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This study places the “cognitive elaboration model” on news gathering and political behavior within the dual-processing “elaboration likelihood model” to derive hypotheses about the effects of incidental news exposure and tests them using two-wave panel data. Results indicate incidental news exposure predicts online participation but not offline participation – underlining the importance of differentiating between political behaviors in the two environments. The key finding, however, is that news elaboration mediates the positive relationship between incidental exposure and political participation, which is theorized as taking place through the peripheral route of elaboration – as opposed to intentional exposure, which engages the central route.
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Peripheral elaboration model: The impact of incidental news exposure on
political participation
Saif Shahin , Magdalena Saldaña , and Homero Gil de Zúñiga
ABSTRACT
This study places the “cognitive elaboration model” on news gathering and political behavior
within the dual-processing “elaboration likelihood model” to derive hypotheses about the eects
of incidental news exposure and tests them using two-wave panel data. Results indicate incidental
news exposure predicts online participation but not oine participation – underlining the impor-
tance of dierentiating between political behaviors in the two environments. The key nding,
however, is that news elaboration mediates the positive relationship between incidental exposure
and political participation, which is theorized as taking place through the peripheral route of
elaboration – as opposed to intentional exposure, which engages the central route.
KEYWORDS
Incidental exposure; social
media; political participation;
elaboration likelihood
model; cognitive elaboration
model
When individuals use mass media with the inten-
tion of consuming news as opposed to, say, get-
ting entertained they become more likely to
participate in politics (Boulianne, 2018). But what
happens when people stumble upon news without
seeking it consciously (Yoo & Gil De Zúñiga,
2019)? This question is becoming increasingly
important as new information and communication
technologies, especially social media, become the
primary source of news and public information
for citizens, especially in the United States (Pew
Research Center, 2018a).
Extant research on incidental news exposure
and its political effects is deeply divided.
Scholars differ over whether such exposure has
increased with the arrival of the internet (Fletcher
& Nielsen, 2018; Prior, 2005); whether it
enhances political knowledge, discussion, and
participation (Kim, Chen, & Gil de Zúñiga,
2013; Valeriani & Vaccari, 2016); and who bene-
fits more from incidental exposure to news
(Baum, 2003; Kim, 2008). This incoherence
stems from two limitations of the scholarship –
one theoretical and one empirical. First, unlike
intentional news exposure, research on incidental
news exposure hasn’t attempted to theorize the
cognitive mechanisms through which its effects
take place. Second, scholars also haven’t com-
pared the effects of incidental and intentional
news exposure concurrently. The scholarship
thus lacks a systematic contrast between inciden-
tal and intentional news exposure.
To address these shortcomings, the present study
integrates two theories of information processing –
Petty and Cacioppo’s (1986) “elaboration likeli-
hood model” and Eveland’s (2002) “cognitive ela-
boration model” to delineate the different
cognitive pathways through which intentional and
incidental news exposure lead to political participa-
tion. The cognitive elaboration model has shown
that news elaboration – or the cognitive association
of fresh information with existing stocks of knowl-
edge to give it context and value – mediates the
relationship between intentional news exposure
and political knowledge. We place this model
within the broader framework of the dual-
processing elaboration likelihood model, which
suggests that the impact of a message is determined
by the cognitive route through which an individual
elaborates upon it. When individuals intentionally
elaborate on information and consider its meaning
deeply, they engage the “central” route of elabora-
tion – and it leads to strong behavioral effects. But
individuals can also elaborate on a message more
superficially through the “peripheral” route, which
is activated by heuristic cues in the message – and it
leads to weaker effects. In this study, we therefore
argue that while intentional news exposure engages
the central route of elaboration, incidental news
exposure engages the peripheral route, also leading
CONTACT Saif Shahin shahin@american.edu School of Communication, American University, Washington, D.C. 20016
JOURNAL OF INFORMATION TECHNOLOGY & POLITICS
https://doi.org/10.1080/19331681.2020.1832012
© 2020 Taylor & Francis
to similar, albeit weaker, effects on political beha-
vior – specifically participation. The hypothesized
model is tested using a comprehensive two-wave
survey that enables us to study the longitudinal
effects of intentional and incidental exposure in
concurrent fashion.
Our peripheral elaboration model is both theore-
tically and socially significant. Theoretically, it
explicates the cognitive mechanism through which
incidental news exposure can influence political
behavior. In doing so, it positions both incidental
and intentional news exposure within the broader
framework of the elaboration likelihood model,
thus contributing to future research on intentional
news exposure as well and potentially laying the
grounds for more studies in which incidental and
intentional news exposure are examined concur-
rently. Socially, it draws attention to the novel and
unexpected ways in which engagement with news
and public information remains significant for
a healthy civil society and democracy in the age of
social media.
Literature review
News exposure and political participation
Intentional news exposure
Individuals can use mass media for various reasons
(Katz & Gurevitch, 1974). Scholars have long
argued that using mass media with the purpose of
gathering news and public affairs information –
intentional news exposure – has a positive effect
on political knowledge and participation (Chaffee
& Frank, 1996; Lemert, 1984; Wellman, Haase,
Witte, & Hampton, 2001). The same argument
has, in the past two decades, been extended to
emerging media technologies. Study after study
has shown that intentional news exposure on the
internet was positively associated with political
knowledge and participation (Kanervo, Zhang, &
Sawyer, 2005; Shah, Rojas, & Cho, 2009; Valeriani
& Vaccari, 2016). Research focusing on particular
online channelsfrom blogs (Kaye & Johnson,
2011) to social networking sites (Gil de Zúñiga,
Jung, & Valenzuela, 2012)has yielded similar
results. More recently, scholars have recognized
that political participation in online and offline
contexts, although closely related, can mean
different things (Strömbäck, Falasca, &
Kruikemeier, 2018). Both are however, positively
influenced by intentional news exposure.
Therefore, we hypothesize that,
H1: Intentional news exposure positively predicts (a)
oine political participation and (b) online political
participation.
Incidental news exposure
But news exposure need not always be intentional.
First, it is possible, even likely, to stumble upon
news and current affairs programming while using
mass media for purposes such as entertainment. As
Tewksbury, Weaver, and Maddex (2001, p. 535)
noted, “A television news viewer may be interested
in only sports and weather, but the viewer will often
sit through the opening news stories to get to those
segments.” Second, a lot of media content, such as
late-night comedy shows and satirical news maga-
zines and television programs, blurs the boundary
between news and entertainment by cracking jokes
about people and issues in the news (Baek &
Wojcieszak, 2009; Prior, 2003). Although indivi-
duals may choose to consume such content for
entertainment, they do simultaneously get exposed
to news as well. Third, using the media for news on
one subject can also cause unintentional exposure
to news on other subjects (Tewksbury et al., 2001).
The emergence of new media technologies has
expanded opportunities for incidental news expo-
sure manifold (Feezell, 2018; Fletcher & Nielsen,
2018). Early web “portals” integrated various kinds
of content on centralized services and pages, simul-
taneously exposing users to both informational and
entertainment content. Search engines, another pop-
ular port of call for online users, throw up a variety of
results for search queries, thus raising the chances of
stumbling upon unsolicited content (Tewksbury
et al., 2001). More recently, as social networking
has become one of the most common online activ-
ities, people often get exposed to news content
shared by their “friends” or those whom they “fol-
low” directly on their Twitter and Facebook feeds
even though they don’t go online for such content in
particular (Gil de Zúñiga et al., 2012).
A number of scholars have focused on the acqui-
sition of political knowledge through incidental
news exposure, drawing on Downs’s (1957)
2S. SHAHIN ET AL.
rational voter model, according to which people try
to acquire information with the least effort. On
similar lines, Popkin (1994) proposed the idea of
low information rationality, which claims that peo-
ple use information shortcuts and heuristic cues to
gain information. As incidental news exposure is
a byproduct of other activities, learning in this
manner requires minimal effort, thereby making it
more likely that people will gain more political
knowledge. Tewksbury et al. (2001) influential
study from the early days of the internet reported
that incidental exposure did not predict knowledge
acquisition in 1996 “because the paucity of news on
non-news sites meant that a measurable acquisition
of information was likely to occur only through
purposeful news seeking” (p. 546). By 1998, how-
ever, the situation had already changed and inci-
dental exposure had emerged as a reliable, positive
predictor of knowledge. Baum (2003) also found
that entertainment content was a significant means
of enhancing public affairs knowledge of those who
did not use media for newsparticularly on for-
eign policy issues. Lee and Kim (2017) experimen-
tal study reported that incidental exposure had
a positive impact on recognition and recall when
readers read the full text of an article they stumbled
upon online.
In contrast, other scholars have contended that
new media technologies curtail opportunities for
incidental exposure by offering users unprece-
dented choice and control over media content and
producing “filter bubbles” in the process – having
a negative effect on political knowledge in the pro-
cess (Pariser, 2011; Prior, 2005). Sunstein (2007)
suggested the internet was spawning “information
cocoons,” allowing users to seek out like-minded
people and selectively expose themselves to infor-
mation and opinion channels close to their existing
views (see also, Stroud, 2010). With such control
over information choices, opportunities for inci-
dental exposure to unsolicited content were
decreasing, and so was any chance of learning
from it.
While concerns about information cocoons and
filter bubbles are valid in general, their impact on
political knowledge is both theoretically weak and
empirically unsupported. First, even if the nature of
information that people inside these “cocoons” are
exposed to is partisan, the fact that it is
informationrather than entertainmentis more
relevant. After all, partisan information is typically
related to public affairs. Second, as Brundidge
(2010) noted, while users may opt to expose them-
selves to particular kinds of information, they don’t
necessarily avoid other kinds of information on the
internet. In line with this argument, recent studies
continue to reveal high levels of incidental news
exposure online (Fletcher & Nielsen, 2018; Serrano-
Puche, Beatriz Fernández, & Rodríguez-Virgili,
2018; Weeks, Lane, Kim, Lee, & Kwak, 2017).
Finally, political knowledge is a desirable but not
a necessary antecedent to political participation.
Information shortcuts and heuristic cues enable
even citizens with limited knowledge to make poli-
tical judgments and participate in the political pro-
cess (Kaiser, Keller, & Kleinen-von Königslöw,
2018; Lupia & McCubbins, 1998). As Oeldorf-
Hirsch (2018) has found in a study of Facebook
and Twitter, incidental news exposure is linked
with engagement but not with political knowledge.
Accordingly, we hypothesize that,
H2: Incidental news exposure positively predicts (a)
oine political participation and (b) online political
participation.
News elaboration and political participation
Intentional news exposure
Explaining the cognitive processes through which
intentional news exposure leads to behavioral
effects has been a focus of political communica-
tion scholarship. A key finding of this body of
research is the mediating role of cognitive ela-
boration – the mental process that “relates the
incoming information to existing knowledge and
images and attaches connotative and associative
meanings” (Perse, 1990, p. 19). The cognitive
elaboration model (Eveland, 2001, 2002) shows
that news elaboration, along with attention to
news, fully mediates the positive relationship
between motivation to use the media for news
and the learning that results from such use. In
other words, it is only after information gained
through intentional media use is cognitively pro-
cessed and elaborated upon that it is able to add
to an individual’s political knowledge. Jung, Kim,
JOURNAL OF INFORMATION TECHNOLOGY & POLITICS 3
and Gil de Zúñiga (2011) have extended this idea
to show that news elaboration also mediates the
relationship between intentional news exposure
and political participation. Therefore, we
hypothesize that,
H3: News elaboration positively predicts (a) oine
political participation and (b) online political
participation.
H4: News elaboration mediates the relationship
between (a) intentional news exposure and oine
political participation, and (b) intentional news
exposure and online political participation.
Incidental news exposure
The cognitive elaboration model assumes inten-
tional exposure to news (Eveland, 2001, 2002).
But if elaborationrather than intentionalityis
the proximal cause of political learning and parti-
cipation, it is pertinent to ask what happens if there
is elaboration without intentionality. To better
explore this question, we examine the cognitive
elaboration model within the broader framework
of Petty and Cacioppo’s (1986) elaboration likeli-
hood model (ELM).
For three decades, psychologists have been gath-
ering evidence on the “dual” nature of cognitive
information processing. Various dual process mod-
els have been proposed; ELM is the most widely
used among them, especially in the field of media
effects research (Meirick, 2013; White, 1997).
According to this theory, the attitudinal and beha-
vioral impact of a message is determined by the
manner in which a receiver elaborates upon it
(Petty and Cacioppo, 1986). Elaboration, however,
can be of two types: it does not always involve deep
thinking and can take place through cognitive
shortcuts as well. These two “routes” of elaboration
are called the central route and the peripheral route
respectively. Elaboration through the central route
is relatively high, and happens when the receiver
thoughtfully examines the message and considers
its arguments before deciding what to do.
Elaboration through the peripheral route is rela-
tively low, taking place when the receiver relies on
some attendant characteristics of the message-
such as their liking of the communicatorto be
influenced by it.
When individuals intentionally gather news
about public affairs from the media, they are likely
to elaborate on it through the central route lead-
ing to strong behavioral effects. But when they are
incidentally exposed to it, certain attendant char-
acteristics of news can also lead to elaboration –
albeit of the peripheral variety. The communica-
tor’s credibility, the receiver’s liking for the com-
municator, and the general response to the message
(consensus heuristic) have been identified as some
of the peripheral cues that can lead to elaboration
(Petty and Cacioppo, 1986).
These cues have all become more significant in
the emerging media environment, when people
stumble upon news on web portals they regularly
visit (“credibility” heuristic), from friends, family
members and other people they choose to follow on
social network sites (“liking” heuristic), or when
they see large numbers of people reading or com-
menting on particular news-related items on blogs,
social media and other sites (“consensus” heuristic)
(Bergström & Belfrage, 2018; Boczkowski,
Mitchelstein, & Matassi, 2018; Gil de Zúñiga,
Molyneux, & Zheng, 2014). All these factors can
attract attention to news and trigger the elaborative
process, with possible attitudinal and behavioral
effectseven when the user is not motivated to
learn from media exposure. Accordingly, we
propose,
H5: Incidental news exposure positively predicts
news elaboration.
RQ1: Does news elaboration mediate the relation-
ship between incidental news exposure and (a) o-
line political participation and (b) online political
participation?
For clarity, the figures below illustrate the
hypothesized relationships between intentional
and incidental news exposure, news elaboration,
and online and offline political participation
(Figures 1 and 2).
4S. SHAHIN ET AL.
Method
This study relies on data obtained from a two-wave
U.S. national panel study conducted by the Digital
Media Research Program at the University of
Texas at Austin. Both waves of the study were
administered using Qualtrics, an online surveyor
to which authors have a university-wide subscrip-
tion account. Respondents for the initial survey
were selected from among those who registered to
participate in an online panel administered by the
media-polling group AS Nielsen. Nielsen employs
a stratified quota sampling to recruit the respon-
dents from over 200,000 people. That is, to over-
come some of the limitations of using internet users
only and assure national representativeness,
a quota based on gender, age, education and
income was established so that the sample would
tend to match as much as possible the distribution
of these demographic variables as reported by the
U.S. Census (to learn more about this data collec-
tion strategy, please see Bode, Vraga, Borah, &
Shah, 2013; Iyengar & Hahn, 2009).
The first wave of the survey was conducted in
December 2013 from an initial sample of 5,000
individuals. In total, 2,060 participants responded,
and 247 cases were deleted for incomplete or inva-
lid data. The response rate for the first wave panel
was 34.6%, based on the American Association for
Public Opinion Research (2016) response rate cal-
culator (RR5). This response rate falls within accep-
table parameters for web-based surveys (Sax,
Gilmartin, & Bryant, 2003). In the second wave of
the survey, conducted in March 2014, 1,024 of the
original interviewees completed the questionnaire,
for a retention rate of 57% (for a detailed discussion
on the importance on retention rate for web panels,
see Watson & Wooden, 2006). Compared to U.S.
Census data, both wave samples were older, better
educated, with slightly higher income and a higher
presence of white respondents. No significant dif-
ferences were found in terms of gender (for further
comparisons, see Appendix 1).
Statistical analysis
To test the hypotheses posed by this study, first,
zero-order and partial-order Pearson’s correlations
were performed to ascertain the ways in which all
variables of interest related to each other.
Furthermore, a series of hierarchical regressions
H5
H2a
Incidental
Exposure
H1a
H3a
Intentional
Exposure
News
Elaboration
Offline
Political
Participation
H4a
RQ1a
Figure 1. Structure of the theoretical model proposed for Offline Political Participation.
H5
H4b
H2b
Incidental
Exposure
H1b
H3b
Intentional
Exposure
News
Elaboration
Online
Political
Participation
RQ1b
Figure 2. Structure of the theoretical model proposed for Online Political Participation.
JOURNAL OF INFORMATION TECHNOLOGY & POLITICS 5
were conducted. These analyses allowed testing the
effects of the key independent variables in the study
(intentional news exposure, incidental news expo-
sure, and news elaboration) while controlling for
the effects of a set of key influential variables pre-
viously identified in the literature, such as demo-
graphics, political efficacy and discussion network
size. Finally, to answer the research questions posed
above, we used the Hayes’ PROCESS macro for
SPSS – model 4 (Hayes, 2013) to initially test the
effect of intentional exposure and incidental expo-
sure to news on online and offline political partici-
pation, using news elaboration as a mediating
variable. To fully isolate the effect of each type of
news exposure over political participation, we
included the other type of news exposure variable
as a control in the mediating path tested. That is,
when testing the mediating path of intentional
news exposure over participation, we controlled
for incidental news exposure, and vice versa.
Dependent variables
Political participation
This is the key dependent variable in this study. We
aim to understand which factors affect political
participation both offline and online. Therefore,
two variables were created to measure this phe-
nomenon drawing on data from the second wave
of the panel survey. The items we used to build
these variables come from Verba, Schlozman, and
Brady (1995), McLeod, Scheufele, and Moy (1999),
and Shah et al. (2007).
For oine political participation (t2), we created
an index from seven items asking how often
respondents had engaged in any of the following
activities during the past 12 months, on a 10-point
scale where 1 = Never and 10 = All the time:
“Attended/watched a public hearing, neighborhood
or school meeting,” “Contacted an elected public
official,” “Attended a political rally,” “Participated
in any demonstrations, protests, or marches,”
“Donated money to a campaign or political
cause,” “Participated in groups that took any local
action for social or political reform,” and “Been
involved in public interest groups, political action
groups, political clubs, political campaigns, or poli-
tical party committees.” We also asked how often
respondents voted on (a) local or statewide
elections, and (b) federal or presidential elections.
We used all these items to create an index of offline
political participation (9 items; α =.85, range = 1 to
10, M = 3.5, SD = 1.7).
For online political participation (t2), respon-
dents were asked about the following activities dur-
ing the past 12 months, also on a 10-point scale:
“Signed or shared an online petition,” “Participated
in online political polls,” “Participated in an online
question and answer session with a politician or
public official,” “Created an online petition,”
“Signed up online to volunteer to help with
a political cause,” and “Used a mobile phone to
donate money to a campaign or political cause via
text message or app” (6 items; α = .82, range = 1 to
10, M = 2.1, SD = 1.6).
Control variables
Demographics
Five standard demographic variables were used as
an initial control block in the regression models.
The respondent’s age (M = 52.7, SD = 14.8), gender
(1 = male, 50.1%), and ethnicity (1 = White, 78.2%)
were asked in the survey. Respondents were also
asked about their highest level of formal education
attained, which ranged from 1 = Less than high
school to 8 = Doctoral degree (M = 3.6, SD = 1.4,
median = some college). Income was measured with
eight categories, where 1 = Less than 10,000 USD
and 8 = 200,000 USD or more (M = 4.5, SD = 1.4,
median = from 50,000 USD to 99,999 USD).
Political orientations
In a second control block, drawing on from data
based on the first wave, variables discussion network
size (t
1
), political aliation (t
1
) and political ecacy
(t
1
) were included. To measure the size of their
discussion networks, respondents were asked to
estimate the number of people they had talked to
about politics or public affairs, both offline (face-to-
face or over the phone) and via Internet (e-mail,
chat rooms, social networking sites and micro-
blogging sites) during the past month. These two
open-ended items were combined into a single
scale to create an index of discussion network size.
However, the index was highly skewed (Spearman-
Brown coef. = .18, range = 0 to 275, M = 4.4,
SD = 17, skewness = 10.9) so we transformed it
6S. SHAHIN ET AL.
using the natural logarithm (Spearman-Brown
coef. = .60, range = 0 to 2.2, M = .33, SD = .37,
skewness = 1.3). To control for political aliation
(t
1
), respondents were asked to place themselves on
scale ranging from 1 = Strong Republican to
11 = Strong Democrat (range = 1 to 11, M = 6.3,
SD = 2.8). Political ecacy (t
1
), on the other hand, is
an important control because it has been identified
as a robust predictor of participatory behaviorsin
this case, political participation (Anderson &
Tverdova, 2001; Bennett, 1997). Although previous
research has studied different influential types of
efficacy, internal and external are the most com-
monly used as controls in the literature (for an
extended discussion on political efficacy see
Morrell, 2003). Thus, this variable was created by
combining five items measured on a 10-point scale
that tapped on both dimensions altogether, ranging
from 1 = Strongly disagree, to 10 = Strongly agree
with each of the following statements: “People like
me can influence government,” “I consider myself
well qualified to participate in politics,” “I have
a good understanding of the important political
issues facing our country,” “No matter whom
I vote for, it won’t make a difference” (reverse
coded), and “People like me don’t have any say in
what the government does” (reverse coded) (5
items; α = .71, range = 1 to 10, M = 5.4, SD = 1.9).
Independent variables
News exposure
Also from wave one, the third block in the regres-
sion model includes two independent variables,
intentional news exposure (t
1
) and incidental news
exposure (t
1
). For intentional news exposure (t
1
),
respondents were asked how often they used differ-
ent media outlets (cable/network/local TV news,
national/local newspapers, radio news, online
news websites, citizen journalism websites and
hyperlocal news sites), as well as different social
media platforms (Twitter, Facebook, Reddit,
Google+, LinkedIn, Tumblr, Instagram, Google
News and Pinterest) to get news, on a 10-point
scale where 1 = Never and 10 = All the time. All
these items were added to create an index of inten-
tional news exposure (18 items; α = .79, range = 1 to
10, M = 3.0, SD = 1.2).
To measure incidental news exposure (t
1
), we
based our questions on measurements previously
developed by Tewksbury et al. (2001) and Kim
et al. (2013), which differentiate the construct from
intentional news exposure. Respondents were asked
how often they encountered news when watching
TV or listening to radio for different purposes
(other than getting news). They were also asked
how often they stumbled upon news via e-mails,
search engines, online portals (such as MSN and
Yahoo!), blogs, and social networking sites (such as
Facebook or Twitter), on a 10-point scale where
1 = Never and 10 = All the time. All these items
were combined to create an index of incidental news
exposure (9 items; α = .85, range = 1 to 10, M = 3.9,
SD = 1.8). These measures have been validated by
several recent studies on incidental exposure to news
(e.g. Ardèvol-Abreu, Diehl, & Gil de Zuniga, 2019;
Fletcher & Nielsen, 2018; Valeriani & Vaccari, 2016;
Yamamoto & Morey, 2019).
News elaboration
Finally, the fourth block in the model included the
variable news elaboration (t2). Drawing on
Eveland’s original work on cognitive elaboration
(Eveland, 2001; Eveland, Shah, & Kwak, 2003),
this composite variable combined two items from
the second wave measured on a 10-point scale
where 1 = Strongly disagree and 10 = Strongly
agree: “I often think about how the news
I encountered relates to other things I know,” and
“I often find myself thinking about what I’ve
encountered in the news” (2 items; r. = .95; range = 1
to 10, M = 4.6; SD = 2.8).
Results
All independent and dependent variables were
together tested for zero-order and partial correla-
tions (see Table 1). Online political participation
was positively correlated with all the key indepen-
dent variables in the study: intentional news expo-
sure (r = .41, p < .001), incidental news exposure
(r = .33, p < .001), and news elaboration (r = .41;
p < .001). After controlling for demographics (age,
gender, education, income, and ethnicity), and
political orientations (discussion network size, poli-
tical affiliation, and political efficacy), correlations
JOURNAL OF INFORMATION TECHNOLOGY & POLITICS 7
remained statistically significant. Offline political
participation was also positively correlated with
intentional news exposure (r = .40, p < .001), inci-
dental news exposure (r = .25, p < .001), and news
elaboration (r = .44, p < .001). All correlations held
their statistical significance after controlling for
demographics and political orientations. In addi-
tion, online and offline political participation were
highly correlated themselves, showing that people
were consistent in their political participation levels
in both environments (r = .75, p < .001). This
relationship remained significant after controlling
for our two control blocks (r = .73, p < .001).
Model 1 accounted for a total variance of 37% for
offline political participation and 25% for online
political participation (see Table 2, Model 1).
From the first block of independent variables, age
(β = .102, p < .01) and education (β = .088, p < .01)
had a positive effect on offline political participa-
tion, as well as being White (β = .077, p < .05). In
terms of online political engagement, income had
a negative effect (β = −.098, p < .01), but no other
effects were found in Block 1.
In Block 2, political efficacy was positively related
to offline political participation (β = .267, p < .001) as
well as online political participation (β = .197,
p < .001), indicating that the more people thought
they could affect the government, the more they
tended to participate. Discussion network size also
affected political participation. According to Model
1, the number of people with whom an individual
discussed news or public affairs had a positive effect
on both offline (β = .228, p < .001) and online
(β = .210, p < .001) political participation, indicating
that the more ties an individual had in her discussion
network, the more she tended to engage politically.
Table 1. Zero-order and partial-order Pearson’s correlations.
Intentional News
Exposure
Incidental News
Exposure
News
Elaboration
Offline Political
Participation
Online Political
Participation
Intentional News Exposure .60*** .37*** .40*** .41***
Incidental News Exposure .57*** .29*** .25*** .33***
News Elaboration .25*** .24*** .44*** .41***
Offline Political
Participation
.30*** .20*** .22*** .75***
Online Political
Participation
.30*** .25*** .28*** .73***
Cell entries are two-tailed zero-order Pearson’s correlations (top diagonal) and partial correlations (bottom diagonal) controlling for age, gender, education,
income, ethnicity, discussion network size, and political efficacy. N = 889 for zero-order correlations, N = 753; df = 744 for partial correlations.
* p <.05, ** p <.01, *** p <.001.
Table 2. Regression models predicting political participation.
Offline Political Participation (W2) Online Political Participation (W2)
Model 1 Model 2 Model 1 Model 2
Block 1: Demographics
Age .102*** .084** −.026 −.051
Education .088** .082** −.010 −.022
Household Income .007 .005 −.098** −.099**
Ethnicity .077* .073* .005 .001
Gender .038 .042 .047 .054
∆R2 (%) 9.0*** 8.8*** 1.1 1.0
Block 2: Political Orientations
Discussion Network Size (W1) .228*** .182*** .210*** .138***
Political Affiliation (W1) .042 .041 .030 .027
Political Efficacy (W1) .267*** .237*** .197*** .154***
∆R2 (%) 21.3*** 21.3*** 16.3*** 16.2***
Block 3: News Exposure
Intentional News Exposure (W1) .247*** .224*** .217*** .180***
Incidental News Exposure (W1) .037 .020 .118** .097*
∆R2 (%) 6.3*** 6.4*** 7.8*** 7.9***
Block 4: Elaboration
News Elaboration (W2) .147*** .215***
∆R2 (%) 1.4*** 3.0***
Total R2 (%) 36.7*** 37.9*** 25.3*** 28.2***
N = 750 for offline political participation and 764 for online political participation. Cell entries are final-entry OLS standardized Beta (β) coefficients.
* p <.05, ** p <.01, *** p <.001.
8S. SHAHIN ET AL.
In Block 3, intentional news exposure had
a positive effect on offline (β = .247, p < .001) as
well as on online political participation (β = .217,
p < .001), showing that the more people consumed
news intentionally, the more they participated.
Incidental news exposure, however, predicted par-
ticipation in online settings (β = .118, p < .01) but
not in offline environments. This finding suggests
that stumbling upon news had a positive impact on
people’s online political engagement, but it did not
motivate them to engage in offline political action.
Model 2 introduced news elaboration in the equa-
tion (see Table 2, Model 2). Block 4 increased the
total variance explained by 1.4% for offline political
participation and 3% for online political participa-
tion. News elaboration was significantly related to
both offline (β = .147, p < .001) and online political
participation (β = .215, p < .001). Model 2 explained
38% of the variance for offline political participation
and 28.2% for online political participation.
This first set of hypotheses in this study sug-
gested that intentional news exposure would posi-
tively predict political participation, both offline
(H1a) and online (H1b). Next, incidental news
exposure was expected to positively predict offline
(H2a) and online political participation (H2b). The
study also hypothesized that news elaboration
would positively predict offline and online political
participation (H3a and H3b). The evidence from
the regressions supported all but one of these
hypotheses. According to Models 1 and 2 for offline
political participation, incidental news exposure
did not predict offline political participation.
Therefore, H2a was not supported.
News elaboration was predicted to mediate the
relationship between intentional news exposure on
the one hand, and political participation on the
other in both offline (H4a) and online (H4b) environ-
ments. Next, this study also hypothesized that inci-
dental news exposure would positively predict news
elaboration (H5), and proceeded to ask if news ela-
boration mediated the relationship between incidental
news exposure and offline (RQ1a) and online (RQ1b)
political participation. To test these hypotheses and
answer the research questions, we used the Hayes’
PROCESS macro for SPSS – model 4 (Hayes, 2013),
controlling for the same two variable blocks we used
in the regression tests – demographics and political
orientations. Additionally, we controlled for inten-
tional news exposure when incidental news exposure
was the dependent variable, and vice versa.
Based on the mediation test, the relationship
between intentional news exposure and offline poli-
tical participation was partially mediated by news
elaboration. As observed in both the regression (see
Table 2) and the mediation tests (see Figure 3),
intentional news exposure was positively related
to offline political participation, but when we intro-
duced news elaboration in the analysis, this rela-
tionship became weaker. In a similar manner, the
relationship between intentional news exposure
and online political participation was partially
mediated by news elaboration – when we intro-
duced news elaboration in the analysis, the
News
Elaboration
(t²)
b=.332*** (.057)
Intentional
exposure
(t¹)
Offline Political
Participation
(t²)
b=. 428*** (.102) b=.084*** (.020)
b=. 368*** (.057)
Intentional
exposure
(t¹)
Offline Political
Participation
(t²)
Figure 3. Mediation test results for Intentional Exposure and Offline Political Participation. Note: N = 750. Unstandardized beta
coefficients and standard error values (in parentheses) are reported, controlling for demographics (age, gender, ethnicity, education,
and income), political orientations (political efficacy, political affiliation, and discussion network size) and incidental news exposure. ***
p <.001
JOURNAL OF INFORMATION TECHNOLOGY & POLITICS 9
relationship between intentional news exposure
and online political participation became weaker,
although it remained significant (see Figure 4). In
both cases, the indirect effect of intentional news
exposure on political participation was positive and
significant (see Table 3). This evidence supports
both H4a and H4b.
The mediation test also shows that the relation-
ship between incidental news exposure and online
political participation is partially mediated by news
elaboration (see Figure 5). The evidence from the
mediation tests supported H5 (incidental news
exposure positively predicts news elaboration) and
answered RQ1b – news elaboration mediated the
relationship between incidental news exposure and
online political participation. As the regression
models indicated incidental news exposure did
not predict offline political participation, we did
not conduct mediation tests for these two variables
and news elaboration (RQ1a).
Table 3 illustrates the indirect effect of the inde-
pendent variables (intentional and incidental news
News
Elaboration
(t²)
b=.260*** (.059)
Intentional
Exposure
(t¹)
Online Political
Participation
(t²)
b=.439*** (.101) b=.118*** (.021)
b=.311*** (.059)
Intentional
Exposure
(t¹)
Online Political
Participation
(t²)
Figure 4. Mediation test results for Intentional Exposure and Online Political Participation. Note: N= 764. Unstandardized beta
coefficients and standard error values (in parentheses) are reported, controlling for demographics (age, gender, ethnicity, education,
and income), political orientations (political efficacy, political affiliation, and discussion network size) and incidental news exposure. ***
p <.001
Table 3. Mediating effects of news elaboration on the relation-
ship between news exposure (intentional and incidental) and
political participation (online and offline).
Individual indirect effect Point estimate SE CI
Lower Upper
A
1
-B-C
1
.036*** .014 .013 .066
A
1
-B-C
2
.052*** .017 .023 .089
A
2
-B-C
2
.021*** .009 .005 .042
CIs are bias-corrected and bias-accelerated 99% confidence intervals (boot-
strap N = 1,000). A
1
= Intentional News Exposure; A
2
= Incidental News
Exposure; B = News Elaboration; C
1
= Offline Political Participation; C
2
= Online Political Participation. N = 765, * p <.05, ** p <.01, *** p <.001.
Figure 5. Mediation test results for Incidental Exposure and Online Political Participation. Note: N = 764. Unstandardized beta
coefficients and standard error values (in parentheses) are reported, controlling for demographics (age, gender, ethnicity, education,
and income), political orientations (political efficacy, political affiliation, and discussion network size) and intentional news exposure. *
p <.05, ** p <.01, *** p <.001
10 S. SHAHIN ET AL.
exposure) on the dependent variables (online and
offline political participation), when news elabora-
tion mediates the relationships. Unstandardized
beta coefficients, standard error values, and confi-
dence intervals for all the mediated relationships
are reported.
We also conducted a robustness check to confirm
our findings. Specifically, while our measurement of
the two independent variables (intentional and inci-
dental news exposure) is in line with previous
research using these variables, our study is the first
to bring these variables together in a concurrent
model. Differences in the operationalization of
these variables – specifically the presence of news-
focused media channels and platforms in our mea-
surement of intentional news exposure that are
absent from our measurement of incidental expo-
sure – could therefore be considered as a confounder
of our results. To offset this concern, we carried out
the regression analyses and mediation tests described
above with an alternative intentional exposure vari-
able that only included media channels and plat-
forms also used in our measure of incidental
exposure, such as TV, radio, and social networking
sites (11 items; α = .72, range = 1 to 10, M= 2.8,
SD = 1.2). All the results remained significant as
before, with marginally different beta coefficients.
Discussion
Our analysis has yielded a number of significant
findings about the relationship between different
forms of news exposure and their effects of online
and offline political participation. First, we tested
if incidental news exposure would predict offline
and online political participation. The evidence
supported only one of these hypotheses: incidental
exposure does lead to political participation in
online environments but is not a significant pre-
dictor for participation in offline settings.
Previous research on the political effects of inci-
dental news exposure has yielded mixed and
unconvincing results (Kaiser et al., 2018; Oeldorf-
Hirsch, 2018; Prior, 2005; Sunstein, 2007;
Tewksbury et al., 2001; Valeriani & Vaccari,
2016; Weeks et al., 2017). By making a crucial
distinction between offline and online political
participation, our study helps explain why that
might be the case.
While incidental news exposure did occur in
legacy media too, it has become particularly signif-
icant in the online environment (Fletcher &
Nielsen, 2018). It thus stands to reason that while
it significantly predicts online political participa-
tion, it does not have a significant effect on offline
participation. Scholars have argued that online par-
ticipation comes at a much lower cost than tradi-
tional forms of political engagement (Morozov,
2009). Our study indicates that online participation
is also more likely than offline participation to be
predicted by “lower cost” or incidental news expo-
sure that takes place through information shortcuts
and heuristic cues (Downs, 1957; Popkin, 1994; Gil
de Zúñiga et al., 2014). Thus, even though online
and offline political participation are highly corre-
lated, they are fundamentally different forms of
engagement. In time, this distinction can be
expected to grow broader. We therefore suggest
that future research on political behavior treat
online and offline participation as distinct
phenomena.
We also predicted that news elaboration would
be positively related to offline and online political
participation. This hypothesis was based on the
cognitive elaboration model, which shows that
news elaboration mediates the positive effect of
intentional news exposure on political knowledge
(Beaudoin & Thorson, 2004; Eveland, 2001, 2002)
and political participation (Cho et al., 2009; Jung
et al., 2011). While this hypothesis replicated pre-
vious research, our use of two-wave panel data
makes this result an empirical contribution to the
literature.
Our study’s primary contribution is theorizing
and empirically testing the role of news elabora-
tion as a mediator between incidental news
exposure and political participation.
Conceptually, this study situates Eveland’s
(2001, 2002) cognitive elaboration model within
the broader framework of Petty and Cacioppo’s
(1986) elaboration likelihood model (ELM) to
argue that the link between intentional news
exposure and political participation can be
viewed as engaging the central route of elabora-
tion. When individuals intentionally expose
themselves to news, there is a greater chance
that they would think deeply about news items,
which in turn enhances their likelihood of
JOURNAL OF INFORMATION TECHNOLOGY & POLITICS 11
participation. But we argue that elaboration
doesn’t always have to be high for a message to
take effect. As ELM shows, elaboration can also
occur peripherally through heuristic cues inher-
ent in messages that can facilitate cognitive asso-
ciations and elaboration – such as the sender’s
credibility, the receiver’s liking of the sender, or
when a number of people seem to be following
the message (Bergström & Belfrage, 2018;
Boczkowski et al., 2018; Cho et al., 2009; Kaiser
et al., 2018). Incidental news exposure, based on
such heuristic cues, can thus be seen as engaging
ELM’s peripheral route of elaboration.
Accordingly, we hypothesized that incidental
news exposure would predict news elaboration
through this alternative cognitive pathway. Our
results bear out this hypothesis. This link is
expectedly weaker than the link between inten-
tional exposure to news and news elaboration, as
the latter engages the deeper, central route to
elaboration, while the former engages the per-
ipheral route.
We next examined if news elaboration mediates
the positive relationship between incidental news
exposure and online political participation. We
found that news elaboration does partially mediate
this relationship, thus empirically verifying the the-
orized mechanism by which incidental news expo-
sure takes effect. This mechanism constitutes what
we call the peripheral elaboration model. According
to this model, exposure to news in the information
age can occur through an unprecedented number of
media channels. This glut of media sources and plat-
forms has increased the possibility that an individual
stumbles upon news and information even when
they are not consciously seeking it. When such inci-
dental exposure happens, then various heuristic cues
in the news item can trigger the elaborative processes
in their mind engaging the peripheral route.
Peripheral elaboration is not as high as when indivi-
duals intentionally use the media for news and
engage the central route of elaboration, but our
empirical analysis indicates that it is strong enough
to lead to important behavioral effects, such as poli-
tical participation.
By postulating and testing this mechanism and
drawing crucial distinctions between (1) central
and peripheral routes of elaboration and (2) off-
line and online political participation, this study
hopes to clarify the lingering confusion over the
behavioral effects of incidental news exposure.
Several previously researched questionsfor
example, who benefits the most from incidental
exposure to newscan continue to be studied
with the help of the peripheral elaboration
model. In addition, a clutch of new questions can
now be asked, such as the relative strength of
various heuristic cues (credibility, liking, consen-
sus) in mediating the incidental exposure-political
participation relationship, or how network size
and strength of ties influence these heuristic
cues. Methodologically, experimental research
can control for as yet unidentified confounders
to take the variable relationships we identify closer
to causation. Finally, this study underlines the
importance of empirically examining incidental
and intentional news exposure concurrently and
proffers a theoretical framework within which
such analysis can be conducted meaningfully. We
hope that future research would follow this exam-
ple and study the antecedents and effects of both
incidental and intentional news exposure in con-
solidated research designs (see also, Serrano-
Puche et al., 2018).
Our results should be viewed with regard to the
limitations of our research design. Our data come
from surveys conducted in 2013–2014. Although the
cognitive processes we examine don’t necessarily
change in the duration of a few years, social media
platforms have continued to evolve and, if anything,
are leading to even higher levels of both intentional
and incidental news exposure (Fletcher & Nielsen,
2018). While Facebook, Twitter, and Reddit remain
dominant as before, platforms such as Snapchat and
WhatsApp have also gained in significance in terms
of news exposure (Pew Research Center, 2018b).
Newer surveys can address this limitation.
Our instruments for measuring incidental and
intentional news exposure are based on self-
assessment. So, clearly, these indexes are not per-
fectly suited to capturing people’s behavior as
observational media use measurements might be.
However, tapping on individuals’ own assessment
of their motivations for exposure or lack thereof,
the instrument minimizes the potential measure-
ment error between incidental and intentional
exposure to news. Theoretically, we treat central
and peripheral routes of elaboration as distinct
12 S. SHAHIN ET AL.
categories, but some research also suggests that
they might lie along a continuum (Kitchen, Kerr,
Schultz, McColl, & Pals, 2014). Emerging methods
such as eye-tracking and brain-mapping can help
make the proposed peripheral elaboration model
more nuanced, in line with advancements in ELM.
Notes on contributors
Dr. Saif Shahin is Assistant Professor in the School of
Communication at American University, Washington DC,
and Associate Editor of the Journal of Information
Technology & Politics. His research interests include critical
data studies, digital culture, and global media and politics.
Dr. Magdalena Saldaña is Assistant Professor in the School of
Communications at Pontificia Universidad Católica de Chile,
and Researcher at the Millennium Institute for Foundational
Research on Data. Her research interests include digital jour-
nalism, social media, political communication, and Latin
American studies.
Dr. Homero Gil de Zúñiga is Distinguished Research
Professor at University of Salamanca where he directs the
Democracy Research Unit (DRU), Professor at Pennsylvania
State University, and as Senior Research Fellow at Universidad
Diego Portales, Chile. His research addresses the influence of
information and communication technologies (i.e., digital and
social media) over people's daily lives, as well as the effect of
such use on the overall democratic process.
ORCID
Saif Shahin http://orcid.org/0000-0002-7608-7283
Magdalena Saldaña http://orcid.org/0000-0002-1218-0091
Homero Gil de Zúñiga http://orcid.org/0000-0002-4187-
3604
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JOURNAL OF INFORMATION TECHNOLOGY & POLITICS 15
Appendix 1.Demographic Prole of Study Survey and Other Comparable Surveys
Digital Media Research
Program Study Survey Dec.
2013 – Jan. 2014
Digital Media Research Program
Study Survey Second Wave
March 2014
Pew Research Center for the
People & the Press Political
Survey July 2013
U.S. Census American
Community Survey 2012
(1-Year Estimates)
(%) (%) (%) (%)
Age:
18–24 5.0 2.7 10.1 10.0
25–34 13.5 11.1 11.3 13.4
35–44 15.7 14.7 11.9 13.0
45–64 43.0 47.5 38.8 26.4
65 or more 22.8 24.1 28.6 13.7
Gender:
Male 50.0 51.0 49.9 49.2
Female 50.0 49.0 50.1 50.8
Race/Ethnicity:
White 78 79.1 72.2 73.9
Hispanic 5.8 5.2 11.2 16.9
African American 10 9.6 10.3 12.6
Asian 3.2 2.9 2.5 5.0
Education:
High school or less 19.3 18.4 32.5 41.6
Some college 34.5 33.9 27.6 29.2
Bachelor’s degree 30.5 31.9 22.6 18.2
Graduate degree 8.8 11.4 14.9 10.9
Household Income:
Less than $49,999 46.0 44.3 45.9 51.9
$50,000 to $99,999 36.5 37.8 26.1 32.7
$100,000 or more 17.4 17.9 17.2 15.4
16 S. SHAHIN ET AL.
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