics
and political variables (direct effect).
H2: The relationship between news app
use and (a) political knowledge and
(b) voting is contingent upon political
interest (differential effect).
Search Engines
A national survey conducted before the
2008 presidential election indicated that nearly
half of voters who went online for information about candidates and issues used search
engines to conduct their research (Icrossing,
2007). Search engines are essentially tied with email as the most popular activity on the Internet,
and two-thirds of search engine users consider
the IAT a fair and unbiased source of information (Purcell, Brenner, & Rainie, 2012). While
few studies have directly investigated the relationship between search engines and political
attitudes and behaviors, the process of using
search engines has been linked to information
seeking (Johnson & Kaye, 2003b), which is
a motivation strongly associated with positive
political measures such as knowledge and likelihood to vote (McLeod & Becker, 1981). In light
of these studies, we offer the following hypotheses in order to test the direct and the differential
effect:
Stephens et al.
H3: Search engine use will predict (a)
political knowledge and (b) voting frequency, after controlling for
demographics and political variables
(direct effect).
H4: The relationship between search
engine use and (a) political knowledge and (b) voting is contingent
upon political interest (differential
effect).
Online Social Networks
Scholars have argued that social networks
are fundamentally different from other news
sources in terms of interactivity, involvement,
and motivations, which affect their relationship
with political knowledge and voting (Dimitrova
et al., 2014). While most studies suggest that
social network sites (SNSs) do not boost knowledge (Baumgartner & Morris, 2010; Dimitrova
et al., 2014; Johnson & Kaye, 2012), Pasek,
More, and Romer (2009) found that Facebook
use was strongly related to political knowledge.
The researchers explained that it is the use of an
aggregate measure of online social network use
that leads to a nonsignificant relationship with
political knowledge; however, when disaggregated by Web site used, there was a discrepancy
in the effect of SNSs among Facebook and
MySpace users. The distinct features of each
Web site, which create separate “Web site cultures” between SNSs, account for the large differences. These different cultures affect not only
the type of users that are drawn to the site but
also the effects that a given site can have on users
(Pasek, More, & Romer, 2009). Yet Dimitrova
et al. (2014) argue that because people use
social media to reinforce their ideas and connect
with friends rather than seek information, SNS
use is unrelated to political knowledge. A uses
and gratifications study of 30 different news
sources found Facebook finished last for gratifying information-seeking needs and Twitter did
not fare much better; both scored higher for gratifying social news needs (Lee, 2013). Similarly,
Kushin and Yamamoto (2010) contend that SNS
use does not lead to political knowledge because
users simply stumble upon political information
while looking for entertainment.
387
Nonetheless, social media is ideal for connecting online users with political actors and
mobilizing them to political action; therefore,
network sites such as Facebook and Twitter
should lead to increased political participation,
including voting (Dimitrova et al., 2014). Most
recent studies, particularly those that center on
using SNSs for political information, indicate
their use increases political participation in general (Dimitrova et al., 2014; Johnson, Zhang,
Bichard, & Seltzer, 2010; Valenzuela, Park, &
Kee, 2009; Vitak et al., 2011), although studies are divided on whether social network use
increases voting intention (Bode, 2012; Bond
et al., 2012; Groshek & Dimitrova, 2011). While
the literature regarding SNS use allows us to
test the competing hypotheses for voting behavior, most previous research suggests that there
is no effect (direct or differential) for political knowledge gain from SNS use. Additionally,
the studies are mixed on whether use will lead
to increased voting. Therefore, this study will
advance two research questions:
RQ1: Will online social network use predict (a) political knowledge and (b)
voting frequency, after controlling
for demographics and political variables? (direct effect)
RQ2: Will the relationship between online
social network use and (a) political
knowledge and (b) voting frequency
be contingent upon political interest? (differential effect)
METHOD
This study analyzes a data set from the Texas
Poll (the official state poll), funded by The
University of Texas at Austin and run jointly
with the Texas Tribune. YouGov gathered the
data from February 8 to February 15, 2012, via
their proprietary opt-in online survey panel of
1.2 million voters (20,000 of them in Texas).
The sample frame included 909 respondents
who opted to participate in the online survey.
To approximate a representative sample of registered Texas voters, respondents were weighted
based on age, gender, race, education, party
identification, ideology, and political interest.
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JOURNAL OF INFORMATION TECHNOLOGY & POLITICS
The weighting process reduced the sample size
to 800. The American Association for Public
Opinion Research (2013) has recognized the
importance and necessity of well-constructed,
transparent nonprobability samples such as optin panels. Several news organizations recognized YouGov for being one of the most accurate
polling organizations in the 2012 election campaign (Byers, 2012; Lauter, 2012; Matthews,
2012).
Dependent Variables
This study examined two political variables: political knowledge and voting frequency.
The political knowledge variable was comprised of three questions asking which political
party held the majority in the U.S. House of
Representatives, how many votes were needed
to override a presidential veto, and who was
the current attorney general of Texas.2 These
questions were multiple choice, and respondents received one point for each correct answer
(“don’t know” responses were coded as incorrect answers), creating a political knowledge
variable ranging from 0 to 3 (M = 2.15, SD =
.94, KR−20 = .47). Voting frequency was based
on a question about voting behavior over the
past two or three years in the state of Texas.
Respondents were asked whether they voted
in every election, almost every election, about
half, one or two, none at all, or don’t know.
The voting experience variable was recoded
before conducting analysis because it was originally measured from high to low (M = 3.92,
SD = 1.11).
Independent Variables
In order to measure interest in politics,
respondents were asked a question about their
level of interest in politics and public affairs
on a four-point scale ranging from extremely
interested to not at all interested in politics.
Responses were reverse-coded so that high
scores equaled high interest (M = 3.50, SD =
.67). Also, respondents were asked how much
time they spent using certain IATs for news.
Three different IATs were investigated: news
applications, search engines, and online social
network sites. Response choices for IAT use
were never, hardly ever, sometimes, and regularly. In total, 302 participants had used news
apps (M = 1.81, SD = 1.15), 685 used search
engines (M = 2.98, SD = 1.04), and 544 used
SNSs (M = 2.40, SD = 1.16).
Additional demographic variables included
age, gender, race, highest level of education completed, and household income. Of the
800 weighted respondents, 426 were female
(53.3%). More than half of the respondents were
White (n = 502, 62.8%), 124 were Hispanic
(18.6%), and 99 were Black (12.4%). Ages
ranged from 18 to 89 years old (M = 51.79,
SD = 14.02). Twenty-three respondents (2.9%)
did not graduate from high school, and 226
(28.3%) completed a high school degree. Two
hundred and eighty-two respondents (35.3%)
graduated with a technical education or from
a two-year college; 191 graduated from a
four-year college; and 78 respondents (9.8%)
reported earning graduate degrees (M = 3.51,
SD = 1.43). Considering household income,
239 respondents (33.7%) earned between zero
and $39,999, while 244 respondents (34.4%)
earned from $40,000 to $79,999, and 227
(32.0%) earned more than $80,000 (M = 1.98,
SD = .81). Ninety respondents (11.3%) did not
report their annual household income. Missing
data were treated with multiple imputations.
Data Analysis
This study first conducted one-tailed correlation tests to determine the relationship between
the three IATs before controlling for other measures. The models presented in the regression
tables are final with all blocks entered. To measure the direct and differential effects of the
three IATs, this study conducted six hierarchical regression analyses. All ß values presented
in this paper are unstandardized. The dependent
variables, political knowledge, and voting frequency were regressed upon four blocks, including demographic variables (age, gender, race,
education, and household income) at the first
block, political interest at the second block, the
three respective IATs at the third block, and
the interaction terms of political interest and
each of the individual IATs at the fourth block.
Centered means were calculated for each IAT
Stephens et al.
before including interactions to avoid multicollinearity issues, which can negatively affect
the quality of the data.
RESULTS
Before conducting hierarchical linear regressions, one-tailed correlations of individual IATs
(and their interaction with political interest)
and voting behavior and political knowledge
were examined. As Table 1 shows, correlation
tests indicated numerous significant relationships between the variables. News app use was
significantly correlated with voting (Pearson’s
r = .064, p < .05) while the interaction of news
app use and political interest resulted in a negative correlation (Pearson’s r = −.078, p < .05).
Search engine use was correlated with both voting (Pearson’s r = .096, p < .01) and political
knowledge (Pearson’s r = .157, p < .01) while
the interaction terms for both voting (Pearson’s
r = −.083, p < .05) and political knowledge
(Pearson’s r = −.114, p < .01) were negatively
correlated. Finally, neither online social network
use nor the interaction term was significantly
correlated with voting and political knowledge.
To answer the hypotheses and research questions, hierarchical linear regressions were analyzed. H1 stated that news application use would
predict (a) political knowledge and (b) voting
frequency, after controlling for demographics
and political variables. Furthermore, H2 asserted
that the relationship between news app use
389
and (a) political knowledge and (b) voting was
contingent upon political interest. Neither direct
effects nor differential effects of application use
predicted political knowledge (total R2 = .178,
see Table 2) and voting frequency (total R2 =
.304, see Table 3). Thus, H1a, H1b, H2a, and
H2b were not supported.
H3a and H3b stated that search engine use
would predict political knowledge and voting,
TABLE 2. Influence of News Application Use
and Political Interests on Political Knowledge
ß
Block 1
Age
Gender
Race
Education
Family Income
R2
Block 2
Political Interest
R2
Block 3
Search Engine Use
Social Network Use
R2
Block 4
Main Effects
R2
Block 5
Interaction Term
R2
Total R2
N
∗p
−.002
−.316∗∗∗
−.187∗∗
.048∗ (.052)
.112∗
.117
.352∗∗∗
.058
.083∗
−.007
.006
−.047
.000
.005
.000
.178
696
< .05, ∗∗ p < .01, ∗∗∗ p < .001, unstandardized ß
TABLE 1. One-tailed Correlation Table (Demographic and Political Variables, N = 800)
1. Age
2. Gender
3. Race
4. Education
5. Income
6. Po. Interest
7. Application
8. Search Engine
9. Social Network
10. InterestXApp
11. InterestXS.E
12. InterestXSNS
13.Voting
14. Knowledge
∗p
1
2
1
−.059∗
−.166∗ ∗
−.136∗ ∗
.008
.216∗ ∗
−.229∗ ∗
−.107∗ ∗
−.225∗ ∗
−.111∗ ∗
−.021
−.016
.239∗ ∗
.091∗ ∗
1
.017
−.066∗ ∗
.012
−.245∗ ∗
−.105∗ ∗
−.068∗ ∗
.094∗ ∗
.028∗
.105∗ ∗
.065∗ ∗
−.166∗ ∗
−.244∗ ∗
< .05, ∗∗ p < .01
3
4
5
6
7
8
1
.073∗
1
−.022
.012
1
−.100∗ ∗
.157∗ ∗
.037
1
.153∗ ∗
.189∗ ∗
.032
.136∗ ∗
1
∗∗
∗∗
.034
.161
.010
.197∗ ∗
.287∗ ∗
1
.048
.021
−.048 −.007
.227∗ ∗
.408∗ ∗
.013
.080∗
.018 −.160∗ ∗
.152∗ ∗ −.050∗ ∗
.039
−.057
.010 −.267∗ ∗ −.042
−.121∗ ∗
.069∗ −.019
.008 −.004
−.035
−.050
−.115∗ ∗
.180∗ ∗
.044
.482∗ ∗
.064∗
.096∗ ∗
−.128∗ ∗
.152∗ ∗
.040
.359∗ ∗
.047
.157∗ ∗
9
10
11
12
13
1
−.038
1
−.047∗ ∗
.389∗ ∗
1
−.004
.291∗ ∗
.421∗ ∗
1
−.022
−.078∗ −.083∗ −.002
1
.002
−.025∗ −.114∗ ∗ −.049 .267∗ ∗
14
1
390
JOURNAL OF INFORMATION TECHNOLOGY & POLITICS
TABLE 3. Influence of News Application Use
and Political Interest on Voting Frequency
TABLE 4. Influence of Search Engine Use
and Political Interest on Political Knowledge
ß
ß
Block 1
Age
Gender
Race
Education
Family Income
R2
Block 2
Political Interest
R2
Block 3
Search Engine Use
Social Network Use
R2
Block 4
Main Effects
R2
Block 5
Interaction Term
R2
Total R2
N
∗p
.011∗∗
−.100
−.119
.102∗∗∗∗∗
.032
.125
.733∗∗∗
.174
−.012
.048
.003
.040
.001
−.050
.001
.304
688
< .05, ∗∗ p < .01, ∗∗∗ p < .001, unstandardized ß
respectively. Also, H4a and H4b predicted that
the relationships were contingent upon political interest. Table 4 shows that after controlling
for demographic variables and political interest,
search engine use significantly predicted political knowledge (ß = .082, p < .05, total R2 =
.184, see Table 4).There was no significant relationship for search engine use and voting (total
R2 = .305, see Table 5). In terms of differential
effects, the interaction between political interest
and search engine use for news was not significant for political knowledge or voting frequency.
Only H3a was supported.
RQ1 examined whether online social network use would predict (a) political knowledge
and (b) voting frequency. Also, RQ2 explored
whether those relationships were contingent
upon political interest. There were no direct
or differential effects of SNS use for political knowledge (total R2 = .185, see Table 6)
or for voting frequency (total R2 = .303, see
Table 7).
In sum, none of the interaction terms, which
explained the differential effects of IATs, were
Block 1
Age
Gender
Race
Education
Family Income
R2
Block 2
Political Interest
R2
Block 3
Application Use
Social Network Use
R2
Block 4
Main Effects
R2
Block 5
Interaction Term
R2
Total R2
N
∗p
−.002
−.314∗∗∗
−.187∗∗
.048∗ (.053)
.113∗
.117
.346∗∗∗
.058
−.046
−.008
.002
.082∗
.006
−.018
.001
.184
696
< .05, ∗∗ p < .01, ∗∗∗ p < .001, unstandardized ß
TABLE 5. Influence of Search Engine Use
and Political Interest on Voting Frequency
ß
Block 1
Age
Gender
Race
Education
Family Income
R2
Block 2
Political Interest
R2
Block 3
Application Use
Social Network Use
R2
Block 4
Main Effects
R2
Block 5
Interaction Term
R2
Total R2
N
∗p
.011∗∗∗
−.107
−.116
.101∗∗∗
.025
.125
.760∗∗∗
.174
.033
.050
.004
−.007
.000
.059
.002
.305
688
< .05, ∗∗ p < .01, ∗∗∗ p < .001, unstandardized ß
Stephens et al.
TABLE 6. Influence of SNS Use and Political
Interest on Political Knowledge
391
significant; however, the relationship between
search engine use and political knowledge was
significant.
ß
Block 1
Age
Gender
Race
Education
Family Income
R2
Block 2
Political Interest
R2
Block 3
Application Use
Search Engine Use
R2
Block 4
Main Effects
R2
Block 5
Interaction Term
R2
Total R2
N
∗p
−.002
−.310∗∗∗
−.184∗∗
.047∗ (.055)
.115∗
.117
.351∗∗∗
.058
−.047
.082∗
.006
−.007
.000
−.044
.002
.185
696
< .05, ∗∗ p < .01, ∗∗∗ p < .001, unstandardized ß
TABLE 7. Influence of SNS Use and Political
Interest on Voting Frequency
ß
Block 1
Age
Gender
Race
Education
Family Income
R2
Block 2
Political Interest
R2
Block 3
Application Use
Search Engine Use
R2
Block 4
Main Effects
R2
Block 5
Interaction Term
R2
Total R2
N
∗p
.011∗∗∗
−.102
−.115
.100∗∗∗
.026
.125
.743∗∗∗
.174
.034
−.011
.002
.050
.002
−.002
.000
.303
688
< .05, ∗∗ p < .01, ∗∗∗ p < .001, unstandardized ß
DISCUSSION
While previous studies have examined how
different types of Internet components influence
political knowledge and voting, less attention
has been paid to the evolving information acquisition tools available to the public. This study
contributes to the conversation regarding information technologies and political outcomes at a
time when few studies have had the opportunity
to examine the various tools with one data set.
The results of this study suggest that IATs function differently for users with respect to political
outcomes.
Hierarchical regression models reveal that
search engines were the only IAT to significantly
contribute to political knowledge. One explanation for this difference could be that search
engines gratify different motivations than news
apps and online social networks. For example,
search engines involve motivated searches for
political news and information, and using search
engines has been linked to information seeking
(Johnson & Kaye, 2003b), which is a motivation strongly associated with positive political measures such as interest, knowledge, and
likelihood to vote (McLeod & Becker, 1981).
Still, the results for search engine use confirm
the instrumental hypothesis and parallel Xenos
and Moy’s (2007) findings regarding knowledge acquisition from online information while
adding a more nuanced use measure to the literature. Users rely on search engines extensively to
find information, including campaign information (Pew Research Center, 2012b), as well as
information about the candidates, because they
trust the information they find through searches
(Pan et al., 2007).
One reason for the insignificant political
knowledge findings for news apps and online
social media could be the uses associated with
the different IATs. As Weiss (2013) found,
young adults mainly use news apps for social
reasons such as checking maps (92.4%) and
finding restaurants and other local businesses
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JOURNAL OF INFORMATION TECHNOLOGY & POLITICS
(82.6%), compared with the 66.7% of participants who said they used the applications for
traditional news. Smith (2011) reported that
individuals mainly use online social networks
such as Facebook to stay connected with others. Although news apps and SNSs provide a
convenient way to get news, these IATs may
function differently than the activeness required
of looking for news via search engines.
None of the IATs contributed to voting behavior. Previous studies found mixed results regarding whether SNSs would boost intent to vote
(Bode, 2012; Bond et al., 2012; Groshek &
Dimitrova, 2011). One reason for the nonsignificant results in this study may be context. Past
studies often asked whether people were specifically searching for campaign information, or the
studies looked for political engagement in more
specific contexts such as the Arab Spring. This
study was conducted during the 2012 primary
campaign but asked about news and information
acquisition in general.
The differential-effects hypothesis was not
confirmed in any of the analyses in this study.
One explanation could be the contingent variable of choice: political interest. Although others have found that political interest acts as the
individual characteristic that explains the interaction between Internet use and political variables (Kaye & Johnson, 2004; Xenos & Moy,
2007), the political interest scores in this sample were relatively high and could account for
much of the variance in these analyses. A sample
with more varied political interest scores could
provide different results.
While search engine use was the only IAT to
show a positive, direct relationship to political
knowledge in this analysis, correlation results
reveal additional information. Significant positive correlations existed between news app use
and voting, search engine use and voting, and
search engine use and political knowledge; however, there were no significant relationships for
social network use. It may be the case that online
social network use primarily serves social motivations, such as to meet interesting people and to
have something to talk about with others (Kaye,
2010; Park, Kee, & Valenzuela, 2009; Raake &
Bonds-Raake, 2008). SNS users, then, are more
likely to stumble upon news by, for instance,
reading a status on Facebook. Although individuals do learn from incidental exposure, particularly those who are lower in political interest and
knowledge, incidental exposure is less likely to
boost knowledge and voting behavior as much
as an intentional, motivated search for information, which requires more cognitive work
(Baum, 2003; Tewksbury, Weaver, & Maddex,
2001). Additionally, the interaction of political
interest with both news app and search engine
use resulted in a negative correlation with voting, suggesting a methodological explanation:
political interest is such a strong predictor of
political knowledge that it impacts the effects of
the IAT.
Limitations and Suggestions for Future
Research
This study of how IATs influence political
attitudes and behaviors was based on an opt-in
Texas Poll, conducted jointly by the University
of Texas and the Texas Tribune. Although the
data was weighted to match voting parameters,
it is unclear whether results from this study can
be generalized to the U.S. population or to an
international audience. Additionally, the current
study relies on cross-sectional data and cannot speak to the cause-and-effect relationship
between use and outcome. Although there is
reason to suspect that Internet use would lead
to political outcomes, it is also possible that
use and outcome work together. In other words,
although search engine use might have a direct
effect on knowledge, it may be that more knowledgeable individuals use search engines. Still,
the present study acts as an initial inquiry into
the effects of IATs, which are becoming more
common among the public.
Like many secondary analyses of data, this
study was limited in what it was able to explore.
This study used a four-point additive measure
to assess political knowledge. A measure with
more variance, or even a different knowledge
measure incorporating knowledge structure density information, which has been shown to be
a better measure for online political knowledge (see, Evelande et al., 2004), could provide
results that are more aligned with the literature. Future studies may want to include other
Stephens et al.
dependent measures, such as knowledge structure density, civic and political participation
beyond voting, and different moderating variables such as trust and efficacy. Future studies
may also want to explore IATs in more depth by
examining motivations for using different technologies and how these motivations help explain
the relationship between information acquisition
techniques and political measures. Additionally,
this study did not account for such IATs as
RSS feeds and news aggregators. Finally, this
study was conducted as news apps were just
emerging as a way to gather news and information. While it is important to remain cautious of the time-dependent nature of evolving
digital media (Althaus & Tewksbury, 2000),
investigating these three information acquisition
tools begins a conversation about specific online
activities as they relate to political knowledge
and voting. Perhaps news application use will
change from a social tool to more of an information tool in the future. This study should be
repeated during the 2014 off-year election and
2016 election to see if more experience with
these information acquisition tools translates
into increased influence on political knowledge
and voting.
ACKNOWLEDGMENT
The authors would like to thank the Texas
Poll for their assistance with survey questions.
NOTES
1. The Texas Poll makes its data available for
study and replication at http://texaspolitics.laits.utexas.edu/
11_9_13.html
2. See Appendix A for exact questions.
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APPENDIX A: SURVEY QUESTIONS
Independent Variables
Information Gathering: Please indicate how
often you read, watch, or listen to news and
information from the following mediums—
smartphone or tablet applications, search
engines, online social network sites: never,
hardly ever, sometimes, or regularly
Control Variables
Political Interest: Generally speaking would
you say you are extremely interested in politics
and public affairs, somewhat interested, not very
interested, or not at all interested?
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Political Ideology: On a scale from 1 to 7,
where 1 is extremely liberal, 7 is extremely conservative, and 4 is exactly in the middle, where
would you place yourself?
Age: Please indicate your age group: 18–29,
30–44, 45–64, 65 and up?
Income: In which category would you place
your household income last year? Less than
$10,000,
$10,000–$19,999,
$20–29,999,
$30,000–$39,999, $40,000–$49,999, $50,000 to
$59,999, $60,000–$69,999, $70,000–$79,999,
$80,000–$99,999,
$100,000–$119,999,
$120,000–$149,999, more than $150,000
Dependent Variables
Education: What is the highest level of education that you received? Less than high school,
high school degree, some college, two-year college degree, four-year college degree, postgraduate degree.
Race: What race do you consider yourself?
White, African American, Hispanic or Latino,
Asian/Pacific Islander, Native American,
Multiracial
Gender: What is your gender?
Voting: There are many elections in the state of
Texas. Furthermore, many people intend to vote
in a given election, but sometimes personal and
professional circumstances keep them from the
polls. Thinking back over the past two or three
years, would you say that you voted in all elections, almost all, about half, one or two, or none
at all?
Political Knowledge: Which political party
holds the majority in the U.S. House of
Representatives? What majority of both houses
of the U.S. Congress is needed to override a
presidential veto? Who is the current Attorney
General of Texas? (Multiple choice questions
with the number of correct answers summed).