ArticlePDF Available

Assessing Political Second Screening Behavior and Personality Traits: The Roles of Economic Development, Freedom of Expression and Monochromatic vs. Polychromatic Cultures

Running head: Personality and Second Screening
Accepted for publication
in Telematics and Informatics
Acceptance date: February 5th, 2020
Assessing Political Second Screening Behavior and Personality Traits: The Roles of Economic
Development, Freedom of Expression and Monochromatic vs. Polychromatic Cultures
Brigitte Huber a*, Homero Gil de Zúñiga a, b, James Liu c
a Department of Communication, MiLab, University of Vienna, Währinger Str. 29, A-1090
Vienna, Austria. E-mail address:
b Facultad de Comunicación y Letras, Universidad Diego Portales, Vergara 240, Santiago,
Chile. E-mail address:
c School of Psychology, Massey University, Private Bag 102904, North Shore Auckland 0745,
New Zealand. E-mail Address:
* Corresponding author. Tel.: +43-1-4277-493 68; eFax: +43-1-4277-8493 68. E-mail
address: (B. Huber).
This research was supported by the Asian Office of Aerospace Research and
Development (Grant FA2386-15-1-0003). Responsibility for the information and views set out in
this study lies entirely with the authors.
Two-wave panel data from 19 countries worldwide.
Extraverted people tend to second screen more than introverts do.
Agreeableness and openness are negatively related to second screening.
Economic, political and cultural indicators explain differences between countries.
This study focuses on an emerging media multitasking phenomenon called second screening or dual
screening. Employing two-wave panel-data from 19 countries, we test whether the Big Five
personality traits help explain the use of an additional screen or device while watching political
content on TV to discuss the program with others or to look up for additional information. Results
show that extraversion positively predicts political second screening. In contrast, agreeableness and
openness to new experience are negatively related to political second screening. Moreover,
multilevel analysis is performed to test whether the between-country variation is related to economic,
political and cultural indicators.
Keywords: personality traits, second screening, dual screen use, multitasking, social media,
cross-national research
Assessing Political Second Screening Behavior and Personality Traits: The Roles of Economic
Development, Freedom of Expression and Monochromatic vs. Polychromatic Cultures
1. Introduction
Second screening or dual screening can be defined as a set of communicative practices in
which individuals use an additional ‘screen’ to go online while watching TV (Gil de Zúñiga et
al., 2015; Vaccari et al., 2015). In the literature, this emerging media multitasking practice is also
referred to as “multi-screening” (Dias, 2016) or “complementary simultaneous media use
(CSMU)” (Nee and Dozier, 2015). While there is a growing body of research investigating the
effects of media multitasking (e.g., Kazakova et al., 2015; Kononova et al., 2014) and
specifically of second screening (e.g., Barnidge et al., 2017; Houston et al., 2013; Lin and Yi-
Hsuan, 2017), less is known about the antecedents of second screening. Accordingly, we are
interested in investigating predictors of this behavior: Why do some people second screen while
watching TV? How do different individual personality traits drive this process?
People engage in second screening activities across different genres such as sports, sitcoms,
dramas, news, etc. Research has shown that news is one of the genres where people tend to
second screen most (Rubenking, 2016; Voorveld and Viswanathan, 2015). Moreover, studies
suggest that second screening while watching political content on TV has implications for
democracy due to its potential to increase people’s level of political participation (Lin and
Chiang, 2017; McGregor and Mourão, 2017). Ran et al. (2016) stress that researchers should pay
more attention to media multitasking during political news consumption. Hence, we decided to
focus on second screening activities during watching the news, political speeches or debates, and
during election coverage. We call this specific type of second screening “political second
screening”, i.e. using an additional screen or device while watching political content on TV to
discuss the program with others or to look up for additional information.
Building on studies investigating personality traits as determinants of different kinds of
digital media use (e.g., Amichai-Hamburger and Vinitzky, 2010; Andreassen et al., 2013; Buckels
et al., 2014; Marshall et al., 2015; Correa et al., 2010; Russo and Amna, 2015; Ryan and Xenos,
2011; Seidmann, 2013), as well as of media multitasking (e.g., Becker et al., 2013; Cain et al., 2016;
Wang and Tchernev, 2012), the current study examines how the Big Five personality traits help
explain political second screening behavior.
There are several possibilities to investigate media multitasking behavior such as conducting a
questionnaire study (e.g., Duff et al., 2014; Jeong and Fishbein, 2007), a media diary study (e.g.,
Papper et al., 2004; Voorveld and Van der Goot, 2013), a laboratory experiment (e.g., Brasel and
Gips, 2011; Garaus et al., 2017), or video analysis (Rigby et al., 2017). Since our study aims to
investigate media multitasking behavior in different countries worldwide, we decided to use survey
data as a less time-consuming and resources-intensive approach than collecting observational data or
conducting experiments (for an overview of methods for studying media multitasking, see Segijn et
al., 2019).
What makes comparative research especially fruitful is its recognition of the relevance of
contextual conditions, i.e. the attempt to link macro-level system conditions and micro-level
variables (Esser, 2013). This study contributes to this kind of research by including macro-
variables (economic, political and cultural indicators) in order to explain differences between
2. Literature review
2.1 Second screening as a media multitasking behavior
Already fifteen years ago, 26.5% of people in the US reported to regularly go online while
watching TV (Pilotta et al., 2004). In 2013, 46% of people in the US who have a smartphone and
43% of people with tablet reported to use these devices to go online during television
consumption every day (Nielsen, 2014). The Nielsen survey also provides an overview of
activities users engage with while watching TV ranging from looking up information in general
or surfing the web to further engage with the program watched on TV such as reading
conversations about the program on social network site, voting or sending comments to live
program. In our study, we focus on dual screen activities that are related to the content watched
on TV. Research on media multitasking reveals differences between genres in that people show
lower levels of multitasking while watching entertainment than while watching sports or the
news (Voorveld and Viswanathan, 2015). Following the work of Gil de Zúñiga et al. (2015), we
focus on media multitasking while watching the news, political speeches or debates, and during
election coverage.
In the literature, second screening is described as a media multitasking activity (Gottfried et
al., 2017). Theoretical approaches that help explain what happens when people perform multiple
tasks simultaneously are the multiple resource theory (Wickens, 2002) as well as thread
cognition (Salvucci and Taatgen, 2008). Their basic assumption is that people’s cognitive
capacity to process information is restricted. If two tasks are not using the same cognitive
resources, multiple tasks can be handled effectively (Wickens, 2002). However, if two tasks are
competing for the same resources, people are not able to perform both tasks at the same level and
have to decide how to interleave these tasks (David et al., 2013). Research on media multitasking
shows that people use different strategies to allocate their attention between tasks; they do not
necessarily apply one fixed strategy but are rather flexible in how to prioritize attention between
tasks (Farmer et al., 2018). Hence, when it comes to second screening while watching political
content on TV, individuals might apply different strategies to allocate attention between the two
tasks: watching news on TV and using a smartphone or another device to go online. While some
will pay more attention to the content on TV, others will engage more in what is happening on
the “second” screen. While some people’s attention will shift back and forth continuously
between the two screens, others will show higher attention for one screen than for the other or
prefer longer slots per each screen (Ran et al., 2016).
Based on the model of media exposure (Webster et al., 2006), research distinguishes between
media factors and audience factors when investigating antecedents of media multitasking (Jeong
and Fishbein, 2007; Voorveld and Viswanathan, 2015). Media factors include media market,
technology availability, etc. (see chapter on macro variables below), and audience factors include
program type preferences, tastes, gratifications sought, etc. One set of audience factors are
related to demographics: Research found that younger people engage more often in media
simultaneously than older people do (Duff et al., 2014; Pilotta et al., 2004). This is also true for
second screening; research found that younger people tend to second screen more often than
older people do (Barnidge et al., 2019; Gil de Zúñiga and Liu, 2017). Additionally, highly
educated people show higher levels of multitasking (Hwang et al., 2014). While in general,
females are more likely to engage highly in media multitasking activities (Duff et al., 2014),
when it comes to media multitasking during news use, males are more likely to multitask
(Hwang et al., 2014).
Additionally, personality traits have become a fruitful framework for predicting media
multitasking (e.g., Becker et al., 2013; Chang, 2017; Cain et al., 2016; Loh and Kanai, 2014; Wang
and Tchernev, 2012). More specifically, research has linked personality traits to multitasking
preference and multitasking behavior. One way of measuring multitasking preference is the
Multitasking Preference Inventory (MPI). It assesses “an individual’s preference for shifting
attention among ongoing tasks, rather than focusing on one task until completion and then
switching to another task” (Poposki and Oswald, 2010, p. 250). As a meta-study reveals the Big
Five can help explain individual’s multitasking preference (Sanderson, 2012). For instance,
people who prefer multitasking show high levels of sociability – a characteristic of the Big Five
dimension extraversion (Mesmer-Magnus et al., 2014). Studies that investigate the relationship
between the Big Five and actual multitasking behavior show similar results. One widely used
measurement for assessing media multitasking behavior is the Media Multitasking Index (MMI).
It is a questionnaire-based index introduced by Ophir et al. (2009) that captures the number of
media a person simultaneously uses when consuming media. The index is used to identify heavy
vs. light media multitaskers. Recent research using a naturalistic field study has shown that the
MMI is a good predictor of actual media multitasking behavior (Rigby et al., 2017).
Drawing on results from these lines of research, in the following we theorize about the
relationship between personality traits and second screening as a specific type of media
multitasking. In addition, we refer to research on personality traits and different kinds of online
behavior that can be part of second screening activities, such as social media use (Andreassen et
al., 2013; Correa et al., 2010; Ryan and Xenos, 2011; Seidmann, 2013), posting comments online
(Buckels, et al., 2014), or online political engagement (Russo and Amna, 2015).
2.2 Personality and second screening
There are different ways to classify personality traits; one that is often used is the five-factor
theory of personality (McCrae and John, 1992; McCrae and Costa, 1999). The five personality traits -
also known as the Big Five - are: extraversion, agreeableness, conscientiousness, emotional stability
(opposite = neuroticism), and openness to new experience.
The first personality trait is extraversion. Extroverted people are active, assertive, energetic,
enthusiastic, outgoing, and talkative (McCrae and John, 1992). They have high social skills and
numerous friendships (McCrae and Costa, 1999). Research suggests that extraversion is positively
related to social media use (Annisette and Lafreniere, 2017; Correa et al., 2010; Hong et al, 2014;
Ryan and Xenos, 2011; Seidmann, 2013; Tan and Yang, 2014). Research distinguishing between
different motives for using social network sites reveals that extraversion significantly predicts the
motivation of new connections (Orchard et al., 2014). Extraversion is generally associated with more
frequent political talk (Mondak et al. 2010). Since one motivation to dual screen is to further discuss
with others in their social networks issues about the program individuals watch, it stands to reason
personality traits may also explain this behavior. Finally, research shows a positive relationship
between extraversion and media multitasking activities (Loh and Kanai, 2014).
Building on these findings and given the positive link that has been established for extraversion
and other communication practices where people interact online, we formulate the following
H1. Extraversion will be positively associated with political second screening.
The next trait is agreeableness. Agreeable people are appreciative, trust others and are generous
and kind (McCrae and John, 1992). They prefer using inoffensive language and believe in
cooperation (McCrae and Costa, 1999). Since agreeable persons are good team players and value
others, they might like engaging in conversations with others online (Mark and Ganzach, 2014).
However, empirical findings show that less agreeable individuals tend to be higher users of the
internet (Andreassen et al., 2013; Landers and Lounsbery, 2006), are more likely to post comments
online and discuss politics online (Russo and Amna, 2015) as well as to troll online (Buckels et al.,
2014). One reason might be that people scoring high on agreeableness try to avoid conflicts and
hostile comments (Barnes et al., 2017). While reason-based conversations might attract agreeable
persons, interactions entailing incivility might discourage them to discuss the program with others.
Hence, both directions seem plausible. Research investigating specifically media multitasking found
no relationship between MMM (media-media multitasking) and agreeableness (Shih, 2013).
Also, for conscientiousness research shows no clear pattern. Conscientious people are efficient,
reliable, responsible, and thorough (McCrae and John, 1992). They like planning and organizing
(McCrae and Costa, 1999). In general, they feel less comfortable in situations that require
multitasking (Mesmer-Magnus et al., 2014). Conscientious people may not spend that much time
online to avoid procrastination or distraction from their daily tasks (Ross et al., 2009). However,
empirical findings regarding conscientiousness and online behavior are limited and inconsistent
(Barnes et al., 2017, p. 567). Recent research shows that less conscientious people use Facebook
more often (Eşkisu et al., 2017) and engage in posting on online news comments more often (Wu
and Atkin, 2017). Other studies found that more conscientious people use Twitter for information
(Hughes et al., 2012) as well as internet for academic purposes (Landers and Lounsbery, 2006) more
often. Finally, research found no association between conscientiousness and media multitasking
(Cain et al., 2016). When it comes to second screening, different scenarios are possible: While
conscientious people may go online while watching the news in order to search for additional
information, check for facts, or discuss with others, they may avoid doing so if it distracts them from
paying close attention to a political debate where a person wants to make sure s/he understand all
the issues thoroughly.
Also the findings for emotional stability are mixed. The opposite of emotional stability is
neuroticism. Neurotic people are anxious, unstable, and worrying (McCrae and John, 1992). They
tend to be pessimistic, show lower levels of self-esteem, and have high perfectionistic beliefs
(McCrae and Costa, 1999). Neurotics use the internet in search of a sense of belonging and social
support (Amichai-Hamburger and Ben-Artzi, 2000). Prior research shows that neuroticism
positively predicts media multitasking (Wang and Tchernev, 2012), social media use (Correa et al.,
2010; Hong et al, 2014; Tan and Yang, 2014) and Facebook informational use (Hughes et al., 2012),
but negatively predicts sharing content online (Hunt and Langstedt, 2014), Twitter use for
informational purposes (Hughes et al, 2012) as well as engaging in online political engagement on
Facebook (Quintelier and Theocharis, 2012). Finally, emotional stability is not a predictor of
commenting online news (Wu and Atkin, 2017). Accordingly, neurotic people might avoid going
online and discuss with others while watching the news, as they might not like to engage in
confrontational conversations about politics because they get easily upset. On the other hand,
neurotic people might seek to interact with others that consume the same program as this might also
create a sense of belonging.
The last personality trait is openness to new experience. Open people like new things and
changes (McCrae and Costa, 1999). They have many different interests and are curious to learn more
about the world around them (McCrae and John, 1992). Prior research shows that open people show
higher levels of social media use (Correa et al, 2010), online political participation and activities
(Jordan et al., 2015; Russo and Amna, 2015), and Facebook use for news and information (Ryan and
Xenos, 2011). This might also be true for second screening: People scoring high on openness might
like to use an additional device while watching politics on TV to learn about different viewpoints and
ideas on a topic related to the program. They might also do so to get in contact with others and
coordinate for engaging in political activities as research suggests that people open to new
experience engage in politics such as protest behavior more often than people scoring low on this
trait (Opp and Brandstätter, 2010). On the other hand, other studies did not find significant
association between being open and using Facebook (Moore and Elroy, 2012) or posting on online
news comments sections (Wu and Atkin, 2017). As commenting on online news is very similar to
commenting on topics related to a political program on TV, this could also be true for second
Since for the last four mentioned personality traits research shows no clear pattern, we pose the
following set of research questions:
RQ1. How do agreeableness (RQ1a), conscientiousness (RQ1b), emotional stability (RQ1c)
and openness to new experiences (RQ1d) relate to political second screening?
2.3 Cross-national comparison: Economic development, freedom of expression and
monochromatic/polychromatic cultures
Recent research has shown that political second screening behavior differs between
countries. For instance, political second screening is more common in Brazil than in the US
(McGregor et al., 2017). What might help explain differences between countries? As mentioned
above, when investigating antecedents of media multitasking behavior, not only individual
factors but also macro level factors are relevant to look at (Jeong and Fishbein, 2007; Voorveld
and Viswanathan, 2015). More specifically, at least the following three indicators should be
considered when investigating media multitasking behavior from a comparative perspective
(Kononova et al., 2014): economic, political and cultural indicators.
Second screening behavior is technologically dependent, and users need to access to the
internet on portable devices (Nee and Dozier, 2017). Hence, the economic development of a
country might influence the second screening behavior of individuals as economically higher
developed countries will be able to provide better infrastructure (Grupp, 1995), and GDP has
been shown to positively influence media ownership, i.e., residents in high GDP countries will
have more resources to buy new media devices (Kononova et al., 2014). GDP is a widely used
indicator to measure the economic development of a country. Accordingly, we include GDP as a
context-level variable in our analysis. We test whether or not individuals in high GDP countries
will be more likely to second screen than individuals in countries with low GDP.
Beside the economic development of a country, also its political development might be of
relevance when investigating media multitasking behavior. More specifically, in countries where
information can circulate more freely, individuals may have more content options to multitask
with (Kononova et al., 2014). Hence, we assume that countries where journalists and citizens are
more free to express themselves, individuals second screen more. Accordingly, we include the V-
dem freedom of expression index in our study. The index is provided by the “Varities of
Democracy”-Institute, an independent research institute based at the Department of Political
Science at the University of Gothenburg in Sweden (for more details, see Coppedge et al., 2016).
Finally, also cultural indicators might provide a better understanding of second screening
behavior in different societies. According to Hall (1983), not all individuals do have the same
orientation toward time. While individuals in some cultures organize their lives around time,
emphasize schedules and prefer doing one thing at a time (monochronic), individuals in other
cultures are more concerned on the present moment than with schedules and prefer doing many
things at once (polychronic). Not surprisingly, research found a positive relationship between
polychronicity and frequency of media multitasking (Srivastava et al., 2016).
As we are interested in assessing whether these three macro variables – the economic,
political and cultural indicator – may help explain differences between countries, we formulate
the following hypotheses:
H2. Individuals in countries with high GDP (H2a), high levels of freedom of expression
(H2b) and polychronic orientation (H2c) tend to second screen politics more.
Comparative research is especially fruitful when macro-level system conditions and micro-
level variables are linked to each other (Esser, 2013). Hence, besides testing the direct relationship
between macro-variables and second screening behavior, we are also interested to investigate
whether individual-level variables and macro-variables may interact to explain political second
screening behavior. More specifically, we want to explore whether the relationship between the Big
Five and political second screening activities may differ between countries with different economic,
political and cultural context. Accordingly, we pose the following research questions:
RQ2. Are there cross-level interactions between personality traits and the macro variables
GDP (RQ2a), freedom of expression (RQ2b) and monochronicity/polychronicity (RQ2c)
that affect political second screening?
3. Method
Prior research has used an array of different methods to investigate media multitasking
behavior (for an overview, see Segijn et al., 2019). To test our hypotheses and answer our research
question, we conducted an online survey in 19 countries worldwide.
3.1 Sample and data
The study at hand is part of a larger international project (Digital Influence). Hence, it uses
data from a larger data set where other research has been published (Gil de Zúñiga et al., 2017;
Barnidge et al., 2018). Data have been collected in 19 countries worldwide (see Table 1). It is a
two-wave panel study: The first wave of the study was fielded online in September 2015, and the
second wave of the study in February/March 2016. A large group of scholars translated the items
for each country. Afterwards, scholars have translated the answers back into English. The survey
was distributed by Nielsen, which curates a worldwide online panel with more than 10 million
potential participants. Nielsen used stratified quota sampling techniques. The aim was to create
samples in each country with demographics similar to those provided in reports of official census
agencies (see Callegaro et al., 2014). The sample size in Wave 1 is 20,361 and the sample size in
Wave 2 is 8,708. For more details on the survey and a demographic breakdown by country see
Gil de Zúñiga et al. (2017).
3.2 Measures
3.2.1 Political second screening
The dependent variable in the analysis is political second screening, i.e., using an additional
screen (e.g., tablet or smartphone) while watching political content on TV to access the Internet
or a social media to get more information or to talk about the program. Survey respondents were
asked how often (1= never; 7 = all the time) they second screen a) while watching political
speeches or debates, b) while watching the news, and c) while following election coverage. The
items were averaged (W1 Cronbach’s alpha (α) = .92, M = 3.1, SD =1.73; W2 α = .93, M = 2.88,
SD =1.73).
The independent variables in this study are the personality traits. They have been measured
based on prior instruments (Costa and McCrae, 1992; Gosling et al., 2003; Greaves et al., 2015;
John and Srivastava, 1999). All items are measured on a seven-point scale (1= strongly disagree;
7 = strongly agree):
3.2.2 Extraversion
We asked individuals how much they agree or disagree with the following statement: like to
start conversations, don’t like to speak in front of groups (recoded), comfortable introducing
themselves to new people, being shy around strangers (recoded), speak to a lot of different
people at events, and have difficulties to approach to others (recoded). The items were averaged
(α = .81, M= 4.23, SD = 1.23).
3.2.3 Agreeableness
We asked individuals how much they sympathize with others’ feelings, whether or not they
feel little concern for others (recoded), to what extend they are indifferent to others’ feelings
(recoded), if they love children, if they try their best to comfort others, and if they find it
tiresome when others ask for help (recoded) (α = .75, M= 5.1, SD = .99).
3.2.4 Conscientiousness
We wanted to know whether or not people get chores done right away, if they don’t like to
pay attention to detail (recoded), if they like order, to what extend they do things according to a
plan, if they are always prepared, if they like making plans and stick to it (α = .72, M= 4.76, SD
= .93).
3.2.5 Emotional stability
This construct captures people’s level of emotional and sensitive equilibrium and steadiness.
We asked people about frequent mood swings (recoded), getting upset easily (recoded), being
obsess over problems (recoded), rarely getting irritated’, don’t getting upset when problems
arise, and being calm most of the time (α = .72, M= 4.33, SD = 1.04).
3.2.6 Openness to new experiences
This index assesses people’s willingness to try new things and live new experiences. The
following statements were used: having difficulty imagining things (recoded), not being
interested in new ideas (recoded), do not like to try new things (recoded), being full of ideas,
taking a long time to learn anything new (recoded), and being quick to understand (α = .70, M=
4.98, SD = .96).
3.2.7 Controls
Additionally, the study includes political interest, political discussion, news use variables,
and demographics as controls. We used two items to measure political interest. We measured
people’s attention to information about what is going on in politics and their levels of interest in
about what is going on in politics (Spearman-Brown Coefficient = .94, M = 4.52, SD = 1.45). To
measure political discussion, respondents were asked how often they talk with weak ties
(acquaintances, strangers) and strong ties (spose/partner, family/friends) about politics online and
face to face. The eight items were averaged (α = .88, M = 2.91, SD = 1.27). Respondents’ social
media news use was measured based on prior research (Gil de Zúñiga et al., 2012; Valenzuela et
al., 2012). We asked how often respondents use social media to get news, stay informed about
current events and public affairs, get news about their local communities, and get news about
current events from mainstream media (α = .87, M = 4.27, SD = 1.51). To measure traditional
news use, individuals were asked how often they get news from TV, newspapers (printed
version), and radio (α = .60, M = 4.52, SD = 1.32). Since general social media use has also found
to be related to the Big Five, we included it as control. We asked respondents how often they use
“social media” and “instant messaging”. The two items were averaged (Spearman-Brown
coefficient = .63, M = 4.82, SD = 1.53). Moreover, we included the following demographics as
controls in our analysis: Age (M = 41, SD = 14.64), gender (51% female), education (measured
on an eight-point scale where 1 = none and 7 = post-graduate degree; M = 4.34, SD = 1.30),
income (annual household income, M = 2.94, SD = 1.1), and ethnicity or race (86% majority).
3.2.8 Country-level measures
GDP per capita (in thousand) per country was collected from the website of the World Bank
(M = 21.59, SD = 15.82) and ranges from 2 (Ukraine) to 57 (USA). The values for the freedom of
expression index were taken directly from the Vdem website (see Coppedge et
al., 2016). The index is based on nine indicators, namely government censorship effort media,
harassment of journalists, media self-censorship, freedom of discussion for men, freedom of
discussion for women, freedom of academic and cultural expression, media bias, print/broadcast
media critical, and print/broadcast-media perspectives). The index (M = 0.82, SD = .22) ranges
from 0.25 (China) to 0.99 (UK). Monochronicity/Polychronicity: based on the categorization by
Morden (1999) and O’Brien (2016), each country was assigned to one of the two types of
cultures in regard to their orientation toward time (0 = monochron culture; 1 = polychron
3.3 Analysis
First, one-sample t-tests were used to show how each country differs from the grand means
for political second screening. Second, hierarchical OLS regressions were used to test the first set
of hypotheses and research questions. The models include cross-sectional, lagged, and
autoregressive regressions using SPSS. While cross-sectional data do not allow for causal
inferences, this study relies on two-wave panel data and includes respondent’s prior scores on the
dependent variable in an autoregressive association in order to better deal with issues of
endogeneity and causal inference (for more details see, e.g., Greenberg, 2008; Kleinnijenhuis,
2016). While the cross-sectional model includes the independent and dependent variables from
Wave 1, the lagged model includes the independent variables from Wave 1 and the dependent
variable from Wave 2, and the autoregressive model includes the independent variables from
Wave 1, the dependent from Wave 2 and also the dependent variable from Wave 1 as
autoregressive term. Finally, multi-level models and cross-level interactions were conducted
using R to test whether the macro-variable helps explaining differences between countries.
4. Results
4.1 Overview of political second screening behavior in different countries
One-sample t-tests were first conducted to assess each country’s difference with the overall
sample in terms of mean levels of second screening behavior2 (M = 3.1, SD = 1.73). Results are
summarized in Table 1. Results show that people in Turkey, China, Brazil, Indonesia, Taiwan,
Philippines, and Japan tend to second screen more while people in Germany, New Zealand,
USA, Russia, UK, Poland, Ukraine, and Estonia tend to second screen less.
[Table 1 about here]
4.2 Personality traits predicting political second screening
Next, we run regression models testing the effect of our control variables and personality
traits on political second screening. Results in Table 2 support H1. Extraversion at time 1 is a
positive predictor of political second screening in all three models: Cross-sectional model
(ß=.045, p<.001), the lagged model (ß=.047, p<.001), and the autoregressive model (ß=.028,
p<.05). Agreeableness (RQ1a) is negatively associated with political second screening in all
three models: People that show lower levels of agreeableness are more likely to second
[Table 2 about here]
Additionally, results in Table 2 indicate that neither conscientiousness (RQ1b), nor emotional
stability (RQ1c) predicts political second screening, but openness to new experiences does
(RQ1d). More specifically, openness to new experiences at time 1 negatively predicts political
second screening in all three models: Cross-sectional model (ß= -.083, p<.001), the lagged model
(ß=-.058, p<.001), and the autoregressive model (ß= -.025, p<.05). That is, people less open to
new experiences tend to second screen more.
[Table 3 about here]
4.3 Comparative results
Finally, we were interested to test whether macro-variables help explain differences between
countries. First, results show that a model with a random intercept (i.e., a model with no
predictors and a random intercept) is a better fit (Log Likelihood = -38867.31) than a model with
a fixed intercept (Log Likelihood = -39791.40). That is, without accounting for the predictors,
mean levels of political second screening vary from country to country. About 5% of the
variance in political second screening is due to country differences (ICC = .053). Table 3 shows
the full model including individual-level variables, macro-level variables, and the cross-level
interactions. We expected individuals in countries with high GDP (H2a), high levels of freedom
of expression (H2b) and polychronic orientation (H2c) to show higher levels of political second
screening behavior. Results in Table 3 show that none of the three macro-level variables is
directly related to second screening. However, when it comes to the cross-level interactions the
country variables do matter. More specifically, we were interested to explore whether there are
cross-level interactions between personality traits and the macro variables GDP (RQ2a), freedom
of expression (RQ2b) and monochronicity/polychronicity (RQ2c) that affect second screening.
Results suggest that there are significant interaction effects for extraversion and GDP, for
agreeableness and freedom of expression, for conscientiousness and freedom of expression, and
for conscientiousness and monochronicity/polychronicity.
[Figure 1 about here]
Figure 1 shows that the positive relationship between extraversion and political second
screening is the strongest in countries with low GDP. Figure 2 shows that the negative
relationship between agreeableness and political second screening is the strongest in countries
with high freedom of expression.
[Figure 2 about here]
Figure 3 illustrates the positive relationship between conscientiousness and political second
screening to be the strongest in countries with high freedom of expression. Finally, Figure 4
shows the positive relationship between conscientiousness and political second screening in
polychron cultures (right panel).
[Figure 3 about here]
[Figure 4 about here]
5. Discussion
Media multitasking is a common phenomenon worldwide. We tested whether personality
traits help explain a specific type of media multitasking behavior - second screening politics -
across 19 societies from all continents. Our results show that three of the five personality traits
included in the analysis are statistically significant predictors of political second screening in the
pooled sample. Political second screeners can be characterized as follows: First, our results
suggest that extraverted people are more likely to engage in second screening while consuming
news and political debates. This makes perfect sense as one reason to second screening politics is
discussing with others. Second, agreeableness people are less likely to second screen. One
possible explanation for this might be that these individuals want to avoid conflicts and hostile
comments (Barnes et al., 2017). More specific, when engaging in second screening behavior,
people are very likely to encounter some kind of counter-attitudinal information. For example,
people in look for additional information online might be exposed to diverse viewpoints while
following user comments related to a political debate on TV or by reading hate comments on
social media. Moreover, one common practice of second screening is engaging in discussion
with others, which allows for politically “uncomfortable” conversations. Hence, agreeableness
people are likely to avoid all these second screening situations. Finally, our results indicate that
people scoring high on openness, are less likely to second screen. At first glance, this result
might be surprising as second screening is a relatively new communication practice and one
would expect open people to engage to this new communication practice. However, one possible
explanation might be that while open people - due to their manifold hobbies and interests - have
a lot of face-to-face contacts to discuss with, connecting with others and getting feedback have
found to be important motives for less open people to engage in online commenting (Wu and
Atkin, 2017).
These findings have important implications for our understanding of how to foster
democracy. While in general second screening about politics and public affairs is discussed to be
beneficial for democracy given its potential to increase political participation (Lin and Chiang,
2017; McGregor and Mourão, 2017), our findings imply that this new form of engaging with
politics is not evenly appealing for people with certain personality traits. Digital media might
help introverted people to partake of political behavior online (see social compensation theory,
McKenna & Bargh, 1998; Kim et al., 2013). For instance, social media has helped introverted
people to discuss political issues more frequently (Kim et al., 2013). However, when it
particularly relates to second screening this premise does not completely hold true. It is
extraverted people who are more likely to second screen – a finding that speaks toward a “rich
get richer” model (Kraut et al., 2002). Hence, there is a need to provide digital spaces for
engaging with politics that attract people who are not feeling that comfortable interacting with
others. One important factor to be addressed in this context is the diversity of online discourse
participants. Prior research shows that only 14% of online news users comment on news;
compared to those who read news but do not comment, they tend to often be male, are less
educated, and belong to lower-income groups (Stroud, Duyn and Peacock, 2016). So, one
challenge for democracy is to find a way of enabling more diverse and inclusive discourses in the
online public sphere. Another challenge is to better deal with hate speech since hate speech
might be one decisive factor that detracts agreeable persons from engaging in second screening
Finally, our results show that economic, political and cultural indicators help explaining
differences between countries. More specifically, the positive relationship between extraversion
and second screening is the strongest in countries with low GDP. That is, individuals tend to
second screen more if they are extroverted and this is especially true in less economically
developed countries. Moreover, the negative relationship between agreeableness and second
screening is the strongest in countries with high levels of freedom of expression. In countries
where individuals are free to express themselves, less agreeable people take advantage of the
opportunity to use and additional device while watching politics on TV to discuss the program
with others. However, this finding has also a downside: In countries where individuals are less
free to express themselves, even less agreeable individuals who are not afraid of conflicting
situations do not benefit from the possibility to digitally further engage with the content watched
on TV. It might be that they prefer to do so face-to-face because online conversations might be
5.1. Limitations and future directions
These conclusions are limited in certain ways. First, one should consider that our
measurement of political second screening did not differentiate between various forms of
political second screening, e.g., second screening for information purposes or second screening
for discussing with others. Future studies should distinguish between these and further motives
for second screening and explore how personality traits are related to them. Second, media
multitasking behavior can never be fully assessed by using self-reported data. For instance,
asking respondents if they use an additional device while watching TV does not allow to capture
details such as whether or not the TV is blurring into the background and they stop following the
content on TV (Rigby et al., 2017). Moreover, individuals tend to underestimate the extent to
which they media multitask (Voorveld and Viswanathan, 2015). Hence, observational data is
needed to measure political second screening behavior more precisely and to better understand
specific facets of political second screening behavior. Scholars could also try to combine self-
reports and registration data (de Vreese and Neijens, 2016). Moreover, while investigating the
direct effect of personality traits on dual screening was a first important step in exploring this
relationship, further research investigating also indirect paths is needed to provide a more
nuanced understanding of the way personality traits affect second screening behavior. Another
limitation is the use of controls in this study. While we were able to control for an array of
important antecedents of second screening behavior such as media habits, political antecedents,
general frequency of social media use, etc; we did not measure general media multitasking
frequency in our survey. However, this would be an important control since the Big Five have
shown to be related to the general media multitasking frequency. Accordingly, future studies
should consider this general media multitasking variable when investigating a specific media
multitasking behavior. Finally, we made an important first step in exploring the role of macro
variables for explaining second screening behavior in different countries worldwide. Future
studies should include additional macro-level factors in order to better understand how the
context of a country might influence second screening behavior.
5.2 Conclusions
Despite these limitations, our results help to explain why some people tend to use an additional
device to go online while watching politics on TV whereas others do not. More specifically, while
extraversion positively predicts political dual screening, the opposite is true for agreeableness
and openness. In addition, results suggest that combining personality traits as individual-level
variables and economic, political and cultural indicators as macro-level variables is a fruitful
approach. By delivering first important insights on the antecedents of political second screening
worldwide, this study provides a solid basis for the further investigation of this rising
communication practice.
1 Whenever prior research allows formulating specific expectations regarding the relationship of
the variables, we put a hypothesis. In the case of lacking theoretical expectation and/or due to
mixed empirical findings, we pose a research question.
2 Using the grand mean is common in comparative research when investigating differences
between countries (e.g., Gainous, Wagner and Gray, 2016; Gil de Zúñiga et al, 2019). Following
this strand of literature, we decided to calculate the grand mean. However, another possibility is
to calculate a one-way ANOVA with country as the independent variable and political second
screening as the dependent variable. Results show that there are statistically significant
differences between group means (F(18, 20235) = 114.003, p = .001)).
Amichai-Hamburger, Y., Ben-Artzi, E., 2000. The relationship between extraversion and
neuroticism and the different uses of the internet. Computers in Human Behavior 16,
Amichai-Hamburger, Y., Vinitzky, G., 2010. Social network use and personality. Computers in
Human Behavior 26, 1289-1295.
American Association for Public Opinion Research (2011). Standard definitions: Final
dispositions of case codes and outcome rates for surveys. Retrieved from
Andreassen, C. S., Griffiths, M. D., Gjertsen, S. R., Krossbakken, E., Kvam, S., Pallesen, S.,
2013. The relationship between behavioral addictions and the five-factor model of
personality. Journal of Behavioral Addictions 2(2), 90-99.
Annisette, L. E., Lafreniere, K. D., 2017. Social media, texting, and personality: A text of the
shallowing hypothesis. Personality and Individual Differences, 115, 154-158.
Barnes, R., 2014. The “ecology of participation”. Digital Journalism 2(4), 542-557.
Barnes, R., Mahar, D., Wong, I., Rune, K., 2017. A neurotic extrovert who is open to new
experiences? Understanding how personality traits may impact the commenting
behaviors of online news readers. Journal of Broadcasting & Electronic Media 61(3),
Barnidge, M., Gil de Zúñiga, H., Diehl, T., 2017. Second screening and political persuasion on
social media. Journal of Broadcasting & Electronic Media 61(2), 309-331.
Barnidge, M., Diehl, T., Rojas, H., 2019. Second screening for news and digital divides. Social
Science Computer Review 37(1), 55-72.
Behling, O., Law, K. S., 2000. Translating questionnaires and other research instruments:
Problems and solutions. Thousand Oaks, CA: Sage.
Becker, M. W., Alzahabi, R., Hopwood, C. J., 2013. Media Multitasking is associated with
symptoms of depression and social anxiety. Cyberpsychology, Behavior, and Social
Networking 16(2), 132-135.
Bosnjak, M., Das, M., Lynn, P., 2016. Methods for probability-based online and mixed-model
panels selected: Recent trends and future perspectives. Social Science Computer Review
34(1), 3-7.
Buckels, E. E., Trapnell, P. D., Paulhus, D. L., 2014. Trolls just want to have fun. Personality
and Individual Differences 67, 97-102.
Cain, M. S., Leonard, J. A., Gabrieli, J. D. E., Finn, A. S., 2016. Media multitasking in
adolescence. Psychonomic Bulletin Review 23, 1932–1941.
Callegaro, M., Baker, R. P., Bethlehem, J., Goritz, A. S., Krosnick, J.A., Lavrakas, P. J. (Eds).,
2014. Online panel research: A data quality perspective. Sussex: John Wiley & Sons.
Cha, E., Kim, K. H., Erlen, J. A., 2007. Translation of scales in cross-cultural research: Issues
and techniques. Journal of Advanced Nursing 58(4), 386-395.
Chadwick, A., O’Loughlin, B., Vaccari, C., 2017. Why people dual screen political debates and
why it matters for democratic engagement. Journal of Broadcasting & Electronic Media
61(2), 220-239.
Chang, Y., 2017. Why do young people multitask with multiple media? Explicating the
relationships among sensation seeking, needs, and media multitasking behavior. Media
Psychology 20(4), 685-703.
Choi, S. M., Kim, Y., Sung, Y., Sohn, D., 2011. Bridging or bonding? A cross-cultural study of
social relationships in social networking sites. Information, Communication and Society
14(1), 107–129.
Correa, T., Hinsley, A. W., Gil de Zúñiga, H., 2010. Who interacts on the Web? The intersection
of users’ personality and social media use. Computers in Human Behavior 26(2), 247-
Costa P. T. Jr, McCrae, R. R., 1992. Revised NEO Personality Inventory (NEO-PI-R) and NEO
Five-Factor Inventory (NEO-FFI) professional manual. Odessa, FL: Psychological
de Vreese, C. H., Neijens, P., 2016. Measuring media exposure in a changing communications
environment. Communication Methods and Measures 10(2-3), 69-80.
Dias, P., 2016. Motivations for multi-screening: An exploratory study on motivations and
gratifications. European Journal of Communication 31(6), 678-693.
Duff, B. R.-L., Yoon, G., Wang, Z., Anghelcev, G., 2014. Doing it all: An exploratory study of
predictors of media multitasking. Journal of Interactive Advertising 14(1), 11-23.
Eşkisu, M., Hoşoğlu, R., Rasmussen, K., 2017. An investigation of the relationship between
Facebook usage, Big Five, self-esteem and narcissism. Computers in Human Behavior
69, 294-301.
Esser, F., 2013. The emerging paradigm of comparative communication enquiry: Advancing
cross-national research in times of globalization. International Journal of Communication
7, 113-128.
Farmer, G.D., Janssen, C.P., Nguyen, A.T., Brumby, D.P., 2018. Dividing attention between
tasks: Testing whether explicit payoff functions elicit optimal dual-task performance.
Cognitive Science 42, 820-849.
Gainous, J., Wagner, K., Gray, T., 2016. Internet freedom and social media effects: Democracy
and citizen attitudes in Latin America. Online Information Review 40(5), 712-738.
Garaus, M., Wagner, U., Bäck, A. M., 2017. The effect of media multitasking on advertising
message effectiveness. Psychology & Marketing 34(2), 138–156
Gil de Zúñiga, H, Ardèvol-Abreu, A., Diehl, T., Gómez Patiño, M., Liu, J. H., 2019. Trust in
institutional actors across 22 countries: Examining political, science, and media trust
around the world. Revista Latina de Comunicación Social 74, 237-262.
Gil de Zúñiga, H., García, V., McGregor, S., 2015. What is second screening? Exploring
motivations of second screen use and its effects on online political participation. Journal
of Communication 65(5), 793–815.
Gil de Zúñiga, H., Jung, N., Valenzuela, S., 2012. Social media use for news and individuals’
social capital, civic engagement, and political participation. Journal of Computer-
Mediated Communication 17(3), 319-336.
Gil de Zúñiga, H., Liu, J.H., 2017. Second screening politics in the social media sphere:
Advancing research on dual screen use in political communication with evidence from 20
countries. Journal of Broadcasting & Electronic Media 61(2), 193-219.
Gosling, S. D., Rentfrow, P. J., Swann, W. B. J., 2003. A very brief measure of the big five
personality domains. Journal of Research in Personality 37, 504–528.
Gottfried, J., Hardy, B. W., Holbert, R. L., Winneg, K., Jamieson, K. H., 2017. The changing
nature of political debate consumption: Social media, multitasking, and knowledge
generation. Political Communication 34, 172-199.
Greaves, L. M., Cowie, L. J., Fraser, G., et al., 2015. Regional differences and similarities in the
personality of New Zealanders. New Zealand Journal of Psychology 44(1), 4-16.
Greenberg, D. V., 2008. Causal analysis with nonexperimental panel data, in: Scott, M. (Ed.),
Handbook of Longitudinal Research: Design, Measurement, and Analysis. Elsevier, New
York, pp. 259-278.
Grupp, H. 1995., Science, high technology and the competitiveness of EU countries. Cambridge
Journal of Economics 19, 209-209.
Hall, E. T., 1989. The Dance of Life: The other Dimension of Time. Anchor Books, New York.
Hong, F.-Y., Huang, D.-H., Lin H.-Y., Chiu, S.-L., 2014. Analysis of the psychological traits,
Facebook usage, and Facebook addiction model of Taiwanese university students.
Telematics and Informatics 31(4), 597-606.
Houston, J. B., Hawthorne, J., Spialek, M. L., Greenwood, M., McKinney, M. S., (2013).
Tweeting during presidential debates: Effect on candidate evaluations and debate
attitudes. Argumentation and Advocacy 49(4), 301-311.
Hughes, D. J., Rowe, M., Batey, M, Lee, A., 2012. A tale of two sites: Twitter vs. Facebook and
the personality predictors of social media usage. Computers in Human Behavior 28, 561-
Hunt, D. S., Langstedt, E., 2014. The Influence of Personality on Digital Photo Sharing. The
Journal of Social Media in Society 3(2), 42-64.
Hwang, Y., Kim, H., Jeong, S. H., 2014. Why do media users multitask? Motives for general,
medium-specific, and content-specific types of media multitasking. Computers in Human
Behavior 36, 542-548.
Jackson, L. A., Wang, J.-L., 2013. Cultural differences in social network site use: A comparative
study of China and the United States. Computers in Human Behavior 29(3), 910-921.
Jeong, S.-H., Fishbein, M., 2007. Predictors of multitasking with media: Media factors and
audience factors. Media Psychology 10, 364-384.
John, O. P., Srivastava, S., 1999. The Big Five trait taxonomy: History, measurement, and
theoretical perspectives, in: Pervin, L. A., John, O. P. (Eds.), Handbook of personality:
Theory and Research. The Guilford Press, New York, London, pp. 102-138.
Jordan, G., Pope, M., Wallis, P., Iyer, S., 2015. The relationship between openness to experience
and willingness to engage in online political participation is influenced by news
consumption. Social Science Computer Review 33(2), 181-197.
Kazakova, S., Cauberghe, V., Pandelaere, M., de Pelsmacker, P., 2015. Can’t see the forest for
the trees? The effect of media multitasking on cognitive processing style. Media
Psychology 18(4), 425-450.
Kim, Y., Hsu, S., Gil de Zúñiga, H., 2013. Influence of social media use on discussion network
heterogeneity and civic engagement: The moderating role of personality traits. Journal of
Communication 63(3), 498-516.
Kim, Y., Sohn, D., Choi, S. M., 2011. Cultural difference in motivations for using social network
sites: A comparative study of American and Korean college students. Computers in
Human Behavior 27, 365–372.
Kleinnijenhuis, J., 2016. Chapter 22: Multilevel regression analysis, in: Keman, H., Woldendorp,
J. J. (Eds.), Handbook of Research Methods and Applications in Political Science. Edgar
Elward Publishing, Cheltenham, pp. 323-340.
Kononova, A., Zasorina, T., Diveeva, N., Kokoeva, A., Chelokyan, A., 2014. Multitasking goes
global: Multitasking with traditional and new electronic media and attention to media
messages among college students in Kuwait, Russia, and the USA. International
Communication Gazette 76(8), 617-640.
Kraut, R., Kiesler, S., Boneva, B., Cummings, J., Helgeson, V., Crawford, A., 2002. Internet
paradox revised. Journal of Social Issues 58, 49–74.
Landers, R. N., Loundsbury, J. W., 2006. An investigation of Big Five and narrow personality
traits in relation to internet usage. Computers in Human Behavior 22(2), 283-293.
Lin, T.T.C., Chiang, Y.-H., 2017. Dual screening: Examining social predictors and impact on
online and offline political participation among Taiwanese Internet users. Journal of
Broadcasting & Electronic Media 61, 240-263
Loh, K. K., Kanai, R., 2014. Higher media multi-tasking activity is associated with smaller gray-
matter density in the anterior cingulate cortex. PLoS ONE 9(9), e106698.
Nielsen, 2013. Action figures: How second screens are transforming TV viewing.
Marshall, T. C., Lefringhausen, K., Ferenczi, N., 2015. The Big Five, self-esteem, and narcissism as
predictors of the topics people write about in Facebook status updates. Personality and
Individual Differences 85(C), 35-40.
Mark, G., Ganzach, Y., 2014. Personality and Internet usage: A large-scale representative study of
young adults. Computers in Human Behavior 36(C), 274-281.
McCrae, R. R., Costa, P. T. Jr., 1999. A five-factor theory of personality, in: Pervin, L. A., John,
O. P. (Eds.), Handbook of personality: Theory and Research. Guilford, New York, pp.
McCrae, R. R., John, O. P., 1992. An Introduction to the Five-Factor model and its applications.
Journal of Personality 60(2), 175-215.
McGregor, S.C., Mourão, R.R., 2017. Second screening Donald Trump: Conditional indirect
effects on political participation. Journal of Broadcasting & Electronic Media 61(2), 264–
McGregor, S.C., Mourão, R.R., Neto, I., Straubhaar, J.D., Angeluci, A., 2017. Second screening
as convergence in Brazil and the United States. Journal of Broadcasting & Electronic
Media 61(1), 163-181.
McKenna, K. Y. A., Bargh, J., 1998. Coming out in the age of the Internet: Identity
“demarginalization” through virtual group participation. Journal of Personality and Social
Psychology, 75, 681–694.
Mesmer-Magnus, J., Viswesvaran, C., Bruk-Lee, V., Sanderson, K., Sinha, N., 2014. Personality
correlates of preference for multitasking in the workplace. Journal of Organizational
Psychology 14(1), 67-76.
Mondak, J. J., Halperin, K. D., 2008. A framework for the study of personality and political
behavior. British Journal of Political Science 38, 335–362.
Mondak, J. J., Hibbing, M. V., Canache, D., Seligson, M. A., Anderson, M. R., 2010. Personality
and civic engagement: An integrative framework for the study of trait effects on political
behavior. American Political Science Review 104, 85–110.
Moore, K., McElroy, J. 2012. The influence of personality on Facebook usage, wall postings,
and regret. Computers in Human Behavior 28, 267-274.
Morden, T., 1999. Models of national culture - a management review. Cross Cultural
Management: An International Journal 6(1), 19-44.
Nee, R. C, Dozier, D. M., 2017. Second screen effects: Linking multiscreen media use to
television engagement and incidental learning. Convergence: The International Journal of
Research into New Media Technologies 23(2), 214–226.
O'Brien, J., 2016. Negotiation for procurement professionals: A proven approach that puts the
buyer in control (2nd ed.). Kogan Page, London.
Ophir, E., Nass, C., Wagner, A. D., 2009. Cognitive control in media multitaskers. Proceedings
of the National Academy of Sciences 106(37), 15583–15587.
Opp, K.-D., Brandstätter, H., 2010. Political protest and personality traits: A neglected link.
Mobilization 15, 323–346.
Orchard, L. J., Fullwood, C., Galbraith, N., Morris, N., 2014. Individual differences as predictors
of social networking. Journal of Computer-Mediated Communication 19(32), 388-402.
Papper, R. A., Holmes, M. E., Popovich, M. N., 2004. Middletown media studies: Media
multitasking and how much people really use the media. International Digital Media
& Arts Association Journal 1, 5–50.
Pilotta, J. J., Schultz, D. E., Drenik, G., Rist, P., 2004. Simultaneous media usage: A critical
consumer orientation to media planning. Journal of Consumer Behaviour 3(3), 285-292.
Quantelier, E., Theocharis, Y., 2012. Online Political Engagement, Facebook, and Personality
Traits. Social Science Computer Review 31(3), 280-290.
Ran, W., Yamamoto, M., Xu, S., 2016. Media multitasking during political news consumption:
A relationship with factual and subjective political knowledge. Computers in Human
Behavior 56, 352–359.
Rigby, J., Brumby, D. P., Gould, S. J. J., Cox, A., 2017. Media multitasking at home: A video
observation study of concurrent TV and mobile device usage, in: Proceedings of the 2017
ACM International Conference on Interactive Experiences for TV and Online Video
(TVX '17). ACM, New York, USA, pp. 3-10.
Ross, C., Orr, E. S., Sisic, M., Arseneault, J. M., Simmering, M. G., Orr, R. R., 2009. Personality
and motivations associated with Facebook use. Computers in Human Behavior 25, 578–
Russo, S., Amna, E., 2015. The personality divide. Do personality traits differentially predict
online political engagement? Social Science Computer Review 34(3), 259-277.
Ryan, T., Xenos, S., 2011. Who uses Facebook? An investigation into the relationship between
the Big Five, shyness, narcissism, loneliness, and Facebook usage. Computers in Human
Behavior 27(5), 1658-1664.
Salvucci, D. D., Taatgen, N. A., 2008. Threaded cognition: An integrated theory of concurrent
multitasking. Psychological Review 115(1), 101–130.
Sanderson, K. R., 2012. Time orientation in organizations: Polychronicity and multitasking.
Dissertation. Florida International University. Retrieved from
Segijn, C. M., Xiong, S., Duff, B. R. L., 2019. Manipulating and measuring media multitasking:
Implications of previous research and guidelines for future research. Communication
Methods and Measures 13(2), 83-101.
Seidman, G., 2013. Self-presentation and belonging on Facebook: How personality influences
social media use and motivations. Personality and Individual Differences 54, 402-407.
Shih S. I., 2013. A null relationship between media multitasking and well-being. PLoS ONE
8(5), e64508.
Srivastava, Nakazawa, M., Chen, Y.-W., 2016. Online, mixed, and offline media multitasking:
Role of cultural, socio-demographic, and media factors. Computers in Human Behavior
62, 720-729.
Stiff, J. B., Dillard, J. P., Somera, L., Kim, H., Sleight, C., 1988. Empathy, communication, and
prosocial behavior. Communications Monographs 55(2), 198-213.
Stroud, N. J., Duyn, E. V., Peacock, C., 2016. Engaging news project. Retrieved from
Tan, W., Yang, C., 2013. Internet applications use and personality. Telematics and Informatics
31(1), 27-38.
Thato, S., Hanna, K. J., Rodcumdee, B., 2005. Translation and validation of the condom self-
efficacy scale with Thai adolescents and young adults. Journal of Nursing Scholarship
37(1), 36-40.
Valenzuela, S., Arriagada, A., Scherman, A., 2012. The social media basis of youth protest
behavior: The case of Chile. Journal of Communication 62(2), 299-314.
Vasalou, A., Joinson, A. N., Courvoisier, D., 2010. Cultural differences, experience with social
networks and the nature of ‘true commitment’ in Facebook. International Journal of
Human-Computer Studies 68, 719-728.
Voorveld, H., Van Der Goot, M., 2013. Age differences in media multitasking: A diary study.
Journal of Broadcasting & Electronic Media 57(3), 392-408.
Voorveld, H. A. M., Viswanathan, V., 2015. An observational study on how situational factors
influence media multitasking with TV: The role of genres, dayparts, and social viewing.
Media Psychology 18(4), 499-526.
Wang, Z., Tchernev, J. M., 2012. The “myth” of media multitasking: Reciprocal dynamics of
media multitasking, personal needs, and gratifications. Journal of Communication 62(3),
493– 513.
Webster, J. G., Phalen, P. F., Lichty, L. W., 2006. Ratings analysis. The theory and practice of
audience research (3rd edition). Erlbaum, Mahwah.
Wu, T.-Y., Atkin, D., 2017. Online news discussions: Exploring the role of user personality and
motivations for posting comments on news. Journalism & Mass Communication
Quarterly 94(1), 61-80.
Yang, K. C. C., Kang, Y., 2015. Exploring big data and privacy in strategic communication
campaigns: A cross-cultural study of mobile social media users’ daily experiences.
International Journal of Strategic Communication 9, 87–101.
Wickens, C. D., 2002. Multiple resources and performance prediction. Theoretical Issues in
Ergonomics Science 3(2), 159–177.
Table 1
Tests of Mean Differences between Country Mean and the Grand Mean for Second Screening
Second Screening
M (SD)
3.14 (1.84)
3.80 (1.66) +
3.92 (1.42) +
2.85 (1.62) -
2.35 (1.67) -
3.55 (1.57) +
3.06 (1.73)
3.49 (1.60) +
3.17 (1.55)
New Zealand
2.41 (1.66) -
3.49 (1.54) +
2.64 (1.64) -
2.58 (1.63) -
3.27 (1.75)
3.53 (1.66) +
4.12 (1.47) +
2.80 (1.60) -
United Kingdom
2.54 (1.80) -
United States
2.48 (1.75) -
Notes. Cell entries are means (M), standard deviations (SD), test statistics (t) and degrees
freedom (df) from one-sample t-tests assessing the difference between each country mean and
the grand mean for second screening (M = 3.10, SD = 1.73). Significance values are indicated as
follows: *p < .05; ** p < .01; *** p < .001 (two-tailed tests). + or – signs denote whether the
difference with the grand mean in a positive or a negative one.
Table 2
Cross-sectional, Lagged, and Autoregressive Regression Models Testing Personality Traits over
Second Screening (19 countries)
Block1: Demographics
Gender (Female=1)
Race (Majority=1)
Block 2: Political Antecedents
Political InterestW1
Political DiscussionW1
Block 3: Media Use
Traditional News UseW1
Social Media News UseW1
Frequency of Social Media
Block 4: Autoregressive Term
Second ScreeningW1
Block 5: Personality Traits
Emotional StabilityW1
Total R2
Note. Cell entries are final-entry ordinary least squares (OLS) standardized coefficients (β).
* p < .05; ** p < .01; *** p < .001. Ncross = 16,277; Nlagged / auto = 6,915.
Table 3
Multi-level models and cross-level interactions predicting second screening
Second Screening
Individual Level Indicators
-.018 (.001)***
-.018 (.001)***
Gender (Female=1)
-.123 (.024)***
-.119 (.024)***
.021 (.010)*
.020 (.010)*
.026 (.011)*
.027 (.011)*
Race (Majority=1)
-.103 (.035)**
-.107 (.035)**
Political Interest
.055 (.010)***
.055 (.010)***
Political Discussion
.360 (.011)***
.355 (.011)***
Traditional News Use
.065 (.010)***
.067 (.010)***
Social Media News Use
.242 (.012)***
.241 (.012)***
Frequency of Social Media Use
.062 (012)***
.063 (.012)***
.046 (.011)***
.050 (.011)***
-.096 (.015)***
-.096 (.015)***
.003 (.015)
.006 (.015)
Emotional Stability
-.002 (.013)
-.003 (.013)
-.113 (.015)***
-.116 (.016)***
Country-Level Predictor
.011 (.007)
.011 (.007)
Freedom of Expression
-.550 (.421)
-.568 (.422)
.032 (.222)
.042 (.223)
Cross-Level Interactions
Extraversion * GDP
-.002 (.001)*
Agreeableness * GDP
.001 (.001)
Conscientiousness * GDP
-.000 (.001)
Emotional Stability * GDP
.001 (.001)
Openness * GDP
.002 (.001)
Extraversion * Freedom Ex.
.040 (.063)
Agreeableness * Freedom Ex.
-.199 (.077)**
Conscientiousness * Freedom Ex.
.219 (.078)**
Emotional Stability * Freedom Ex.
-.072 (.069)
Openness * Freedom Ex.
.002 (.081)
Extraversion * Mono/Poly
-.002 (.033)
Agreeableness * Mono/Poly
.015 (.042)
Conscientiousness * Mono/Poly
.151 (.043)***
Emotional Stability* Mono/Poly
-.032 (.035)
Openness * Mono/Poly
-.066 (.045)
Var. of Random Comp. (SD)
Variance Within Country
2.04 (1.43)
2.04 (1.43)
Variance Between Country
.12 (.34)
.12 (.34)
Log Likelihood
Note: Ncross = 16,277. *p < .05; ** p < .01; *** p < .001. Predictors and dependent variables centered to the
grand mean. Based on 19 groups. Table shows results from cross-sectional model; results from lagged and
autoregressive models (Nlagged / auto = 6,915) are not shown in the table as the cross-level interactions were no
longer significant.
Figure 1. Cross-level interaction between extraversion and GDP as estimated by the model in
Table 3. Charts represent the effect for extraversion on second screening at the 10th, 50th, and
90th quantiles of the moderator.
Figure 2. Cross-level interaction between agreeableness and freedom of expression as estimated
by the model in Table 3. Charts represent the effect for agreeableness on second screening at the
10th, 50th, and 90th quantiles of the moderator.
Figure 3. Cross-level interaction between conscientiousness and freedom of expression as
estimated by the model in Table 3. Charts represent the effect for conscientiousness on second
screening at the 10th, 50th, and 90th quantiles of the moderator.
Figure 4. Cross-level interaction between conscientiousness and monochronicity / polychronicity
as estimated by the model in Table 3. Charts represent the effect for conscientiousness on second
screening in monochron (left panel) vs. polychron cultures (right panel).
... As polychronicity is thought to be a stable individual trait (Capdeferro et al., 2014;Sanderson, 2012), existing research has explored the relationship between polychronicity and a variety of differences in personality (e.g., Conte & Gintoft, 2005;Huber, de Zúñiga, & Liu, 2020;Kantrowitz, Grelle, Beaty, & Wolf, 2012). The Big Five factors of personality, including extraversion, agreeableness, conscientiousness, neuroticism and openness, provide a comprehensive framework for measuring personality (Goldberg, 1990). ...
Full-text available
Information technology provides the potential for polychronic learning. However, research on polychronicity in the educational field is scarce. The purposes of this study were to develop a multidimensional polychronicity scale for information technology learning and explore the relationship between polychronicity in information technology‐supported learning and personality traits. This study is divided into two phases. First, a questionnaire survey was used to develop a multidimensional polychronicity scale for information technology learning, and 602 questionnaires were included in the analysis. Second, 129 participants reported their polychronicity in information technology‐supported learning and Big Five personality traits. The first phase results showed that the scale of polychronicity in IT‐supported learning (SPITSL) contained three constructs, that is, time tangibility (α = .89), scheduling preference (α = .84) and involvement with people (α = .85) and that the total variance explained was 69.43%. The confirmatory factor analysis results indicated that the preliminary model fit, overall model fit and internal model fit were acceptable. The second phase results showed that polychronicity was related to the Big Five personality traits. This study showed that the SPITSL has a multidimensional structure that is consistent with the definition of polychronicity advocated by many scholars.
Full-text available
Social trust has long attracted the interest of researchers across different disciplines. Most of previous studies rely on single-country data and consider only one dimension of social trust at a time (e.g., trust in science, the media or political institutions). This research extends a framework developed by the Global Trust Inventory (GTI) by discussing several dimensions of social trust, while simultaneously analyzing how trust in institutions varies across societies. Drawing on an online panel survey collected in 22 countries (N = 22,033), we examine cross-country differences in social trust—including government trust, trust in governing bodies, security, and knowledge producers. Additionally, this paper fills a gap in current literature by including a measure of trust in the media. Findings are discussed in the context of comparing emerging and developed countries based on the Human Development Index.
Full-text available
Media multitasking has been receiving increased attention from communication scholars as well as scholar in other fields, with studies focusing on the prevalence, predictors, behavior, and effects. Several recent papers have provided overviews of findings from media multitasking research, or provided frameworks to help researchers think about conceptual issues around multitasking. This article expands on those efforts by refining the methodological elements that are important to consider in media multitasking research. We discuss the validity of operationalizations in previous studies, and the impact that design and measures had on the conclusions drawn. In order to do this, we map the different options for manipulating and measuring media multitasking, discuss the implications, and provide guidelines for future research examining media multitasking to help connect disparate findings and provide additional guidance for researchers to move forward with this topic.
Full-text available
In the context of the United States, research shows a positive relationship between network heterogeneity and political expression on social media at the individual level. This study builds on that research, relying on multilevel analysis that (1) leverages a twenty-country comparative survey and (2) includes country-level data on freedom of expression. Results show a positive relationship between network heterogeneity and political expression on social media across countries, but that relationship is stronger where freedom of expression is more limited. Keywords social media, political expression, network heterogeneity, freedom of expression index, democracy, political communication This paper examines how a country's freedom of expression shapes "risky" political expression on social media. Political expression has flourished on social media (Halpern and Gibbs 2013; Vaccari et al. 2015), and individuals who express their political views in these environments enjoy an increasingly substantial role, alongside political and media elites, in shaping public narratives about political issues in Western societies (Chadwick 2017; K. Thorson and Wells 2015). Recent research in the
Full-text available
Second screening is a relatively new set of media practices that arguably empower audiences to shape public narratives alongside news organizations and political elites. But in developing countries such as Colombia, it is important to examine who participates in this process, as substantial inequalities in both access to and use of information and communication technologies (ICTs) persist. This study examines how socioeconomic status (SES) relates to the adoption of second screening practices in Colombia, a country in which the technological access and literacy necessary to engage in these practices are becoming widespread but are not yet ubiquitous. Based on a random sample of face-to-face interviews, results show evidence of persistent digital divides in Colombia in terms of ICT access, ICT use, and second screening for news. Additionally, results indicate that the relationship between SES and second screening for news is indirect, mediated through technological access and public affairs engagement.
Full-text available
This study examines the relationship between peoples' personality traits and social media uses with data from 20 societies (N = 21,314). A measure of the ''Big Five'' personality traits is tested on key social media dimensions: frequency of use, social interaction, and news consumption. Across diverse societies, findings suggest that while extraversion, agreeableness, and conscientiousness are all positive predictors of different types of social media use, emotional stability and openness are negatively related to them.
Full-text available
We test people's ability to optimize performance across two concurrent tasks. Participants performed a number entry task while controlling a randomly moving cursor with a joystick. Participants received explicit feedback on their performance on these tasks in the form of a single combined score. This payoff function was varied between conditions to change the value of one task relative to the other. We found that participants adapted their strategy for interleaving the two tasks, by varying how long they spent on one task before switching to the other, in order to achieve the near maximum payoff available in each condition. In a second experiment, we show that this behavior is learned quickly (within 2-3 min over several discrete trials) and remained stable for as long as the payoff function did not change. The results of this work show that people are adaptive and flexible in how they prioritize and allocate attention in a dual-task setting. However, it also demonstrates some of the limits regarding people's ability to optimize payoff functions.
Full-text available
Conference Paper
Increasingly people interact with their mobile devices while watching television. We evolve an understanding of this kind of everyday media multitasking behaviour through an analysis of video data. In our study, four households were recorded watching television over three evenings. We analysed 55 hours of footage in which participants were watching the TV. We found that mobile device habits were highly variable between participants during this time, ranging from 0% to 23% of the time that the TV was on. To help us understand this variability, participants completed the Media Multitasking Index (MMI) questionnaire. Results showed that participants with a higher MMI score used their mobile device more while watching TV at home. We also saw evidence that the TV was being used as a hub in the home: multiple people were often present when the time the TV was on, providing a background for other household activities. We argue that video analysis can give valuable insights into media multitasking in the home.
Full-text available
This article seeks to explain political persuasion in relation to second screening—people’s use of a second screen (i.e., smartphone/laptop) while watching television to access further information or discuss TV programs. Employing a two-wave-panel survey in the United States, results show this emergent practice makes people more open to changing their political opinions, particularly among those who habitually use social media for news or frequently interact with others in social media contexts.
This article develops a theoretical context for the influence of personality traits on commenting behaviors on online news Web sites. Drawing on the literature of personality, and online behaviors, we examine how personality traits may influence which individuals make contributions on news Web sites. Additionally the article focuses on how the commenting “landscape,” in particular moderation policies and user interfaces, may impact those who contribute.
As second screening becomes more widespread, this study addresses its mediating role on the impact of TV news in political participation online and offline, and how this impact varies across groups. We expand the existing line of research by assessing the moderating role of support for Donald Trump on the established mediated model. Through a cross-lagged autoregressive panel survey design applied to the communication mediation model, our results support the link between second screening and political participation—but the mediating role of second screening is contingent upon attitudes towards Trump. For those who do not view Trump favorably, second screening during news leads to a decrease in political participation, both online and offline. As such, this article adds to the communication mediation model by suggesting that discussion and elaboration may not always be positive antecedents to political participation. When individuals disagree with the message dominating TV news and social media, deliberation via second screening leads to political disengagement.